Research Computing Center Wiki - User contributions [en]

Duo authentication changes

2026-09-02T19:02:39Z

Shtsai:

==Duo Phone Call and SMS Text Verification Retirement Sept.17, 2026==

To enhance security and reduce phishing attempts, Enterprise Information Technology Services (EITS) is updating Duo multi-factor authentication (MFA) methods for all Duo users at the University of Georgia. These changes are part of EITS’ ongoing efforts to better protect UGA accounts.

As part of these improvements, EITS will remove the two less secure verification methods, SMS text messages and phone calls, and introduce more secure authentication options to safeguard UGA accounts.

===What is Changing?===

Beginning September 17, 2026, SMS text messages and phone calls will no longer be available as verification methods in Duo for students, faculty and staff. These methods are being retired because they are less secure.

The same change will go into effect for Departmental MyID accounts on November 5, 2026. Beginning on that date, SMS text messages and phone calls will no longer be available as Duo verification methods for Departmental MyIDs.

SMS text messages and phone calls were removed as verification methods for UGA retirees and owners of IT administrative accounts this summer.

===New Secure Authentication Options===

Effective Summer 2026, users have access more secure options, including:

* Duo Mobile app (push notifications and passcodes)

* Platform authenticators (such as fingerprint or facial recognition on your device)

* Roaming authenticators (such as USB security keys)

* Duo Desktop authentication (Duo authentication for your computer)

* Duo Hard Tokens (physical keychain devices that provide a Duo code)

===Why This Change Is Happening===

This change will remove two less secure MFA options in Duo while introducing more secure methods for logging in to UGA systems. These updates align Duo MFA with current security best practices and support enhanced protection for UGA systems and data.

===How will these changes affect access to GACRC clusters and servers===

Prior to these changes most users who has a mobile phone registered with Archpass Duo were offered three options when then ssh into e.g. Sapelo2.

After September 17, most users will only see one option, as illustrated below:

<pre class="gscript">
[shtsai@localhost ~]$ ssh shtsai@sapelo2.gacrc.uga.edu
(shtsai@sapelo2.gacrc.uga.edu) Password:
(shtsai@sapelo2.gacrc.uga.edu) UGA DUO authentication is required for SSH/SCP access to
GACRC systems.

UGA DUO is a two-factor authentication service which
requires a password (one factor) and a code, phone,
or device (second factor) to successfully authenticate.

If you are not enrolled in the UGA DUO service please
visit the UGA DUO service self-service portal to enroll
and configure or manage your DUO enabled devices.

https://archpass.uga.edu

For additional help with UGA DUO authentication or to
report an issue please visit:

https://uga.teamdynamix.com/TDClient/3190/eitsclientportal/KB/Category/23825/ArchPass-powered-by-Duo
Duo two-factor login for shtsai

Enter a passcode or select one of the following options:

1. Duo Push to XXX-XXX-1234

Passcode or option (1-1): 1
Success. Logging you in...
Success. Logging you in...
Last login: Wed Sep 2 13:42:52 2026 from 172.22.72.26
[shtsai@ss-sub4 ~]$
</pre>

At the prompt

<pre class="gscript">
Passcode or option (1-1):
</pre>

you can either enter '''1''' to receive a Duo push to the app on the mobile phone, or open the Duo app in the mobile phone and enter the 6-digit code displayed in the app into the prompt above. For example, if the Duo app in the mobile phone shows "Passcode 401321", you could enter

<pre class="gscript">
Passcode or option (1-1): 401321
</pre>

Users who do not have a mobile phone registered with Archpass Duo, or who cannot or prefer not to install the Duo app on the mobile have the option to use a hard token for Duo authentication.

Users who do not have a mobile phone registered for Duo push and who opt to use a hard token for Duo authentication will see something like this when they ssh into Sapelo2:

<pre class="gscript">
[shtsai@localhost ~]$ ssh shtsai@sapelo2.gacrc.uga.edu
(shtsai@sapelo2.gacrc.uga.edu) Password:
(shtsai@sapelo2.gacrc.uga.edu) UGA DUO authentication is required for SSH/SCP access to
GACRC systems.

UGA DUO is a two-factor authentication service which
requires a password (one factor) and a code, phone,
or device (second factor) to successfully authenticate.

If you are not enrolled in the UGA DUO service please
visit the UGA DUO service self-service portal to enroll
and configure or manage your DUO enabled devices.

https://archpass.uga.edu

For additional help with UGA DUO authentication or to
report an issue please visit:

https://uga.teamdynamix.com/TDClient/3190/eitsclientportal/KB/Category/23825/ArchPass-powered-by-Duo
Duo two-factor login for shtsai

Enter a passcode or select one of the following options:

Passcode:

</pre>

Entering the passcode provided by the hard token will enable Duo authentication.

===Additional Information===

To update your Duo Verification method, visit the [https://archpass.uga.edu/ Duo Self-Service portal].

For assistance in updating your method, contact the [https://eitshelpdesk.uga.edu/ EITS Help Desk].

For more information about these updates, available authentication methods and step-by-step resources, [https://eits.uga.edu/about-us/major-initiatives/duo-verification-methods-improvements/duo-improvements-overview/ visit the EITS Major Initiatives page: Duo Verification Methods Improvements].

Duo authentication changes

2026-09-02T19:00:56Z

Shtsai:

==Duo Phone Call and SMS Text Verification Retirement Sept.17, 2026==

To enhance security and reduce phishing attempts, Enterprise Information Technology Services (EITS) is updating Duo multi-factor authentication (MFA) methods for all Duo users at the University of Georgia. These changes are part of EITS’ ongoing efforts to better protect UGA accounts.

As part of these improvements, EITS will remove the two less secure verification methods, SMS text messages and phone calls, and introduce more secure authentication options to safeguard UGA accounts.

===What is Changing?===

Beginning September 17, 2026, SMS text messages and phone calls will no longer be available as verification methods in Duo for students, faculty and staff. These methods are being retired because they are less secure.

The same change will go into effect for Departmental MyID accounts on November 5, 2026. Beginning on that date, SMS text messages and phone calls will no longer be available as Duo verification methods for Departmental MyIDs.

SMS text messages and phone calls were removed as verification methods for UGA retirees and owners of IT administrative accounts this summer.

===New Secure Authentication Options===

Effective Summer 2026, users have access more secure options, including:

* Duo Mobile app (push notifications and passcodes)

* Platform authenticators (such as fingerprint or facial recognition on your device)

* Roaming authenticators (such as USB security keys)

* Duo Desktop authentication (Duo authentication for your computer)

* Duo Hard Tokens (physical keychain devices that provide a Duo code)

===Why This Change Is Happening===

This change will remove two less secure MFA options in Duo while introducing more secure methods for logging in to UGA systems. These updates align Duo MFA with current security best practices and support enhanced protection for UGA systems and data.

===How will these changes affect access to GACRC clusters and servers===

Prior to these changes most users who has a mobile phone registered with Archpass Duo were offered three options when then ssh into e.g. Sapelo2.

After September 17, most users will only see one option, as illustrated below:

<pre class="gscript">
[shtsai@localhost ~]$ ssh shtsai@sapelo2.gacrc.uga.edu
(shtsai@sapelo2.gacrc.uga.edu) Password:
(shtsai@sapelo2.gacrc.uga.edu) UGA DUO authentication is required for SSH/SCP access to
GACRC systems.

UGA DUO is a two-factor authentication service which
requires a password (one factor) and a code, phone,
or device (second factor) to successfully authenticate.

If you are not enrolled in the UGA DUO service please
visit the UGA DUO service self-service portal to enroll
and configure or manage your DUO enabled devices.

https://archpass.uga.edu

For additional help with UGA DUO authentication or to
report an issue please visit:

https://uga.teamdynamix.com/TDClient/3190/eitsclientportal/KB/Category/23825/ArchPass-powered-by-Duo
Duo two-factor login for shtsai

Enter a passcode or select one of the following options:

1. Duo Push to XXX-XXX-1234

Passcode or option (1-1): 1
Success. Logging you in...
Success. Logging you in...
Last login: Wed Sep 2 13:42:52 2026 from 172.22.72.26
[shtsai@ss-sub4 ~]$
</pre>

At the prompt

<pre class="gscript">
Passcode or option (1-1):
</pre>

you can either enter '''1''' to receive a Duo push to the app on the mobile phone, or open the Duo app in the mobile phone and enter the 6-digit code displayed in the app into the prompt above. For example, if the Duo app in the mobile phone shows "Passcode 401321", you could enter

<pre class="gscript">
Passcode or option (1-1): 401321
</pre>

Users who do not have a mobile phone registered with Archpass Duo, or who cannot or prefer not to install the Duo app on the mobile have the option to use a hard token for Duo authentication.

Users who do not have a mobile phone registered for Duo push and who opt to use a hard token for Duo authentication will see something like this when they ssh into Sapelo2:

===Additional Information===

To update your Duo Verification method, visit the [https://archpass.uga.edu/ Duo Self-Service portal].

For assistance in updating your method, contact the [https://eitshelpdesk.uga.edu/ EITS Help Desk].

For more information about these updates, available authentication methods and step-by-step resources, [https://eits.uga.edu/about-us/major-initiatives/duo-verification-methods-improvements/duo-improvements-overview/ visit the EITS Major Initiatives page: Duo Verification Methods Improvements].

Duo authentication changes

2026-09-02T18:19:45Z

Shtsai:

==Duo Phone Call and SMS Text Verification Retirement Sept.17, 2026==

To enhance security and reduce phishing attempts, Enterprise Information Technology Services (EITS) is updating Duo multi-factor authentication (MFA) methods for all Duo users at the University of Georgia. These changes are part of EITS’ ongoing efforts to better protect UGA accounts.

As part of these improvements, EITS will remove the two less secure verification methods, SMS text messages and phone calls, and introduce more secure authentication options to safeguard UGA accounts.

===What is Changing?===

Beginning September 17, 2026, SMS text messages and phone calls will no longer be available as verification methods in Duo for students, faculty and staff. These methods are being retired because they are less secure.

The same change will go into effect for Departmental MyID accounts on November 5, 2026. Beginning on that date, SMS text messages and phone calls will no longer be available as Duo verification methods for Departmental MyIDs.

SMS text messages and phone calls were removed as verification methods for UGA retirees and owners of IT administrative accounts this summer.

===New Secure Authentication Options===

Effective Summer 2026, users have access more secure options, including:

* Duo Mobile app (push notifications and passcodes)

* Platform authenticators (such as fingerprint or facial recognition on your device)

* Roaming authenticators (such as USB security keys)

* Duo Desktop authentication (Duo authentication for your computer)

* Duo Hard Tokens (physical keychain devices that provide a Duo code)

===Why This Change Is Happening===

This change will remove two less secure MFA options in Duo while introducing more secure methods for logging in to UGA systems. These updates align Duo MFA with current security best practices and support enhanced protection for UGA systems and data.

===Additional Information===

To update your Duo Verification method, visit the [https://archpass.uga.edu/ Duo Self-Service portal].

For assistance in updating your method, contact the [https://eitshelpdesk.uga.edu/ EITS Help Desk].

For more information about these updates, available authentication methods and step-by-step resources, [https://eits.uga.edu/about-us/major-initiatives/duo-verification-methods-improvements/duo-improvements-overview/ visit the EITS Major Initiatives page: Duo Verification Methods Improvements].

===How will these changes affect access to GACRC clusters and servers===

Prior to these changes most users who has a mobile phone registered with Archpass Duo were offered three options when then ssh into e.g. Sapelo2.

After September 17, most users will only see one option, as illustrated below:

<pre class="gscript">
[shtsai@localhost ~]$ ssh shtsai@sapelo2.gacrc.uga.edu
(shtsai@sapelo2.gacrc.uga.edu) Password:
(shtsai@sapelo2.gacrc.uga.edu) UGA DUO authentication is required for SSH/SCP access to
GACRC systems.

UGA DUO is a two-factor authentication service which
requires a password (one factor) and a code, phone,
or device (second factor) to successfully authenticate.

If you are not enrolled in the UGA DUO service please
visit the UGA DUO service self-service portal to enroll
and configure or manage your DUO enabled devices.

https://archpass.uga.edu

For additional help with UGA DUO authentication or to
report an issue please visit:

https://uga.teamdynamix.com/TDClient/3190/eitsclientportal/KB/Category/23825/ArchPass-powered-by-Duo
Duo two-factor login for shtsai

Enter a passcode or select one of the following options:

1. Duo Push to XXX-XXX-1234

Passcode or option (1-1): 1
Success. Logging you in...
Success. Logging you in...
Last login: Wed Sep 2 13:42:52 2026 from 172.22.72.26
[shtsai@ss-sub4 ~]$
</pre>

At the prompt

<pre class="gscript">
Passcode or option (1-1):
</pre>

you can either enter '''1''' to receive a Duo push to the app on the mobile phone, or open the Duo app in the mobile phone and enter the 6-digit code displayed in the app into the prompt above. For example, if the Duo app in the mobile phone shows "Passcode 401321", you could enter

<pre class="gscript">
Passcode or option (1-1): 401321
</pre>

Duo authentication changes

2026-09-02T18:13:50Z

Shtsai: /* How will these changes affect access to GACRC clusters and servers */

Duo authentication changes

2026-09-02T18:13:27Z

Shtsai: /* How will these changes affect access to GACRC clusters and servers */

Duo authentication changes

2026-09-02T18:10:55Z

Shtsai:

Duo authentication changes

2026-09-02T17:34:27Z

Shtsai: /* New Secure Authentication Options */

Duo authentication changes

2026-09-02T17:33:51Z

Shtsai: Created page with " ==Duo Phone Call and SMS Text Verification Retirement Sept.17, 2026== To enhance security and reduce phishing attempts, Enterprise Information Technology Services (EITS) is updating Duo multi-factor authentication (MFA) methods for all Duo users at the University of Georgia. These changes are part of EITS’ ongoing efforts to better protect UGA accounts. As part of these improvements, EITS will remove the two less secure verification methods, SMS text messages and ph..."

Quick Reference Guide

2026-08-29T20:26:54Z

Shtsai:

=Linux=
Below are the minimum Linux definitions, commands, and shortcuts that you should know to do work on an HPC cluster. Some videos covering introductory Linux commands are available the '''[https://kaltura.uga.edu/playlist/dedicated/176125031/1_uwkiealj/1_81u2kfi2 Kaltura Linux video link ]'''.

==Definitions==
1. '''Command''' - Word or letters typed into a terminal to make a computer do something. Executed by pressing enter after typing the command.

2. '''Command line''' - Literally, the line on which your cursor is on in the terminal. Sometimes used to describe the whole experience of not a graphical user interface, i.e. "Command Line Interface" or "CLI".

3. '''Command prompt''' - The text at the beginning of the command line, before your cursor. Typically [''username''@''computername''].

4. '''Directory''' - Another word for "Folder" (just like a folder in Windows or Mac). Often shortened to "dir" in commands, documentation, conversation, etc...

5. '''Terminal''' - Category of software that allows you to interact with your computer via commands, and can allow you to connect to a remote computer (via a CLI).

6. '''Path''' - The location of a file or directory. A series of subsequent directories to get to some file or directory. e.g., /home/john/file.txt

7. '''First /''' (in a path) - The root directory, where everything starts in the Linux file system.

8. '''Absolute path''' - a path that is written beginning with / (root)

9. '''Relative path''' - a path that is written relative to some other directory (does not begin with /)

10. (Command) '''Option''' - extra letter or word (-''SomeLetter'' or --''SomeWord'') put after a command to modify its behavior.
----
==Commands==
<big>'''Getting Help'''</big> (most important)
{| class="wikitable"
! Command !! Description !! Examples
|-
| <code>''command'' --help</code> || Print a commands help output to the terminal || <code>ls --help</code>
|-
| <code>man ''command''</code> || Read a command's manual (press q to exit) || <code>man ls</code>
|-
|}
<big>'''Directories'''</big>
{| class="wikitable"
! Command !! Description !! Examples
|-
| <code>ls</code> || List the contents of a particular directory (current dir if no path specified) || <code>ls /scratch/'''MyID'''</code>
|-
| <code>mkdir</code> || Make a new directory at the specified path || <code>mkdir /scratch/'''MyID'''/workdir</code>
|-
| <code>cd</code> || Change directory to a particular directory (personal /home dir if no path specified) || <code>cd /scratch/'''MyID'''/workdir</code>
|-
| <code>rmdir</code> || Remove an empty directory || <code>rmdir tmpdir</code>
|}
<big>'''Files'''</big>
{| class="wikitable"
|-
! Command !! Description !! Examples
|-
| <code>nano</code> || Create or edit a file with nano (to save and exit: ctrl + x, y, enter)|| <code>nano sub.sh</code>
|-
| <code>cat</code> || Print the contents of a file to the terminal (better for shorter files) || <code>cat slurm-42342.out</code>
|-
| <code>less</code> || View the contents of a file (better for longer files) || <code>less slurm-42342.out</code>
|-
| <code>cp</code> || Copy a file or files (<code>cp ''sourcepath(s) destinationpath''</code>) || <code>cp /scratch/'''MyID'''/workdir/sub.sh /work/'''labgroup'''</code>
|-
| <code>mv</code> || Move a file or files (<code>mv ''sourcepath(s) destinationpath''</code>) || <code>mv /scratch/'''MyID'''/workdir/sub.sh /work/'''labgroup'''</code>
|-
| <code>rm</code> || Remove a file or files || <code>rm oldfile.txt</code>
|-
| <code>rm -r</code> || Remove a directory and '''all''' of its contents, recursively (starting with the last dir in the path)|| <code>rm -r /home/'''MyID'''/olddir</code>
|-
| <code>dos2unix</code> || Removes DOS/Windows line endings in file || <code>dos2unix file.txt</code>
|-
| <code>mac2unix</code> || Removes Mac line endings in file. || <code>mac2unix file.txt</code>
|-
|}

<big>'''Software'''</big>
{| class="wikitable"
|-
!Command!!Description!!Examples
|-
|<code>ml spider <software name></code>||See if a software is available, will also show you which versions are available||<code>ml spider Blast+</code>
|-
|<code>ml <software name></code>||Load a software module||<code>ml BLAST+/2.16.0-gompi-2024a</code>
|-
|}
Also see the [[Software]] page and [[Frequently Asked Questions#Software|Software]] section of our FAQ.
----
[[#top|Back to Top]]
==Shortcuts ==

'''Linux'''

{| class="wikitable"
|-
!Shortcut!! Description
|-
| ctrl + l (lowercase L)||Clear your terminal screen (scroll up to see previous output)
|-
|up/down arrow keys||Cycle through previously entered commands
|-
|tab||auto-complete a file or directory name when typing a path (tab tab if more than one file begins with the letters you've typed)
|-
|esc, . (escape, then period)||Cycle through previous arguments to commands
|-
|ctrl + w||Backspace typed text on the command line, one word at a time
|-
|ctrl + c|| Cancel typed text on the command line
|-
|}

'''less'''

{| class="wikitable"
|-
!Shortcut!!Description
|-
| shift + g ||Go to the end of file
|-
|gg||Go to the beginning of file
|-
| -N then enter ||Display/hide line numbers
|-
|/''SomePattern''||Search forward through a file for ''SomePattern'' (/ then enter to keep searching forward for the same pattern)
|-
|?''SomePattern''||Search backwards through a file for ''SomePattern'' (? then enter to keep searching forward for the same pattern)
|-
|q ||Exit less
|-
|}
----
[[#top|Back to Top]]

=Slurm=
Below are the minimum Slurm commands that you should know to do work on an HPC cluster. The first table are default Slurm commands. The next table are GACRC pre-formatted wrapper scripts of the Slurm commands to provide more information and flexibility.
<br />
<br />
'''Default Slurm Commands'''
{| class="wikitable"
!Command!!Description!!Examples
|-
|<code>sbatch</code>|| Submit a submission script to the cluster||<code>sbatch sub.sh</code>
|-
|<code>squeue</code>||Check status currently running jobs (--me for only your jobs)||<code>squeue --me</code>
|-
|<code>sacct -X</code>||Check status of your jobs started since midnight of current day (-X for one line per job)||<code>sacct -X</code>
|-
|<code>sacct -X -S yyyy-mm-dd</code>||Check status of your jobs since yyyy-mm-dd||<code>sacct -X -S 2021-10-20</code>
|-
|<code>sacct -j ''jobID'' ||Check status of a particular job||<code>sacct -j 234213</code>
|-
|<code>scancel ''jobID'' ||Cancel one of your running jobs ||<code>scancel 456781</code>
|-
|<code>sinfo</code>||Check the status of partitions||<code>sinfo -p batch</code>
|-
|}
'''GACRC Slurm Commands'''
{| class="wikitable"
!Command!!Description/Slurm Equivalent!!Examples
|-
|<code>interact</code>||Start an interactive job. (Same as <code>qlogin</code> but with more flexibility)||<code>interact -c 4 --mem 10gb --time 01:00:00</code>
|-
|<code>sq</code>||<code>squeue</code>||<code>sq --me</code>
|-
|<code>sacct-gacrc -X</code>||<code>sacct</code>||<code>sacct-gacrc -X</code>
|-
|}

Training

2026-08-24T20:16:25Z

Shtsai: /* Out-Reach/In-Class Talk */

==GACRC Training==

The GACRC regularly hosts training sessions on a number of subjects relevant to the use of our computational and storage resources. Scheduled trainings will be announced through the GACRC mailing list.

'''NOTE: New users are required to attend a Sapelo2 cluster introductory training session and information about that will be sent once an account is requested.'''

==Regular Training Announcement==

In '''September 2026''', the GACRC is hosting 9 training sessions listed below. These training workshops will be offered remotely via Zoom Meeting. Detailed instructions for joining the Zoom meeting will be sent to your UGA email account before each training session you register for.

We will offer:

1. Linux training for Linux-inexperienced cluster new users (3 sessions)

2. Sapelo2 cluster new user training (3 sessions)

3. Using Sapelo2 Cluster at the GACRC, Part II (1 session)

4. Installing Software Packages in Virtual Environments on Sapelo2 (1 session)

5. Job Parallelization with GNU Parallel and Slurm Arrays (1 session)

==Event Schedule==

This section describes the training workshops that we offer, along with the sessions that are currently scheduled.

===Sapelo2 Cluster New User Training===

This mandatory training consists of an overview of the structure of Sapelo2 as well as hands-on practice submitting a job along with guidance and best practices when using the Sapelo2 cluster.

'''Prerequisites:'''

*Linux basics. A Linux-inexperienced user must complete a prerequisite Linux training for Linux-inexperienced cluster new users.

'''Workshop Training Goals:'''

*Understand the layout of Sapelo2

*Understand the Sapelo2 file systems

*Understand the Sapelo2 partitions

*Understand the Sapelo2 software environment

*Understand how to request computing resources and submit a computational batch job following the Sapelo2 cluster general workflow

*Understand how to initiate an interactive job

*Understand how to transfer files to and from the cluster

*Understand how to get support from GACRC support team when you have any issues on cluster

'''Scheduled Sessions:'''

{| class="wikitable unsortable" width="100%" border="1" cellspacing="0" cellpadding="2" align="center"
|-
! scope="col" |Title
! scope="col" |Date/Time
|-
|Using Sapelo2 Cluster at the GACRC
|August 20th, Thursday, 2:00 PM - 4:00 PM
|-
|Using Sapelo2 Cluster at the GACRC
|September 11th, Friday, 2:00 PM - 4:00 PM
|-
|Using Sapelo2 Cluster at the GACRC
|September 16th, Wednesday, 2:00 PM - 4:00 PM
|-
|Using Sapelo2 Cluster at the GACRC
|September 24th, Thursday, 2:00 PM - 4:00 PM
|}

===Linux Training for Linux-inexperienced Cluster New Users===
The Sapelo2 High Performance Computing (HPC) cluster runs a headless Linux distribution as the operating system on each of its constituent nodes. The term headless refers to the fact that these nodes do not have a desktop graphical user interface (GUI) installed by default. Graphical desktop environments consume resources that analyses could otherwise use, so users employ a command-line interface (CLI) instead. To interact with these resources, users connect to a remote terminal via SSH and execute commands.

The Linux Training workshop provides hands-on practice of the fundamental Linux commands necessary to interact with HPC resources.

'''Prerequisite:'''

Please watch the introductory videos on Linux, basic Linux terms, and Linux Paths and Directories (total ~17 minutes) '''before attending the training workshop'''.

*[https://kaltura.uga.edu/media/t/1_81u2kfi2/176125031 Linux]
*[https://kaltura.uga.edu/media/t/1_ol51cuyn/176125031 basic Linux terms]
*[https://kaltura.uga.edu/media/t/1_wdyxhgdg/176125031 Linux Paths and Directories]

'''Training Goals:'''

1. Understand fundamental concepts of Linux working environment (filesystem hierarchy, path, PATH, etc.)

2. Know how to use Linux common commands (ls, cd, pwd, cat, more, nano, mkdir, rm, cp, mv, etc.)

3. Understand what is Linux bash shell and know how to make a simple Linux script and run it in Linux environment

'''Scheduled Sessions:'''

{| class="wikitable unsortable" width="100%" border="1" cellspacing="0" cellpadding="2" align="center"
|-
! scope="col" | Title
! scope="col" |Date/Time
|-
|Use Linux on Cluster
|August 18th, Tuesday, 1:00 PM - 3:00 PM
|-
|Use Linux on Cluster
|September 9th, Wednesday, 1:00 PM - 3:00 PM
|-
|Use Linux on Cluster
|September 14th, Monday, 1:00 PM - 3:00 PM
|-
|Use Linux on Cluster
|September 22nd, Tuesday, 1:00 PM - 3:00 PM
|}

===Using Sapelo2 Cluster at the GACRC, Part II ===
This workshop will cover high-performance computing on Sapelo2, including job scheduling, resource requests (CPU, memory, GPU), and techniques for optimizing job performance.

'''Prerequisites:'''
*Linux basics. A Linux-inexperienced user must complete a prerequisite Linux training for Linux-inexperienced cluster new users.
*Sapelo2 cluster new user training. Fundamental HPC and Sapelo2 knowledge is required for this advanced Sapelo2 workshop.

'''Training Goals:'''

1. Learn about high-performance computing framework

2. Why is my job pending? How can I get my job to start sooner? How to find available computing resources on Sapelo2?

3. How to request computing resources such as nodes, CPU cores, memory, GPU device, etc. to run serial, threaded, MPI, and GPU jobs on Sapelo2?

4. How can I make my job run more efficiently (through the correct use of software and hardware)?

5. A quick intro to MPI library and how to compile/run MPI jobs on Sapelo2

'''Scheduled Sessions:'''

{| class="wikitable unsortable" width="100%" border="1" cellspacing="0" cellpadding="2" align="center"
|-
! scope="col" |Title
! scope="col" |Date/Time
|-
|Using Sapelo2 Cluster at the GACRC, Part II
|September 18th, Friday, 1:00 PM - 3:00 PM
|}

===Installing Software Packages in Virtual Environments on Sapelo2 ===
This workshop will cover the basics of virtual environments as well as provide practical guidance and best practices for using virtual environments on the Sapelo2 cluster. Participants will learn the basics of creating and configuring virtual environments, how to install software packages in both a Conda virtual environment and a Python virtual environment, and manage dependencies in their environments.

'''Prerequisite:'''

Must already have strong understanding of the Linux environment and Sapelo2.

'''Training Goals:'''

1. Learn how to create a virtual environment on Sapelo2

2. Be able to install software packages into both Conda and Python virtual environments

3. Understand how to manage dependencies of their virtual environments

'''Scheduled Sessions:'''

{| class="wikitable unsortable" width="100%" border="1" cellspacing="0" cellpadding="2" align="center"
|-
! scope="col" |Title
! scope="col" |Date/Time
|-
|Installing Software Packages in Virtual Environments on Sapelo2
|September 23rd, Wednesday, 2:00 PM - 2:45 PM
|}

===Job Parallelization with GNU Parallel and Slurm Arrays ===
Learn how to run multiple commands in parallel using GNU Parallel and Slurm Arrays. These tools greatly reduce the runtime of certain types of jobs by running multiple instances of the same command in parallel. The workshop focuses on problems that involve executing the same command on multiple different inputs. This workshop is intended for users comfortable writing job submission scripts and using a command line. Concurrent and parallel programming techniques are not covered in this workshop.

'''Prerequisite:'''

Must already have strong understanding of the Linux environment and Sapelo2.

'''Training Goals:'''

1. Understand which jobs benefit from GNU Parallel and Slurm Arrays

2. Use GNU Parallel and Slurm Arrays to parallelize jobs

3. Understand the differences and similarities between GNU Parallel and Slurm Arrays

'''Scheduled Sessions:'''

{| class="wikitable unsortable" width="100%" border="1" cellspacing="0" cellpadding="2" align="center"
|-
! scope="col" |Title
! scope="col" |Date/Time
|-
|Job Parallelization with GNU Parallel and Slurm Arrays
|September 25th, Friday, 1:00 PM - 3:00 PM
|}



==How to Register==

The training workshops '''Using Sapelo2 Cluster at the GACRC''' and '''Use Linux on Cluster''' are '''ONLY''' offered to '''new users''' who need user accounts on the GACRC Sapelo2 cluster or current Sapelo2 users seeking a refresher. If you would like to use the cluster, please ask your group PI/UGA faculty member to send us an account creation request for you, using the [https://uga.teamdynamix.com/TDClient/Requests/ServiceDet?ID=25839 GACRC User Account Request Form].

If you would like to attend the '''Using Sapelo2 Cluster at the GACRC, Part II''', the '''Installing Software Packages in Virtual Environments on Sapelo2''', and/or the '''Job Parallelization with GNU Parallel and Slurm Arrays''' training workshops, please send us a request using the [https://uga.teamdynamix.com/TDClient/Requests/ServiceDet?ID=25852 GACRC Training Request Form]. In your request, please tell us which session(s) you would like to attend.



== Download==

This section provides the slides that we use for our current workshops and material used for several of our past training events and presentations.

===Sapelo2 Cluster Training===

{| class="wikitable unsortable" width="100%" border="1" cellspacing="0" cellpadding="2" align="center"
|[[Media:GACRC_Sapelo2_cluster_new_user_training_workshop_v10.8.pdf]]
|-
|}

===Teaching Cluster Training===

{| class="wikitable unsortable" width="100%" border="1" cellspacing="0" cellpadding="2" align="center"
|-
|[[Media:GACRC-Teaching-cluster-new-user-training-workshop-Spring2026.pdf]]
|-
|}

===Linux Training for New Cluster Users===

{| class="wikitable unsortable" width="100%" border="1" cellspacing="0" cellpadding="2" align="center"
|-
|[[Media:Linux_Training_For_New_Users_Of_Cluster_Suchi_04252019.pdf]]
|-
|}

===Installing Software Packages in Virtual Environments on Sapelo2===

{| class="wikitable unsortable" width="100%" border="1" cellspacing="0" cellpadding="2" align="center"
|-
|[[Media:GACRC_virtual_environments_training_v1.2.pdf]]
|}

===Job Parallelization with GNU Parallel and Slurm Arrays===

{| class="wikitable unsortable" width="100%" border="1" cellspacing="0" cellpadding="2" align="center"
|-
|[[Media:GNU_Parallel_and_SLURM_Arrays_v1.2.pdf]]
|}



===Topical Sessions===

{| class="wikitable unsortable" width="100%" border="1" cellspacing="0" cellpadding="2" align="center"
|
|-
|[[Media:AI_Resources_on_the_GACRC_Sapelo2_Cluster.pdf]]
|-
|[[Media:Using_Sapelo2_Cluster_at_the_GACRC_Part_II_Rocky8.pdf]]

|}

===Out-Reach/In-Class Talk===

{| class="wikitable unsortable" width="100%" border="1" cellspacing="0" cellpadding="2" align="center"
|-
! scope="col" |Dept./Center/Institute
! scope="col" |Type
! scope="col" |Workshop PDF
|-
|Fall 2026
|
|[[Media:GACRC_Teaching_Cluster_Training-Python.pdf]]
|-
|Fall 2026
|
|[[Media:GACRC_Teaching_Cluster_Training-Amber.pdf]]
|-
|GEOG - Spring2026 || In-Class || [[Media:GACRC-Teaching-cluster-new-user-training-workshop-python-Spring2026.pdf]]
|-
|BCMB8330 - Spring2026 || In-Class || [[Media:GACRC-Teaching-cluster-new-user-training-workshop_bcmb8330_Spring2026.pdf]]
|-
|CSP seminar - Fall 2026|| Out-Reach || [[Media:GACRC_overview_20260825_CSP.pdf]]

|}

'''NOTE:''' The slides may become outdated and you should always check GACRC Wiki for up to date information.

File:GACRC overview 20260825 CSP.pdf

2026-08-24T20:14:57Z

Shtsai:

Job Submission partitions on Sapelo2

2026-08-16T01:23:50Z

Shtsai:

[[category:sapelo2]]

== Overview ==
This page describes the Slurm partitions available on the Sapelo2 cluster, including job limits and the resources available in each partition.

In Slurm, queues are called ''partitions''. When you submit a job, you must request both:
* the partition to use, and
* the resources your job needs, such as CPU cores, memory, or GPU devices.

Slurm will reject a job submission if no nodes match the resources you request. For background on Slurm, see [[Migrating from Torque to Slurm]].

== How to use this page ==
Use the first table to choose a partition based on job type and time limit.

Use the second table to confirm that your requested resources fit within the hardware available in that partition.

== Partition limits ==
{| class="wikitable unsortable"
|+ Sapelo2 partitions, time limits, and per-user job limits
|-
! scope="col" | Partition name
! scope="col" | Time limit
! scope="col" | Maximum running jobs per user
! scope="col" | Maximum submitted jobs per user
! scope="col" | Intended use and notes
|-
! scope="row" | <code>batch</code>
| 7 days
| 250
| 10,000
| Standard partition for regular compute jobs on general-purpose nodes.
|-
! scope="row" | <code>batch_30d</code>
| 30 days
| 1
| 2
| Standard partition for long-running jobs on regular nodes. A user may have one running job and one pending job, or two pending jobs and no running job. A third submission to this partition will be rejected.
|-
! scope="row" | <code>highmem_p</code>
| 7 days
| 6
| 100
| High-memory partition for jobs that require more memory than standard nodes provide.
|-
! scope="row" | <code>highmem_30d_p</code>
| 30 days
| 1
| 2
| High-memory partition for long-running jobs. A user may have one running job and one pending job, or two pending jobs and no running job. A third submission to this partition will be rejected.
|-
! scope="row" | <code>hugemem_p</code>
| 7 days
| 4
| 4
| Huge-memory partition for jobs needing up to 3 TB of memory.
|-
! scope="row" | <code>hugemem_30d_p</code>
| 30 days
| 4
| 4
| Huge-memory partition for long-running jobs needing up to 3 TB of memory.
|-
! scope="row" | <code>gpu_p</code>
| 7 days
| 8
| 20
| GPU-enabled partition for jobs that require one or more GPUs.
|-
! scope="row" | <code>gpu_30d_p</code>
| 30 days
| 2
| 2
| GPU-enabled partition for long-running jobs. A user may have one running job and one pending job, or two pending jobs and no running job. A third submission to this partition will be rejected.
|-
! scope="row" | <code>inter_p</code>
| 2 days
| 3
| 20
| Interactive partition for interactive jobs on regular nodes.
|-
! scope="row" | <code>''name''_p</code>
| Variable
| Variable
| Variable
| Partition for a specific group's buy-in nodes. Replace <code>''name''</code> with the group-specific partition prefix.
|}

== Resource limits by partition ==
Before submitting a job, make sure your requested memory, CPU cores, and GPU count fit within the limits of the partition you choose.

In the table below, the phrase '''partition maximum''' identifies the largest per-node resource values available within that partition. This replaces color-only emphasis so that the information is available to all users.

{| class="wikitable unsortable"
|+ Node resources available in each Sapelo2 partition
|-
! scope="col" | Partition
! scope="col" | Number of nodes
! scope="col" | Memory per node (GB)
! scope="col" | CPU cores per node
! scope="col" | Processor type
! scope="col" | GPU configuration
! scope="col" | Notes
|-
! scope="row" | <code>batch</code>, <code>batch_30d</code>
| 1
| 740
| 384
| AMD EPYC Turin (5th gen)
| None
| Partition maximum for memory and cores is available on this node type.
|-
! scope="row" | <code>batch</code>, <code>batch_30d</code>
| 64
| 740
| 128
| AMD EPYC Genoa (4th gen)
| None
| Partition maximum for memory is available on this node type.
|-
! scope="row" | <code>batch</code>, <code>batch_30d</code>
| 120
| 500
| 128
| AMD EPYC Milan (3rd gen)
| None
| Standard-capacity general-purpose nodes.
|-
! scope="row" | <code>batch</code>, <code>batch_30d</code>
| 4
| 250
| 64
| AMD EPYC Milan (3rd gen)
| None
| Standard-capacity general-purpose nodes.
|-
! scope="row" | <code>batch</code>, <code>batch_30d</code>
| 2
| 120
| 64
| AMD EPYC Milan (3rd gen)
| None
| Standard-capacity general-purpose nodes.
|-
! scope="row" | <code>batch</code>, <code>batch_30d</code>
| 123
| 120
| 64
| AMD EPYC Rome (2nd gen)
| None
| Standard-capacity general-purpose nodes.
|-
! scope="row" | <code>batch</code>, <code>batch_30d</code>
| 25
| 120
| 32
| AMD EPYC Naples (1st gen)
| None
| Lower-core-count general-purpose nodes.
|-
! scope="row" | <code>batch</code>, <code>batch_30d</code>
| 40
| 180
| 32
| Intel Xeon Skylake
| None
| Lower-core-count general-purpose nodes.
|-
! scope="row" | <code>highmem_p</code>, <code>highmem_30d_p</code>
| 10
| 500
| 32
| AMD EPYC Naples (1st gen)
| None
| High-memory nodes.
|-
! scope="row" | <code>highmem_p</code>, <code>highmem_30d_p</code>
| 2
| 990
| 128
| AMD EPYC Milan (3rd gen)
| None
| Partition maximum for memory and cores is available on this node type.
|-
! scope="row" | <code>highmem_p</code>, <code>highmem_30d_p</code>
| 12
| 990
| 32
| AMD EPYC Milan (3rd gen)
| None
| High-memory nodes with fewer available cores per node.
|-
! scope="row" | <code>hugemem_p</code>, <code>hugemem_30d_p</code>
| 3
| 3000
| 48
| AMD EPYC Genoa (4th gen)
| None
| Partition maximum for memory and cores is available on this node type.
|-
! scope="row" | <code>hugemem_p</code>, <code>hugemem_30d_p</code>
| 2
| 2000
| 32
| AMD EPYC Rome (2nd gen)
| None
| Huge-memory nodes.
|-
! scope="row" | <code>gpu_p</code>, <code>gpu_30d_p</code>
| 1
| 180
| 32
| Intel Xeon Skylake
| 1 NVIDIA P100
| Older GPU nodes.
|-
! scope="row" | <code>gpu_p</code>, <code>gpu_30d_p</code>
| 2
| 120
| 64
| AMD EPYC Rome (2nd gen)
| 1 NVIDIA V100S
| Single-GPU nodes with 64 cores.
|-
! scope="row" | <code>gpu_p</code>, <code>gpu_30d_p</code>
| 14
| 1000
| 64
| AMD EPYC Milan (3rd gen)
| 4 NVIDIA A100
| Partition maximum for memory is available on this node type.
|-
! scope="row" | <code>gpu_p</code>, <code>gpu_30d_p</code>
| 12
| 1000
| 64
| Intel Xeon Sapphire Rapids
| 4 NVIDIA H100
| Partition maximum for memory is available on this node type.
|-
! scope="row" | <code>gpu_p</code>, <code>gpu_30d_p</code>
| 12
| 740
| 128
| AMD EPYC Genoa (4th gen)
| 4 NVIDIA L4
| Partition maximum for cores is available on this node type.
|-
! scope="row" | <code>''name''_p</code>
| Variable
| Variable
| Variable
| Variable
| Variable
| Resource limits depend on the group's buy-in nodes.
|}

== Choosing a partition ==
A general rule of thumb is:

* Use <code>batch</code> for most non-GPU jobs.
* Use <code>batch_30d</code> only when your job genuinely needs a longer wall time.
* Use <code>highmem_p</code> or <code>highmem_30d_p</code> when your memory requirements exceed what standard nodes provide.
* Use <code>hugemem_p</code> or <code>hugemem_30d_p</code> for jobs that need very large memory allocations, including jobs approaching 3 TB of memory.
* Use <code>gpu_p</code> or <code>gpu_30d_p</code> for GPU jobs.
* Use <code>inter_p</code> for interactive work.
* Use <code>''name''_p</code> only if your group has access to a buy-in partition with that name.



== Terms used on this page ==
; Partition
: A Slurm queue that determines which nodes your job may run on.
; Time limit
: The maximum wall-clock runtime allowed for a job in that partition.
; Running jobs
: Jobs currently executing for a user in that partition.
; Submitted jobs
: Total jobs a user may have in the partition, including running and pending jobs.
; Buy-in nodes
: Nodes purchased by a specific group and made available through a group-specific partition.

== Related documentation ==
* [[Migrating from Torque to Slurm]]

Job Submission partitions on Sapelo2

2026-08-16T01:19:30Z

Shtsai:

[[category:sapelo2]]

== Overview ==
This page describes the Slurm partitions available on the Sapelo2 cluster, including job limits and the resources available in each partition.

In Slurm, queues are called ''partitions''. When you submit a job, you must request both:
* the partition to use, and
* the resources your job needs, such as CPU cores, memory, or GPU devices.

Slurm will reject a job submission if no nodes match the resources you request. For background on Slurm, see [[Migrating from Torque to Slurm]].

== How to use this page ==
Use the first table to choose a partition based on job type and time limit.

Use the second table to confirm that your requested resources fit within the hardware available in that partition.

== Partition limits ==
{| class="wikitable unsortable"
|+ Sapelo2 partitions, time limits, and per-user job limits
|-
! scope="col" | Partition name
! scope="col" | Time limit
! scope="col" | Maximum running jobs per user
! scope="col" | Maximum submitted jobs per user
! scope="col" | Intended use and notes
|-
! scope="row" | <code>batch</code>
| 7 days
| 250
| 10,000
| Standard partition for regular compute jobs on general-purpose nodes.
|-
! scope="row" | <code>batch_30d</code>
| 30 days
| 1
| 2
| Standard partition for long-running jobs on regular nodes. A user may have one running job and one pending job, or two pending jobs and no running job. A third submission to this partition will be rejected.
|-
! scope="row" | <code>highmem_p</code>
| 7 days
| 6
| 100
| High-memory partition for jobs that require more memory than standard nodes provide.
|-
! scope="row" | <code>highmem_30d_p</code>
| 30 days
| 1
| 2
| High-memory partition for long-running jobs. A user may have one running job and one pending job, or two pending jobs and no running job. A third submission to this partition will be rejected.
|-
! scope="row" | <code>hugemem_p</code>
| 7 days
| 4
| 4
| Huge-memory partition for jobs needing up to 3 TB of memory.
|-
! scope="row" | <code>hugemem_30d_p</code>
| 30 days
| 4
| 4
| Huge-memory partition for long-running jobs needing up to 3 TB of memory.
|-
! scope="row" | <code>gpu_p</code>
| 7 days
| 8
| 20
| GPU-enabled partition for jobs that require one or more GPUs.
|-
! scope="row" | <code>gpu_30d_p</code>
| 30 days
| 2
| 2
| GPU-enabled partition for long-running jobs. A user may have one running job and one pending job, or two pending jobs and no running job. A third submission to this partition will be rejected.
|-
! scope="row" | <code>inter_p</code>
| 2 days
| 3
| 20
| Interactive partition for interactive jobs on regular nodes.
|-
! scope="row" | <code>''name''_p</code>
| Variable
| Variable
| Variable
| Partition for a specific group's buy-in nodes. Replace <code>''name''</code> with the group-specific partition prefix.
|}

== Resource limits by partition ==
Before submitting a job, make sure your requested memory, CPU cores, and GPU count fit within the limits of the partition you choose.

In the table below, the phrase '''partition maximum''' identifies the largest per-node resource values available within that partition. This replaces color-only emphasis so that the information is available to all users.

{| class="wikitable unsortable"
|+ Node resources available in each Sapelo2 partition
|-
! scope="col" | Partition
! scope="col" | Number of nodes
! scope="col" | Memory per node (GB)
! scope="col" | CPU cores per node
! scope="col" | Processor type
! scope="col" | GPU configuration
! scope="col" | Notes
|-
! scope="row" | <code>batch</code>, <code>batch_30d</code>
| 1
| 740
| 384
| AMD EPYC Turin (5th gen)
| None
| Partition maximum for memory and cores is available on this node type.
|-
! scope="row" | <code>batch</code>, <code>batch_30d</code>
| 64
| 740
| 128
| AMD EPYC Genoa (4th gen)
| None
| Partition maximum for memory is available on this node type.
|-
! scope="row" | <code>batch</code>, <code>batch_30d</code>
| 120
| 500
| 128
| AMD EPYC Milan (3rd gen)
| None
| Standard-capacity general-purpose nodes.
|-
! scope="row" | <code>batch</code>, <code>batch_30d</code>
| 4
| 250
| 64
| AMD EPYC Milan (3rd gen)
| None
| Standard-capacity general-purpose nodes.
|-
! scope="row" | <code>batch</code>, <code>batch_30d</code>
| 2
| 120
| 64
| AMD EPYC Milan (3rd gen)
| None
| Standard-capacity general-purpose nodes.
|-
! scope="row" | <code>batch</code>, <code>batch_30d</code>
| 123
| 120
| 64
| AMD EPYC Rome (2nd gen)
| None
| Standard-capacity general-purpose nodes.
|-
! scope="row" | <code>batch</code>, <code>batch_30d</code>
| 25
| 120
| 32
| AMD EPYC Naples (1st gen)
| None
| Lower-core-count general-purpose nodes.
|-
! scope="row" | <code>batch</code>, <code>batch_30d</code>
| 40
| 180
| 32
| Intel Xeon Skylake
| None
| Lower-core-count general-purpose nodes.
|-
! scope="row" | <code>highmem_p</code>, <code>highmem_30d_p</code>
| 10
| 500
| 32
| AMD EPYC Naples (1st gen)
| None
| High-memory nodes.
|-
! scope="row" | <code>highmem_p</code>, <code>highmem_30d_p</code>
| 2
| 990
| 128
| AMD EPYC Milan (3rd gen)
| None
| Partition maximum for memory and cores is available on this node type.
|-
! scope="row" | <code>highmem_p</code>, <code>highmem_30d_p</code>
| 12
| 990
| 32
| AMD EPYC Milan (3rd gen)
| None
| High-memory nodes with fewer available cores per node.
|-
! scope="row" | <code>hugemem_p</code>, <code>hugemem_30d_p</code>
| 3
| 3000
| 48
| AMD EPYC Genoa (4th gen)
| None
| Partition maximum for memory and cores is available on this node type.
|-
! scope="row" | <code>hugemem_p</code>, <code>hugemem_30d_p</code>
| 2
| 2000
| 32
| AMD EPYC Rome (2nd gen)
| None
| Huge-memory nodes with lower maximums than the partition peak.
|-
! scope="row" | <code>gpu_p</code>, <code>gpu_30d_p</code>
| 2
| 180
| 32
| Intel Xeon Skylake
| 1 NVIDIA P100
| Older GPU nodes.
|-
! scope="row" | <code>gpu_p</code>, <code>gpu_30d_p</code>
| 2
| 120
| 64
| AMD EPYC Rome (2nd gen)
| 1 NVIDIA V100S
| Single-GPU nodes with 64 cores.
|-
! scope="row" | <code>gpu_p</code>, <code>gpu_30d_p</code>
| 14
| 1000
| 64
| AMD EPYC Milan (3rd gen)
| 4 NVIDIA A100
| Partition maximum for memory is available on this node type.
|-
! scope="row" | <code>gpu_p</code>, <code>gpu_30d_p</code>
| 12
| 1000
| 64
| Intel Xeon Sapphire Rapids
| 4 NVIDIA H100
| Partition maximum for memory is available on this node type.
|-
! scope="row" | <code>gpu_p</code>, <code>gpu_30d_p</code>
| 12
| 740
| 128
| AMD EPYC Genoa (4th gen)
| 4 NVIDIA L4
| Partition maximum for cores is available on this node type.
|-
! scope="row" | <code>''name''_p</code>
| Variable
| Variable
| Variable
| Variable
| Variable
| Resource limits depend on the group's buy-in nodes.
|}

== Choosing a partition ==
A general rule of thumb is:

* Use <code>batch</code> for most non-GPU jobs.
* Use <code>batch_30d</code> only when your job genuinely needs a longer wall time.
* Use <code>highmem_p</code> or <code>highmem_30d_p</code> when your memory requirements exceed what standard nodes provide.
* Use <code>hugemem_p</code> or <code>hugemem_30d_p</code> for jobs that need very large memory allocations, including jobs approaching 3 TB of memory.
* Use <code>gpu_p</code> or <code>gpu_30d_p</code> for GPU jobs.
* Use <code>inter_p</code> for interactive work.
* Use <code>''name''_p</code> only if your group has access to a buy-in partition with that name.



== Terms used on this page ==
; Partition
: A Slurm queue that determines which nodes your job may run on.
; Time limit
: The maximum wall-clock runtime allowed for a job in that partition.
; Running jobs
: Jobs currently executing for a user in that partition.
; Submitted jobs
: Total jobs a user may have in the partition, including running and pending jobs.
; Buy-in nodes
: Nodes purchased by a specific group and made available through a group-specific partition.

== Related documentation ==
* [[Migrating from Torque to Slurm]]

Systems

2026-08-16T01:15:34Z

Shtsai:

[[Category:Sapelo2]]
[[Category:Teaching]]



=== Sapelo2 ===

Sapelo2 is a Linux cluster that runs a 64-bit Rocky 9.5 operating system and it is managed using Warewulf. Several virtual login nodes are available, with Intel Xeon Gold 6230 processors, 32GB of RAM, and 16 cores per node. The queueing system on Sapelo2 is Slurm.

Internodal communication among the compute nodes and between these nodes and the storage systems serving the home directories and the scratch directories is provided by an EDR Infiniband network (100Gbps).

The cluster is currently comprised of the following resources:

'''Regular nodes'''

* 64 compute nodes with AMD EPYC (Genoa 4th gen) processors (128 cores and 745GB of RAM per node)
* 120 compute nodes with AMD EPYC (Milan 3rd gen) processors (128 cores and 512GB of RAM per node)
* 4 compute nodes with AMD EPYC (Milan 3rd gen) processors (64 cores and 256GB of RAM per node)
* 2 compute nodes with AMD EPYC (Milan 3rd gen) processors (64 cores and 128GB of RAM per node)
* 123 compute nodes with AMD EPYC (Rome 2nd gen) processors (64 cores and 128GB of RAM per node)
* 25 compute nodes with AMD EPYC (Naples 1st gen) processors (32 cores and 128GB of RAM per node)
* 42 compute nodes with Intel Xeon Skylake processors (32 cores and 192GB of RAM per node)

'''High memory nodes (3TB/node)'''

* 3 compute nodes with AMD EPYC (Genoa 4th gen) processors (48 cores and 3TB of RAM per node)

'''High memory nodes (2TB/node)'''

* 2 compute nodes with AMD EPYC (Rome 2nd gen) processors (32 cores and 2TB of RAM per node)

'''High memory nodes (1TB/node)'''

* 2 compute nodes with AMD EPYC (Milan 3rd gen) processors (128 cores and 1TB of RAM per node)
* 12 compute nodes with AMD EPYC (Milan 3rd gen) processors (32 cores and 1TB of RAM per node)

'''High memory nodes (512GB/node)'''

* 10 compute nodes with AMD EPYC (Naples 1st gen) processors (32 cores and 512GB of RAM per node)


'''GPU nodes'''

* 12 compute nodes with Intel Xeon SapphireRapids processors (64 cores and 1TB of RAM) and 4x NVIDIA H100 GPU cards.
* 12 compute nodes with AMD EPYC (Genoa 4th gen) processors (128 cores and 745GB of RAM) and 4x NVIDIA L4 GPU cards.
* 14 compute nodes with AMD EPYC (Milan 3rd gen) processors (64 cores and 1TB of RAM) and 4x NVIDIA A100 GPU cards.
* 2 compute nodes with Intel Xeon Skylake processors (32 cores and 187GB of RAM) and 1x NVIDIA P100 GPU card per node


'''Buy-in nodes'''

* Various configurations


====[[Connecting#Connecting_to_Sapelo2 |Connecting to Sapelo2]]====

====[[Transferring Files]]====

====[[Disk Storage]]====

====[[Software on Sapelo2]]====

====[[Available Toolchains and Toolchain Compatibility]]====

====[[Code Compilation on Sapelo2]]====

====[[Running Jobs on Sapelo2]]====

====[[Monitoring Jobs on Sapelo2]]====

====[[Migrating from Torque to Slurm]]====

'''Training material'''

To help users familiarize with Slurm and the test cluster environment, we have prepared some training videos that are available from the GACRC's Kaltura channel at https://kaltura.uga.edu/channel/GACRC/176125031 (login with MyID and password is required). Training sessions and slides are available at https://wiki.gacrc.uga.edu/wiki/Training

----
[[#top|Back to Top]]



=== Teaching cluster ===

The teaching cluster is a Linux cluster that runs a 64-bit Linux, with Rocky 9.5. The login node is a VM that has 4 cores (Intel Xeon Gold 6230 processor) and 16GB of RAM. An EDR Infiniband network (100Gbps) provides internodal communication among compute nodes, and between the compute nodes and the storage systems serving the home directories and the work directories.

The cluster is currently comprised of the following resources:

'''Regular nodes:'''

* 10 compute nodes with AMD EPYC (Naples 1st gen) processors (32 cores and 128GB or RAM per node)

'''High-memory nodes:'''

* 2 compute nodes with AMD EPYC (Naples 1st gen) processors (64 cores and 1TB of RAM per node)

'''GPU nodes:'''

* 1 compute node with Intel Skylake processors (32 cores, 192GB RAM per node) and a P100 GPU card

* 2 compute nodes with Intel SapphireRapids processors (96 cores, 1TB RAM per node) and 4 A30 GPU cards (for instructors associated with the Franklin College only)



The queueing system on the teaching cluster is Slurm.

====[[Connecting#Connecting_to_the_teaching_cluster |Connecting to the teaching cluster]]====

====[[Transferring Files]]====

====Software Installed on the teaching cluster====

The teaching cluster has access to the same software stack installed on Sapelo2.

====[[Code Compilation on the teaching cluster]]====

====[[Running Jobs on the teaching cluster]]====

====[[Monitoring Jobs on the teaching cluster]]====

How to Install and Configure PuTTY

2026-08-14T15:46:23Z

Shtsai: /* Downloading PuTTY */

[[Category:Sapelo2]][[Category:Teaching]]
__TOC__

==Introduction==

[https://www.chiark.greenend.org.uk/~sgtatham/putty/latest.html PuTTY] is an SSH and telnet client, developed originally by Simon Tatham for the Windows platform. PuTTY is open source software that is available with source code and is developed and supported by a group of volunteers.

Please note, these screenshots may become out of date as newer versions of PuTTY are released.

==Downloading PuTTY==

PuTTY can be directly downloaded from [https://www.chiark.greenend.org.uk/~sgtatham/putty/latest.html Link to Download PuTTY]. Click the '''64-bit x86''' MSI installer as shown in the following screenshot:

[[File:Puttydl.png]]

==Installing PuTTY==

Now that you have downloaded the PuTTY installer, click the installer as shown in the bottom-left corner of your browser

[[File:Putty19.png]]

You will see a new window appear. Click "Next."

[[File:Putty4.png]]

Click "Next once more.

[[File:Putty5.png]]

In the next setup screen, click the red "X" next to "Add shortcut to PuTTY on the Desktop, and select "Entire feature will be installed on local hard drive."

[[File:Putty6.png]]

[[File:Putty7.png]]

Next, click "Install." A window will appear in the middle of your screen asking "Do you want to allow this app to make changes to your device?" Click "Yes."

[[File:Putty8.png]]

Once the installation has completed, you will see this screen as shown below. You may un-check the "View README" file if you wish, and then click "Finish."

[[File:Putty9.png]]

==Configuring PuTTY==

'''Please Note:''' If you're off-campus or not connected to a UGA network, you will need to connect to UGA's '''remote.uga.edu''' VPN via the '''Cisco AnyConnect''' client before you can successfully connect to GACRC resources with PuTTY (or any other similar program). If you are on campus, connected to a UGA network, you don't have to worry about that. If you have set up Cisco AnyConnect before, more information can be found [https://eits.uga.edu/access_and_security/infosec/tools/vpn/ here]

Now That PuTTY is installed on your computer, you can follow these steps to connect to GACRC resources (to which you are authorized to connect) with it. Double-click the PuTTY shortcut on your desktop, and you will see the following window (If you didn't put a shortcut on your desktop, search for "putty" in the windows search bar in the bottom left of your screen, next to the start button. Click the one that says "PuTTY", ''not'' PuTTYgen). You only have to edit these two text fields:

[[File:Putty10_2.png]]

First, type ''YourMyID''@''hostname'' in the "Host Name (or IP address)" text field. ''hostname'' is the hostname of the GACRC resource to which you are connecting. Examples of those may be:
{| class="wikitable"
|+
!GACRC Resource
!Hostname
|-
|Linux Training Computer
|trainer.gacrc.uga.edu
|-
|Sapelo2 Login Nodes
|sapelo2.gacrc.uga.edu
|-
|Sapelo2 Transfer Nodes
|xfer.gacrc.uga.edu
|-
|Teaching Cluster Login Nodes
|teach.gacrc.uga.edu
|-
|Teaching Cluster Transfer Nodes
|txfer.gacrc.uga.edu
|}

So for example, if my MyID were abc12345, and I was going to connect to the GACRC Linux training computer, then in the "Host Name (or IP address)" field, I would type abc12345@trainer.gacrc.uga.edu

To save yourself the time, you can have PuTTY save what you type in the "Host Name (or IP address)" field by giving the connection a name in the "Saved Sessions" text field. In the screenshot below, I called mine "sapelo2", because I'm connecting to the Sapelo2 login nodes in that screenshot. The naming here is arbitrary. It's just a label for the text in the "Host Name (or IP address)" field. After typing a name for the connection in the "Saved Sessions" field, click the "Save" button as shown below.

[[File:Putty11_2.png]]

Now the next time you open PuTTY, you can just click the label you made before, and then click "Load", and then "Open", to open a connection to the GACRC resource.

[[File:Putty12.png]]

The first time you connect to a new computer with PuTTY, it will give you this security alert window, basically asking if you trust the computer you're connecting to. Answer affirmatively to the prompt shown and it won't ask you that again from that computer.

[[File:Puttyacceptkey.png |frameless|850px]]]

Now you're ready to enter your MyID password. <big>'''It will look like you're not typing anything, but it is accepting your keyboard input.'''</big> Just type your password and press enter. '''If it asks you for your password again''', that could mean several things:

* Your password was typed incorrectly (make sure caps lock is off)
* Your username was typed incorrectly in PuTTY
* You are trying to access something you have not been given access to (in this case it would say something like "Access Denied".

Most GACRC resources (except the Linux training computer), require authentication with ArchPass Duo, after a successful username/password authentication. As you can see in the screenshot below, you can type a 1, 2, or 3, depending on how you want to authenticate with ArchPass Duo. Most people use option 1. So you could just type 1 in the prompt, and then press enter. If have not set up ArchPass Duo on your mobile device, you can find more information about setting that up [https://eits.uga.edu/access_and_security/infosec/tools/archpass/ here]

[[File:Puttyduo.png | frameless|850px]]]

Once you successfully connect to a GACRC resource, you will see a command prompt with your username @ the name of the computer you're connected to, followed by a cursor, waiting for you to type a command. In the screenshot below, I connected to the Sapelo2 login nodes, and it put me on ss-sub3.

[[File:Puttyduosuccess.png | frameless|850px]]

Using a Conda environment in Jupyter

2026-08-11T20:20:00Z

Shtsai: /* Open Jupyter in Open OnDemand */

== Conda environment ==
A Conda environment is a type of virtual environment. Conda environments are used to contain a set of software/packages that you install (via Conda) into that environment that may be accessed any time you activate the environment. To learn more about Conda environments, please see our wiki page on creating Conda environments at '''[[Installing Applications on Sapelo2#How to install Conda packages|Link to wiki page on how to install Conda environments]].'''

== Open OnDemand ==
Open OnDemand is a web-based service that allows users to run software with a graphical user interface (GUI) such as Jupyter Notebook. Please see our [[OnDemand|Open OnDemand wiki page]] to learn more.

== Using a Conda environment in Jupyter ==
These steps will allow you to access the software you install into a Conda environment within Jupyter Notebook running on Open OnDemand

=== Create the Conda environment ===
Create your Conda environment and install any packages you will want to use.
=== Install ipykernel ===
While your Conda environment is still activated, install ipykernel to your Conda environment with:

<pre class="gcommand">
conda install ipykernel
python -m ipykernel install --user --name=my_env
</pre>
Note: change my_env to whatever the name of your Conda environment is. This is the name that you will see in Jupyter Notebook as an available kernel.

=== Open Jupyter in Open OnDemand ===
Click on the Jupyter app in [https://ondemand.gacrc.uga.edu/ Open OnDemand access URL] and choose your desired resources. Please ensure that you choose the Miniforge3 version of Jupyter.

Click launch at the bottom of the page to start a Jupyter Notebook session. You will be redirected to "My Interactive Sessions" where you can wait for the job to be allocated resources.<br>

[[File:Screenshot 2023-12-13 at 10.25.00 AM.png|alt=|border|700x700px]]

Once the job starts, click on Connect to Jupyter. This will open Jupyter in a new page in your browser.

'''For Jupyter versions < 7.0.0 please see below''' (for versions > 7.0.0, scroll down).

From here, click "new" and you should see the name of the ipykernel you created. Clicking on that will start a Jupyter notebook with a kernel that has access to all of the packages in your Conda environment.

[[File:Load_condakernel.png|alt=|border|1111x1111px]]

'''For Jupyter versions > 7.0.0 please see below.'''

From here, click File > new > notebook. A new Jupyter notebook will open and a window will appear titled "Select Kernel". Click the drop down menu and under "Start Other Kernel" you should see the name of your Conda environment. Clicking on that will start your Jupyter notebook with a kernel that has access to all of the packages in your Conda environment.

[[File:New-jupyter-interface.png|alt=|border|1111x1111px]]
[[File:Load_new_condakernel.png|alt=|border|1111x1111px]]

Using a Conda environment in Jupyter

2026-08-11T20:18:53Z

Shtsai:

== Conda environment ==
A Conda environment is a type of virtual environment. Conda environments are used to contain a set of software/packages that you install (via Conda) into that environment that may be accessed any time you activate the environment. To learn more about Conda environments, please see our wiki page on creating Conda environments at '''[[Installing Applications on Sapelo2#How to install Conda packages|Link to wiki page on how to install Conda environments]].'''

== Open OnDemand ==
Open OnDemand is a web-based service that allows users to run software with a graphical user interface (GUI) such as Jupyter Notebook. Please see our [[OnDemand|Open OnDemand wiki page]] to learn more.

== Using a Conda environment in Jupyter ==
These steps will allow you to access the software you install into a Conda environment within Jupyter Notebook running on Open OnDemand

=== Create the Conda environment ===
Create your Conda environment and install any packages you will want to use.
=== Install ipykernel ===
While your Conda environment is still activated, install ipykernel to your Conda environment with:

<pre class="gcommand">
conda install ipykernel
python -m ipykernel install --user --name=my_env
</pre>
Note: change my_env to whatever the name of your Conda environment is. This is the name that you will see in Jupyter Notebook as an available kernel.

=== Open Jupyter in Open OnDemand ===
Click on the Jupyter app in [https://ondemand.gacrc.uga.edu/ Open OnDemand] and choose your desired resources. Please ensure that you choose the Miniforge3 version of Jupyter.

Click launch at the bottom of the page to start a Jupyter Notebook session. You will be redirected to "My Interactive Sessions" where you can wait for the job to be allocated resources.<br>

[[File:Screenshot 2023-12-13 at 10.25.00 AM.png|alt=|border|700x700px]]

Once the job starts, click on Connect to Jupyter. This will open Jupyter in a new page in your browser.

'''For Jupyter versions < 7.0.0 please see below''' (for versions > 7.0.0, scroll down).

From here, click "new" and you should see the name of the ipykernel you created. Clicking on that will start a Jupyter notebook with a kernel that has access to all of the packages in your Conda environment.

[[File:Load_condakernel.png|alt=|border|1111x1111px]]

'''For Jupyter versions > 7.0.0 please see below.'''

From here, click File > new > notebook. A new Jupyter notebook will open and a window will appear titled "Select Kernel". Click the drop down menu and under "Start Other Kernel" you should see the name of your Conda environment. Clicking on that will start your Jupyter notebook with a kernel that has access to all of the packages in your Conda environment.

[[File:New-jupyter-interface.png|alt=|border|1111x1111px]]
[[File:Load_new_condakernel.png|alt=|border|1111x1111px]]

Disk Storage

2026-08-11T00:59:46Z

Shtsai:

[[Category:Sapelo2]][[Category:Storage]]

== Storage Overview ==

Network attached storage systems at the GACRC are tiered in three levels based on speed and capacity. Ranked in order of decreasing speed, the file systems are "scratch" and "work", "home", and "offline" storage.

The home filesystem is the "landing zone" when users login, and the scratch filesystem is where jobs should be run. Scratch is considered temporary and files are not to be left on it long-term. The work file system is a group-shared space that can be used to store common files needed by jobs. The offline storage filesystem is where data that is currently being used should be stored when it is not being used on scratch.

Each compute node has local physical hard drives that the user can utilize as temporary storage, aka lscratch. The lscratch device is a very fast storage device compared to the network attached storage systems. The drawback is that the capacity is low and it cannot be accessed from outside the compute node. The data in lscratch is not backed up and it can be deleted anytime after the job on the compute node is finished.

=== Home file system ===

When you login into a system (e.g. sapelo2 or xfer nodes), you will land on your home directory. Home directories are "auto mounted" on the login nodes and xfer nodes when you login. Your home directory on the xfer nodes is the same as your home directory on sapelo2. Sapelo2 compute nodes will also mount a user's home directory when a job starts (be that interactive or batch). Users of the teaching cluster have a separate home directory, which is not the same as on Sapelo2.

Home directories have a per user quota and have snapshots. Snapshots are like backups in that they are read-only moment-in-time captures of files and directories which can be used to restore files that may have been accidentally deleted or overwritten.


The recommended data workflow is to have files in the home directory *change* as little as possible. These should be databases, applications that you use frequently but do not need to modify that often and other things that you, primarily, *read from*. 

Summary of the home directory characteristics for a sample user 'jsmith' in 'abclab':
<pre>
sapelo2
home dir quota = 200GB
home dir path = /home/jsmith
snapshots = yes
subject to 30-day purge = no
</pre>

=== Scratch file system ===

The scratch file system resides on a high-speed storage device and it should be used to store temporary files needed for current jobs. Files that are not needed for current jobs should not be left on the scratch file system. This file system is mounted on the login nodes, xfer nodes, and compute nodes.

The recommended data workflow will have jobs write output files, including intermediate data, such as checkpoint files, and final results into the scratch file system. Final results, intermediate files, and other data should then be transferred out of and immediately deleted from the scratch file system, if these are not needed for other jobs that are being submitted soon.

Because the scratch file system stores large amounts of data that change a lot, it is does not have snapshots turned on and it is not backed up in anyway. Files deleted from a scratch directory cannot be recovered.

There is no per user quota in the scratch file system, but a file retention policy is implemented to help prevent this file system from filling up.

'''Scratch file system "30-day purge" policy'''

<blockquote style="background-color: lightyellow; border: solid thin grey;">
Any file that is not accessed/read or modified by a compute job in a time period of at least 30 days will be automatically deleted off the /scratch file system. Measures circumventing this policy will be monitored and actively discouraged.

There is no storage size quota for /scratch usage. Space is only limited by the physical size of the scratch space being used. If usage across the entire file system is more than 80% of total capacity, the GACRC will take additional measures to reduce usage to a more suitable level. Amongst possible actions, request/force users to clean up their /scratch directories or reduce temporarily the 30 day limit to a lower limit.

Please see [[Policies#Policy_Statement_for_SCRATCH_File_System|purge policy]] for more info.
</blockquote>

Summary of the scratch directory characteristics for a sample user 'jsmith' in 'abclab':
<pre>
sapelo2
scratch dir quota = Currently no per user quota
scratch dir path = /scratch/jsmith
snapshots = no
subject to 30-day purge = yes
</pre>

=== Work file system ===

The work file system resides on a high-speed storage device and it should be used to store files needed for jobs. Each group has a directory in the work file system and this space can be used to store files needed by multiple users within a group. The work file system has a per group quota and files stored there are not subject to the auto-purge policy that is applied to the scratch file system.

The work file system is mounted on the login nodes, xfer nodes, and compute nodes.

The recommended data workflow is to have files that are often needed for repeated jobs, possibly by multiple users within a group, such as reference data and model data, be stored in the group's work directory. This directory is not intended as a place for jobs to write output files.

The work file system does not have snapshots turned on and it is not backed up in anyway. Files deleted from a work directory cannot be recovered.

Summary of the work directory characteristics for a sample user 'jsmith' in 'abclab':
<pre>
sapelo2
work dir group quota = 500GB and a maximum of 100,000 files
work dir path = /work/abclab
snapshots = no
subject to 30-day purge = no
</pre>

=== lscratch file system ===

Each compute node has local physical hard drives that the user can utilize as temporary storage. The file system defined on the hard drives is called /lscratch. The lscratch device is a very fast storage device compared to the network attached storage systems. The drawback is that the capacity is low and it cannot be accessed from outside the compute node. This file system can be used for single-core jobs and for multi-thread jobs that run within a single node. In general, parallel jobs that use more than one node (e.g. MPI jobs) cannot use the /lscratch file system.

The data in lscratch is not backed up and it needs to be deleted when job on the compute node is finished.

Jobs that do not need to write large output files, but that need to access the files often (for example, to write small amounts of data into disk), can benefit from using /lscratch. Jobs that use /lscratch should request the amount of space in /lscratch. For information on how to request lscratch space for jobs, please refer to [https://wiki.gacrc.uga.edu/wiki/Running_Jobs_on_Sapelo2#How_to_run_a_job_using_the_local_scratch_.2Flscratch_on_a_compute_node How to run a job from lscratch]

Summary of the lscratch directory characteristics for a sample user 'jsmith' in 'abclab':
<pre>
sapelo2
quota = Limited by device size (Approx. 210GB on the AMD nodes and 800GB on the Intel nodes)
path = /lscratch
snapshots = no
subject to purge = yes (files to be deleted when job exits the node)
</pre>

=== Project file system ===

The offline storage filesystem is named "project" and is configured for use by lab groups. By default, each lab group has a 1TB quota. Individual members of a lab group can create subdirectories under their lab's project directory. PI's of lab groups can request additional storage on project as needed. Please note that this storage is not meant for long-term (e.g., archive) storage of data. That type of storage is the responsibility of the user.

The project filesystem is not mounted on the compute nodes and cannot be accessed by running jobs. It is mounted on the "xfer" nodes when it is first accessed using its full path.

The project filesystem has snapshots turned on.

The recommended data workflow is to have data not needed for current jobs, but that are still needed for future jobs on the cluster, be transferred into the project file system and deleted from the scratch area.

Summary of the project directory characteristics for a sample group 'abclab':
<pre>
sapelo2
quota = default of 1TB per group
path = /project/abclab
snapshots = yes
subject to 30-day purge = no
</pre>

[[#top|Back to Top]]

== Storage Architecture Summary ==

Mount path for home, scratch, work, and lscratch filesystems using an example user 'jsmith' in a lab group 'abclab':
<pre>
sapelo2

home= /home/jsmith
scratch= /scratch/jsmith
work= /work/abclab
lscratch= /lscratch

</pre>

Quota for home, scratch, work, and lscratch filesystems:
<pre>
sapelo2

home= 200GB
scratch= Currently no quota
work= (to be added)
lscratch= Limited by device size (Approx. 210GB on the AMD nodes and 800GB on the Intel nodes)

</pre>

== Auto Mounting Filesystems ==

Some filesystems are "auto mounted" when they are first accessed on a server. For the xfer nodes, this includes Sapelo2 home directories and the project filesystems. Sapelo2 compute nodes will mount a user's home directory when a job starts.

== Snapshots ==

'''Home directories'''

Home directories are snapshotted. Snapshots are like backups in that they are read-only moment-in-time captures of files and directories which can be used to restore files that may have been accidentally deleted or overwritten.

Home directories on Sapelo2 have snapshots taken once a day and are maintained on Sapelo2 for 14 days, giving the user the ability to retrieve old files for up to 14 days after they have deleted them.

'''Note: Users can access the previous 14 days of snapshots of their own home directories and restore their files.'''

If you would like to recover a file that you have deleted from your home directory within the last 14 days, you can check if the file is available in any of the snapshots and, if so, copy the file back. This can be done on a transfer node (xfer.gacrc.uga.edu) or on a Sapelo2 compute node.

Here is an example for user jsmith, on an xfer node:

<pre>
[jsmith@xfer1 ]$ pwd
/home/jsmith

[jsmith@xfer1 ]$ ls /home/.zfs/snapshot/
zrepl_20220907_063420_000 zrepl_20220920_220422_000 zrepl_20220921_200422_000
zrepl_20220908_070420_000 zrepl_20220920_230422_000 zrepl_20220921_210422_000
zrepl_20220909_073421_000 zrepl_20220921_000422_000 zrepl_20220921_220422_000
zrepl_20220910_073421_000 zrepl_20220921_010422_000 zrepl_20220921_230422_000
zrepl_20220911_073421_000 zrepl_20220921_020422_000 zrepl_20220922_000422_000
zrepl_20220912_073421_000 zrepl_20220921_030422_000 zrepl_20220922_010421_000
zrepl_20220913_073421_000 zrepl_20220921_040422_000 zrepl_20220922_020422_000
zrepl_20220914_073421_000 zrepl_20220921_050422_000 zrepl_20220922_030422_000
zrepl_20220915_073421_000 zrepl_20220921_060422_000 zrepl_20220922_040422_000
zrepl_20220916_073421_000 zrepl_20220921_070422_000 zrepl_20220922_050421_000
zrepl_20220917_073421_000 zrepl_20220921_080422_000 zrepl_20220922_060422_000
zrepl_20220918_080421_000 zrepl_20220921_090421_000 zrepl_20220922_070422_000
zrepl_20220919_080422_000 zrepl_20220921_100422_000 zrepl_20220922_080422_000
zrepl_20220920_083422_000 zrepl_20220921_113421_000 zrepl_20220922_090422_000
zrepl_20220920_143422_000 zrepl_20220921_123422_000 zrepl_20220922_100422_000
zrepl_20220920_153422_000 zrepl_20220921_133423_000 zrepl_20220922_110422_000
zrepl_20220920_163422_000 zrepl_20220921_150422_000 zrepl_20220922_120422_000
zrepl_20220920_173422_000 zrepl_20220921_160422_000 zrepl_20220922_130422_000
zrepl_20220920_190421_000 zrepl_20220921_170422_000 zrepl_20220922_140422_000
zrepl_20220920_200422_000 zrepl_20220921_180421_000 zrepl_20220922_143422_000
zrepl_20220920_210422_000 zrepl_20220921_190422_000 zrepl_20220922_150422_000

[jsmith@xfer1 ]$ cd /home/.zfs/snapshot/zrepl_20220907_063420_000/jsmith

[jsmith@xfer1 ]$ cp my-to-restore-file /home/jsmith
</pre>

Weekly and monthly snapshots are also made going as far back as 6 months, but GACRC staff must retrieve these snapshots for you upon [https://uga.teamdynamix.com/TDClient/2060/Portal/Requests/ServiceDet?ID=25844 request].

'''Project file systems'''

The project file systems are also snapshotted and the method to recover a file from a snapshot depends on whether your project directory is located on the Panasas storage device or on a ZFS storage device (SN13). One of the two methods below should work for you.

'''Note: ANY user in a lab can access the snapshots of his/her group project file system and restore files he/she has there.'''

'''Method 1 (for project folders on the Panasas)'''

Each /project filesystem on the Panasas contains a completely invisible directory named ".snapshot". This directory cannot be listed with ls or viewed by any program at all. Only the "cd" command can be used to enter this directory. Users of /project directories may retrieve files from these snapshots by changing into their snapshot directory /project/abclab/.snapshot and then cd into an appropriate snapshot directory and copying files from the that snapshot to any location they would like.

Here is an example for user jsmith who is in the abclab group, on an xfer node:

<pre>
[jsmith@xfer1 ]$ pwd
/home/jsmith

[jsmith@xfer1 ]$ cd /project/abclab/.snapshot

[jsmith@xfer1 .snapshot]$ ls
2019.02.17.04.00.03.Weekly 2019.03.01.06.00.03.Daily
2019.02.26.06.00.03.Daily 2019.03.02.06.00.03.Daily
2019.02.27.06.00.03.Daily 2019.03.03.04.00.03.Weekly
2019.02.28.06.00.03.Daily 2019.03.03.06.00.03.Daily

[jsmith@xfer1 snapshot]$ cd 2019.03.03.06.00.03.Daily

[jsmith@xfer1 2019.03.03.06.00.03.Daily]$ cp my-to-restore-file /home/jsmith/test
</pre>

'''Method 2 (for project folders on SN13)'''

Each /project filesystem on SN13 contains a hidden directory called .zfs and the snapshots are located in the directory /project/abclab/.zfs/snapshot (you can <code>cd</code> into this directory and list the snapshots with the <code>ls</code> command).

Here is an example for user jsmith who is in the abclab group, on an xfer node:

<pre>
[jsmith@xfer1 ]$ pwd
/home/jsmith
[jsmith@xfer1 ]$ date
Wed Aug 18 11:40:59 EDT 2021

[jsmith@xfer1 ]$ cd /project/abclab/.zfs/snapshot

[jsmith@xfer1 snapshot]$ ls
zrepl_20210729_211245_000 zrepl_20210811_052246_000 zrepl_20210818_012411_000
zrepl_20210730_215245_000 zrepl_20210812_054245_000 zrepl_20210818_022410_000
zrepl_20210731_222244_000 zrepl_20210813_201246_000 zrepl_20210818_035041_000
zrepl_20210801_225245_000 zrepl_20210816_152410_000 zrepl_20210818_052018_000
zrepl_20210802_235244_000 zrepl_20210817_152411_000 zrepl_20210818_062051_000
zrepl_20210804_003245_000 zrepl_20210817_172411_000 zrepl_20210818_075046_000
zrepl_20210805_021244_000 zrepl_20210817_182411_000 zrepl_20210818_092039_000
zrepl_20210806_025245_000 zrepl_20210817_202410_000 zrepl_20210818_115019_000
zrepl_20210807_035245_000 zrepl_20210817_212410_000 zrepl_20210818_125036_000
zrepl_20210808_045245_000 zrepl_20210817_222410_000 zrepl_20210818_135021_000
zrepl_20210809_050244_000 zrepl_20210817_232411_000 zrepl_20210818_145018_000
zrepl_20210810_050245_000 zrepl_20210818_002411_000 zrepl_20210818_152028_000

[jsmith@xfer1 snapshot]$ cd zrepl_20210818_152028_000
[jsmith@xfer1 zrepl_20210818_152028_000]$ cp my-to-restore-file /home/jsmith/test
</pre>

[[#top|Back to Top]]

== Current Storage Systems ==

* Aeon hybrid Lustre appliance (2.4PB flash & 9.6PB spinning drives) - $SCRATCH & $WORK on Sapelo2

* ZFS storage appliance (500TB) - $HOME on Sapelo2

* ZFS storage appliance (8PB) - $PROJECT research groups' long-term space - only for active projects requiring Sapelo2 access

* ZFS storage appliances (8PB) - backup & mirroring environments for $HOME and $PROJECT.

CryoSPARC-Sapelo2

2026-08-05T17:35:31Z

Shtsai:

[[Category:Sapelo2]][[Category:Software]][[Category:Engineering]]

== Category ==

Engineering

== Program On ==

Sapelo2

== Version ==

4.3.1

== Author / Distributor ==

See https://guide.cryosparc.com/

== Description ==

"CryoSPARC (Cryo-EM Single Particle Ab-Initio Reconstruction and Classification) is a state of the art HPC software solution for complete processing of single-particle cryo-electron microscopy (cryo-EM) data. CryoSPARC is useful for solving cryo-EM structures of membrane proteins, viruses, complexes, flexible molecules, small particles, phase plate data and negative stain data." For more information, please see https://guide.cryosparc.com/.

'''NOTE''': Users are required to be added into GACRC '''cryosparc''' group before they can run this software from Sapelo2. Please fill out the [https://uga.teamdynamix.com/TDClient/2060/Portal/Requests/ServiceDet?ID=25844 GACRC General Support form] to request. We will reach out to you after we received your request.

== Configurations ==
'''Master node VM:'''

* Host name: '''cryosparc.gacrc.uga.edu'''
* Intel Xeon processors (16 cores) and 64GB of RAM
* mongodb is installed and run on the master node
'''Worker nodes:'''
* One NVIDIA Tesla A100 node: Intel Xeon processors (64 cores), 1TB host RAM memory, 4 NVIDIA Tesla A100 GPU cards (80GiB device memory per card), and NVMe SSD 3570GB local drive.
* cryoSPARC recommends using SSD for caching particle data. /lscratch/gacrc-cryo is set up on the worker node for this purpose.
* The amount of space that cryoSPARC can use in /lscratch/gacrc-cryo is capped at 100GB.
'''cryoSPARC group:''' '''cryosparc'''

'''cryoSPARC service account:''' '''gacrc-cryo'''

* gacrc-cryo is the '''service account''' that will run cryoSPARC workflow jobs for all cryoSPARC users.
* Some tasks can only be handled by gacrc-cryo, like make a new lane of worker node(s), user management, and connect or update worker node(s) to master, etc..
* Regular CryoSPARC users can run cryosparcm on the master node to check cryoSPARC status, using cryosparcm status or cryosparcm checkdb.

'''cryoSPARC project space:''' '''/scratch/gacrc-cryo'''

== User Login ==
The method to access the CryoSPARC web interface is to connect through the UGA VPN and access the CryoSPARC web server directly.


=== VPN + Direct Web Access ===
# Connect to the UGA VPN using the Cisco Secure Client (Cisco AnyConnect) and select '''remote.uga.edu'''.
#: '''Note:''' Even if you are working on campus, you must still connect to the UGA VPN before accessing the CryoSPARC web server.
# Open the URL for your assigned CryoSPARC instance in your web browser. For example:
#:
#: <code>http://cryosparc.gacrc.uga.edu:39000</code>
# Log in using your registered UGA email address and CryoSPARC password.
#:
#: '''Note:''' Your registered UGA email address may be an email alias rather than your official '''MyID@uga.edu''' address. Please use the email address that was registered with your CryoSPARC account.



























=== Troubleshooting ===
If you are unable to access the CryoSPARC web interface:

* Verify that you are connected to the UGA VPN if you are using the recommended VPN + Direct Web Access method.
* Confirm that you are using the correct URL for your assigned CryoSPARC instance.
* Make sure you are logging in with your registered UGA email address and CryoSPARC password. Your registered UGA email address may be an email alias rather than your official MyID@uga.edu address.
* If you are using the SSH tunnel method, ensure that the SSH session remains active while accessing the web interface.
* If you have forgotten your CryoSPARC password, contact GACRC Support to request a password reset token.
* If the problem persists, please contact GACRC Support and include:
** The URL you are using to access CryoSPARC.
** Your UGA MyID and your registered UGA email address.
** A screenshot of the error message or the login/password reset screen, if applicable.

== Run cryoSPARC workflow jobs ==

=== Project space selection ===
A project in cryoSPARC is a high level container corresponding with a project directory on the file system, which stores all associated Jobs of a project. Each project in cryoSPARC is entirely contained within a file system directory. All the jobs and their respective intermediate and output data created within a project will be stored within the project directory.

* When you start a new project in cryoSPARC GUI, please select and use '''/scratch/gacrc-cryo''' as the cryoSPARC project space.
* This folder is owned by gacrc-cryo. Regular cryoSPARC users have access and read permissions which allow them to browse files in this folder and copy files from this folder to their own storage spaces on Sapelo2.

=== Run job on the master node ===
cryoSPARC will use the master node to run some types of workflow jobs, for example, "Import Movies", "Inspect Picks", and the interactive job "Select 2D Classes". When a job is created, if cryoSPARC will use the master node to run the job, you will be notified about this in cryoSPARC GUI.

=== Run job using "Lane Sapelo2 Default (cluster)" ===
* In cryoSPARC, queue a job to "'''Lane Sapelo2 Default (cluster)'''"; The job will be dispatched to the worker node via Slurm. Please note, gacrc-cryo, instead of your own Sapelo2 user account, is the user account owning and running the job. We highly recommend you to use this method to run cryoSPARC workflow jobs on Sapelo2.
* cryoSPARC will decide on how many CPU cores and how much memory it will use to run a workflow job, depend on the type of the job and your data size. Currently, we configured that from each worker node cryoSPARC can use up to '''20GB''' memory and '''4''' CPU cores.
* If the job needs to run on GPU devices, cryoSPARC will queue the job with a default number of GPU devices , for example 1. You can change this number by yourself in cryoSPARC GUI when you create the job. Please note that the maximum number of GPU devices installed on the worker node is '''4'''.

==Documentation (v4.0+)==

About cryoSPARC: https://guide.cryosparc.com/

Get Started with CryoSPARC: Introductory Tutorial: https://guide.cryosparc.com/processing-data/get-started-with-cryosparc-introductory-tutorial

A Tour of the CryoSPARC Interface: https://guide.cryosparc.com/application-guide-v4.0+/a-tour-of-the-cryosparc-interface

Using the CryoSPARC Interface: https://guide.cryosparc.com/application-guide-v4.0+/using-the-cryosparc-interface

Creating and Running Jobs: https://guide.cryosparc.com/application-guide-v4.0+/creating-and-running-jobs

Tutorial videos: https://guide.cryosparc.com/processing-data/tutorial-videos

==Installation==

*Version 4.3.1 master is installed on the master node (cryosparc.gacrc.uga.edu).
*Version 4.3.1 workers are installed on one worker GPU node (NVIDIA Tesla A100 GPU node).

==System==
64-bit Linux

Training

2026-08-05T14:08:57Z

Shtsai:

==GACRC Training==

The GACRC regularly hosts training sessions on a number of subjects relevant to the use of our computational and storage resources. Scheduled trainings will be announced through the GACRC mailing list.

'''NOTE: New users are required to attend a Sapelo2 cluster introductory training session and information about that will be sent once an account is requested.'''

==Regular Training Announcement==

In '''August 2026''', the GACRC is hosting 6 training sessions listed below. These training workshops will be offered remotely via Zoom Meeting. Detailed instructions for joining the Zoom meeting will be sent to your UGA email account before each training session you register for.

We will offer:

1. Linux training for Linux-inexperienced cluster new users (3 sessions)

2. Sapelo2 cluster new user training (3 sessions)

==Event Schedule==

This section describes the training workshops that we offer, along with the sessions that are currently scheduled.

===Sapelo2 Cluster New User Training===

This mandatory training consists of an overview of the structure of Sapelo2 as well as hands-on practice submitting a job along with guidance and best practices when using the Sapelo2 cluster.

'''Prerequisites:'''

*Linux basics. A Linux-inexperienced user must complete a prerequisite Linux training for Linux-inexperienced cluster new users.

'''Workshop Training Goals:'''

*Understand the layout of Sapelo2

*Understand the Sapelo2 file systems

*Understand the Sapelo2 partitions

*Understand the Sapelo2 software environment

*Understand how to request computing resources and submit a computational batch job following the Sapelo2 cluster general workflow

*Understand how to initiate an interactive job

*Understand how to transfer files to and from the cluster

*Understand how to get support from GACRC support team when you have any issues on cluster

'''Scheduled Sessions:'''

{| class="wikitable unsortable" width="100%" border="1" cellspacing="0" cellpadding="2" align="center"
|-
! scope="col" |Title
! scope="col" |Date/Time
|-
|Using Sapelo2 Cluster at the GACRC
|July 16th, Thursday, 2:00 PM - 4:00 PM
|-
|Using Sapelo2 Cluster at the GACRC
|July 22nd, Wednesday, 2:00 PM - 4:00 PM
|-
|Using Sapelo2 Cluster at the GACRC
|August 7th, Friday, 2:00 PM - 4:00 PM
|-
|Using Sapelo2 Cluster at the GACRC
|August 12th, Wednesday, 2:00 PM - 4:00 PM
|-
|Using Sapelo2 Cluster at the GACRC
|August 20th, Thursday, 2:00 PM - 4:00 PM
|}

===Linux Training for Linux-inexperienced Cluster New Users===
The Sapelo2 High Performance Computing (HPC) cluster runs a headless Linux distribution as the operating system on each of its constituent nodes. The term headless refers to the fact that these nodes do not have a desktop graphical user interface (GUI) installed by default. Graphical desktop environments consume resources that analyses could otherwise use, so users employ a command-line interface (CLI) instead. To interact with these resources, users connect to a remote terminal via SSH and execute commands.

The Linux Training workshop provides hands-on practice of the fundamental Linux commands necessary to interact with HPC resources.

'''Prerequisite:'''

Please watch the introductory videos on Linux, basic Linux terms, and Linux Paths and Directories (total ~17 minutes) '''before attending the training workshop'''.

*[https://kaltura.uga.edu/media/t/1_81u2kfi2/176125031 Linux]
*[https://kaltura.uga.edu/media/t/1_ol51cuyn/176125031 basic Linux terms]
*[https://kaltura.uga.edu/media/t/1_wdyxhgdg/176125031 Linux Paths and Directories]

'''Training Goals:'''

1. Understand fundamental concepts of Linux working environment (filesystem hierarchy, path, PATH, etc.)

2. Know how to use Linux common commands (ls, cd, pwd, cat, more, nano, mkdir, rm, cp, mv, etc.)

3. Understand what is Linux bash shell and know how to make a simple Linux script and run it in Linux environment

'''Scheduled Sessions:'''

{| class="wikitable unsortable" width="100%" border="1" cellspacing="0" cellpadding="2" align="center"
|-
! scope="col" | Title
! scope="col" |Date/Time
|-
|Use Linux on Cluster
|July 14th, Tuesday, 1:00 PM - 3:00 PM
|-
|Use Linux on Cluster
|July 20th, Monday, 1:00 PM - 3:00 PM
|-
|Use Linux on Cluster
|August 5th, Wednesday, 1:00 PM - 3:00 PM
|-
|Use Linux on Cluster
|August 10th, Monday, 1:00 PM - 3:00 PM
|-
|Use Linux on Cluster
|August 18th, Tuesday, 1:00 PM - 3:00 PM
|}

===Using Sapelo2 Cluster at the GACRC, Part II ===
This workshop will cover high-performance computing on Sapelo2, including job scheduling, resource requests (CPU, memory, GPU), and techniques for optimizing job performance.

'''Prerequisites:'''
*Linux basics. A Linux-inexperienced user must complete a prerequisite Linux training for Linux-inexperienced cluster new users.
*Sapelo2 cluster new user training. Fundamental HPC and Sapelo2 knowledge is required for this advanced Sapelo2 workshop.

'''Training Goals:'''

1. Learn about high-performance computing framework

2. Why is my job pending? How can I get my job to start sooner? How to find available computing resources on Sapelo2?

3. How to request computing resources such as nodes, CPU cores, memory, GPU device, etc. to run serial, threaded, MPI, and GPU jobs on Sapelo2?

4. How can I make my job run more efficiently (through the correct use of software and hardware)?

5. A quick intro to MPI library and how to compile/run MPI jobs on Sapelo2

'''Scheduled Sessions:'''

{| class="wikitable unsortable" width="100%" border="1" cellspacing="0" cellpadding="2" align="center"
|-
! scope="col" |Title
! scope="col" |Date/Time
|-
|Using Sapelo2 Cluster at the GACRC, Part II
|
|}

===Installing Software Packages in Virtual Environments on Sapelo2 ===
This workshop will cover the basics of virtual environments as well as provide practical guidance and best practices for using virtual environments on the Sapelo2 cluster. Participants will learn the basics of creating and configuring virtual environments, how to install software packages in both a Conda virtual environment and a Python virtual environment, and manage dependencies in their environments.

'''Prerequisite:'''

Must already have strong understanding of the Linux environment and Sapelo2.

'''Training Goals:'''

1. Learn how to create a virtual environment on Sapelo2

2. Be able to install software packages into both Conda and Python virtual environments

3. Understand how to manage dependencies of their virtual environments

'''Scheduled Sessions:'''

{| class="wikitable unsortable" width="100%" border="1" cellspacing="0" cellpadding="2" align="center"
|-
! scope="col" |Title
! scope="col" |Date/Time
|-
|Installing Software Packages in Virtual Environments on Sapelo2
|
|}

===Job Parallelization with GNU Parallel and Slurm Arrays ===
Learn how to run multiple commands in parallel using GNU Parallel and Slurm Arrays. These tools greatly reduce the runtime of certain types of jobs by running multiple instances of the same command in parallel. The workshop focuses on problems that involve executing the same command on multiple different inputs. This workshop is intended for users comfortable writing job submission scripts and using a command line. Concurrent and parallel programming techniques are not covered in this workshop.

'''Prerequisite:'''

Must already have strong understanding of the Linux environment and Sapelo2.

'''Training Goals:'''

1. Understand which jobs benefit from GNU Parallel and Slurm Arrays

2. Use GNU Parallel and Slurm Arrays to parallelize jobs

3. Understand the differences and similarities between GNU Parallel and Slurm Arrays

'''Scheduled Sessions:'''

{| class="wikitable unsortable" width="100%" border="1" cellspacing="0" cellpadding="2" align="center"
|-
! scope="col" |Title
! scope="col" |Date/Time
|-
|Job Parallelization with GNU Parallel and Slurm Arrays
|
|}



==How to Register==

The training workshops '''Using Sapelo2 Cluster at the GACRC''' and '''Use Linux on Cluster''' are '''ONLY''' offered to '''new users''' who need user accounts on the GACRC Sapelo2 cluster or current Sapelo2 users seeking a refresher. If you would like to use the cluster, please ask your group PI/UGA faculty member to send us an account creation request for you, using the [https://uga.teamdynamix.com/TDClient/Requests/ServiceDet?ID=25839 GACRC User Account Request Form].

If you would like to attend the '''Using Sapelo2 Cluster at the GACRC, Part II''', the '''Installing Software Packages in Virtual Environments on Sapelo2''', and/or the '''Job Parallelization with GNU Parallel and Slurm Arrays''' training workshops, please send us a request using the [https://uga.teamdynamix.com/TDClient/Requests/ServiceDet?ID=25852 GACRC Training Request Form]. In your request, please tell us which session(s) you would like to attend.



== Download==

This section provides the slides that we use for our current workshops and material used for several of our past training events and presentations.

===Sapelo2 Cluster Training===

{| class="wikitable unsortable" width="100%" border="1" cellspacing="0" cellpadding="2" align="center"
|[[Media:GACRC_Sapelo2_cluster_new_user_training_workshop_v10.8.pdf]]
|-
|}

===Teaching Cluster Training===

{| class="wikitable unsortable" width="100%" border="1" cellspacing="0" cellpadding="2" align="center"
|-
|[[Media:GACRC-Teaching-cluster-new-user-training-workshop-Spring2026.pdf]]
|-
|}

===Linux Training for New Cluster Users===

{| class="wikitable unsortable" width="100%" border="1" cellspacing="0" cellpadding="2" align="center"
|-
|[[Media:Linux_Training_For_New_Users_Of_Cluster_Suchi_04252019.pdf]]
|-
|}

===Installing Software Packages in Virtual Environments on Sapelo2===

{| class="wikitable unsortable" width="100%" border="1" cellspacing="0" cellpadding="2" align="center"
|-
|[[Media:GACRC_virtual_environments_training_v1.2.pdf]]
|}

===Job Parallelization with GNU Parallel and Slurm Arrays===

{| class="wikitable unsortable" width="100%" border="1" cellspacing="0" cellpadding="2" align="center"
|-
|[[Media:GNU_Parallel_and_SLURM_Arrays_v1.1.pdf]]
|}



===Topical Sessions===

{| class="wikitable unsortable" width="100%" border="1" cellspacing="0" cellpadding="2" align="center"
|
|-
|[[Media:AI_Resources_on_the_GACRC_Sapelo2_Cluster.pdf]]
|-
|[[Media:Using_Sapelo2_Cluster_at_the_GACRC_Part_II_Rocky8.pdf]]

|}

===Out-Reach/In-Class Talk===

{| class="wikitable unsortable" width="100%" border="1" cellspacing="0" cellpadding="2" align="center"
|-
! scope="col" |Dept./Center/Institute
! scope="col" |Type
! scope="col" |Workshop PDF
|-
|Fall 2026
|
|[[:Media:GACRC Teaching Cluster Training-Python.pdf]]
|-
|Fall 2026
|
|[[:Media:GACRC Teaching Cluster Training-Amber.pdf]]
|-
|GEOG - Spring2026 || In-Class || [[Media:GACRC-Teaching-cluster-new-user-training-workshop-python-Spring2026.pdf]]
|-
|BCMB8330 - Spring2026 || In-Class || [[Media:GACRC-Teaching-cluster-new-user-training-workshop_bcmb8330_Spring2026.pdf]]

|}

'''NOTE:''' The slides may become outdated and you should always check GACRC Wiki for up to date information.

Georgia Advanced Computing Resource Center

2026-07-31T20:10:43Z

Shtsai:

__NOTOC__
Welcome to the Georgia Advanced Computing Resource Center wiki. The information provided here is a supplement to the GACRC webpage. The GACRC online information resources include:

*[http://gacrc.uga.edu/ Web Site] – General overview
*[https://wiki.gacrc.uga.edu/ Wiki] – Software docs and how-to’s - "You Are Here"
*[https://kaltura.uga.edu/channel/GACRC/176125031 Kaltura] – Linux and HPC training videos









<div style="width=100%; margin:0; background:white; font-size:120%; font-weight:bold; border:4px solid #00CC33; text-align:left; color:black; padding:0.2em 0.4em;"> Current Status: <span style="color:black"> Online </span></div>







<h2>Important News</h2>
The following is an important notice for all of our current users:



* [[Rocky 9 Transition Guide]]




<h2> Getting Started </h2>
Welcome to the Georgia Advanced Computing Resource Center at the University of Georgia. If you're new to the GACRC, start with these links to get acquainted with our resources.
*[[User Accounts|User Accounts]]
*[[Instructional Accounts]]
*[[Connecting]]
*[[Transferring Files]]
*[[Password | Changing your Password]]
*[[Frequently Asked Questions | FAQ]]
*[[Quick_Reference_Guide|Command List]]
*[[Getting Help]]
*[[Policies]]
*[[Consulting]]
*[[Training]]

A Webinar introducing the GACRC was presented on March 10, 2025. A recording of this [https://kaltura.uga.edu/media/t/1_jfku2g8l/176125031 webinar] is found on GACRC's Kaltura channel.

<h2> System Information </h2>
Hardware information and operational procedures are described below.
*[[Systems]]
*[[Disk Storage]]



<h2> Job and Data Management </h2>
Information on how to run jobs and data management.
*[[Running Jobs]]
*[[Monitoring Jobs]]
*[[Job Submission Partitions]]
*[[Sample Scripts | Sample Job Submission Scripts]]
*[[Migrating from Torque to Slurm]]
*[[Troubleshooting on Sapelo2]]
*[[Best Practices]]
*[[Globus]]
*[[OnDemand | Open OnDemand]]


<h2> Software and Libraries </h2>
Documentation for software applications, programming tools, and usage.
*[[Software]]
*[[Available Toolchains and Toolchain Compatibility]]
*[[Bioinformatics Databases]]
*[[OpenMP]]
*[[MPI | Message Passing Interface (MPI)]]
*[[Compilers]]
*[[GPU|GPU and CUDA Programming]]
*[[Installing Applications]]

Nsight-Compute-CLI-Sapelo2

2026-07-30T18:25:35Z

Shtsai:

[[Category:Sapelo2]][[Category:Software]][[Category:Other]]

== Category ==

Other

== Program On ==

Sapelo2

== Version ==

2023.1.1.0, 2024.1.0.0, 2024.3.0.0, 2025.1.0.0, 2025.4.0.0

== Author / Distributor ==

NVIDIA

== Description ==

From https://docs.nvidia.com/nsight-compute/NsightComputeCli/index.html: "NVIDIA Nsight Compute CLI (ncu) provides a non-interactive way to profile applications from the command line. "

== Running Program ==

Also refer to [[Running Jobs on Sapelo2]]

Compute nodes equipped with GPU cards have access to all applications installed in /apps.

* Version 2025.4.0.0 is installed with CUDA v. 13.1.0 in /apps/eb/CUDA/13.1.0

To use it, please first load the module with:
<pre class="gcommand">
module load CUDA/13.1.0
</pre>

* Version 2025.1.0.0 is installed with CUDA v. 12.8.0 in /apps/eb/CUDA/12.8.0

To use it, please first load the module with:
<pre class="gcommand">
module load CUDA/12.8.0
</pre>

* Version 2024.3.0.0 is installed with CUDA v. 12.6.0 in /apps/eb/CUDA/12.6.0

To use it, please first load the module with:
<pre class="gcommand">
module load CUDA/12.6.0
</pre>

* Version 2024.1.0.0 is installed with CUDA v. 12.4.0 in /apps/eb/CUDA/12.4.0

To use it, please first load the module with:
<pre class="gcommand">
module load CUDA/12.4.0
</pre>

* Version 2023.1.1.0 is installed with CUDA v. 12.1.1 in /apps/eb/CUDA/12.1.1

To use it, please first load the module with:
<pre class="gcommand">
module load CUDA/12.1.1
</pre>

'''Please note:'''

'''1.''' Before running ncu to profile an application, please set the environment variable TMPDIR to point to a directory of yours. For example, use
<pre class="gcommand">
export TMPDIR=/scratch/$USER
</pre>

'''2.''' To profile applications on the H100 devices, please use the ncu in CUDA version 12.6.0 or higher. The older versions of ncu do not support the Hopper architecture.

'''3.''' The ncu command should work on all the GPU nodes on Sapelo2.


If you would like to request a specific node, you can do so with the Slurm <code>--nodelist</code> option. For example, <code>--nodelist=ra8-3 </code>.

'''Sample command to request an interactive job on a specific node:'''
<pre class="gcommand">
interact -p gpu_p --gres=gpu:H100:1 --nodelist=ra8-3
</pre>

'''Sample Slurm header line to request a specific node for a batch job:'''
<pre class="gcommand">
#SBATCH --nodelist=ra8-3
</pre>

== Documentation ==

Please see https://docs.nvidia.com/nsight-compute/NsightComputeCli/index.html

== Installation ==

Downloaded from NVIDIA site.

== System ==

64-bit Linux

Using a Python environment in Jupyter

2026-07-28T19:54:06Z

Shtsai:

== Python environment ==
A Python environment is a type of virtual environment. Python environments are used to contain a set of software/packages that you install (via pip) into that environment that may be accessed any time you activate the environment. To learn more about Python environments, please see our wiki page on creating Python environments at '''[[Installing Applications on Sapelo2#How_to_install_Python_packages|Link to wiki page on creating Python environments]].'''

== Open OnDemand ==
Open OnDemand is a web-based service that allows users to run software with a graphical user interface (GUI) such as Jupyter Notebook. Please see our wiki page on [[OnDemand|'''Open OnDemand''']] to learn more.

== Using a Python environment in Jupyter ==
These steps will allow you to access the software you install into a Python environment within Jupyter Notebook running on Open OnDemand

=== Create the Python environment ===
Create your Python environment and install any packages you will want to use.
=== Install ipykernel ===
While your Python environment is still activated, install ipykernel to your Python environment with:

<pre class="gcommand">
pip install ipykernel
python -m ipykernel install --user --name=my_env
</pre>
Note: change my_env to whatever the name of your Python environment is. This is the name that you will see in Jupyter Notebook as an available kernel.

=== Open Jupyter in Open OnDemand ===
Click on the Jupyter app in [https://ondemand.gacrc.uga.edu/ Open OnDemand] and click launch at the bottom of the page to start a Jupyter Notebook session.

Note: please request a version of Jupyter Notebook with the same Python version and GCCcore toolchain version as the one you use to create your Python Virtual Environment<br>

[[File:Screenshot 2023-12-13 at 10.25.00 AM.png|alt=|border|700x700px]]

Once the job starts, click on Connect to Jupyter. This will open Jupyter in a new page in your browser.

'''For Jupyter versions < 7.0.0 please see below''' (for versions > 7.0.0, scroll down).

From here, click "new" and you should see the name of the ipykernel you created. Clicking on that will start a Jupyter notebook with a kernel that has access to all of the packages in your Python environment.

[[File:Load_pythonkernel.png|alt=|border|1111x1111px]]

'''For Jupyter versions > 7.0.0 please see below.'''

From here, click File > new > notebook. A new Jupyter notebook will open and a window will appear titled "Select Kernel". Click the drop down menu and under "Start Other Kernel" you should see the name of your python environment. Clicking on that will start your Jupyter notebook with a kernel that has access to all of the packages in your Python environment.

[[File:New-jupyter-interface.png|alt=|border|1111x1111px]]

[[File:Load_new_pythonkernel.png|alt=|border|1111x1111px]]

Transferring Files

2026-07-28T18:33:02Z

Shtsai:

[[Category:Sapelo2]][[Category:Teaching]]

=Transferring Files to and from Sapelo2=

To transfer files from your local storage device to Sapelo2 or from Sapelo2 to your local storage device, please connect to xfer.gacrc.uga.edu (use your UGA MyID, the MyID password to authenticate). You will also be prompted for two-factor authentication with Archpass Duo.

==The File Transfer nodes for Sapelo2 (xfer.gacrc.uga.edu)==

Users can transfer files between their local machines and GACRC storage using various programs, such as (a) Filezilla, (b) WinSCP, and (c) a secure copy (scp). To transfer files, you must have a file transfer program installed on your local machine and a connection to the UGA campus network.

All Sapelo2 users, as well as all GACRC PIs, can access the xfer nodes using the hostname '''xfer.gacrc.uga.edu''' and using their UGA MyID (not the 810 or 811 number) and the MyID password to authenticate. Two-factor authentication using Archpass Duo is also necessary. For more details of Archpass Duo, please refer to
[https://uga.teamdynamix.com/TDClient/3190/eitsclientportal/KB/Category/23825/ArchPass-powered-by-Duo archpass_duo ].

The GACRC file transfer nodes (xfer) are configured to facilitate file transfer. These nodes, which have a hostname of '''xfer.gacrc.uga.edu''', are connected via a higher-bandwidth network connection. Hence, maximum transfer speed between a user's local machine and the GACRC systems can be achieved by transferring files to/from the host xfer.gacrc.uga.edu (instead of the login nodes of the clusters).

It is not necessary to connect to the UGA VPN when connecting to a file transfer node from off-campus. If you are transferring data from/to a server outside of campus (such as your local computer), you will get much faster transfer speeds if you are not connected to the VPN.

Note that a user's home directory on the xfer nodes is the same as the user's Sapelo2 home directory, which is '''not''' the same as the user's home directory on the teaching cluster.

The xfer node can access the following file systems using the full path:

1. Your home directory on an xfer node is the same as your Sapelo2 home directory, and the path is /home/username

2. The Sapelo2 scratch directory: /scratch/username

3. The Sapelo2 work directory: /work/groupname

4. The project file system for the lab: /project/groupname

==File Transfer Methods for Sapelo2==

===Globus===
Globus is a high-performance data-transfer platform that allows you to perform and/or automate data transfers and is the recommended method for transferring files to/from the Sapelo2 Cluster. These transfers can be to and from other institutions, your local computer, or your Sapelo2 directories, including your /project area. Collaborators can also share data with you by sharing a Collection or an "endpoint" with you. The shared Collection/Endpoint can be on another institution, on a desktop or laptop, or on their GACRC storage.
Data transfers happen unattended and are faster than SCP/SFTP, data verification is on by default, and automatic restarts or continuation of transfers happen after a disruption.

Information about how to get started with and how to use Globus can be found on our Globus [[Globus|wiki page]].

===Using FileZilla===

In order to use [http://filezilla-project.org/ FileZilla] to transfer files between a local machine and Sapelo2, users will need to install the [http://filezilla-project.org/ FileZilla] software on the local machine. After installation, start FileZilla and follow these steps:

1. Go to File -> Site Manager

2. Add a New Site with the following configuration.

In the '''General''' tab select

<pre class="gcomment">

Protocol: SFTP - SSH File Transfer Protocol

Host: sftp://xfer.gacrc.uga.edu

Port: 22

Logon Type: Interactive

User: [your UGA MyID]
</pre>

In the '''Transfer Settings''' tab, check the box for "Limit number of simultaneous connections" and set "Maximum number of connections" to 1.

If this step is omitted, then you will need to enter your password and Duo authentication for each file that you upload or download. By limiting the number of simultaneous connections to 1, you only need to enter your password and Duo authentication once per session.

With the above settings saved, you should be able to transfer files to txfer using filezilla. When you open the connection, you will be prompted for your MyID password (unless you use key based ssh). If the password authenticates successfully, then another pop-up window will prompt for Duo. Note that the Duo window looks very similar to the password window, but the text in the box will describe the options you can use for Duo (for example, a push, enter a passcode, etc).

Once the connection is established you can upload files from your local machine to Sapelo2 or download files from Sapelo2 to your local machine.

Here is the PDF to check step-by-step screen shots:
{| width="100%" border="1" cellspacing="0" cellpadding="2" align="center" class="wikitable unsortable"
|-
| [[Media:Xfer_Filezilla_07282026.pdf]]
|-
|}

===Using WinSCP===

In order to use [http://winscp.net/ WinSCP] to transfer files between a local Windows machine and Sapelo2, users will need to install the [http://winscp.net/ WinSCP] software on the local machine. After installation, when you click the WinSCP shortcut button, Login window pops up, in that you select new site and enter the following information to start the new session:

<pre class="gcomment">

Session:

File Protocol: SFTP

Host name: xfer.gacrc.uga.edu

Port number: 22

Username: [your UGA MyID]

Password: [your MyID password]

</pre>

If the password authenticates successfully, then another pop-up window will prompt for Duo two-factor authentication. It says, Using Keyboard-interactive authentication and lists the Duo options you can choose from: Duo push, Phone Call, or SMS passcode, etc. Once the connection is established you can upload or download files from Local Machine to Cluster or vice-versa. Note: Under Advanced, keep the "Default" option for Transfer settings Rule. With this default option, you will be able to transfer multiple files or folders during the session.


===Using scp===

To transfer files using scp you must have scp on your local machine and a connection to the UGA campus network. An scp software is included in recent releases of Unix based operating systems (including Linux and Mac OS X).

Sample commands to transfer files from your local Unix/Linux/Mac OS X machine to Sapelo2:

Open a terminal in your local machine, change directory (cd) to where the files are located in your local machine, and at the command prompt type:

<pre class="gcommand">
scp filename yourUGAMyID@xfer.gacrc.uga.edu:subdirectory
</pre>

where ''filename'' is the name of the file to be transferred, ''yourUGAMyID'' is your UGA MyID, and ''subdirectory'' is the subdirectory in your Sapelo2 home directory to which files are being transferred. You will then be asked to enter your UGA MyID password. You will also be prompted for two-factor authentication with Archpass Duo.

To transfer files from Sapelo2 to your local machine, use the full path to your file on Sapelo2 followed by the location you would like to transfer the files locally. The following example transfers a file called "filename" to the local current working directory.

<pre class="gcommand">
scp yourUGAMyID@xfer.gacrc.uga.edu:full/path/to/filename ./
</pre>

(Note: yourUGAMyID@ can be omitted if your username on Sapelo2 is the same as on your local machine). Multiple files (e.g. file1, file2, and file3) can be transferred with a single command:

<pre class="gcommand">
scp file1 file2 file3 yourUGAMyID@xfer.gacrc.uga.edu:subdirectory
</pre>

Wildcards can be used for multiple file transfer, for example, to upload all files with .dat extension to your sapelo2 home directory, to a subdirectory called subdir:

<pre class="gcommand">
scp *.dat yourUGAMyID@xfer.gacrc.uga.edu:subdir
</pre>

An example that uses a wildcard to download all files with .dat extension in your Sapelo2 home dir, in a subdirectory called subdir to your local machine:

<pre class="gcommand">
scp yourUGAMyID@xfer.gacrc.uga.edu:subdir/\*.dat ./
</pre>

(Note the backslash “ \ ” preceeding * in the last example.)

A directory and all its contents can be transferred using the scp option '''-r''', for recursive file transferring. For example, to transfer a directory on your local machine called programs and all files in it to your Sapelo2 home directory, use:

<pre class="gcommand">
scp -r programs yourUGAMyID@xfer.gacrc.uga.edu:
</pre>

You can also use the scp option '''-p''' to preserve the file features, such as last modification time, file permissions, etc.

To transfer files directly to your Sapelo2 /scratch area (such as /scratch/johndoe/):

<pre class="gcommand">
scp filename yourUGAMyID@xfer.gacrc.uga.edu:/scratch/johndoe/
</pre>

To transfer files directly to your Sapelo2 work directory:

<pre class="gcommand">
scp filename yourUGAMyID@xfer.gacrc.uga.edu:/work/groupname/subdirname
</pre>

To transfer files directly to your lab's project file system (such as /project/groupname):

<pre class="gcommand">
scp filename yourUGAMyID@xfer.gacrc.uga.edu:/project/groupname
</pre>

===Download directly from internet to Sapelo2===

Here is an example on how to download [http://hannonlab.cshl.edu/fastx_toolkit/index.html FastaX] software from internet directly to Sapelo2

<pre class="gcommand">
wget https://github.com/agordon/fastx_toolkit/releases/download/0.0.14/fastx_toolkit-0.0.14.tar.bz2
</pre>

Some websites use indirect connections, it could be downloaded as
<pre class="gcommand">
curl -OL https://github.com/agordon/fastx_toolkit/releases/download/0.0.14/fastx_toolkit-0.0.14.tar.bz2
</pre>


==Transferring Files between two file systems on Sapelo2==

Use ssh to login to xfer.gacrc.uga.edu using your UGA MyID username and password (and two-factor authentication via Archpass Duo) to transfer files between different file systems on Sapelo2, including the project area. To transfer data between two file systems that are available on the xfer node, you can use the '''cp''' or the '''rsync''' commands. If you have many files to copy, then a good option is to use the '''fpsync''' command, as that will allow the use of multiple cores.

For example, if you want to use 4 cores to transfer all files from /scratch/myid/mydata to /project/mylab/myid/mydata, you can use the following command on an xfer node shell prompt:

<pre class="gcomman">
fpsync -n 4 -t $HOME/fpsync /scratch/myid/mydata /project/mylab/myid/mydata
</pre>

When using the '''fpysnc''' command, please use the <code> -t $HOME/fpsync</code> option to set the fpsync's temporary directory to be in your home directory.



=Transferring Files to and from the Teaching Cluster=

==The File Transfer node for the Teaching Cluster (txfer.gacrc.uga.edu)==

Users can transfer files between their local machines and the teaching cluster using (a) Filezilla, (b) a secure copy (scp), or (c) WinSCP. To transfer files using scp (or SSH file transfer) you must have scp (or SSH) on your local machine and a connection to the UGA campus network. An scp software is included in recent releases of Unix based operating systems (including Linux and Mac OS X).

Access to txfer.gacrc.uga.edu requires an active MyID and password, and two-factor authentication with Archpass Duo. For more details of Archpass Duo, please refer to
[https://eits.uga.edu/access_and_security/infosec/tools/archpass archpass_duo] .

==File Transfer Methods for the Teaching Cluster==
To transfer files from your local storage device to the teaching cluster, please connect to the file transfer node, txfer.gacrc.uga.edu (use your UGA MyID and the MyID password to authenticate). You will also be prompted for two-factor authentication with Archpass Duo.

===Using FileZilla===

In order to use [http://filezilla-project.org/ FileZilla] to transfer files between a local machine and the teaching cluster, users will need to install the [http://filezilla-project.org/ FileZilla] software on the local machine. After installation, start FileZilla and follow these steps:

1. Go to File -> Site Manager

2. Add a New Site with the following configuration.

In the '''General''' tab select

<pre class="gcomment">

Protocol: SFTP - SSH File Transfer Protocol

Host: sftp://txfer.gacrc.uga.edu

Port: 22

Logon Type: Interactive

User: [your UGA MyID]
</pre>

In the '''Transfer Settings''' tab, check the box for "Limit number of simultaneous connections" and set "Maximum number of connections" to 1.

If this step is omitted, then you will need to enter your password and Duo authentication for each file that you upload or download. By limiting the number of simultaneous connections to 1, you only need to enter your password and Duo authentication once per session.

With the above settings saved, you should be able to transfer files to txfer using filezilla. When you open the connection, you will be prompted for your MyID password (unless you use key based ssh). If the password authenticates successfully, then another pop-up window will prompt for Duo. Note that the Duo window looks very similar to the password window, but the text in the box will describe the options you can use for Duo (for example, a push, enter a passcode, etc).

===Using WinSCP===

In order to use [http://winscp.net/ WinSCP] to transfer files between a local Windows machine and the teaching cluster, users will need to install the [http://winscp.net/ WinSCP] software on the local machine. After installation, when you click the WinSCP shortcut button, Login window pops up, in that you select new site and enter the following information to start the new session:

<pre class="gcomment">

Session:

File Protocol: SFTP

Host name: txfer.gacrc.uga.edu

Port number: 22

Username: [your UGA MyID]

Password: [your MyID password]

</pre>

If the password authenticates successfully, then another pop-up window will prompt for Duo two-factor authentication. It says, Using Keyboard-interactive authentication and lists the Duo options you can choose from: Duo push, Phone Call, or SMS passcode, etc. Once the connection is established you can upload or download files from Local Machine to Cluster or vice-versa. Note: Under Advanced, kept the "Default" option for Transfer settings Rule. With this default option, you will be able to transfer multiple files or folders during the session.


===Using scp===

Sample commands to transfer files from your local Unix/Linux/Mac OS X machine to the teaching cluster:

Open a terminal in your local machine, change directory (cd) to where the files are located in your local machine, and at the command prompt type:

<pre class="gcommand">
scp filename yourUGAMyID@txfer.gacrc.uga.edu:subdirectory
</pre>

where ''filename'' is the name of the file to be transferred, ''yourUGAMyID'' is your UGA MyID, and ''subdirectory'' is the subdirectory in your teaching cluster home directory to which files are being transferred. You will then be asked to enter your UGA MyID password.

(Note: yourUGAMyID@ can be omitted if your username on the teaching cluster is the same as on your local machine). Multiple files (e.g. file1, file2, and file3) can be transferred with a single command:

<pre class="gcommand">
scp file1 file2 file3 yourUGAMyID@txfer.gacrc.uga.edu:subdirectory
</pre>

Wildcards can be used for multiple file transfer (e.g. all files with .dat extension):

<pre class="gcommand">
scp *.dat yourUGAMyID@txfer.gacrc.uga.edu:subdirectory
</pre>

=Transferring Files between Sapelo2 and the Teaching Cluster=

To transfer files between your Sapelo2 directories (e.g. your Sapelo2 home directory or /scratch directory) and your teaching cluster home directory, please first use ssh to login into xfer.gacrc.uga.edu. Then use the scp command to copy files to/from your home directory at txfer.gacrc.uga.edu.

For example, to transfer a directory called subdir2 in your /scratch directory to your teaching cluster home directory. On xfer.gacrc.uga.edu issue the command:
<pre class="gcommand">
scp -r -p /scratch/yourUGAMyID/subdir2 txfer.gacrc.uga.edu:

UGA DUO authentication is required for SSH/SCP access to
GACRC systems.

UGA DUO is a two-factor authentication service which
requires a password (one factor) and a code, phone,
or device (second factor) to successfully authenticate.

If you are not enrolled in the UGA DUO service please
visit the UGA DUO service self-service portal to enroll
and configure or manage your DUO enabled devices.

https://eits.uga.edu/access_and_security/infosec/tools/duo/portal/

For additional help with UGA DUO authentication or to
report an issue please visit:

https://eits.uga.edu/access_and_security/infosec/tools/archpass/

Password:

Duo two-factor login for yourUGAMyID

Enter a passcode or select one of the following options:

1. Duo Push to XXX-XXX-4304
2. Phone call to XXX-XXX-4304
3. SMS passcodes to XXX-XXX-4304

Passcode or option (1-3):

</pre>
When you enter your password, you will be prompted for the two-factor authentication step. Once you enter a passcode or select an option, the file transfer will start.

==Transferring Files between your Sapelo2 project area and the Teaching Cluster==

To transfer files between your /project area and your teaching cluster home directory, please first use ssh to login into xfer.gacrc.uga.edu. Then use the scp command to e.g. copy files from /project to your home directory at txfer.gacrc.uga.edu (or to copy files from your home directory at txfer.gacrc.uga.edu to your /project area).

For example, to copy a file called species.fa from your teaching cluster home directory to a subdirectory called binf1234 in your /project area, use the following command on xfer.gacrc.uga.edu:

<pre class="gcommand">
scp txfer.gacrc.uga.edu:species.fa /project/abclab/binf1234
</pre>
where ''abclab'' needs to be changed to the name of your group.

==Other Methods==
===Using iCommands to access the Cyverse Data Store===
[https://cyverse.org/data-store Cyverse] is a secure data storage and management service. iCommands is a collection of commands for Linux and Mac OS operating systems that are used to interact with the CyVerse Data Store. iCommands can be used by CyVerse account users to download files that have been shared by other users and to upload files to the Data Store, as well as add metadata, change permissions, and more. A CyVerse account is not required to download a public data file via iCommands.

version 4.2.8 of iCommands is installed on Sapelo2 IRODS '''transfer node''' (xfer-irods.gacrc.uga.edu). Please note that this command is not available on the other transfer nodes.

You will need to log on a Sapelo2 transfer node xfer-irods.gacrc.uga.edu and then initialize the connection to iRODS by using the iinit command to use iCommands

More information about using iCommands can be found on the [[ICommands-Sapelo2|'''ICommands-Sapelo2''']] page. You can also read [https://cyverse.atlassian.net/wiki/spaces/DS/pages/241869855/Using+iCommands Official iCommands Documentation]

File:Xfer Filezilla 07282026.pdf

2026-07-28T18:31:25Z

Shtsai:

Using a Conda environment in Jupyter

2026-07-28T18:28:30Z

Shtsai:

== Conda environment ==
A Conda environment is a type of virtual environment. Conda environments are used to contain a set of software/packages that you install (via Conda) into that environment that may be accessed any time you activate the environment. To learn more about Conda environments, please see our wiki page on creating Conda environments at '''[[Installing Applications on Sapelo2#How to install Conda packages|Link to wiki page on how to install Conda environments]].'''

== Open OnDemand ==
Open OnDemand is a web-based service that allows users to run software with a graphical user interface (GUI) such as Jupyter Notebook. Please see our wiki page on Open OnDemand [[OnDemand|'''here''']] to learn more.

== Using a Conda environment in Jupyter ==
These steps will allow you to access the software you install into a Conda environment within Jupyter Notebook running on Open OnDemand

=== Create the Conda environment ===
Create your Conda environment and install any packages you will want to use.
=== Install ipykernel ===
While your Conda environment is still activated, install ipykernel to your Conda environment with:

<pre class="gcommand">
conda install ipykernel
python -m ipykernel install --user --name=my_env
</pre>
Note: change my_env to whatever the name of your Conda environment is. This is the name that you will see in Jupyter Notebook as an available kernel.

=== Open Jupyter in Open OnDemand ===
Click on the Jupyter app in [https://ondemand.gacrc.uga.edu/ Open OnDemand] and choose your desired resources. Please ensure that you choose the Miniforge3 version of Jupyter.

Click launch at the bottom of the page to start a Jupyter Notebook session. You will be redirected to "My Interactive Sessions" where you can wait for the job to be allocated resources.<br>

[[File:Screenshot 2023-12-13 at 10.25.00 AM.png|alt=|border|700x700px]]

Once the job starts, click on Connect to Jupyter. This will open Jupyter in a new page in your browser.

'''For Jupyter versions < 7.0.0 please see below''' (for versions > 7.0.0, scroll down).

From here, click "new" and you should see the name of the ipykernel you created. Clicking on that will start a Jupyter notebook with a kernel that has access to all of the packages in your Conda environment.

[[File:Load_condakernel.png|alt=|border|1111x1111px]]

'''For Jupyter versions > 7.0.0 please see below.'''

From here, click File > new > notebook. A new Jupyter notebook will open and a window will appear titled "Select Kernel". Click the drop down menu and under "Start Other Kernel" you should see the name of your Conda environment. Clicking on that will start your Jupyter notebook with a kernel that has access to all of the packages in your Conda environment.

[[File:New-jupyter-interface.png|alt=|border|1111x1111px]]
[[File:Load_new_condakernel.png|alt=|border|1111x1111px]]

Visual Studio Code SSH

2026-07-28T17:52:28Z

Shtsai: /* Downloading VS Code */

==Introduction==

Visual Studio Code, or "VS Code", is a popular code editor. It is very customizable and allows the user to install many extensions.

==Correct Usage of VS Code and other IDEs on the Sapelo2 Cluster==

Please keep in mind that if you log in to the Sapelo2 Cluster via VS Code, you will be placed on a '''login node'''. This is acceptable only to edit code/files and other low-level tasks, but please do not run code there. It will likely overload our login nodes and cause disruptions to the performance of the login nodes. If you want to run tests in VS Code, please do one of the following first:

'''Running Code/Tests in VS Code on the Cluster'''

# Start an interactive job within VS Code after logging in to the Sapelo2 Cluster
# Use VS Code through On Demand ([[OnDemand|https://wiki.gacrc.uga.edu/wiki/OnDemand]])

''Note'': Some VS Code extensions run in the background even if you are only editing code when you log in via your own VS Code installation on your computer (not through OnDemand). This can still overload our login nodes and cause significant slowness for users logged in to the same login nodes. To remedy this, you can disable these extensions for remote hosts using the instructions below.

==Disabling Heavy-Hitting Extensions==

Disabling extensions using "Disable (remote)" is simple and re-enabling them is also very quick and easy and does not require re-installation. "'''Disable (remote)" disables your VS Code extensions when on a remote host (like the Sapelo2 Cluster), but still allows you to use them locally.'''

If you log in to the Sapelo2 Cluster via VS Code (not through OnDemand), your enabled extensions may run in the background even if you are only editing code, which will still contribute to the load on the login node. High load on login nodes causes major disruptions in the performance of those nodes for all users logged in to those nodes.

The following VS Code extensions are some that commonly run in the background even when you are not running code. Please consider disabling them for remote hosts with "Disable (remote)" if you plan to log in to the Sapelo2 Cluster via the VS Code IDE. You can easily and quickly enable the extension when you are ready to run your code on the Sapelo2 Cluster within an interactive job in your VS Code session.

Extensions:
*C/C++ (ms-vscode.cpptools)
*Python (ms-python.python)
*Pylance (ms-python.vscode-pylance)
*Jupyter (ms-toolsai.jupyter)

* GitHub Copilot (github.copilot)

* GitHub Copilot Chat (github.copilot-chat)
* Codeium (codeium.codeium)
* Claude-related extensions
* Docker (ms-azuretools.vscode-docker)

Steps to disable extensions for remote hosts using "Disable (remote)":

#In VS Code, click on the extension panel to view your extensions
#Click on the gear icon next to the extension that you want to disable
#Click on 'Disable (remote)'

* You can then re-enable your extensions if you need to run tests on the Sapelo2 Cluster by starting an interactive job in your VS Code session and clicking "Enable" on your desired extension

==Downloading VS Code==

If you haven't already, you can download VS Code this [https://code.visualstudio.com/download "Link to Download VS Code"]. If you are using Windows, you will almost certainly want to download the 64-bit User Installer.

===Installing the SSH extension===
Here we will go over how to set up its SSH extension. This will allow you to have VS Code connect to Sapelo2 so that you can edit files on the cluster with your local instance of VS Code.

====Open the Extensions Interface====

When you open VS Code, you'll see on the left the Activity Bar, with several buttons displayed vertically.

[[File:Vscode_activity_bar.png | frameless]]

----

====Search for the SSH Extension====

Clicking the button with the four squares opens up the extensions interface in the sidebar. Below you can see some of extensions I already have installed. Some of these are just simply color theme packages, to change syntax highlighting colors. Others provide features relevant to a particular programming language, such as the Python extension providing [https://code.visualstudio.com/docs/editor/intellisense#:~:text=IntelliSense%20is%20a%20general%20term,%2C%20and%20%22code%20hinting.%22 IntelliSense] for Python.

[[File:Vscode_extensions.png | 400px | frameless]]

----

At the top of the extensions interface is a search bar, where you can search for official or community-provided extensions. This is where you could search for the SSH extension, for example. Typically you'll want to install extensions provided by a known source, such as Microsoft in this case (the first option), and/or an extension that has many downloads and a high rating, as shown in this search output.

[[File:Vscode_ssh_extension.png | 600px | frameless]]

----

====Install the SSH Extension====

Once you find the extension you want, all you have to do is click the small "Install" button in the search output, and a tab will open in your editor with more information and instructions on how to use the extension that you have installed.

[[File:Vscode_installed.png |700x700px| frameless]]

----

==Using the SSH Extension==

===Click the SSH Button===

You can hide the sidebar if you wish with cmd + b on Mac and ctrl + b on Windows. Once the SSH extension is installed you will see this small greater than & less than button in the bottom left corner of VS Code.

[[File:Vscode_sshbutton.png | frameless]]

----

====Add a new SSH Host Connection====

Upon clicking that small SSH button in the bottom left, a prompt window will appear in the top middle of VS Code, asking how you want to connect to whatever it is you're going to SSH. Select the first option, "Connect Current Window to Host...".

[[File:Vscode_connection_options.png | 600px | frameless]]

----

Next, click the "Add New SSH Host..." option, and type your MyID@sapelo2.gacrc.uga.edu in the prompt, and then press enter.

[[File:Vscode_add_new_ssh_host.png | 600px | frameless]]
[[File:Vscode_connection.png | 600px | frameless]]

----

If it asks you what SSH config file to update, you can just choose the first option.

[[File:Vscode_sshconfig.png | 600px | frameless]]

----

====Connect to Sapelo2====

The prompt will then disappear, but now you can click the small SSH button in the bottom left of VS Code and see that your connection has been saved, you don't have to type it every time. Click the Sapelo2 connection to then be presented with a password prompt. This just wants your MyID password, as if you were normally connecting to Sapelo2 via PuTTY or your terminal. Then you will get an ArchPass DUO prompt. You can respond to this with a 1 for a push notification to your phone, or however you normally authenticate with ArchPass DUO.

[[File:Vscode_chooseconnection.png | 600px | frameless]]
[[File:Vscode_password.png | 600px | frameless]]
[[File:Vscode_duo.png | 600px | frameless]]

----

====Open Directories on Sapelo2====

Once you approve the connection request via DUO, you're connected to Sapelo2! To open up a particular directory on Sapelo2, click the button on the side part that looks like two pieces of paper, which will open the explorer interface within the sidebar.

[[File:Vscode_explorer.png | 600px | frameless]]

----

Click the "Open Folder" button to then be presented with a prompt where you can enter any path on Sapelo2 to which you have permission to access.

[[File:Vscode_opendir.png |700x700px| frameless]]

----

In this example, I've opened my home directory in the side bar. It presents it as a file tree that I can navigate graphically. Directories can be expanded by clicking them. Files can be opened in VS Code by double clicking them, as you can see here, with this file.txt in my home directory.

[[File:Vscode_homedir.png |700x700px| frameless]]

----

You can open multiple directories on Sapelo2 in the sidebar of VS Code by clicking the "Add Folder to Workspace" option in the File menu.

[[File:Vscode_add_dir.png |700x700px| frameless]]

----

====Open the Terminal in VS Code====

I highly recommend toggling the Terminal in VS Code, which will allow you to interact with Sapelo2 as you normally would from within VS Code. To do this, click "Terminal" under the View menu. This will open up a terminal in the bottom half of the editor. You can enter any command as you normally would on Sapelo2.

[[File:Vscode_terminal.png |714x714px| frameless ]]

== VSCode Tunnel Workflow on Sapelo2 (GPU Nodes) ==

=== Overview ===
This guide describes how to run VSCode on Sapelo2 compute nodes using a tunnel-based workflow.

=== Motivation ===
Using VSCode Remote SSH directly from a local machine often:

* Launches backend services on '''login nodes'''
* Causes '''CPU and memory contention''' on login nodes
* Leads to degraded performance for all users

This workflow ensures:

* All VSCode processes run inside a '''Slurm interactive or batch job'''
* Proper resource allocation (CPU / memory / GPU)
* No persistent processes on login nodes

=== Workflow architecture ===
Local Browser (vscode.dev)

↓

VSCode Tunnel (cloud relay)

↓

Compute Node (Slurm job on gpu_p)

↓

VSCode Server + Extensions + User Workload
----

=== Advantages of This Workflow ===
* Prevents login node overload
* Enables GPU-based development
* Supports AI/LLM workflows
* Works entirely in browser

=== Step-by-Step Workflow in an Interactive GPU Job ===

==== 1. Allocate a GPU Interactive Job ====
<syntaxhighlight lang="shell">
interact -c 20 --mem 80gb -p gpu_p --gres gpu:L4:2
</syntaxhighlight>'''Note:''' Computing resources (CPU, memory, GPU) allocated here will be available within your VSCode tunnel working environment.
----

==== 2. Clean Previous VSCode State (Recommended. Note that all VSCode extensions you downloaded will be removed) ====
<syntaxhighlight lang="shell">
rm -rf ~/.vscode
rm -rf ~/.vscode-server
rm -rf ~/.code_tunnel
</syntaxhighlight>
----

==== 3. Prepare Working Directory ====
<syntaxhighlight lang="shell">
cd /scratch/$USER/VSCode_tunnel/
</syntaxhighlight>
----

==== 4. Load VSCode Module ====
<syntaxhighlight lang="shell">
ml VSCode/1.88.1
</syntaxhighlight>'''Note:''' You can load additional compatible central modules at this step. Any modules loaded here will be configured and available within your VSCode tunnel working environment.
----

==== 5. Authenticate VSCode Tunnel (You need a GitHub personal account to complete this step) ====
<syntaxhighlight lang="shell">
code-tunnel tunnel user login --provider github
</syntaxhighlight>Follow the instructions in your local browser:

* Open <nowiki>https://github.com/login/device</nowiki> , choose to use your personal GitHub account.
* Enter the code, for example, 15C1-6D7C
* Authorize access[[File:Vscode-tunnel-1.png|thumb|Open <nowiki>https://github.com/login/device</nowiki>|none|600x600px]][[File:Vscode-tunnel-2.png|none|thumb|600x600px|Enter the code]][[File:Vscode-tunnel-3.png|none|thumb|Authorize access|513x513px]][[File:Vscode-tunnel-4.png|none|thumb|600x600px|Authorization successful]]
----

==== 6. Start the Tunnel ====
<syntaxhighlight lang="shell">
code-tunnel tunnel --name <your vscode tunnel name> --cli-data-dir ~/.code_tunnel --accept-server-license-terms
</syntaxhighlight>For example:<syntaxhighlight lang="shell">
code-tunnel tunnel --name vscode-tunnel-0416 --cli-data-dir ~/.code_tunnel --accept-server-license-terms
</syntaxhighlight>You will be asked how would you like to log in to VSCode (cloud relay). Choose GitHub Account.
[[File:Vscode-tunnel-5.png|none|thumb|1189x1189px|How would you like to log in to claud VSCode]]
----

==== 7. Open VSCode in your Local Browser ====
To grant access to the server, following instructions, you will repeat what you did in Step 5:

*Open <nowiki>https://github.com/login/device</nowiki> , choose to use your personal GitHub account.
* Enter the code generated at this step, for example, E1F9-D7E8
* Authorize access
'''Important Notes:'''

* If another authorization prompt appears, it means the previous login in Step 5 was not fully completed.
* Complete authorization using the latest code provided (for example: <code>E1F9-D7E8</code>).
* Once authorization is successful, the VSCode link will appear and can be used directly.

The VSCode tunnel is now active. Open the provided link in your browser to connect

<nowiki>https://vscode.dev/tunnel/</nowiki><tunnel-name>/<full>/<path>/<to>/<your>/<working folder where you started tunnel>

Example:

<nowiki>https://vscode.dev/tunnel/vscode-tunnel-0416/scratch/zhuofei/VSCode_tunnel</nowiki>
----

==== 8. Trust Workspace ====
VSCode is now connected through the tunnel (cloud relay).
[[File:Vscode-tunnel-6.png|none|thumb|910x910px|VSCode is now active]]
'''Note:''' Initial connection may take several minutes due to VSCode server download and setup.

* The first time VSCode connects, it will automatically download the VSCode server (<code>vscode-server-linux-x64.tar.gz</code>) and temporarily store it in <code>/tmp</code>.
* After the server is downloaded and started, VSCode will establish the connection. This process may take several minutes to complete—please be patient.

You will also need to click to trust workspace that VSCode will use:
[[File:Vscode-tunnel-7.png|none|thumb|908x908px|Trust workspace]]

==== 9. What happens if you cancel the interactive job or the job reaches its time limit on the cluster? ====
If you cancel the interactive job, or if the job is terminated after reaching its time limit, the VSCode tunnel session will stop, and all allocated resources (CPU, memory, and GPU) will be released. You will also lose the active VSCode connection and need to start a new session.
----

=== Step-by-Step Workflow in a batch GPU Job ===

==== 1. Prepare a batch job submission script for VSCode tunnel session ====
Within the working directory of the interactive job described above, you can use a batch job submission script (<code>sub.sh</code>) as shown below:<syntaxhighlight lang="shell">
#!/bin/bash
#SBATCH --job-name=code_tunnel
#SBATCH --partition=gpu_p
#SBATCH --gres=gpu:L4:2
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=4
#SBATCH --mem=32G
#SBATCH --time=4:00:00
#SBATCH --output=log.%j.out

ml VSCode/1.88.1

code-tunnel tunnel user login --provider github
code-tunnel tunnel --name vscode-tunnel-0416 --cli-data-dir ~/.code_tunnel --accept-server-license-terms

</syntaxhighlight>'''Note:'''

# Computing resources (CPU, memory, GPU) allocated for your batch job will be available within your VSCode tunnel working environment.
# You can load additional compatible central modules in your batch job submission script. Any modules loaded for your batch job will be configured and available within your VSCode tunnel working environment.

==== 2. Submit a batch job ====
You can then submit the job to the cluster using:<syntaxhighlight lang="shell">
sbatch sub.sh
</syntaxhighlight>

==== 3. How to complete Steps 5–7 from the interactive workflow described above. ====
Once the job starts running, monitor the job’s standard output log file (e.g., log.44588780.out). You can use commands such as <code>cat</code>to view the log contents from time to time and follow the on-screen instructions to complete Steps 5–7 from the interactive workflow described above.

==== 4. What happens if you cancel the batch job or the job reaches its time limit on the cluster? ====
If you cancel the batch job, or if the job is terminated after reaching its time limit, the VSCode tunnel session will stop, and all allocated resources (CPU, memory, and GPU) will be released. You will also lose the active VSCode connection and need to start a new session.

==== 5. Example steps ====
<syntaxhighlight lang="shell">
zhuofei@ss-sub4 VSCode_tunnel$ pwd
/scratch/zhuofei/VSCode_tunnel

zhuofei@ss-sub4 VSCode_tunnel$ ls
sub.sh

zhuofei@ss-sub4 VSCode_tunnel$ cat sub.sh
#!/bin/bash
#SBATCH --job-name=code_tunnel
#SBATCH --partition=gpu_p
#SBATCH --gres=gpu:L4:1
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=4
#SBATCH --mem=32G
#SBATCH --time=4:00:00
#SBATCH --output=log.%j.out

ml VSCode/1.88.1

code-tunnel tunnel user login --provider github
code-tunnel tunnel --name vscode-tunnel-0416 --cli-data-dir ~/.code_tunnel --accept-server-license-terms

zhuofei@ss-sub4 VSCode_tunnel$ sbatch sub.sh
Submitted batch job 44594144

zhuofei@ss-sub4 VSCode_tunnel$ sq --me
JOBID NAME PARTITION USER NODES CPUS MIN_MEMORY PRIORITY TIME TIME_LIMIT STATE NODELIST(REASON)
44594144 code_tunnel gpu_p zhuofei 1 4 32G 996 0:02 4:00:00 RUNNING ra5-6

zhuofei@ss-sub4 VSCode_tunnel$ ls
log.44594144.out sub.sh

# --- Next Steps ---
# --- 1. Run `cat log` ---
# --- 2. Follow the instructions shown in a browser ---
# --- 3. Return and run `cat log` again to see new instructions at the bottom of the log ---
# --- 4. Repeat until tunnel is fully ready ---

# Open the log file first time
zhuofei@ss-sub4 VSCode_tunnel$ cat log.44594144.out

# --- VS Code tunnel requests GitHub device authentication ---
# --- ACTION: Open a browser and complete authorization using the code shown below ---
To grant access to the server, please log into https://github.com/login/device and use code D9D8-6303

# --- After completing the step above, return and continue ---

# Open the log file second time
zhuofei@ss-sub4 VSCode_tunnel$ cat log.44594144.out

# --- Previous step has been completed ---
# --- Continue following instructions from the log ---
To grant access to the server, please log into https://github.com/login/device and use code D9D8-6303

# --- ACTION ---
# --- If this authorization message appears again and you have NOT completed login, complete it in a browser using the latest code shown ---
# --- If you have already completed authorization, ignore this message and continue ---

*
* Visual Studio Code Server
*
* By using the software, you agree to
* the Visual Studio Code Server License Terms (https://aka.ms/vscode-server-license) and
* the Microsoft Privacy Statement (https://privacy.microsoft.com/en-US/privacystatement).
*

# --- Tunnel provides browser access link ---
# --- ACTION: Open this link in your browser ---
Open this link in your browser https://vscode.dev/tunnel/vscode-tunnel-0416/scratch/zhuofei/VSCode_tunnel

# --- After opening the link, return and continue ---

# Open the log file third time
zhuofei@ss-sub4 VSCode_tunnel$ cat log.44594144.out

# --- Authentication message may still appear ---
# --- No action needed if authorization is already completed ---
To grant access to the server, please log into https://github.com/login/device and use code D9D8-6303

*
* Visual Studio Code Server
*
* License and privacy information
*

# --- Tunnel link remains active ---
# --- ACTION: Ensure your browser is connected using the link above ---
Open this link in your browser https://vscode.dev/tunnel/vscode-tunnel-0416/scratch/zhuofei/VSCode_tunnel

# --- After confirming connection, continue ---

# Open the log file fourth time
zhuofei@ss-sub4 VSCode_tunnel$ cat log.44594144.out

# --- Final stage: client connects to tunnel ---
To grant access to the server, please log into https://github.com/login/device and use code D9D8-6303

*
* Visual Studio Code Server
*
* License and privacy information
*

Open this link in your browser https://vscode.dev/tunnel/vscode-tunnel-0416/scratch/zhuofei/VSCode_tunnel

# --- A client (your browser) connected to the tunnel ---
[2026-04-20 11:06:45] info [tunnels::connections::relay_tunnel_host] Opened new client on channel 2

# --- Internal SSH handshake ---
[2026-04-20 11:06:45] info [russh::server] wrote id
[2026-04-20 11:06:45] info [russh::server] read other id

# --- Tunnel session is active ---
[2026-04-20 11:06:45] info [russh::server] session is running

# --- VS Code checks for existing server ---
[2026-04-20 11:06:45] info [rpc.0] Checking /home/zhuofei/.code_tunnel/servers/Stable-560a9dba96f961efea7b1612916f89e5d5d4d679/log.txt and /home/zhuofei/.code_tunnel/servers/Stable-560a9dba96f961efea7b1612916f89e5d5d4d679/pid.txt for a running server...

# --- Starts new server if none exists ---
[2026-04-20 11:06:45] info [rpc.0] Starting server...

# --- VS Code Server successfully started ---
[2026-04-20 11:06:46] info [rpc.0] Server started

# --- DONE: Tunnel is fully ready ---
# --- You can now use VS Code in your browser ---
</syntaxhighlight>
=== What Happens Under the Hood When Using VSCode ===
We would like VSCode users to understand what happens behind the scenes.
When VSCode starts (via tunnel or SSH remote connection), it launches multiple backend processes, including:

* <code>code-tunnel</code>
* <code>code-server</code>
* <code>node (server-main.js)</code>
* <code>extensionHost</code>
* <code>fileWatcher</code>
* <code>ptyHost</code>

These components work together to provide remote editing, extension support, terminal access, and file system monitoring.

As a result, VSCode behaves much like a '''multi-process application server''', rather than a simple lightweight editor or a simple SSH tool as many users might expect.

Because of this architecture:

* Multiple background processes are always running
* CPU and memory are continuously consumed
* Resource usage persists even when idle

For this reason, VSCode '''must be run on compute nodes''', not login nodes. '''''Running VSCode directly on login nodes can result in increased CPU load and memory usage, leading to degraded responsiveness for all users.'''''

Please keep the following in mind:

* Always treat VSCode as a '''compute workload'''
* Always run it inside a Slurm allocation, either via an interactive job (as shown above), a batch job, or an [[OnDemand|Open OnDemand]] (OOD) VSCode session
* Avoid launching it directly on login nodes, especially when using the SSH Remote extension.

----

=== Compute Node and Login Node Behavior (Observed) ===
When your VSCode tunnel (interactive or batch job) is running on a compute node, you can check the related VS Code processes with:<syntaxhighlight lang="shell">
ssh <node> "pgrep -u $USER -af 'code|vscode|node'"
</syntaxhighlight>To compare, run the same command on the login node where you started the interactive job or submitted the batch job:<syntaxhighlight>
pgrep -u $USER -af "code|vscode|node"
</syntaxhighlight>Example:<syntaxhighlight lang="shell">
zhuofei@ss-sub4 VSCode_tunnel$ ssh ra5-6 "pgrep -u $USER -af 'code|vscode|node'"
3064679 code-tunnel tunnel --name vscode-tunnel-0416 --cli-data-dir /home/zhuofei/.code_tunnel --accept-server-license-terms
3064696 sh /home/zhuofei/.code_tunnel/servers/Stable-560a9dba96f961efea7b1612916f89e5d5d4d679/server/bin/code-server --connection-token=j2x9Z0dD03TwZ5WpnxGpodje1UFflxwsjGy0uDjVIOA --accept-server-license-terms --start-server --enable-remote-auto-shutdown --socket-path=/tmp/code-6ca97adf-e1a0-4b74-a319-bd3dff74b07c
3064700 /home/zhuofei/.code_tunnel/servers/Stable-560a9dba96f961efea7b1612916f89e5d5d4d679/server/node /home/zhuofei/.code_tunnel/servers/Stable-560a9dba96f961efea7b1612916f89e5d5d4d679/server/out/server-main.js --connection-token=j2x9Z0dD03TwZ5WpnxGpodje1UFflxwsjGy0uDjVIOA --accept-server-license-terms --start-server --enable-remote-auto-shutdown --socket-path=/tmp/code-6ca97adf-e1a0-4b74-a319-bd3dff74b07c
3064724 /home/zhuofei/.code_tunnel/servers/Stable-560a9dba96f961efea7b1612916f89e5d5d4d679/server/node /home/zhuofei/.code_tunnel/servers/Stable-560a9dba96f961efea7b1612916f89e5d5d4d679/server/out/bootstrap-fork --type=fileWatcher
3064815 /home/zhuofei/.code_tunnel/servers/Stable-560a9dba96f961efea7b1612916f89e5d5d4d679/server/node --dns-result-order=ipv4first /home/zhuofei/.code_tunnel/servers/Stable-560a9dba96f961efea7b1612916f89e5d5d4d679/server/out/bootstrap-fork --type=extensionHost --transformURIs --useHostProxy=false
3064832 /home/zhuofei/.code_tunnel/servers/Stable-560a9dba96f961efea7b1612916f89e5d5d4d679/server/node /home/zhuofei/.code_tunnel/servers/Stable-560a9dba96f961efea7b1612916f89e5d5d4d679/server/out/bootstrap-fork --type=ptyHost --logsPath /home/zhuofei/.vscode-server/data/logs/20260420T110646
3064843 /bin/bash --init-file /home/zhuofei/.code_tunnel/servers/Stable-560a9dba96f961efea7b1612916f89e5d5d4d679/server/out/vs/workbench/contrib/terminal/common/scripts/shellIntegration-bash.sh

zhuofei@ss-sub4 VSCode_tunnel$ pgrep -u $USER -af 'code|vscode|node'
# (no output)

</syntaxhighlight>

==== Observed Behavior ====
'''On compute node:'''

* VS Code Server process (<code>code-server</code>)
* Node runtime processes (shown as <code>node</code>)
* Extension host (<code>node ... --type=extensionHost</code>)
* File watcher (<code>node ... --type=fileWatcher</code>)
* Terminal backend (<code>node ... --type=ptyHost</code>)

'''On login node:'''

* No VSCode-related processes

----

==== Result ====

* VSCode backend processes run only on the compute node
* No VSCode services remain on the login node

----

==== Interpretation ====

* The VSCode workload is fully isolated to the compute node
* Login nodes remain clean and responsive
* This behavior aligns with HPC best practices

----

=== Best Practices ===

* Always run VSCode (server/tunnel) inside a Slurm interactive or batch job on a compute node.
* Never run VSCode directly on login nodes.
* Request appropriate CPU, memory, and GPU resources for your workload.
* Install only the extensions you need.
* Periodically remove unused extensions to reduce resource usage.
* Avoid leaving idle VSCode sessions running; terminate jobs when not in use.
* VS Code and its extensions are installed under ${HOME}/.vscode-server/, which counts toward your home directory quota.

=== Key Takeaway ===
VSCode is a multi-process system. It must be treated like a compute workload—not a simple SSH tool or text editor.

=== Final Note ===
This workflow is strongly recommended for:

* AI / LLM workloads
* GPU-based development
* Heavy interactive coding sessions

VSCode

2026-07-28T17:50:00Z

Shtsai:

== VSCode ==
Visual Studio Code, or VSCode, is a source code editor/IDE that may be used with various languages (a few include C#, C++, Python, Ruby, Rust, Java, JavaScript, Julia, HTML). VSCode can be used via [[OnDemand|Open OnDemand]].

== Open OnDemand ==
Open OnDemand is a web-based service that allows users to run software with a graphical user interface (GUI) such as VSCode. Please see our wiki page on [[OnDemand|'''Open OnDemand''']] to learn more. To access the Open OnDemand interface on Sapelo2, point your browser to https://ondemand.gacrc.uga.edu/

== Opening VSCode IDE/Editor ==
* The first step is to go to Open OnDemand and click on Interactive Apps in the top bar. Click on VSCode under "Servers".

[[File:start_vscode.png|alt=|border|868x868px]]

* This will open a page where you can specify various computational resources to be used in the VSCode job.

[[File:vscode_resources.png|alt=|border|647x647px]]

* You can also specify a particular workspace under Load existing VSCode workspace. The default for this is /home/MyID and can be changed by clicking "Select Path"
* If you would like to setup your environment in VSCode by sourcing a shell or bash script, you can do so by specifying the absolute path to the script in the Environment Setup Script

[[File:Vscode-setup1.png|alt=|border|580x580px]]

* Clicking on "Launch" will queue up the job and take you to the Interactive Sessions page. You will see your VSCode job appear like below.

[[File:Vscode-connect.png|alt=|border|580x580px]]

* When the resources have been allocated, you will be able to click "Connect" to start the VSCode session.

== Using the VSCode IDE/Editor ==
* After you click "Connect", the VSCode IDE will open in a new tab.
* In the VSCode IDE, you can create a file (and save it to a specific location on the cluster), or open a workspace (default will be /home/MyID) and create a file there.
* To create a file for a specific coding language:
** Hover over the Open Editors bar and click "Open new text file"
** Click on "Select a language" and choose whichever language you want your code to be interpreted in.
** Your file will automatically have the file suffix that goes with the language you chose.
* To run code in the terminal:
** The code file must be saved (to a location on the cluster) before you can run it in the terminal.
** To open the terminal click the menu button in the top left, then View, then Terminal and a terminal window will open in the bottom half of the screen.
** To run the file you have currently open, click the menu, then Terminal, then Run Active File
* You can also install extensions through VSCode. To do so:
** Click the extensions button on the left, a few down from the menu button.
** An extensions menu will open that allows you to search for extensions and manage what you have installed or enabled/disabled.
** Extensions are installed to /home/MyID/.local/share/code-server/extensions
** The extensions you install will be available to you every time you open a new VSCode session

== Potential Issues ==
* If you accidentally close the tab containing the VSCode IDE and want to get it back:
** As long as the job is still running in Open OnDemand, you can reopen your session exactly as it was when the browser was closed (tabs and files you had open will still be there).
** Simply go back to the My Interactive Sessions page in Open OnDemand and click "Connect" again.
*** If the "Connect" button is no longer there, your job ran out of time and you need to start a new one. Doing so will open a new VSCode IDE (files and tabs you had open will NOT be there, you need to reopen anything you had open before).
* If you get a browser in VSCode that asks you to login and requests a password (see below), simply close the tab and reopen VSCode by clicking on the "Connect" button under the My Interactive Sessions page in OOD.

[[File:Vscode-password.png|alt=|border|580x580px]]

Training

2026-07-28T17:07:36Z

Shtsai: /* Out-Reach/In-Class Talk */

==GACRC Training==

The GACRC regularly hosts training sessions on a number of subjects relevant to the use of our computational and storage resources. Scheduled trainings will be announced through the GACRC mailing list.

'''NOTE: New users are required to attend a Sapelo2 cluster introductory training session and information about that will be sent once an account is requested.'''

==Regular Training Announcement==

In '''August 2026''', the GACRC is hosting 6 training sessions listed below. These training workshops will be offered remotely via Zoom Meeting. Detailed instructions for joining the Zoom meeting will be sent to your UGA email account before each training session you register for.

We will offer:

1. Linux training for Linux-inexperienced cluster new users (3 sessions)

2. Sapelo2 cluster new user training (3 sessions)

==Event Schedule==

This section describes the training workshops that we offer, along with the sessions that are currently scheduled.

===Sapelo2 Cluster New User Training===

This mandatory training consists of an overview of the structure of Sapelo2 as well as hands-on practice submitting a job along with guidance and best practices when using the Sapelo2 cluster.

'''Prerequisites:'''

*Linux basics. A Linux-inexperienced user must complete a prerequisite Linux training for Linux-inexperienced cluster new users.

'''Workshop Training Goals:'''

*Understand the layout of Sapelo2

*Understand the Sapelo2 file systems

*Understand the Sapelo2 partitions

*Understand the Sapelo2 software environment

*Understand how to request computing resources and submit a computational batch job following the Sapelo2 cluster general workflow

*Understand how to initiate an interactive job

*Understand how to transfer files to and from the cluster

*Understand how to get support from GACRC support team when you have any issues on cluster

'''Scheduled Sessions:'''

{| class="wikitable unsortable" width="100%" border="1" cellspacing="0" cellpadding="2" align="center"
|-
! scope="col" |Title
! scope="col" |Date/Time
|-
|Using Sapelo2 Cluster at the GACRC
|July 16th, Thursday, 2:00 PM - 4:00 PM
|-
|Using Sapelo2 Cluster at the GACRC
|July 22nd, Wednesday, 2:00 PM - 4:00 PM
|-
|Using Sapelo2 Cluster at the GACRC
|August 7th, Friday, 2:00 PM - 4:00 PM
|-
|Using Sapelo2 Cluster at the GACRC
|August 12th, Wednesday, 2:00 PM - 4:00 PM
|-
|Using Sapelo2 Cluster at the GACRC
|August 20th, Thursday, 2:00 PM - 4:00 PM
|}

===Linux Training for Linux-inexperienced Cluster New Users===
The Sapelo2 High Performance Computing (HPC) cluster runs a headless Linux distribution as the operating system on each of its constituent nodes. The term headless refers to the fact that these nodes do not have a desktop graphical user interface (GUI) installed by default. Graphical desktop environments consume resources that analyses could otherwise use, so users employ a command-line interface (CLI) instead. To interact with these resources, users connect to a remote terminal via SSH and execute commands.

The Linux Training workshop provides hands-on practice of the fundamental Linux commands necessary to interact with HPC resources.

'''Prerequisite:'''

Please watch the introductory videos on Linux, basic Linux terms, and Linux Paths and Directories (total ~17 minutes) '''before attending the training workshop'''.

*[https://kaltura.uga.edu/media/t/1_81u2kfi2/176125031 Linux]
*[https://kaltura.uga.edu/media/t/1_ol51cuyn/176125031 basic Linux terms]
*[https://kaltura.uga.edu/media/t/1_wdyxhgdg/176125031 Linux Paths and Directories]

'''Training Goals:'''

1. Understand fundamental concepts of Linux working environment (filesystem hierarchy, path, PATH, etc.)

2. Know how to use Linux common commands (ls, cd, pwd, cat, more, nano, mkdir, rm, cp, mv, etc.)

3. Understand what is Linux bash shell and know how to make a simple Linux script and run it in Linux environment

'''Scheduled Sessions:'''

{| class="wikitable unsortable" width="100%" border="1" cellspacing="0" cellpadding="2" align="center"
|-
! scope="col" | Title
! scope="col" |Date/Time
|-
|Use Linux on Cluster
|July 14th, Tuesday, 1:00 PM - 3:00 PM
|-
|Use Linux on Cluster
|July 20th, Monday, 1:00 PM - 3:00 PM
|-
|Use Linux on Cluster
|August 5th, Wednesday, 1:00 PM - 3:00 PM
|-
|Use Linux on Cluster
|August 10th, Monday, 1:00 PM - 3:00 PM
|-
|Use Linux on Cluster
|August 18th, Tuesday, 1:00 PM - 3:00 PM
|}

===Using Sapelo2 Cluster at the GACRC, Part II ===
This workshop will cover high-performance computing on Sapelo2, including job scheduling, resource requests (CPU, memory, GPU), and techniques for optimizing job performance.

'''Prerequisites:'''
*Linux basics. A Linux-inexperienced user must complete a prerequisite Linux training for Linux-inexperienced cluster new users.
*Sapelo2 cluster new user training. Fundamental HPC and Sapelo2 knowledge is required for this advanced Sapelo2 workshop.

'''Training Goals:'''

1. Learn about high-performance computing framework

2. Why is my job pending? How can I get my job to start sooner? How to find available computing resources on Sapelo2?

3. How to request computing resources such as nodes, CPU cores, memory, GPU device, etc. to run serial, threaded, MPI, and GPU jobs on Sapelo2?

4. How can I make my job run more efficiently (through the correct use of software and hardware)?

5. A quick intro to MPI library and how to compile/run MPI jobs on Sapelo2

'''Scheduled Sessions:'''

{| class="wikitable unsortable" width="100%" border="1" cellspacing="0" cellpadding="2" align="center"
|-
! scope="col" |Title
! scope="col" |Date/Time
|-
|Using Sapelo2 Cluster at the GACRC, Part II
|
|}

===Installing Software Packages in Virtual Environments on Sapelo2 ===
This workshop will cover the basics of virtual environments as well as provide practical guidance and best practices for using virtual environments on the Sapelo2 cluster. Participants will learn the basics of creating and configuring virtual environments, how to install software packages in both a Conda virtual environment and a Python virtual environment, and manage dependencies in their environments.

'''Prerequisite:'''

Must already have strong understanding of the Linux environment and Sapelo2.

'''Training Goals:'''

1. Learn how to create a virtual environment on Sapelo2

2. Be able to install software packages into both Conda and Python virtual environments

3. Understand how to manage dependencies of their virtual environments

'''Scheduled Sessions:'''

{| class="wikitable unsortable" width="100%" border="1" cellspacing="0" cellpadding="2" align="center"
|-
! scope="col" |Title
! scope="col" |Date/Time
|-
|Installing Software Packages in Virtual Environments on Sapelo2
|
|}

===Job Parallelization with GNU Parallel and Slurm Arrays ===
Learn how to run multiple commands in parallel using GNU Parallel and Slurm Arrays. These tools greatly reduce the runtime of certain types of jobs by running multiple instances of the same command in parallel. The workshop focuses on problems that involve executing the same command on multiple different inputs. This workshop is intended for users comfortable writing job submission scripts and using a command line. Concurrent and parallel programming techniques are not covered in this workshop.

'''Prerequisite:'''

Must already have strong understanding of the Linux environment and Sapelo2.

'''Training Goals:'''

1. Understand which jobs benefit from GNU Parallel and Slurm Arrays

2. Use GNU Parallel and Slurm Arrays to parallelize jobs

3. Understand the differences and similarities between GNU Parallel and Slurm Arrays

'''Scheduled Sessions:'''

{| class="wikitable unsortable" width="100%" border="1" cellspacing="0" cellpadding="2" align="center"
|-
! scope="col" |Title
! scope="col" |Date/Time
|-
|Job Parallelization with GNU Parallel and Slurm Arrays
|
|}



==How to Register==

The training workshops '''Using Sapelo2 Cluster at the GACRC''' and '''Use Linux on Cluster''' are '''ONLY''' offered to '''new users''' who need user accounts on the GACRC Sapelo2 cluster or current Sapelo2 users seeking a refresher. If you would like to use the cluster, please ask your group PI/UGA faculty member to send us an account creation request for you, using the [https://uga.teamdynamix.com/TDClient/Requests/ServiceDet?ID=25839 GACRC User Account Request Form].

If you would like to attend the '''Using Sapelo2 Cluster at the GACRC, Part II''', the '''Installing Software Packages in Virtual Environments on Sapelo2''', and/or the '''Job Parallelization with GNU Parallel and Slurm Arrays''' training workshops, please send us a request using the [https://uga.teamdynamix.com/TDClient/Requests/ServiceDet?ID=25852 GACRC Training Request Form]. In your request, please tell us which session(s) you would like to attend.



== Download==

This section provides the slides that we use for our current workshops and material used for several of our past training events and presentations.

===Sapelo2 Cluster Training===

{| class="wikitable unsortable" width="100%" border="1" cellspacing="0" cellpadding="2" align="center"
|[[Media:GACRC_Sapelo2_cluster_new_user_training_workshop_v10.8.pdf]]
|-
|}

===Teaching Cluster Training===

{| class="wikitable unsortable" width="100%" border="1" cellspacing="0" cellpadding="2" align="center"
|-
|[[Media:GACRC-Teaching-cluster-new-user-training-workshop-Spring2026.pdf]]
|-
|}

===Linux Training for New Cluster Users===

{| class="wikitable unsortable" width="100%" border="1" cellspacing="0" cellpadding="2" align="center"
|-
|[[Media:Linux_Training_For_New_Users_Of_Cluster_Suchi_04252019.pdf]]
|-
|}

===Installing Software Packages in Virtual Environments on Sapelo2===

{| class="wikitable unsortable" width="100%" border="1" cellspacing="0" cellpadding="2" align="center"
|-
|[[Media:GACRC_virtual_environments_training_v1.2.pdf]]
|}

===Job Parallelization with GNU Parallel and Slurm Arrays===

{| class="wikitable unsortable" width="100%" border="1" cellspacing="0" cellpadding="2" align="center"
|-
|[[Media:GNU_Parallel_and_SLURM_Arrays_v1.1.pdf]]
|}



===Topical Sessions===

{| class="wikitable unsortable" width="100%" border="1" cellspacing="0" cellpadding="2" align="center"
|
|-
|[[Media:AI_Resources_on_the_GACRC_Sapelo2_Cluster.pdf]]
|-
|[[Media:Using_Sapelo2_Cluster_at_the_GACRC_Part_II_Rocky8.pdf]]

|}

===Out-Reach/In-Class Talk===

{| class="wikitable unsortable" width="100%" border="1" cellspacing="0" cellpadding="2" align="center"
|-
! scope="col" |Dept./Center/Institute
! scope="col" |Type
! scope="col" |Workshop PDF
|-
|GEOG - Spring2026 || In-Class || [[Media:GACRC-Teaching-cluster-new-user-training-workshop-python-Spring2026.pdf]]
|-
|BCMB8330 - Spring2026 || In-Class || [[Media:GACRC-Teaching-cluster-new-user-training-workshop_bcmb8330_Spring2026.pdf]]
|-
|PHYS8601 - Spring2026 || In-Class || [[Media:GACRC_Teaching_cluster_new_user_training_workshop-phys8601-Spring2026.pdf]]
|-
|Anthropology Department || Out-Reach || [[Media:GACRC_overview_20251117_Anthropology.pdf]]
|-
|CSP seminar - Fall 2025|| Out-Reach || [[Media:GACRC_overview_20250819-CSP.pdf]]

|}

'''NOTE:''' The slides may become outdated and you should always check GACRC Wiki for up to date information.

Georgia Advanced Computing Resource Center

2026-07-28T17:00:25Z

Shtsai:

__NOTOC__
Welcome to the Georgia Advanced Computing Resource Center wiki. The information provided here is a supplement to the GACRC webpage. The GACRC online information resources include:

*[http://gacrc.uga.edu/ Web Site] – General overview
*[https://wiki.gacrc.uga.edu/ Wiki] – Software docs and how-to’s - "You Are Here"
*[https://kaltura.uga.edu/channel/GACRC/176125031 Kaltura] – Linux and HPC training videos










<div style="width=100%; margin:0; background:white; font-size:120%; font-weight:bold; border:4px solid #FF9F40 ; text-align:left; color:black; padding:0.2em 0.4em;"> Current Status: <span style="color:black"> Scheduled maintenance underway - Sapelo2, xfer nodes, GACRC storage systems, and Open OnDemand unavailable </span></div>






<h2>Important News</h2>
The following is an important notice for all of our current users:



* [[Rocky 9 Transition Guide]]




<h2> Getting Started </h2>
Welcome to the Georgia Advanced Computing Resource Center at the University of Georgia. If you're new to the GACRC, start with these links to get acquainted with our resources.
*[[User Accounts|User Accounts]]
*[[Instructional Accounts]]
*[[Connecting]]
*[[Transferring Files]]
*[[Password | Changing your Password]]
*[[Frequently Asked Questions | FAQ]]
*[[Quick_Reference_Guide|Command List]]
*[[Getting Help]]
*[[Policies]]
*[[Consulting]]
*[[Training]]

A Webinar introducing the GACRC was presented on March 10, 2025. A recording of this [https://kaltura.uga.edu/media/t/1_jfku2g8l/176125031 webinar] is found on GACRC's Kaltura channel.

<h2> System Information </h2>
Hardware information and operational procedures are described below.
*[[Systems]]
*[[Disk Storage]]



<h2> Job and Data Management </h2>
Information on how to run jobs and data management.
*[[Running Jobs]]
*[[Monitoring Jobs]]
*[[Job Submission Partitions]]
*[[Sample Scripts | Sample Job Submission Scripts]]
*[[Migrating from Torque to Slurm]]
*[[Troubleshooting on Sapelo2]]
*[[Best Practices]]
*[[Globus]]
*[[OnDemand | Open OnDemand]]


<h2> Software and Libraries </h2>
Documentation for software applications, programming tools, and usage.
*[[Software]]
*[[Available Toolchains and Toolchain Compatibility]]
*[[Bioinformatics Databases]]
*[[OpenMP]]
*[[MPI | Message Passing Interface (MPI)]]
*[[Compilers]]
*[[GPU|GPU and CUDA Programming]]
*[[Installing Applications]]

Georgia Advanced Computing Resource Center

2026-07-28T17:00:14Z

Shtsai:

__NOTOC__
Welcome to the Georgia Advanced Computing Resource Center wiki. The information provided here is a supplement to the GACRC webpage. The GACRC online information resources include:

*[http://gacrc.uga.edu/ Web Site] – General overview
*[https://wiki.gacrc.uga.edu/ Wiki] – Software docs and how-to’s - "You Are Here"
*[https://kaltura.uga.edu/channel/GACRC/176125031 Kaltura] – Linux and HPC training videos









<div style="width=100%; margin:0; background:white; font-size:120%; font-weight:bold; border:4px solid #FF9F40 ; text-align:left; color:black; padding:0.2em 0.4em;"> Current Status: <span style="color:black"> Scheduled maintenance underway - Sapelo2, xfer nodes, GACRC storage systems, and Open OnDemand unavailable </span></div>






<h2>Important News</h2>
The following is an important notice for all of our current users:



* [[Rocky 9 Transition Guide]]




<h2> Getting Started </h2>
Welcome to the Georgia Advanced Computing Resource Center at the University of Georgia. If you're new to the GACRC, start with these links to get acquainted with our resources.
*[[User Accounts|User Accounts]]
*[[Instructional Accounts]]
*[[Connecting]]
*[[Transferring Files]]
*[[Password | Changing your Password]]
*[[Frequently Asked Questions | FAQ]]
*[[Quick_Reference_Guide|Command List]]
*[[Getting Help]]
*[[Policies]]
*[[Consulting]]
*[[Training]]

A Webinar introducing the GACRC was presented on March 10, 2025. A recording of this [https://kaltura.uga.edu/media/t/1_jfku2g8l/176125031 webinar] is found on GACRC's Kaltura channel.

<h2> System Information </h2>
Hardware information and operational procedures are described below.
*[[Systems]]
*[[Disk Storage]]



<h2> Job and Data Management </h2>
Information on how to run jobs and data management.
*[[Running Jobs]]
*[[Monitoring Jobs]]
*[[Job Submission Partitions]]
*[[Sample Scripts | Sample Job Submission Scripts]]
*[[Migrating from Torque to Slurm]]
*[[Troubleshooting on Sapelo2]]
*[[Best Practices]]
*[[Globus]]
*[[OnDemand | Open OnDemand]]


<h2> Software and Libraries </h2>
Documentation for software applications, programming tools, and usage.
*[[Software]]
*[[Available Toolchains and Toolchain Compatibility]]
*[[Bioinformatics Databases]]
*[[OpenMP]]
*[[MPI | Message Passing Interface (MPI)]]
*[[Compilers]]
*[[GPU|GPU and CUDA Programming]]
*[[Installing Applications]]

File Management

2026-07-28T16:34:47Z

Shtsai: /* References */

To help us optimize storage space and improve the /project file system performance, we kindly ask you to compress your files into archives (e.g., tar files) rather than storing numerous individual files in the /project file system. Compressing files not only reduces the amount of storage used but also simplifies file management (e.g. file back up and recovery) and transfer.

==File Compression==

In order to save space, please compress your files before transferring them into your group's /project file system. The <code>gzip</code> command can be used to compress files, but it uses a single thread on a single core.

The <code>pigz</code> command is a parallel implementation of <code>gzip</code> that can run with multiple threads, making use of multiple cores. The <code>unpigz</code> command is equivalent to <code>gunzip</code> and it can be used to uncompress gzip'ed files. The .gz files created by pigz are compatible with gzip/gunzip. The pigz command is particularly helpful to compress a large number of files (or a folder) or to compress large files.

The compute nodes on Sapelo2 and on the teaching cluster have pigz installed centrally, so you don't need to load any modules in order to use this command. The help page for this command shows the available options, and it can be viewed with the command
<pre class="gcommand">
pigz --help
</pre>

<blockquote style="background-color: lightyellow; border: solid thin grey;">
'''Note:''' Please do not run <code>gzip/gunzip/pigz/unpigz</code> commands directly on the Sapelo2 login nodes. Instead, please use either an interactive or a batch job for compressing and uncompressing files. If we detect these commands being run on the login nodes, we may have to cancel them to avoid overloading the login nodes.
</blockquote>

===Some simple examples===

Compress a file
<pre class="gcommand">
pigz filename
</pre>

Compress a file with best compression rate
<pre class="gcommand">
pigz -9 filename
pigz --best filename
</pre>

Uncompress a file
<pre class="gcommand">
unpigz filename.gz
</pre>

We suggest that you run <code>pigz</code> in an interactive job that requests multiple cores and run <code>pigz</code> with the '-p num_thread' option to specify the numnber of threads (num_threads) to use.

For example, start an interactive session with 10 cores and 4GB of RAM with
<pre class="gcommand">
interact -c 10 --mem=4g
</pre>
and then run pigz with 10 threads with
<pre class="gcommand">
pigz --best -p 10 my_big_file
</pre>

To recursive compress all files in a directory (e.g. called dirname) use the -r option. For example, using 10 threads
<pre class="gcommand">
pigz --best -r -p 10 dirname
</pre>

To recursive uncompress all files in a directory:
<pre class="gcommand">
unpigz -r dirname
</pre>

===Sample timing comparison===

This example shows the time required to compress a single 10GB file in an interactive session that has 10 cores available. The gzip command took about 84 seconds, while the pigz command took about 22 seconds, to compress a 10GB file down to 10MB (gzip) and 12MB (pigz).

<pre class="gcommand">
[shtsai@ss-sub3]$ interact -c 10

srun --pty --cpus-per-task=10 --job-name=interact --ntasks=1 --nodes=1 --partition=inter_p --time=12:00:00 --mem=2GB /bin/bash -l

[shtsai@c2-17]$ ls -lh 10g.img
-rw-r--r-- 1 shtsai gclab 10G Dec 16 2021 10g.img

[shtsai@c2-17]$ time gzip 10g.img

real 1m24.437s
user 1m19.214s
sys 0m4.932s

[shtsai@c2-17]$ ls -lh 10g.img.gz
-rw-r--r-- 1 shtsai gclab 10M Dec 16 2021 10g.img.gz

[shtsai@c2-17]$ time gunzip 10g.img.gz

real 1m23.511s
user 1m3.506s
sys 0m18.855s

[shtsai@c2-17]$ ls -lh 10g.img
-rw-r--r-- 1 shtsai gclab 10G Dec 16 2021 10g.img

[shtsai@c2-17]$ time pigz --best -p 10 10g.img

real 0m22.028s
user 1m39.639s
sys 0m9.134s

[shtsai@c2-17]$ ls -lh 10g.img.gz
-rw-r--r-- 1 shtsai gclab 12M Dec 16 2021 10g.img.gz

[shtsai@c2-17 shtsai]$ time unpigz 10g.img.gz

real 0m36.700s
user 0m45.593s
sys 0m21.935s

[shtsai@c2-17]$ ls -lh 10g.img
-rw-r--r-- 1 shtsai gclab 10G Dec 16 2021 10g.img
</pre>

===References===

* pigz home page: https://zlib.net/pigz/

* gzip manual: https://www.gnu.org/software/gzip/manual/gzip.html

==Creating tar files==

Having a large number of files in a file system can overload the storage metadata server and delay data recovery from backups, etc. If you need to store a large number of files in your group's /project area, instead of storing a large number of individual files, please first create a tar file with the files or with a directory, and transfer the tar file to /project.

The <code>tar</code> command can be run in interactive job on Sapelo2 with
<pre class="gcommand">
tar cvf dirname.tar dirname
</pre>

<blockquote style="background-color: lightyellow; border: solid thin grey;">
'''Note:''' Please do not run <code>tar</code> directly on the Sapelo2 login nodes. Instead, please use either an interactive or a batch job for creating or extracting archive files.
</blockquote>

A tar file can be compressed with <code>pigz</code> using multiple cores in an interactive session that requested multiple cores with
<pre class="gcommand">
pigz dirname.tar
</pre>
Alternatively, you could use <code>pigz</code> to compress the files in your directory, before creating a tar file.

To extract the files from a tar file:
<pre class="gcommand">
tar xvf dirname.tar
</pre>

To extract the files from a tar.gz file:
<pre class="gcommand">
tar zxvf dirname.tar.gz
</pre>

==File transfer==

The recommended way to transfer files between file systems on the cluster, or between GACRC and external storage systems is using Globus. For more information, please see https://wiki.gacrc.uga.edu/wiki/Globus

Transferring Files

2026-07-28T16:18:02Z

Shtsai:

[[Category:Sapelo2]][[Category:Teaching]]

==The File Transfer nodes (xfer.gacrc.uga.edu)==

Users can transfer files between their local machines and GACRC storage using various programs, such as (a) Filezilla, (b) WinSCP, and (c) a secure copy (scp). To transfer files, you must have a file transfer program installed on your local machine and a connection to the UGA campus network.

All Sapelo2 users, as well as all GACRC PIs, can access the xfer nodes using the hostname '''xfer.gacrc.uga.edu''' and using their UGA MyID (not the 810 or 811 number) and the MyID password to authenticate. Two-factor authentication using Archpass Duo is also necessary. For more details of Archpass Duo, please refer to
[https://uga.teamdynamix.com/TDClient/3190/eitsclientportal/KB/Category/23825/ArchPass-powered-by-Duo archpass_duo ].

The GACRC file transfer nodes (xfer) are configured to facilitate file transfer. These nodes, which have a hostname of '''xfer.gacrc.uga.edu''', are connected via a higher-bandwidth network connection. Hence, maximum transfer speed between a user's local machine and the GACRC systems can be achieved by transferring files to/from the host xfer.gacrc.uga.edu (instead of the login nodes of the clusters).

It is not necessary to connect to the UGA VPN when connecting to a file transfer node from off-campus. If you are transferring data from/to a server outside of campus (such as your local computer), you will get much faster transfer speeds if you are not connected to the VPN.

Note that a user's home directory on the xfer nodes is the same as the user's Sapelo2 home directory, which is '''not''' the same as the user's home directory on the teaching cluster.

The xfer node can access the following file systems using the full path:

1. Your home directory on an xfer node is the same as your Sapelo2 home directory, and the path is /home/username

2. The Sapelo2 scratch directory: /scratch/username

3. The Sapelo2 work directory: /work/groupname

4. The project file system for the lab: /project/groupname

==Transferring Files from your local storage device to Sapelo2 or vice-versa==

To transfer files from your local storage device to Sapelo2 or from Sapelo2 to your local storage device, please connect to xfer.gacrc.uga.edu (use your UGA MyID, the MyID password to authenticate). You will also be prompted for two-factor authentication with Archpass Duo.

===Using FileZilla===

In order to use [http://filezilla-project.org/ FileZilla] to transfer files between a local machine and Sapelo2, users will need to install the [http://filezilla-project.org/ FileZilla] software on the local machine. After installation, start FileZilla and follow these steps:

1. Go to File -> Site Manager

2. Add a New Site with the following configuration.

In the '''General''' tab select

<pre class="gcomment">

Protocol: SFTP - SSH File Transfer Protocol

Host: sftp://xfer.gacrc.uga.edu

Port: 22

Logon Type: Interactive

User: [your UGA MyID]
</pre>

In the '''Transfer Settings''' tab, check the box for "Limit number of simultaneous connections" and set "Maximum number of connections" to 1.

If this step is omitted, then you will need to enter your password and Duo authentication for each file that you upload or download. By limiting the number of simultaneous connections to 1, you only need to enter your password and Duo authentication once per session.

With the above settings saved, you should be able to transfer files to txfer using filezilla. When you open the connection, you will be prompted for your MyID password (unless you use key based ssh). If the password authenticates successfully, then another pop-up window will prompt for Duo. Note that the Duo window looks very similar to the password window, but the text in the box will describe the options you can use for Duo (for example, a push, enter a passcode, etc).

Once the connection is established you can upload files from your local machine to Sapelo2 or download files from Sapelo2 to your local machine.

Here is the PDF to check step-by-step screen shots:
{| width="100%" border="1" cellspacing="0" cellpadding="2" align="center" class="wikitable unsortable"
|-
| [[Media:Xfer_Filezilla_12032018.pdf]]
|-
|}

===Using WinSCP===

In order to use [http://winscp.net/ WinSCP] to transfer files between a local Windows machine and Sapelo2, users will need to install the [http://winscp.net/ WinSCP] software on the local machine. After installation, when you click the WinSCP shortcut button, Login window pops up, in that you select new site and enter the following information to start the new session:

<pre class="gcomment">

Session:

File Protocol: SFTP

Host name: xfer.gacrc.uga.edu

Port number: 22

Username: [your UGA MyID]

Password: [your MyID password]

</pre>

If the password authenticates successfully, then another pop-up window will prompt for Duo two-factor authentication. It says, Using Keyboard-interactive authentication and lists the Duo options you can choose from: Duo push, Phone Call, or SMS passcode, etc. Once the connection is established you can upload or download files from Local Machine to Cluster or vice-versa. Note: Under Advanced, keep the "Default" option for Transfer settings Rule. With this default option, you will be able to transfer multiple files or folders during the session.


===Using scp===

To transfer files using scp you must have scp on your local machine and a connection to the UGA campus network. An scp software is included in recent releases of Unix based operating systems (including Linux and Mac OS X).

Sample commands to transfer files from your local Unix/Linux/Mac OS X machine to Sapelo2:

Open a terminal in your local machine, change directory (cd) to where the files are located in your local machine, and at the command prompt type:

<pre class="gcommand">
scp filename yourUGAMyID@xfer.gacrc.uga.edu:subdirectory
</pre>

where ''filename'' is the name of the file to be transferred, ''yourUGAMyID'' is your UGA MyID, and ''subdirectory'' is the subdirectory in your Sapelo2 home directory to which files are being transferred. You will then be asked to enter your UGA MyID password. You will also be prompted for two-factor authentication with Archpass Duo.

To transfer files from Sapelo2 to your local machine, use the full path to your file on Sapelo2 followed by the location you would like to transfer the files locally. The following example transfers a file called "filename" to the local current working directory.

<pre class="gcommand">
scp yourUGAMyID@xfer.gacrc.uga.edu:full/path/to/filename ./
</pre>

(Note: yourUGAMyID@ can be omitted if your username on Sapelo2 is the same as on your local machine). Multiple files (e.g. file1, file2, and file3) can be transferred with a single command:

<pre class="gcommand">
scp file1 file2 file3 yourUGAMyID@xfer.gacrc.uga.edu:subdirectory
</pre>

Wildcards can be used for multiple file transfer, for example, to upload all files with .dat extension to your sapelo2 home directory, to a subdirectory called subdir:

<pre class="gcommand">
scp *.dat yourUGAMyID@xfer.gacrc.uga.edu:subdir
</pre>

An example that uses a wildcard to download all files with .dat extension in your Sapelo2 home dir, in a subdirectory called subdir to your local machine:

<pre class="gcommand">
scp yourUGAMyID@xfer.gacrc.uga.edu:subdir/\*.dat ./
</pre>

(Note the backslash “ \ ” preceeding * in the last example.)

A directory and all its contents can be transferred using the scp option '''-r''', for recursive file transferring. For example, to transfer a directory on your local machine called programs and all files in it to your Sapelo2 home directory, use:

<pre class="gcommand">
scp -r programs yourUGAMyID@xfer.gacrc.uga.edu:
</pre>

You can also use the scp option '''-p''' to preserve the file features, such as last modification time, file permissions, etc.

To transfer files directly to your Sapelo2 /scratch area (such as /scratch/johndoe/):

<pre class="gcommand">
scp filename yourUGAMyID@xfer.gacrc.uga.edu:/scratch/johndoe/
</pre>

To transfer files directly to your Sapelo2 work directory:

<pre class="gcommand">
scp filename yourUGAMyID@xfer.gacrc.uga.edu:/work/groupname/subdirname
</pre>

To transfer files directly to your lab's project file system (such as /project/groupname):

<pre class="gcommand">
scp filename yourUGAMyID@xfer.gacrc.uga.edu:/project/groupname
</pre>

===Download directly from internet to Sapelo2===

Here is an example on how to download [http://hannonlab.cshl.edu/fastx_toolkit/index.html FastaX] software from internet directly to Sapelo2

<pre class="gcommand">
wget https://github.com/agordon/fastx_toolkit/releases/download/0.0.14/fastx_toolkit-0.0.14.tar.bz2
</pre>

Some websites use indirect connections, it could be downloaded as
<pre class="gcommand">
curl -OL https://github.com/agordon/fastx_toolkit/releases/download/0.0.14/fastx_toolkit-0.0.14.tar.bz2
</pre>


==Transferring Files between two file systems on Sapelo2==

Use ssh to login to xfer.gacrc.uga.edu using your UGA MyID username and password (and two-factor authentication via Archpass Duo) to transfer files between different file systems on Sapelo2, including the project area. To transfer data between two file systems that are available on the xfer node, you can use the '''cp''' or the '''rsync''' commands. If you have many files to copy, then a good option is to use the '''fpsync''' command, as that will allow the use of multiple cores.

For example, if you want to use 4 cores to transfer all files from /scratch/myid/mydata to /project/mylab/myid/mydata, you can use the following command on an xfer node shell prompt:

<pre class="gcomman">
fpsync -n 4 -t $HOME/fpsync /scratch/myid/mydata /project/mylab/myid/mydata
</pre>

When using the '''fpysnc''' command, please use the <code> -t $HOME/fpsync</code> option to set the fpsync's temporary directory to be in your home directory.



==The File Transfer node for the teaching cluster (txfer.gacrc.uga.edu)==

Users can transfer files between their local machines and the teaching cluster using (a) Filezilla, (b) a secure copy (scp), or (c) WinSCP. To transfer files using scp (or SSH file transfer) you must have scp (or SSH) on your local machine and a connection to the UGA campus network. An scp software is included in recent releases of Unix based operating systems (including Linux and Mac OS X).

Access to txfer.gacrc.uga.edu requires an active MyID and password, and two-factor authentication with Archpass Duo. For more details of Archpass Duo, please refer to
[https://eits.uga.edu/access_and_security/infosec/tools/archpass archpass_duo] .

==Transferring Files from your local storage device to the teaching cluster==

To transfer files from your local storage device to the teaching cluster, please connect to txfer.gacrc.uga.edu (use your UGA MyID and the MyID password to authenticate). You will also be prompted for two-factor authentication with Archpass Duo.

===Using FileZilla===

In order to use [http://filezilla-project.org/ FileZilla] to transfer files between a local machine and the teaching cluster, users will need to install the [http://filezilla-project.org/ FileZilla] software on the local machine. After installation, start FileZilla and follow these steps:

1. Go to File -> Site Manager

2. Add a New Site with the following configuration.

In the '''General''' tab select

<pre class="gcomment">

Protocol: SFTP - SSH File Transfer Protocol

Host: sftp://txfer.gacrc.uga.edu

Port: 22

Logon Type: Interactive

User: [your UGA MyID]
</pre>

In the '''Transfer Settings''' tab, check the box for "Limit number of simultaneous connections" and set "Maximum number of connections" to 1.

If this step is omitted, then you will need to enter your password and Duo authentication for each file that you upload or download. By limiting the number of simultaneous connections to 1, you only need to enter your password and Duo authentication once per session.

With the above settings saved, you should be able to transfer files to txfer using filezilla. When you open the connection, you will be prompted for your MyID password (unless you use key based ssh). If the password authenticates successfully, then another pop-up window will prompt for Duo. Note that the Duo window looks very similar to the password window, but the text in the box will describe the options you can use for Duo (for example, a push, enter a passcode, etc).

===Using WinSCP===

In order to use [http://winscp.net/ WinSCP] to transfer files between a local Windows machine and the teaching cluster, users will need to install the [http://winscp.net/ WinSCP] software on the local machine. After installation, when you click the WinSCP shortcut button, Login window pops up, in that you select new site and enter the following information to start the new session:

<pre class="gcomment">

Session:

File Protocol: SFTP

Host name: txfer.gacrc.uga.edu

Port number: 22

Username: [your UGA MyID]

Password: [your MyID password]

</pre>

If the password authenticates successfully, then another pop-up window will prompt for Duo two-factor authentication. It says, Using Keyboard-interactive authentication and lists the Duo options you can choose from: Duo push, Phone Call, or SMS passcode, etc. Once the connection is established you can upload or download files from Local Machine to Cluster or vice-versa. Note: Under Advanced, kept the "Default" option for Transfer settings Rule. With this default option, you will be able to transfer multiple files or folders during the session.


===Using scp===

Sample commands to transfer files from your local Unix/Linux/Mac OS X machine to the teaching cluster:

Open a terminal in your local machine, change directory (cd) to where the files are located in your local machine, and at the command prompt type:

<pre class="gcommand">
scp filename yourUGAMyID@txfer.gacrc.uga.edu:subdirectory
</pre>

where ''filename'' is the name of the file to be transferred, ''yourUGAMyID'' is your UGA MyID, and ''subdirectory'' is the subdirectory in your teaching cluster home directory to which files are being transferred. You will then be asked to enter your UGA MyID password.

(Note: yourUGAMyID@ can be omitted if your username on the teaching cluster is the same as on your local machine). Multiple files (e.g. file1, file2, and file3) can be transferred with a single command:

<pre class="gcommand">
scp file1 file2 file3 yourUGAMyID@txfer.gacrc.uga.edu:subdirectory
</pre>

Wildcards can be used for multiple file transfer (e.g. all files with .dat extension):

<pre class="gcommand">
scp *.dat yourUGAMyID@txfer.gacrc.uga.edu:subdirectory
</pre>

==Transferring Files between your Sapelo2 directories and the teaching cluster==

To transfer files between your Sapelo2 directories (e.g. your Sapelo2 home directory or /scratch directory) and your teaching cluster home directory, please first use ssh to login into xfer.gacrc.uga.edu. Then use the scp command to copy files to/from your home directory at txfer.gacrc.uga.edu.

For example, to transfer a directory called subdir2 in your /scratch directory to your teaching cluster home directory. On xfer.gacrc.uga.edu issue the command:
<pre class="gcommand">
scp -r -p /scratch/yourUGAMyID/subdir2 txfer.gacrc.uga.edu:

UGA DUO authentication is required for SSH/SCP access to
GACRC systems.

UGA DUO is a two-factor authentication service which
requires a password (one factor) and a code, phone,
or device (second factor) to successfully authenticate.

If you are not enrolled in the UGA DUO service please
visit the UGA DUO service self-service portal to enroll
and configure or manage your DUO enabled devices.

https://eits.uga.edu/access_and_security/infosec/tools/duo/portal/

For additional help with UGA DUO authentication or to
report an issue please visit:

https://eits.uga.edu/access_and_security/infosec/tools/archpass/

Password:

Duo two-factor login for yourUGAMyID

Enter a passcode or select one of the following options:

1. Duo Push to XXX-XXX-4304
2. Phone call to XXX-XXX-4304
3. SMS passcodes to XXX-XXX-4304

Passcode or option (1-3):

</pre>
When you enter your password, you will be prompted for the two-factor authentication step. Once you enter a passcode or select an option, the file transfer will start.

==Transferring Files between your project area and the teaching cluster==

To transfer files between your /project area and your teaching cluster home directory, please first use ssh to login into xfer.gacrc.uga.edu. Then use the scp command to e.g. copy files from /project to your home directory at txfer.gacrc.uga.edu (or to copy files from your home directory at txfer.gacrc.uga.edu to your /project area).

For example, to copy a file called species.fa from your teaching cluster home directory to a subdirectory called binf1234 in your /project area, use the following command on xfer.gacrc.uga.edu:

<pre class="gcommand">
scp txfer.gacrc.uga.edu:species.fa /project/abclab/binf1234
</pre>
where ''abclab'' needs to be changed to the name of your group.

== Globus ==
Globus is a high-performance data-transfer platform that allows you to perform and/or automate data transfers. These transfers can be to and from other institutions, your local computer, or your GACRC /project area. Collaborators can also share data with you by sharing a Collection or an "endpoint" with you. The shared Collection/Endpoint can be on another institution, on a desktop or laptop, or on their GACRC storage.
Data transfers happen unattended and are faster than SCP/SFTP, data verification is on by default, and automatic restarts or continuation of transfers happen after a disruption.

To use Globus you will need a Globus Identity. At a minimum you will need to setup your identity using the University of Georgia organizational login in order to access UGA systems. More information about how to get started using Globus can be found on the [[Globus|'''Globus''']] page. You can also get more information about Globus from the [https://docs.globus.org/ Official Globus Documentation]

== Using iCommands to access the Cyverse Data Store ==
[https://cyverse.org/data-store Cyverse] is a secure data storage and management service. iCommands is a collection of commands for Linux and Mac OS operating systems that are used to interact with the CyVerse Data Store. iCommands can be used by CyVerse account users to download files that have been shared by other users and to upload files to the Data Store, as well as add metadata, change permissions, and more. A CyVerse account is not required to download a public data file via iCommands.

version 4.2.8 of iCommands is installed on Sapelo2 IRODS '''transfer node''' (xfer-irods.gacrc.uga.edu). Please note that this command is not available on the other transfer nodes.

You will need to log on a Sapelo2 transfer node xfer-irods.gacrc.uga.edu and then initialize the connection to iRODS by using the iinit command to use iCommands

More information about using iCommands can be found on the [[ICommands-Sapelo2|'''ICommands-Sapelo2''']] page. You can also read [https://cyverse.atlassian.net/wiki/spaces/DS/pages/241869855/Using+iCommands Official iCommands Documentation]

Jupyter-Sapelo2

2026-07-08T13:09:24Z

Shtsai:

[[Category:Sapelo2]][[Category:Software]][[Category:Programming]]
== Category ==

Programming

== Program On ==

Sapelo2

== Version ==

6.5.6, 7.0.2, 7.2.0, 7.2.3, 7.4.4, 7.4.7

== Author / Distributor ==

[https://jupyter.readthedocs.io/en/latest/index.html Jupyter]

== Description ==

"The Jupyter Notebook is a web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text" [https://jupyter.readthedocs.io/en/latest/index.html Jupyter]

== Running Program ==

===Versions===

Please also refer to [[Running Jobs on Sapelo2]].

*Jupyter 6.5.6 is installed as a module called JupyterNotebook/6.5.6-GCCcore-11.3.0 and it uses Python 3.10.4

*Jupyter 7.0.2 is installed as a module called JupyterNotebook/7.0.2-GCCcore-12.3.0 and it uses Python 3.11.3

*Jupyter 7.2.0 is installed as a module called JupyterNotebook/7.2.0-GCCcore-13.2.0 and it uses Python 3.11.5

*Jupyter 7.2.3 is installed as a module called JupyterNotebook/7.2.3-GCCcore-13.3.0 and it uses Python 3.12.3

*Jupyter 7.4.4 is installed as a module called JupyterNotebook/7.4.4-GCCcore-14.2.0 and it uses Python 3.13.1

*Jupyter 7.4.7 is installed as a module called JupyterNotebook/7.4.7-GCCcore-14.3.0 and it uses Python 3.13.5

'''Please note: You do not have to install jupyter notebook on your local machine.'''

For example, to use Jupyter 7.2.3, please load the module:
<pre class="gcommand">
module load JupyterNotebook/7.2.3-GCCcore-13.3.0
</pre>



There are three options to use Jupyter notebook on Sapelo2.

===Option 1: Use the Jupyter notebook interactive app in the Open OnDemand interface (highly recommended)===

The best way to run a Jupyter notebook on Sapelo2 is using the Jupyter notebook interactive app in the [[OnDemand]] interface to Sapelo2.

<u>Some Important Notes about requesting resources for a Jupyter Notebook session through Open OnDemand:</u>

* With this option, you are able to use jupyter-notebook from centrally installed JupyterNotebook modules, which you can choose under "Jupyter Environment Setup".
* You can also choose which directory to start Jupyter from using the "Notebook Directory" option
** For example, if you put /scratch/MyID in that box, it will start your session from your scratch dir and you will only have access to the files and folders there.
** If you want to be able to access files in multiple directories (like both /home/MyID and /scratch/MyID) in one Jupyter session, you can set the starting Notebook Directory to simply / (root) which will allow you to access all of your directories.
* You are also able to load other modules before launching the Jupyter Notebook with "Extra Modules to Load". Please ensure that the modules you load here are compatible with the toolchain and Python version selected in the "Jupyter Environment Setup"
* The rest of the options are similar to the resources you would request in a normal job submission script (number of cores, time, memory, etc).

[[File:OOD_JupyterNotebook_example.png|alt=|border|850x850px]]

If you would like to use /lscratch for your jupyter notebook, open a Terminal in the jupyter notebook and there you can create an /lscratch/$USER directory and copy files to it. If you use /lscratch for your jupyter session, remember to copy any files you need to keep back to /scratch before closing this application.

To add a Conda virtual environment to Jupyter Notebook please see [[Using a Conda environment in Jupyter]]

To add a Python virtual environment to Jupyter Notebook please see [[Using a Python environment in Jupyter]]

=== Option 2: Use the Jupyter notebook in an X Desktop session in the Open OnDemand interface===

Another option to run jupyter-notebook from the Open OnDemand interface is to first start an X Desktop session in the [[OnDemand]] interface and run jupyter-notebook from that desktop. This option is helpful if
you would like to use a jupyter-notebook that is not installed as a central module, for example, one that is installed in a conda environment in the user's home dir.

Steps to follow:

*2.1: Open a browser on your local machine and point it to https://ondemand.gacrc.uga.edu (if accessing from off-campus, please connect to the UGA VPN first). Authenticate with UGA's SSO.*

*2.2: Start an X Desktop session on Sapelo2, choose the resources to use (e.g. number of cores, memory, walltime, partition, gres, etc) and click Launch.

*2.3: Once the session starts, click on "Launch X Desktop Session on Sapelo2"

*2.4: In the X Desktop session, click on the Terminal icon in the panel at the bottom to start a Terminal session on the compute node allocated to your job.

*2.5: If you would like to use /lscratch for your jupyter notebook, from the Terminal you can create an /lscratch/$USER directory and copy files to it.

*2.6: In the Terminal, load any modules you want to use and/or activate a conda environment, if using jupyter-notebook from a conda environment. For example, if you would like to use the central module JupyterNotebook/6.5.6-GCCcore-11.3.0, you could load it with
<pre class="gcommand">
ml JupyterNotebook/6.5.6-GCCcore-11.3.0
</pre>
You can also load other modules that are compatible with this one (in the example above, you could load other modules that use the GCCcore-11.3.0, GCC-11.3.0, gompi-2022a, or foss-2022a toolchains).

*2.7: Start jupyter-notebook in the Terminal with
<pre class="gcommand">
NOTEBOOKPORT=8888
jupyter-notebook --port $NOTEBOOKPORT --no-browser
</pre>
where NOTEBOOKPORT should be set to some random value between 8000 and 10000. The example above uses 8888, but please choose a different value.

In the terminal you should see some output ending with something like this:
<pre class="gcommand">

[I 09:26:53.882 NotebookApp] Jupyter Notebook 6.4.12 is running at:
[I 09:26:53.882 NotebookApp] http://10.2.1.91:8999/?token=83da4f2fb1f7f95bba6ba1e6d4d6ce5c81cadc09a602c811
[I 09:26:53.883 NotebookApp] or http://127.0.0.1:8999/?token=83da4f2fb1f7f95bba6ba1e6d4d6ce5c81cadc09a602c811
[I 09:26:53.883 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
[C 09:26:53.887 NotebookApp]

To access the notebook, open this file in a browser:
file:///home/shtsai/.local/share/jupyter/runtime/nbserver-1942279-open.html
Or copy and paste one of these URLs:
http://10.2.1.91:8999/?token=83da4f2fb1f7f95bba6ba1e6d4d6ce5c81cadc09a602c811
or http://127.0.0.1:8999/?token=83da4f2fb1f7f95bba6ba1e6d4d6ce5c81cadc09a602c811
</pre>

*2.8: Start another Terminal in the X Desktop session and in it start firefox with
<pre class="gcommand">
/apps/eb/Firefox/141.0/firefox
</pre>

*2.9: In the firefox browser that opens in your X Desktop session, paste the html file listed in the output of the jupyter-notebook command. In the example above the line is
'''file:///home/shtsai/.local/share/jupyter/runtime/nbserver-1942279-open.html'''. This should open the jupyter notebook.

*2.10: If you use /lscratch for your jupyter session, remember to copy any files you need to keep back to /scratch before closing this X Desktop session.

To add a Conda environment to Jupyter Notebook please see [[Using a Conda environment in Jupyter]]

===Option 3: To use Jupyter notebook on sapelo2 with an interactive job (not recommended)===

'''The port number for the jupyter notebook server should be some random value between 8000 and 10000. Assign this value to the variable 'NOTEBOOKPORT' in the submission script below replacing 8888. Please do not use 8888. If more than one person uses that same value you cannot establish the ssh tunnel'''

Sample steps to use jupyter 6.4.12, once you connect to sapelo2:
<pre class="gcommand">
interact [OPTIONS]

NOTEBOOKPORT=8888

IPUSED=$(hostname -i)

echo "NOTEBOOKPORT is " $NOTEBOOKPORT

echo "IPUSED is " $IPUSED

module load Anaconda3/2022.10

jupyter-notebook --port $NOTEBOOKPORT --ip=$IPUSED --no-browser

</pre>

After running the steps above, establish a another ssh tunnel from your desktop or laptop to sapelo2 login node at port NOTEBOOKPORT.

'''For Mac/Linux Users'''

If you are using a Linux or Apple machine for your desktop or laptop you can use the following command to establish the ssh tunnel

'''Make sure you replace NOTEBOOKPORT and IPUSED below with the port number and the IP address you are using ''' (see the output of the '''echo''' commands above)
<pre class="gcommand">
ssh -N -L NOTEBOOKPORT:IPUSED:NOTEBOOKPORT username@sapelo2.gacrc.uga.edu
</pre>

Unless you have ssh key configured, you will be prompted for your MyID password and for Archpass Duo authentication. Once authentication is established, this session prompt will hang and you are ready to go to the next step.

'''For Windows Users'''

If you are using a Windows machine for your desktop or laptop download the '''[https://the.earth.li/~sgtatham/putty/latest/x86/plink.exe plink program]''' to use in place of the ssh client.
The command for windows would be as follows:
Assuming the plink.exe is in the current directory where you have a command window open.

'''Make sure you replace NOTEBOOKPORT and IPUSED below with the port number and the IP address you are using ''' (see the output of the '''echo''' commands above)
<pre class="gcommand">
plink -ssh -N -L NOTEBOOKPORT:IPUSED:NOTEBOOKPORT username@sapelo2.gacrc.uga.edu
</pre>

Once you establish the ssh tunnel by running the command above you can access the Jupyter notebook by going to '''http://localhost:NOTEBOOKPORT''' using the browser on your desktop or laptop (make sure you replace NOTEBOOKPORT with the port number you used in the qlogin session). If the page displayed on the browser asks for a token or password, you can check the token shown in your qlogin session and copy and paste it into the token field in the page displayed in your browser. For example, the qlogin terminal might show something like this:

<pre class="gcommand">
[I 11:16:00.939 NotebookApp] JupyterLab extension loaded from /apps/eb/Anaconda3/2022.10/lib/python3.7/site-packages/jupyterlab
[I 11:16:00.939 NotebookApp] JupyterLab application directory is /apps/eb/Anaconda3/2022.10/share/jupyter/lab
[I 11:16:00.942 NotebookApp] Serving notebooks from local directory: /home/zhuofei
[I 11:16:00.942 NotebookApp] The Jupyter Notebook is running at:
[I 11:16:00.942 NotebookApp] http://10.2.1.30:8888/?token=857f8a9f63cf42f698ac1845db64219516a23e248019cdeb
[I 11:16:00.942 NotebookApp] or http://127.0.0.1:8888/?token=857f8a9f63cf42f698ac1845db64219516a23e248019cdeb
[I 11:16:00.942 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
[C 11:16:00.947 NotebookApp]

To access the notebook, open this file in a browser:
file:///home/zhuofei/.local/share/jupyter/runtime/nbserver-14353-open.html
Or copy and paste one of these URLs:
http://10.2.1.30:8888/?token=857f8a9f63cf42f698ac1845db64219516a23e248019cdeb
or http://127.0.0.1:8888/?token=857f8a9f63cf42f698ac1845db64219516a23e248019cdeb
</pre>

Copy and paste the token, which in the example above is 857f8a9f63cf42f698ac1845db64219516a23e248019cdeb, into the proper field in the page displayed in your browser.

==Documentation==

Details at https://jupyter.readthedocs.io/en/latest/index.html

==System==
64-bit Linux

Jupyter-Sapelo2

2026-07-08T13:08:58Z

Shtsai:

[[Category:Sapelo2]][[Category:Software]][[Category:Programming]]
== Category ==

Programming

== Program On ==

Sapelo2

== Version ==

6.5.6, 7.0.2, 7.2.0, 7.2.3, 7.4.4, 7.4.5, 7.4.7

== Author / Distributor ==

[https://jupyter.readthedocs.io/en/latest/index.html Jupyter]

== Description ==

"The Jupyter Notebook is a web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text" [https://jupyter.readthedocs.io/en/latest/index.html Jupyter]

== Running Program ==

===Versions===

Please also refer to [[Running Jobs on Sapelo2]].

*Jupyter 6.5.6 is installed as a module called JupyterNotebook/6.5.6-GCCcore-11.3.0 and it uses Python 3.10.4

*Jupyter 7.0.2 is installed as a module called JupyterNotebook/7.0.2-GCCcore-12.3.0 and it uses Python 3.11.3

*Jupyter 7.2.0 is installed as a module called JupyterNotebook/7.2.0-GCCcore-13.2.0 and it uses Python 3.11.5

*Jupyter 7.2.3 is installed as a module called JupyterNotebook/7.2.3-GCCcore-13.3.0 and it uses Python 3.12.3

*Jupyter 7.4.4 is installed as a module called JupyterNotebook/7.4.4-GCCcore-14.2.0 and it uses Python 3.13.1

*Jupyter 7.4.7 is installed as a module called JupyterNotebook/7.4.7-GCCcore-14.3.0 and it uses Python 3.13.5

'''Please note: You do not have to install jupyter notebook on your local machine.'''

For example, to use Jupyter 7.2.3, please load the module:
<pre class="gcommand">
module load JupyterNotebook/7.2.3-GCCcore-13.3.0
</pre>



There are three options to use Jupyter notebook on Sapelo2.

===Option 1: Use the Jupyter notebook interactive app in the Open OnDemand interface (highly recommended)===

The best way to run a Jupyter notebook on Sapelo2 is using the Jupyter notebook interactive app in the [[OnDemand]] interface to Sapelo2.

<u>Some Important Notes about requesting resources for a Jupyter Notebook session through Open OnDemand:</u>

* With this option, you are able to use jupyter-notebook from centrally installed JupyterNotebook modules, which you can choose under "Jupyter Environment Setup".
* You can also choose which directory to start Jupyter from using the "Notebook Directory" option
** For example, if you put /scratch/MyID in that box, it will start your session from your scratch dir and you will only have access to the files and folders there.
** If you want to be able to access files in multiple directories (like both /home/MyID and /scratch/MyID) in one Jupyter session, you can set the starting Notebook Directory to simply / (root) which will allow you to access all of your directories.
* You are also able to load other modules before launching the Jupyter Notebook with "Extra Modules to Load". Please ensure that the modules you load here are compatible with the toolchain and Python version selected in the "Jupyter Environment Setup"
* The rest of the options are similar to the resources you would request in a normal job submission script (number of cores, time, memory, etc).

[[File:OOD_JupyterNotebook_example.png|alt=|border|850x850px]]

If you would like to use /lscratch for your jupyter notebook, open a Terminal in the jupyter notebook and there you can create an /lscratch/$USER directory and copy files to it. If you use /lscratch for your jupyter session, remember to copy any files you need to keep back to /scratch before closing this application.

To add a Conda virtual environment to Jupyter Notebook please see [[Using a Conda environment in Jupyter]]

To add a Python virtual environment to Jupyter Notebook please see [[Using a Python environment in Jupyter]]

=== Option 2: Use the Jupyter notebook in an X Desktop session in the Open OnDemand interface===

Another option to run jupyter-notebook from the Open OnDemand interface is to first start an X Desktop session in the [[OnDemand]] interface and run jupyter-notebook from that desktop. This option is helpful if
you would like to use a jupyter-notebook that is not installed as a central module, for example, one that is installed in a conda environment in the user's home dir.

Steps to follow:

*2.1: Open a browser on your local machine and point it to https://ondemand.gacrc.uga.edu (if accessing from off-campus, please connect to the UGA VPN first). Authenticate with UGA's SSO.*

*2.2: Start an X Desktop session on Sapelo2, choose the resources to use (e.g. number of cores, memory, walltime, partition, gres, etc) and click Launch.

*2.3: Once the session starts, click on "Launch X Desktop Session on Sapelo2"

*2.4: In the X Desktop session, click on the Terminal icon in the panel at the bottom to start a Terminal session on the compute node allocated to your job.

*2.5: If you would like to use /lscratch for your jupyter notebook, from the Terminal you can create an /lscratch/$USER directory and copy files to it.

*2.6: In the Terminal, load any modules you want to use and/or activate a conda environment, if using jupyter-notebook from a conda environment. For example, if you would like to use the central module JupyterNotebook/6.5.6-GCCcore-11.3.0, you could load it with
<pre class="gcommand">
ml JupyterNotebook/6.5.6-GCCcore-11.3.0
</pre>
You can also load other modules that are compatible with this one (in the example above, you could load other modules that use the GCCcore-11.3.0, GCC-11.3.0, gompi-2022a, or foss-2022a toolchains).

*2.7: Start jupyter-notebook in the Terminal with
<pre class="gcommand">
NOTEBOOKPORT=8888
jupyter-notebook --port $NOTEBOOKPORT --no-browser
</pre>
where NOTEBOOKPORT should be set to some random value between 8000 and 10000. The example above uses 8888, but please choose a different value.

In the terminal you should see some output ending with something like this:
<pre class="gcommand">

[I 09:26:53.882 NotebookApp] Jupyter Notebook 6.4.12 is running at:
[I 09:26:53.882 NotebookApp] http://10.2.1.91:8999/?token=83da4f2fb1f7f95bba6ba1e6d4d6ce5c81cadc09a602c811
[I 09:26:53.883 NotebookApp] or http://127.0.0.1:8999/?token=83da4f2fb1f7f95bba6ba1e6d4d6ce5c81cadc09a602c811
[I 09:26:53.883 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
[C 09:26:53.887 NotebookApp]

To access the notebook, open this file in a browser:
file:///home/shtsai/.local/share/jupyter/runtime/nbserver-1942279-open.html
Or copy and paste one of these URLs:
http://10.2.1.91:8999/?token=83da4f2fb1f7f95bba6ba1e6d4d6ce5c81cadc09a602c811
or http://127.0.0.1:8999/?token=83da4f2fb1f7f95bba6ba1e6d4d6ce5c81cadc09a602c811
</pre>

*2.8: Start another Terminal in the X Desktop session and in it start firefox with
<pre class="gcommand">
/apps/eb/Firefox/141.0/firefox
</pre>

*2.9: In the firefox browser that opens in your X Desktop session, paste the html file listed in the output of the jupyter-notebook command. In the example above the line is
'''file:///home/shtsai/.local/share/jupyter/runtime/nbserver-1942279-open.html'''. This should open the jupyter notebook.

*2.10: If you use /lscratch for your jupyter session, remember to copy any files you need to keep back to /scratch before closing this X Desktop session.

To add a Conda environment to Jupyter Notebook please see [[Using a Conda environment in Jupyter]]

===Option 3: To use Jupyter notebook on sapelo2 with an interactive job (not recommended)===

'''The port number for the jupyter notebook server should be some random value between 8000 and 10000. Assign this value to the variable 'NOTEBOOKPORT' in the submission script below replacing 8888. Please do not use 8888. If more than one person uses that same value you cannot establish the ssh tunnel'''

Sample steps to use jupyter 6.4.12, once you connect to sapelo2:
<pre class="gcommand">
interact [OPTIONS]

NOTEBOOKPORT=8888

IPUSED=$(hostname -i)

echo "NOTEBOOKPORT is " $NOTEBOOKPORT

echo "IPUSED is " $IPUSED

module load Anaconda3/2022.10

jupyter-notebook --port $NOTEBOOKPORT --ip=$IPUSED --no-browser

</pre>

After running the steps above, establish a another ssh tunnel from your desktop or laptop to sapelo2 login node at port NOTEBOOKPORT.

'''For Mac/Linux Users'''

If you are using a Linux or Apple machine for your desktop or laptop you can use the following command to establish the ssh tunnel

'''Make sure you replace NOTEBOOKPORT and IPUSED below with the port number and the IP address you are using ''' (see the output of the '''echo''' commands above)
<pre class="gcommand">
ssh -N -L NOTEBOOKPORT:IPUSED:NOTEBOOKPORT username@sapelo2.gacrc.uga.edu
</pre>

Unless you have ssh key configured, you will be prompted for your MyID password and for Archpass Duo authentication. Once authentication is established, this session prompt will hang and you are ready to go to the next step.

'''For Windows Users'''

If you are using a Windows machine for your desktop or laptop download the '''[https://the.earth.li/~sgtatham/putty/latest/x86/plink.exe plink program]''' to use in place of the ssh client.
The command for windows would be as follows:
Assuming the plink.exe is in the current directory where you have a command window open.

'''Make sure you replace NOTEBOOKPORT and IPUSED below with the port number and the IP address you are using ''' (see the output of the '''echo''' commands above)
<pre class="gcommand">
plink -ssh -N -L NOTEBOOKPORT:IPUSED:NOTEBOOKPORT username@sapelo2.gacrc.uga.edu
</pre>

Once you establish the ssh tunnel by running the command above you can access the Jupyter notebook by going to '''http://localhost:NOTEBOOKPORT''' using the browser on your desktop or laptop (make sure you replace NOTEBOOKPORT with the port number you used in the qlogin session). If the page displayed on the browser asks for a token or password, you can check the token shown in your qlogin session and copy and paste it into the token field in the page displayed in your browser. For example, the qlogin terminal might show something like this:

<pre class="gcommand">
[I 11:16:00.939 NotebookApp] JupyterLab extension loaded from /apps/eb/Anaconda3/2022.10/lib/python3.7/site-packages/jupyterlab
[I 11:16:00.939 NotebookApp] JupyterLab application directory is /apps/eb/Anaconda3/2022.10/share/jupyter/lab
[I 11:16:00.942 NotebookApp] Serving notebooks from local directory: /home/zhuofei
[I 11:16:00.942 NotebookApp] The Jupyter Notebook is running at:
[I 11:16:00.942 NotebookApp] http://10.2.1.30:8888/?token=857f8a9f63cf42f698ac1845db64219516a23e248019cdeb
[I 11:16:00.942 NotebookApp] or http://127.0.0.1:8888/?token=857f8a9f63cf42f698ac1845db64219516a23e248019cdeb
[I 11:16:00.942 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
[C 11:16:00.947 NotebookApp]

To access the notebook, open this file in a browser:
file:///home/zhuofei/.local/share/jupyter/runtime/nbserver-14353-open.html
Or copy and paste one of these URLs:
http://10.2.1.30:8888/?token=857f8a9f63cf42f698ac1845db64219516a23e248019cdeb
or http://127.0.0.1:8888/?token=857f8a9f63cf42f698ac1845db64219516a23e248019cdeb
</pre>

Copy and paste the token, which in the example above is 857f8a9f63cf42f698ac1845db64219516a23e248019cdeb, into the proper field in the page displayed in your browser.

==Documentation==

Details at https://jupyter.readthedocs.io/en/latest/index.html

==System==
64-bit Linux

Job Submission partitions on Sapelo2

2026-05-15T18:37:45Z

Shtsai:

[[category:sapelo2]]

== Overview ==
This page describes the Slurm partitions available on the Sapelo2 cluster, including job limits and the resources available in each partition.

In Slurm, queues are called ''partitions''. When you submit a job, you must request both:
* the partition to use, and
* the resources your job needs, such as CPU cores, memory, or GPU devices.

Slurm will reject a job submission if no nodes match the resources you request. For background on Slurm, see [[Migrating from Torque to Slurm]].

== How to use this page ==
Use the first table to choose a partition based on job type and time limit.

Use the second table to confirm that your requested resources fit within the hardware available in that partition.

== Partition limits ==
{| class="wikitable unsortable"
|+ Sapelo2 partitions, time limits, and per-user job limits
|-
! scope="col" | Partition name
! scope="col" | Time limit
! scope="col" | Maximum running jobs per user
! scope="col" | Maximum submitted jobs per user
! scope="col" | Intended use and notes
|-
! scope="row" | <code>batch</code>
| 7 days
| 250
| 10,000
| Standard partition for regular compute jobs on general-purpose nodes.
|-
! scope="row" | <code>batch_30d</code>
| 30 days
| 1
| 2
| Standard partition for long-running jobs on regular nodes. A user may have one running job and one pending job, or two pending jobs and no running job. A third submission to this partition will be rejected.
|-
! scope="row" | <code>highmem_p</code>
| 7 days
| 6
| 100
| High-memory partition for jobs that require more memory than standard nodes provide.
|-
! scope="row" | <code>highmem_30d_p</code>
| 30 days
| 1
| 2
| High-memory partition for long-running jobs. A user may have one running job and one pending job, or two pending jobs and no running job. A third submission to this partition will be rejected.
|-
! scope="row" | <code>hugemem_p</code>
| 7 days
| 4
| 4
| Huge-memory partition for jobs needing up to 3 TB of memory.
|-
! scope="row" | <code>hugemem_30d_p</code>
| 30 days
| 4
| 4
| Huge-memory partition for long-running jobs needing up to 3 TB of memory.
|-
! scope="row" | <code>gpu_p</code>
| 7 days
| 8
| 20
| GPU-enabled partition for jobs that require one or more GPUs.
|-
! scope="row" | <code>gpu_30d_p</code>
| 30 days
| 2
| 2
| GPU-enabled partition for long-running jobs. A user may have one running job and one pending job, or two pending jobs and no running job. A third submission to this partition will be rejected.
|-
! scope="row" | <code>inter_p</code>
| 2 days
| 3
| 20
| Interactive partition for interactive jobs on regular nodes.
|-
! scope="row" | <code>''name''_p</code>
| Variable
| Variable
| Variable
| Partition for a specific group's buy-in nodes. Replace <code>''name''</code> with the group-specific partition prefix.
|}

== Resource limits by partition ==
Before submitting a job, make sure your requested memory, CPU cores, and GPU count fit within the limits of the partition you choose.

In the table below, the phrase '''partition maximum''' identifies the largest per-node resource values available within that partition. This replaces color-only emphasis so that the information is available to all users.

{| class="wikitable unsortable"
|+ Node resources available in each Sapelo2 partition
|-
! scope="col" | Partition
! scope="col" | Number of nodes
! scope="col" | Memory per node (GB)
! scope="col" | CPU cores per node
! scope="col" | Processor type
! scope="col" | GPU configuration
! scope="col" | Notes
|-
! scope="row" | <code>batch</code>, <code>batch_30d</code>
| 16
| 740
| 128
| AMD EPYC Genoa (4th gen)
| None
| Partition maximum for memory and cores is available on this node type.
|-
! scope="row" | <code>batch</code>, <code>batch_30d</code>
| 120
| 500
| 128
| AMD EPYC Milan (3rd gen)
| None
| Partition maximum for cores is also available on this node type.
|-
! scope="row" | <code>batch</code>, <code>batch_30d</code>
| 4
| 250
| 64
| AMD EPYC Milan (3rd gen)
| None
| Standard-capacity general-purpose nodes.
|-
! scope="row" | <code>batch</code>, <code>batch_30d</code>
| 2
| 120
| 64
| AMD EPYC Milan (3rd gen)
| None
| Standard-capacity general-purpose nodes.
|-
! scope="row" | <code>batch</code>, <code>batch_30d</code>
| 123
| 120
| 64
| AMD EPYC Rome (2nd gen)
| None
| Standard-capacity general-purpose nodes.
|-
! scope="row" | <code>batch</code>, <code>batch_30d</code>
| 25
| 120
| 32
| AMD EPYC Naples (1st gen)
| None
| Lower-core-count general-purpose nodes.
|-
! scope="row" | <code>batch</code>, <code>batch_30d</code>
| 40
| 180
| 32
| Intel Xeon Skylake
| None
| Lower-core-count general-purpose nodes.
|-
! scope="row" | <code>highmem_p</code>, <code>highmem_30d_p</code>
| 10
| 500
| 32
| AMD EPYC Naples (1st gen)
| None
| High-memory nodes.
|-
! scope="row" | <code>highmem_p</code>, <code>highmem_30d_p</code>
| 2
| 990
| 128
| AMD EPYC Milan (3rd gen)
| None
| Partition maximum for memory and cores is available on this node type.
|-
! scope="row" | <code>highmem_p</code>, <code>highmem_30d_p</code>
| 12
| 990
| 32
| AMD EPYC Milan (3rd gen)
| None
| High-memory nodes with fewer available cores per node.
|-
! scope="row" | <code>hugemem_p</code>, <code>hugemem_30d_p</code>
| 3
| 3000
| 48
| AMD EPYC Genoa (4th gen)
| None
| Partition maximum for memory and cores is available on this node type.
|-
! scope="row" | <code>hugemem_p</code>, <code>hugemem_30d_p</code>
| 2
| 2000
| 32
| AMD EPYC Rome (2nd gen)
| None
| Huge-memory nodes with lower maximums than the partition peak.
|-
! scope="row" | <code>gpu_p</code>, <code>gpu_30d_p</code>
| 2
| 180
| 32
| Intel Xeon Skylake
| 1 NVIDIA P100
| Older GPU nodes.
|-
! scope="row" | <code>gpu_p</code>, <code>gpu_30d_p</code>
| 2
| 120
| 64
| AMD EPYC Rome (2nd gen)
| 1 NVIDIA V100S
| Single-GPU nodes with 64 cores.
|-
! scope="row" | <code>gpu_p</code>, <code>gpu_30d_p</code>
| 14
| 1000
| 64
| AMD EPYC Milan (3rd gen)
| 4 NVIDIA A100
| Partition maximum for memory is available on this node type.
|-
! scope="row" | <code>gpu_p</code>, <code>gpu_30d_p</code>
| 12
| 1000
| 64
| Intel Xeon Sapphire Rapids
| 4 NVIDIA H100
| Partition maximum for memory is available on this node type.
|-
! scope="row" | <code>gpu_p</code>, <code>gpu_30d_p</code>
| 12
| 740
| 128
| AMD EPYC Genoa (4th gen)
| 4 NVIDIA L4
| Partition maximum for cores is available on this node type.
|-
! scope="row" | <code>''name''_p</code>
| Variable
| Variable
| Variable
| Variable
| Variable
| Resource limits depend on the group's buy-in nodes.
|}

== Choosing a partition ==
A general rule of thumb is:

* Use <code>batch</code> for most non-GPU jobs.
* Use <code>batch_30d</code> only when your job genuinely needs a longer wall time.
* Use <code>highmem_p</code> or <code>highmem_30d_p</code> when your memory requirements exceed what standard nodes provide.
* Use <code>hugemem_p</code> or <code>hugemem_30d_p</code> for jobs that need very large memory allocations, including jobs approaching 3 TB of memory.
* Use <code>gpu_p</code> or <code>gpu_30d_p</code> for GPU jobs.
* Use <code>inter_p</code> for interactive work.
* Use <code>''name''_p</code> only if your group has access to a buy-in partition with that name.



== Terms used on this page ==
; Partition
: A Slurm queue that determines which nodes your job may run on.
; Time limit
: The maximum wall-clock runtime allowed for a job in that partition.
; Running jobs
: Jobs currently executing for a user in that partition.
; Submitted jobs
: Total jobs a user may have in the partition, including running and pending jobs.
; Buy-in nodes
: Nodes purchased by a specific group and made available through a group-specific partition.

== Related documentation ==
* [[Migrating from Torque to Slurm]]

Nsight-Compute-CLI-Sapelo2

2026-05-14T21:21:23Z

Shtsai:

[[Category:Sapelo2]][[Category:Software]][[Category:Other]]

== Category ==

Other

== Program On ==

Sapelo2

== Version ==

2023.1.1.0, 2024.1.0.0, 2024.3.0.0, 2025.1.0.0, 2025.4.0.0

== Author / Distributor ==

NVIDIA

== Description ==

From https://docs.nvidia.com/nsight-compute/NsightComputeCli/index.html: "NVIDIA Nsight Compute CLI (ncu) provides a non-interactive way to profile applications from the command line. "

== Running Program ==

Also refer to [[Running Jobs on Sapelo2]]

Compute nodes equipped with GPU cards have access to all applications installed in /apps.

* Version 2025.4.0.0 is installed with CUDA v. 13.1.0 in /apps/eb/CUDA/13.1.0

To use it, please first load the module with:
<pre class="gcommand">
module load CUDA/13.1.0
</pre>

* Version 2025.1.0.0 is installed with CUDA v. 12.8.0 in /apps/eb/CUDA/12.8.0

To use it, please first load the module with:
<pre class="gcommand">
module load CUDA/12.8.0
</pre>

* Version 2024.3.0.0 is installed with CUDA v. 12.6.0 in /apps/eb/CUDA/12.6.0

To use it, please first load the module with:
<pre class="gcommand">
module load CUDA/12.6.0
</pre>

* Version 2024.1.0.0 is installed with CUDA v. 12.4.0 in /apps/eb/CUDA/12.4.0

To use it, please first load the module with:
<pre class="gcommand">
module load CUDA/12.4.0
</pre>

* Version 2023.1.1.0 is installed with CUDA v. 12.1.1 in /apps/eb/CUDA/12.1.1

To use it, please first load the module with:
<pre class="gcommand">
module load CUDA/12.1.1
</pre>

'''Please note:'''

'''1.''' Before running ncu to profile an application, please set the environment variable TMPDIR to point to a directory of yours. For example, use
<pre class="gcommand">
export TMPDIR=/scratch/$USER
</pre>

'''2.''' To profile applications on the H100 devices, please use the ncu in CUDA version 12.6.0 or higher. The older versions of ncu do not support the Hopper architecture.

'''3.''' Currently the ncu command only works on the following nodes:

<pre class="gcomment">
A100: ra4-1, ra4-2, b6-1, b6-2, b6-3, b6-4, b7-1, b7-2,b7-4, b8-4

H100: ra5-2, ra5-3, ra7-2, ra8-3, ra8-4
</pre>

More nodes will be added to the list above, as ncu is enabled on them (it requires a node reboot). Please check back, if of interest.

You can request a specific node with the Slurm <code>--nodelist</code> option. For example, <code>--nodelist=ra8-3 </code>.

'''Sample command to request an interactive job on a specific node:'''
<pre class="gcommand">
interact -p gpu_p --gres=gpu:H100:1 --nodelist=ra8-3
</pre>

'''Sample Slurm header line to request a specific node for a batch job:'''
<pre class="gcommand">
#SBATCH --nodelist=ra8-3
</pre>

== Documentation ==

Please see https://docs.nvidia.com/nsight-compute/NsightComputeCli/index.html

== Installation ==

Downloaded from NVIDIA site.

== System ==

64-bit Linux

Trinity-Sapelo2

2026-05-11T19:56:28Z

Shtsai:

[[Category:Sapelo2]][[Category:Software]][[Category:Bioinformatics]]
== Category ==

Bioinformatics

== Program On ==

Sapelo2

== Version ==

2.5.1, 2.8.4, 2.8.5, 2.15.1, 2.15.2

== Author / Distributor ==

Trinity is now published online at [http://www.nature.com/nbt/journal/v29/n7/abs/nbt.1883.html Nature Biotechnology]. The Broad Institute’s [http://www.broadinstitute.org/blog/suite-tools-takes-flight blog] has a story on how the Trinity project came together.

More details at [https://github.com/trinityrnaseq/trinityrnaseq/ Trinity github page]

== Description ==

From https://github.com/trinityrnaseq/trinityrnaseq/wiki:

"Trinity, developed at the [http://www.broadinstitute.org/ Broad Institute], represents a novel method for the efficient and robust de novo reconstruction of transcriptomes from RNA-Seq data. Trinity combines three independent software modules: Inchworm, Chrysalis, and Butterfly, applied sequentially to process large volumes of RNA-Seq reads. Trinity partitions the sequence data into many individual de Bruijn graphs, each representing the transcriptional complexity at at a given gene or locus, and then processes each graph independently to extract full-length splicing isoforms and to tease apart transcripts derived from paralogous genes. Briefly, the process works like so:

Inchworm assembles the RNA-Seq data into the unique sequences of transcripts, often generating full-length transcripts for a dominant isoform, but then reports just the unique portions of alternatively spliced transcripts.

Chrysalis clusters the Inchworm contigs into clusters and constructs complete de Bruijn graphs for each cluster. Each cluster represents the full transcriptonal complexity for a given gene (or sets of genes that share sequences in common). Chrysalis then partitions the full read set among these disjoint graphs.

Butterfly then processes the individual graphs in parallel, tracing the paths that reads and pairs of reads take within the graph, ultimately reporting full-length transcripts for alternatively spliced isoforms, and teasing apart transcripts that corresponds to paralogous genes."

[[#top|Back to Top]]

== Running Program ==

===General Instructions===

Also refer to [[Running Jobs on Sapelo2]]

Trinity versions 2.5.1, 2.8.4, 2.8.5, 2.15.1, and 2.15.2 are installed on Sapelo2.

* Mostly trinity needs to run at large memory queue, namely highmem_p, as in the sample script below.
* '''<u>Trinity is best run utilizing /lscratch, please see [[Trinity-Sapelo2#Utilizing .2Flscratch in Trinity Job Submission Script|Utilizing /lscratch for Trinity Jobs]] or scroll down to see how to configure your job submission script to use /lscratch</u>'''
* To run Trinity in conjunction with HpcGridRunner, please see [[Trinity-HpcGridRunner|our wiki page on Trinity HpcGridRunner]].

* Memory estimates according to an old Trinity documentation: For a 4 billion base mouse, it uses about 50 GB memory at peak.

* Do not ask for more than 24 CPU cores at the command and double the quantity of requesting CPU from queue. e.g. at the following, command ask for 8 CPU cores and at the header, it asks for --cpus-per-task=8.

* Using --normalize_reads could tremendously reduce the needs of memory. For this feature, please ensure there is no space in sequence name and quality score names, and adding "/1", "/2" to sequence name to make each seq name unique for pair reads in fasta /fq header.

* '''Please use the --full_cleanup option to make sure Trinity cleans up after itself.''' This helps a lot in keeping the number of files on Lustre storage under control.

* If previous jobs left dir trinity_out_dir, remove it before start another trinity job.

===Trinity versions 2.5.1, 2.8.4, and 2.8.5 Singularity Container on Sapelo2===

On the Sapelo2 cluster, singularity containers have access to the users home directory ($HOME), scratch directory (/scratch), lscratch directory (/lscratch), /tmp directory (/tmp) inside the container.

All environment variables set before executing singularity command is available inside the container.

To run Trinity v2.5.1, sample command is as below:

<pre class="gcommand">
singularity exec /apps/singularity-images/trinity-2.5.1.simg COMMAND
</pre>

where COMMAND should be replaced by the command you want to use.

To run Trinity v2.8.4, sample command is as below:

<pre class="gcommand">
singularity exec /apps/singularity-images/trinity-2.8.4.simg COMMAND
</pre>

To run Trinity v2.8.5, sample command is as below:

<pre class="gcommand">
singularity exec /apps/singularity-images/trinity-2.8.5.simg COMMAND
</pre>

where COMMAND should be replaced by the command you want to use.

Example of a shell script sub.sh to run Trinity v2.8.4 on the batch partition:

<pre class="gscript">
#!/bin/bash
#SBATCH --job-name=j_Trinity # Job name (j_Trinity)
#SBATCH --partition=batch # Partition name (batch or highmem_p)
#SBATCH --ntasks=1 # Run job in single task
#SBATCH --cpus-per-task=8 # CPU core count per task
#SBATCH --mem=100G # Memory per node (100GB)
#SBATCH --time=48:00:00 # Time limit hrs:min:sec or days-hours:minutes:seconds
#SBATCH --export=NONE # Do not export any user’s explicit environment variables to compute node
#SBATCH --output=log.%j.out # Standard output log
#SBATCH --error=log.%j.err # Standard error log

#SBATCH --mail-user=username@uga.edu # Where to send mail
#SBATCH --mail-type=ALL # Mail events (BEGIN, END, FAIL, ALL)

cd $SLURM_SUBMIT_DIR

singularity exec /apps/singularity-images/trinity-2.8.4.simg Trinity --seqType <string> --max_memory 100G --CPU 8 --no_version_check --full_cleanup --normalize_reads
</pre>

Example to run Trinity script align_and_estimate_abundance.pl:

<pre class="gscript">
#!/bin/bash
#SBATCH --job-name=j_Trinity # Job name (j_Trinity)
#SBATCH --partition=batch # Partition name (batch or highmem_p)
#SBATCH --ntasks=1 # Run job in single task
#SBATCH --cpus-per-task=1 # CPU core count per task
#SBATCH --mem=20G # Memory per node (100GB)
#SBATCH --time=48:00:00 # Time limit hrs:min:sec or days-hours:minutes:seconds
#SBATCH --export=NONE # Do not export any user’s explicit environment variables to compute node
#SBATCH --output=%x_%j.out # Standard output log
#SBATCH --error=%x_%j.err # Standard error log

#SBATCH --mail-user=username@uga.edu # Where to send mail
#SBATCH --mail-type=ALL # Mail events (BEGIN, END, FAIL, ALL)

cd $SLURM_SUBMIT_DIR

singularity exec /apps/singularity-images/trinity-2.8.4.simg /usr/local/bin/trinityrnaseq/util/align_and_estimate_abundance.pl [options]
</pre>

Where [options] need to be added as appropriate. Other parameters of the job, such as the maximum wall clock time, maximum memory, the number cores per node, and the job name need to be modified appropriately as well.

[[#top|Back to Top]]

===Trinity versions 2.15.1 and 2.15.2 Software Module on Sapelo2 ===

*version 2.15.1 running with Python3 is installed at /apps/eb/Trinity/2.15.1-foss-2022a

To run Trinity v2.15.1, please load the module:

<pre class="gcommand">
module load Trinity/2.15.1-foss-2022a
</pre>

*version 2.15.2 running with Python3 is installed at /apps/eb/Trinity/2.15.2-foss-2023a

To run Trinity v2.15.2, please load the module:

<pre class="gcommand">
module load Trinity/2.15.2-foss-2023a
</pre>

Example of a shell script sub.sh to run Trinity v2.15.1 on the batch partition:

<pre class="gscript">
#!/bin/bash
#SBATCH --job-name=j_Trinity # Job name (j_Trinity)
#SBATCH --partition=batch # Partition name (batch or highmem_p)
#SBATCH --ntasks=1 # Run job in single task
#SBATCH --cpus-per-task=8 # CPU core count per task
#SBATCH --mem=100G # Memory per node (100GB)
#SBATCH --time=48:00:00 # Time limit hrs:min:sec or days-hours:minutes:seconds
#SBATCH --export=NONE # Do not export any user’s explicit environment variables to compute node
#SBATCH --output=log.%j.out # Standard output log
#SBATCH --error=log.%j.err # Standard error log

#SBATCH --mail-user=username@uga.edu # Where to send mail
#SBATCH --mail-type=ALL # Mail events (BEGIN, END, FAIL, ALL)

cd $SLURM_SUBMIT_DIR

module load Trinity/2.15.1-foss-2022a

Trinity --seqType <string> --max_memory 100G --CPU 8 --no_version_check --full_cleanup --normalize_reads
</pre>

Example to run Trinity script align_and_estimate_abundance.pl:

<pre class="gscript">
#!/bin/bash
#SBATCH --job-name=j_Trinity # Job name (j_Trinity)
#SBATCH --partition=batch # Partition name (batch or highmem_p)
#SBATCH --ntasks=1 # Run job in single task
#SBATCH --cpus-per-task=1 # CPU core count per task
#SBATCH --mem=20G # Memory per node (100GB)
#SBATCH --time=48:00:00 # Time limit hrs:min:sec or days-hours:minutes:seconds
#SBATCH --export=NONE # Do not export any user’s explicit environment variables to compute node
#SBATCH --output=%x_%j.out # Standard output log
#SBATCH --error=%x_%j.err # Standard error log

#SBATCH --mail-user=username@uga.edu # Where to send mail
#SBATCH --mail-type=ALL # Mail events (BEGIN, END, FAIL, ALL)

cd $SLURM_SUBMIT_DIR

module load Trinity/2.15.1-foss-2022a

${EBROOTTRINITY}/trinityrnaseq-v2.10.0/util/align_and_estimate_abundance.pl [options]
</pre>

Where EBROOTTRINITY is the env variable storing Trinity installation pat, i.e., /apps/eb/Trinity/2.15.1-foss-2022a ; [options] need to be added as appropriate. Other parameters of the job, such as the maximum wall clock time, maximum memory, the number cores per node, and the job name need to be modified appropriately as well.

[[#top|Back to Top]]

==Utilizing /lscratch in Trinity Job Submission Script==
* Utilizing /lscratch allows Trinity jobs to run much faster and smoother and also negates the effects of heavy IO traffic.
* This /lscratch directory resides on the local hard drive of the compute node that your job gets allocated to (which means you cannot access this directory outside the job submission script).
* Below is a sample job submission script including steps so you can see what you need to add to your job submission script in order to make your Trinity job utilize /lscratch.
** As well as adding the 6 steps below, please also add the Slurm header --gres=lscratch:___ which requests space in /lscratch. The default units for this is GB and in the example submission script below, we are requesting 200GB of space with the line '''''#SBATCH --gres=lscratch:200''''' (it is the last Slurm header). Please only request as much space in /lscratch as is needed for your job.

<pre class="gscript">
#!/bin/bash
#SBATCH --job-name=j_Trinity # Job name (j_Trinity)
#SBATCH --partition=batch # Partition name (batch or highmem_p)
#SBATCH --ntasks=1 # Run job in single task
#SBATCH --cpus-per-task=36 # CPU core count per task
#SBATCH --mem=128G # Memory per node (100GB)
#SBATCH --time=48:00:00 # Time limit hrs:min:sec or days-hours:minutes:seconds
#SBATCH --export=NONE # Do not export any user’s explicit environment variables to compute node
#SBATCH --output=log.%j.out # Standard output log
#SBATCH --error=log.%j.err # Standard error log
#SBATCH --mail-user=username@uga.edu # Where to send mail
#SBATCH --mail-type=ALL # Mail events (BEGIN, END, FAIL, ALL)
#SBATCH --gres=lscratch:200

cd $SLURM_SUBMIT_DIR

# Step 1: create a directory in /lscratch

mkdir -p /lscratch/${USER}/${SLURM_JOB_ID}/trinity_outputs

# Step 2: copy over any input files.

cp file1.fastq.gz /lscratch/${USER}/${SLURM_JOB_ID}/trinity_outputs
cp file2.fastq.gz /lscratch/${USER}/${SLURM_JOB_ID}/trinity_outputs
cp file3.bam /lscratch/${USER}/${SLURM_JOB_ID}/trinity_outputs

# Step 3: change directories into /lscratch

cd /lscratch/${USER}/${SLURM_JOB_ID}/trinity_outputs

# Step 4: your normal job lines (loading Trinity and running Trinity command)

module load Trinity/2.15.1-foss-2022a

Trinity --seqType fq --left 'file1.fastq.gz' --right 'file2.fastq.gz' --CPU 36 --max_memory 120G --output '/lscratch/${USER}/${SLURM_JOB_ID}/trinity_outputs/trinity'

Trinity --genome_guided_bam 'file3.bam' --genome_guided_max_intron 10000 --CPU 36 --max_memory 120G --output '/lscratch/${USER}/${SLURM_JOB_ID}/trinity_outputs/trinity'

### NOTE: the directory specified in --output is the directory created in step 1 with the addition of /trinity at the end. This is because Trinity writes some files in the --output dir and some right above it.

# Step 5: copy output files back over to a certain location in /scratch which you can change below

cp -r /lscratch/${USER}/* /scratch/${USER}/some/directory

# Step 6: clean up /lscratch directory **VERY IMPORTANT STEP**

rm -rf /lscratch/${USER}/${SLURM_JOB_ID}
</pre>

* Please feel free to submit a ticket to us if you would like further help, explanations of how /lscratch works, or to even look over your submission script to ensure it is correctly utilizing /lscratch!

==Job Submission==

Submit a job submission script (sub.sh) to Sapelo2:

<pre class="gcommand">
sbatch sub.sh
</pre>

[[#top|Back to Top]]

==Documentation ==

More details at [https://github.com/trinityrnaseq/trinityrnaseq/wiki/ Trinity wiki page]

<pre class="gcommand">
[cft07037@b1-24 ~]$ ml Trinity/2.15.1-foss-2022a
To execute picard run: java -jar $EBROOTPICARD/picard.jar
[cft07037@b1-24 ~]$ Trinity --show_full_usage_info

###############################################################################
#

______ ____ ____ ____ ____ ______ __ __
| || \ | || \ | || || | |
| || D ) | | | _ | | | | || | |
|_| |_|| / | | | | | | | |_| |_|| ~ |
| | | \ | | | | | | | | | |___, |
| | | . \ | | | | | | | | | | |
|__| |__|\_||____||__|__||____| |__| |____/

Trinity-v2.15.1

#
#
# Required:
#
# --seqType <string> :type of reads: ('fa' or 'fq')
#
# --max_memory <string> :suggested max memory to use by Trinity where limiting can be enabled. (jellyfish, sorting, etc)
# provided in Gb of RAM, ie. '--max_memory 10G'
#
# If paired reads:
# --left <string> :left reads, one or more file names (separated by commas, no spaces)
# --right <string> :right reads, one or more file names (separated by commas, no spaces)
#
# Or, if unpaired reads:
# --single <string> :single reads, one or more file names, comma-delimited (note, if single file contains pairs, can use flag: --run_as_paired )
#
# Or,
# --samples_file <string> tab-delimited text file indicating biological replicate relationships.
# ex.
# cond_A cond_A_rep1 A_rep1_left.fq A_rep1_right.fq
# cond_A cond_A_rep2 A_rep2_left.fq A_rep2_right.fq
# cond_B cond_B_rep1 B_rep1_left.fq B_rep1_right.fq
# cond_B cond_B_rep2 B_rep2_left.fq B_rep2_right.fq
#
# # if single-end instead of paired-end, then leave the 4th column above empty.
#
####################################
## Misc: #########################
#
# --SS_lib_type <string> :Strand-specific RNA-Seq read orientation.
# if paired: RF or FR,
# if single: F or R. (dUTP method = RF)
# See web documentation.
#
# --CPU <int> :number of CPUs to use, default: 2
# --min_contig_length <int> :minimum assembled contig length to report
# (def=200, must be >= 100)
#
# --long_reads <string> :fasta file containing error-corrected or circular consensus (CCS) pac bio reads
# (** note: experimental parameter **, this functionality continues to be under development)
#
# --genome_guided_bam <string> :genome guided mode, provide path to coordinate-sorted bam file.
# (see genome-guided param section under --show_full_usage_info)
#
# --long_reads_bam <string> :long reads to include for genome-guided Trinity
# (bam file consists of error-corrected or circular consensus (CCS) pac bio read aligned to the genome)
#
# --jaccard_clip :option, set if you have paired reads and
# you expect high gene density with UTR
# overlap (use FASTQ input file format
# for reads).
# (note: jaccard_clip is an expensive
# operation, so avoid using it unless
# necessary due to finding excessive fusion
# transcripts w/o it.)
#
# --trimmomatic :run Trimmomatic to quality trim reads
# see '--quality_trimming_params' under full usage info for tailored settings.
#
# --output <string> :name of directory for output (will be
# created if it doesn't already exist)
# default( your current working directory: "/home/cft07037/trinity_out_dir"
# note: must include 'trinity' in the name as a safety precaution! )
#
# --full_cleanup :only retain the Trinity fasta file, rename as ${output_dir}.Trinity.fasta
#
# --cite :show the Trinity literature citation
#
# --verbose :provide additional job status info during the run.
#
# --version :reports Trinity version (Trinity-v2.15.1) and exits.
#
# --show_full_usage_info :show the many many more options available for running Trinity (expert usage).

#
# --no_super_reads :turn off super-reads mode
#
# --prep :Only prepare files (high I/O usage) and stop before kmer counting.
#
# --no_cleanup :retain all intermediate input files.
#
# --no_version_check :dont run a network check to determine if software updates are available.
#
# --no_symlink :dont symlink, just copy files instead (sets env var NO_SYMLINK=TRUE)
#
# --monitoring :use collectl to monitor all steps of Trinity
# --monitor_sec <int> : number of seconds for each interval of runtime monitoring (default: 60)
#
# --no_distributed_trinity_exec :do not run Trinity phase 2 (assembly of partitioned reads), and stop after generating command list.
#
# --workdir <string> :where Trinity phase-2 assembly computation takes place (defaults to --output setting).
# (can set this to a node-local drive or RAM disk)
#
####################################################
# Inchworm and K-mer counting-related options: #####
#
# --min_kmer_cov <int> :min count for K-mers to be assembled by
# Inchworm (default: 1)
# --inchworm_cpu <int> :number of CPUs to use for Inchworm, default is min(6, --CPU option)
#
# --no_run_inchworm :stop after running jellyfish, before inchworm. (phase 1, read clustering only)
#
###################################
# Chrysalis-related options: ######
#
# --max_reads_per_graph <int> :maximum number of reads to anchor within
# a single graph (default: 200000)
# --min_glue <int> :min number of reads needed to glue two inchworm contigs
# together. (default: 2)
#
# --max_chrysalis_cluster_size <int> :max number of Inchworm contigs to be included in a single Chrysalis cluster. (default: 25)
#
# --no_bowtie :dont run bowtie to use pair info in chrysalis clustering.
#
# --no_run_chrysalis :stop after running inchworm, before chrysalis. (phase 1, read clustering only)
#
#####################################
### Butterfly-related options: ####
#
# --bfly_algorithm <string> : assembly algorithm to use. Options: ORIGINAL PASAFLY
#
# --bfly_opts <string> :additional parameters to pass through to butterfly
# (see butterfly options: java -jar Butterfly.jar ).
# (note: only for expert or experimental use. Commonly used parameters are exposed through this Trinity menu here).
#
#
# Butterfly read-pair grouping settings (used to define 'pair paths'):
#
# --group_pairs_distance <int> :maximum length expected between fragment pairs (default: 500)
# (reads outside this distance are treated as single-end)
#
# ///////////////////////////////////////////////
# Butterfly default reconstruction mode settings.
#
# --path_reinforcement_distance <int> :minimum overlap of reads with growing transcript
# path (default: PE: 25, SE: 25)
# Set to 1 for the most lenient path extension requirements.
#
#
# /////////////////////////////////////////
# Butterfly transcript reduction settings:
#
# --no_path_merging : all final transcript candidates are output (including SNP variations, however, some SNPs may be unphased)
#
# By default, alternative transcript candidates are merged (in reality, discarded) if they are found to be too similar, according to the following logic:
#
# (identity=(numberOfMatches/shorterLen) > 98.0% or if we have <= 2 mismatches) and if we have internal gap lengths <= 10
#
# with parameters as:
#
# --min_per_id_same_path <int> default: 98 min percent identity for two paths to be merged into single paths
# --max_diffs_same_path <int> default: 2 max allowed differences encountered between path sequences to combine them
# --max_internal_gap_same_path <int> default: 10 maximum number of internal consecutive gap characters allowed for paths to be merged into single paths.
#
# If, in a comparison between two alternative transcripts, they are found too similar, the transcript with the greatest cumulative
# compatible read (pair-path) support is retained, and the other is discarded.
#
#
# //////////////////////////////////////////////
# Butterfly Java and parallel execution settings.
#
# --bflyHeapSpaceMax <string> :java max heap space setting for butterfly
# (default: 10G) => yields command
# 'java -Xmx10G -jar Butterfly.jar ... $bfly_opts'
# --bflyHeapSpaceInit <string> :java initial heap space settings for
# butterfly (default: 1G) => yields command
# 'java -Xms1G -jar Butterfly.jar ... $bfly_opts'
# --bflyGCThreads <int> :threads for garbage collection
# (default: 2))
# --bflyCPU <int> :CPUs to use (default will be normal
# number of CPUs; e.g., 2)
# --bflyCalculateCPU :Calculate CPUs based on 80% of max_memory
# divided by maxbflyHeapSpaceMax
#
# --bfly_jar <string> : /path/to/Butterfly.jar, otherwise default
# Trinity-installed version is used.
#
#
################################################################################
#### Quality Trimming Options ####
#
# --quality_trimming_params <string> defaults to: "ILLUMINACLIP:/apps/eb/Trinity/2.15.1-foss-2022a/trinityrnaseq-v2.15.1/trinity-plugins/Trimmomatic/adapters/TruSeq3-PE.fa:2:30:10 SLIDINGWINDOW:4:5 LEADING:5 TRAILING:5 MINLEN:25"
#
################################################################################
#### In silico Read Normalization Options ###
#
# --normalize_max_read_cov <int> defaults to 200
# --normalize_by_read_set run normalization separate for each pair of fastq files,
# then one final normalization that combines the individual normalized reads.
# Consider using this if RAM limitations are a consideration.
#
# --just_normalize_reads stop after performing read normalization
#
# --no_normalize_reads :Do *not* run in silico normalization of reads. Defaults to max. read coverage of 200.
# see '--normalize_max_read_cov' under full usage info for tailored settings.
# (Note, as of Sept 21, 2016, normalization is on by default)
# (*Turning off normalization is not recommended for most applications)
#
# --no_parallel_norm_stats :Do not try to run the high-mem normalization stats generator in parallel for paired-end fastqs.
#
###############################################################################
#### Genome-guided de novo assembly
#
# * required:
#
# --genome_guided_max_intron <int> :maximum allowed intron length (also maximum fragment span on genome)
#
# * optional:
#
# --genome_guided_min_coverage <int> :minimum read coverage for identifying and expressed region of the genome. (default: 1)
#
# --genome_guided_min_reads_per_partition <int> :default min of 10 reads per partition
#
#
#######################################################################
# Trinity phase 2 (parallel assembly of read clusters) Options: #######
#
# --grid_exec <string> :your command-line utility for submitting jobs to the grid.
# This should be a command line tool that accepts a single parameter:
# ${your_submission_tool} /path/to/file/containing/commands.txt
# and this submission tool should exit(0) upon successful
# completion of all commands.
#
# --grid_node_CPU <int> number of threads for each parallel process to leverage. (default: 1)
#
# --grid_node_max_memory <string> max memory targeted for each grid node. (default: 1G)
#
# The --grid_node_CPU and --grid_node_max_memory are applied as
# the --CPU and --max_memory parameters for the Trinity jobs run in
# Trinity Phase 2 (assembly of read clusters)
#
# --FORCE ignore failed commands from earlier run, continue on.
# (Note, this should only be used after you've
# already dealt with these failed commands directly as needed)
#
########################################################################
# Singularity-related options
#
# --singularity_img <string> :path to a Trinity singularity image to use
#
# --singularity_extra_params <string> :additional parameters to include for the singularity command execution
#
#

#
#
###############################################################################
#
# *Note, a typical Trinity command might be:
#
# Trinity --seqType fq --max_memory 50G --left reads_1.fq --right reads_2.fq --CPU 6
#
# (if you have multiple samples, use --samples_file ... see above for details)
#
# and for Genome-guided Trinity, provide a coordinate-sorted bam:
#
# Trinity --genome_guided_bam rnaseq_alignments.csorted.bam --max_memory 50G
# --genome_guided_max_intron 10000 --CPU 6
#
# see: /apps/eb/Trinity/2.15.1-foss-2022a/trinityrnaseq-v2.15.1/sample_data/test_Trinity_Assembly/
# for sample data and 'runMe.sh' for example Trinity execution
#
# For more details, visit: http://trinityrnaseq.github.io
#
###############################################################################
</pre>
[[#top|Back to Top]]

== Installation==

Sources are downloaded from https://github.com/trinityrnaseq/trinityrnaseq

==System==
64-bit Linux

[[#top|Back to Top]]

Trinity-Sapelo2

2026-05-11T19:35:14Z

Shtsai:

[[Category:Sapelo2]][[Category:Software]][[Category:Bioinformatics]]
== Category ==

Bioinformatics

== Program On ==

Sapelo2

== Version ==

2.5.1, 2.8.4, 2.8.5, 2.15.1, 2.15.2

== Author / Distributor ==

Trinity is now published online at [http://www.nature.com/nbt/journal/v29/n7/abs/nbt.1883.html Nature Biotechnology]. The Broad Institute’s [http://www.broadinstitute.org/blog/suite-tools-takes-flight blog] has a story on how the Trinity project came together.

More details at [http://trinityrnaseq.github.io/ Trinity]

== Description ==

From [http://trinityrnaseq.github.io/ Trinity]:

"Trinity, developed at the [http://www.broadinstitute.org/ Broad Institute], represents a novel method for the efficient and robust de novo reconstruction of transcriptomes from RNA-Seq data. Trinity combines three independent software modules: Inchworm, Chrysalis, and Butterfly, applied sequentially to process large volumes of RNA-Seq reads. Trinity partitions the sequence data into many individual de Bruijn graphs, each representing the transcriptional complexity at at a given gene or locus, and then processes each graph independently to extract full-length splicing isoforms and to tease apart transcripts derived from paralogous genes. Briefly, the process works like so:

Inchworm assembles the RNA-Seq data into the unique sequences of transcripts, often generating full-length transcripts for a dominant isoform, but then reports just the unique portions of alternatively spliced transcripts.

Chrysalis clusters the Inchworm contigs into clusters and constructs complete de Bruijn graphs for each cluster. Each cluster represents the full transcriptonal complexity for a given gene (or sets of genes that share sequences in common). Chrysalis then partitions the full read set among these disjoint graphs.

Butterfly then processes the individual graphs in parallel, tracing the paths that reads and pairs of reads take within the graph, ultimately reporting full-length transcripts for alternatively spliced isoforms, and teasing apart transcripts that corresponds to paralogous genes."

[[#top|Back to Top]]

== Running Program ==

===General Instructions===

Also refer to [[Running Jobs on Sapelo2]]

Trinity versions 2.5.1, 2.8.4, 2.8.5, 2.15.1, and 2.15.2 are installed on Sapelo2.

* Mostly trinity needs to run at large memory queue, namely highmem_p, as in the sample script below.
* '''<u>Trinity is best run utilizing /lscratch, please see [[Trinity-Sapelo2#Utilizing .2Flscratch in Trinity Job Submission Script|Utilizing /lscratch for Trinity Jobs]] or scroll down to see how to configure your job submission script to use /lscratch</u>'''
* To run Trinity in conjunction with HpcGridRunner, please see [[Trinity-HpcGridRunner|our wiki page on Trinity HpcGridRunner]].

* Here is a post for memory estimates. For a 4 billion base mouse, it uses about 50 GB memory at peak. [http://trinityrnaseq.github.io/performance/index.html performance]

* Do not ask for more than 24 CPU cores at the command and double the quantity of requesting CPU from queue. e.g. at the following, command ask for 8 CPU cores and at the header, it asks for --cpus-per-task=8.

* Using --normalize_reads could tremendously reduce the needs of memory. For this feature, please ensure there is no space in sequence name and quality score names, and adding "/1", "/2" to sequence name to make each seq name unique for pair reads in fasta /fq header.

* '''Please use the --full_cleanup option to make sure Trinity cleans up after itself.''' This helps a lot in keeping the number of files on Lustre storage under control.

* If previous jobs left dir trinity_out_dir, remove it before start another trinity job.

===Trinity versions 2.5.1, 2.8.4, and 2.8.5 Singularity Container on Sapelo2===

On the Sapelo2 cluster, singularity containers have access to the users home directory ($HOME), scratch directory (/scratch), lscratch directory (/lscratch), /tmp directory (/tmp) inside the container.

All environment variables set before executing singularity command is available inside the container.

To run Trinity v2.5.1, sample command is as below:

<pre class="gcommand">
singularity exec /apps/singularity-images/trinity-2.5.1.simg COMMAND
</pre>

where COMMAND should be replaced by the command you want to use.

To run Trinity v2.8.4, sample command is as below:

<pre class="gcommand">
singularity exec /apps/singularity-images/trinity-2.8.4.simg COMMAND
</pre>

To run Trinity v2.8.5, sample command is as below:

<pre class="gcommand">
singularity exec /apps/singularity-images/trinity-2.8.5.simg COMMAND
</pre>

where COMMAND should be replaced by the command you want to use.

Example of a shell script sub.sh to run Trinity v2.8.4 on the batch partition:

<pre class="gscript">
#!/bin/bash
#SBATCH --job-name=j_Trinity # Job name (j_Trinity)
#SBATCH --partition=batch # Partition name (batch or highmem_p)
#SBATCH --ntasks=1 # Run job in single task
#SBATCH --cpus-per-task=8 # CPU core count per task
#SBATCH --mem=100G # Memory per node (100GB)
#SBATCH --time=48:00:00 # Time limit hrs:min:sec or days-hours:minutes:seconds
#SBATCH --export=NONE # Do not export any user’s explicit environment variables to compute node
#SBATCH --output=log.%j.out # Standard output log
#SBATCH --error=log.%j.err # Standard error log

#SBATCH --mail-user=username@uga.edu # Where to send mail
#SBATCH --mail-type=ALL # Mail events (BEGIN, END, FAIL, ALL)

cd $SLURM_SUBMIT_DIR

singularity exec /apps/singularity-images/trinity-2.8.4.simg Trinity --seqType <string> --max_memory 100G --CPU 8 --no_version_check --full_cleanup --normalize_reads
</pre>

Example to run Trinity script align_and_estimate_abundance.pl:

<pre class="gscript">
#!/bin/bash
#SBATCH --job-name=j_Trinity # Job name (j_Trinity)
#SBATCH --partition=batch # Partition name (batch or highmem_p)
#SBATCH --ntasks=1 # Run job in single task
#SBATCH --cpus-per-task=1 # CPU core count per task
#SBATCH --mem=20G # Memory per node (100GB)
#SBATCH --time=48:00:00 # Time limit hrs:min:sec or days-hours:minutes:seconds
#SBATCH --export=NONE # Do not export any user’s explicit environment variables to compute node
#SBATCH --output=%x_%j.out # Standard output log
#SBATCH --error=%x_%j.err # Standard error log

#SBATCH --mail-user=username@uga.edu # Where to send mail
#SBATCH --mail-type=ALL # Mail events (BEGIN, END, FAIL, ALL)

cd $SLURM_SUBMIT_DIR

singularity exec /apps/singularity-images/trinity-2.8.4.simg /usr/local/bin/trinityrnaseq/util/align_and_estimate_abundance.pl [options]
</pre>

Where [options] need to be added as appropriate. Other parameters of the job, such as the maximum wall clock time, maximum memory, the number cores per node, and the job name need to be modified appropriately as well.

[[#top|Back to Top]]

===Trinity versions 2.15.1 and 2.15.2 Software Module on Sapelo2 ===

*version 2.15.1 running with Python3 is installed at /apps/eb/Trinity/2.15.1-foss-2022a

To run Trinity v2.15.1, please load the module:

<pre class="gcommand">
module load Trinity/2.15.1-foss-2022a
</pre>

*version 2.15.2 running with Python3 is installed at /apps/eb/Trinity/2.15.2-foss-2023a

To run Trinity v2.15.2, please load the module:

<pre class="gcommand">
module load Trinity/2.15.2-foss-2023a
</pre>

Example of a shell script sub.sh to run Trinity v2.15.1 on the batch partition:

<pre class="gscript">
#!/bin/bash
#SBATCH --job-name=j_Trinity # Job name (j_Trinity)
#SBATCH --partition=batch # Partition name (batch or highmem_p)
#SBATCH --ntasks=1 # Run job in single task
#SBATCH --cpus-per-task=8 # CPU core count per task
#SBATCH --mem=100G # Memory per node (100GB)
#SBATCH --time=48:00:00 # Time limit hrs:min:sec or days-hours:minutes:seconds
#SBATCH --export=NONE # Do not export any user’s explicit environment variables to compute node
#SBATCH --output=log.%j.out # Standard output log
#SBATCH --error=log.%j.err # Standard error log

#SBATCH --mail-user=username@uga.edu # Where to send mail
#SBATCH --mail-type=ALL # Mail events (BEGIN, END, FAIL, ALL)

cd $SLURM_SUBMIT_DIR

module load Trinity/2.15.1-foss-2022a

Trinity --seqType <string> --max_memory 100G --CPU 8 --no_version_check --full_cleanup --normalize_reads
</pre>

Example to run Trinity script align_and_estimate_abundance.pl:

<pre class="gscript">
#!/bin/bash
#SBATCH --job-name=j_Trinity # Job name (j_Trinity)
#SBATCH --partition=batch # Partition name (batch or highmem_p)
#SBATCH --ntasks=1 # Run job in single task
#SBATCH --cpus-per-task=1 # CPU core count per task
#SBATCH --mem=20G # Memory per node (100GB)
#SBATCH --time=48:00:00 # Time limit hrs:min:sec or days-hours:minutes:seconds
#SBATCH --export=NONE # Do not export any user’s explicit environment variables to compute node
#SBATCH --output=%x_%j.out # Standard output log
#SBATCH --error=%x_%j.err # Standard error log

#SBATCH --mail-user=username@uga.edu # Where to send mail
#SBATCH --mail-type=ALL # Mail events (BEGIN, END, FAIL, ALL)

cd $SLURM_SUBMIT_DIR

module load Trinity/2.15.1-foss-2022a

${EBROOTTRINITY}/trinityrnaseq-v2.10.0/util/align_and_estimate_abundance.pl [options]
</pre>

Where EBROOTTRINITY is the env variable storing Trinity installation pat, i.e., /apps/eb/Trinity/2.15.1-foss-2022a ; [options] need to be added as appropriate. Other parameters of the job, such as the maximum wall clock time, maximum memory, the number cores per node, and the job name need to be modified appropriately as well.

[[#top|Back to Top]]

==Utilizing /lscratch in Trinity Job Submission Script==
* Utilizing /lscratch allows Trinity jobs to run much faster and smoother and also negates the effects of heavy IO traffic.
* This /lscratch directory resides on the local hard drive of the compute node that your job gets allocated to (which means you cannot access this directory outside the job submission script).
* Below is a sample job submission script including steps so you can see what you need to add to your job submission script in order to make your Trinity job utilize /lscratch.
** As well as adding the 6 steps below, please also add the Slurm header --gres=lscratch:___ which requests space in /lscratch. The default units for this is GB and in the example submission script below, we are requesting 200GB of space with the line '''''#SBATCH --gres=lscratch:200''''' (it is the last Slurm header). Please only request as much space in /lscratch as is needed for your job.

<pre class="gscript">
#!/bin/bash
#SBATCH --job-name=j_Trinity # Job name (j_Trinity)
#SBATCH --partition=batch # Partition name (batch or highmem_p)
#SBATCH --ntasks=1 # Run job in single task
#SBATCH --cpus-per-task=36 # CPU core count per task
#SBATCH --mem=128G # Memory per node (100GB)
#SBATCH --time=48:00:00 # Time limit hrs:min:sec or days-hours:minutes:seconds
#SBATCH --export=NONE # Do not export any user’s explicit environment variables to compute node
#SBATCH --output=log.%j.out # Standard output log
#SBATCH --error=log.%j.err # Standard error log
#SBATCH --mail-user=username@uga.edu # Where to send mail
#SBATCH --mail-type=ALL # Mail events (BEGIN, END, FAIL, ALL)
#SBATCH --gres=lscratch:200

cd $SLURM_SUBMIT_DIR

# Step 1: create a directory in /lscratch

mkdir -p /lscratch/${USER}/${SLURM_JOB_ID}/trinity_outputs

# Step 2: copy over any input files.

cp file1.fastq.gz /lscratch/${USER}/${SLURM_JOB_ID}/trinity_outputs
cp file2.fastq.gz /lscratch/${USER}/${SLURM_JOB_ID}/trinity_outputs
cp file3.bam /lscratch/${USER}/${SLURM_JOB_ID}/trinity_outputs

# Step 3: change directories into /lscratch

cd /lscratch/${USER}/${SLURM_JOB_ID}/trinity_outputs

# Step 4: your normal job lines (loading Trinity and running Trinity command)

module load Trinity/2.15.1-foss-2022a

Trinity --seqType fq --left 'file1.fastq.gz' --right 'file2.fastq.gz' --CPU 36 --max_memory 120G --output '/lscratch/${USER}/${SLURM_JOB_ID}/trinity_outputs/trinity'

Trinity --genome_guided_bam 'file3.bam' --genome_guided_max_intron 10000 --CPU 36 --max_memory 120G --output '/lscratch/${USER}/${SLURM_JOB_ID}/trinity_outputs/trinity'

### NOTE: the directory specified in --output is the directory created in step 1 with the addition of /trinity at the end. This is because Trinity writes some files in the --output dir and some right above it.

# Step 5: copy output files back over to a certain location in /scratch which you can change below

cp -r /lscratch/${USER}/* /scratch/${USER}/some/directory

# Step 6: clean up /lscratch directory **VERY IMPORTANT STEP**

rm -rf /lscratch/${USER}/${SLURM_JOB_ID}
</pre>

* Please feel free to submit a ticket to us if you would like further help, explanations of how /lscratch works, or to even look over your submission script to ensure it is correctly utilizing /lscratch!

==Job Submission==

Submit a job submission script (sub.sh) to Sapelo2:

<pre class="gcommand">
sbatch sub.sh
</pre>

[[#top|Back to Top]]

==Documentation ==

More details at [http://trinityrnaseq.github.io/ Trinity]

<pre class="gcommand">
[cft07037@b1-24 ~]$ ml Trinity/2.15.1-foss-2022a
To execute picard run: java -jar $EBROOTPICARD/picard.jar
[cft07037@b1-24 ~]$ Trinity --show_full_usage_info

###############################################################################
#

______ ____ ____ ____ ____ ______ __ __
| || \ | || \ | || || | |
| || D ) | | | _ | | | | || | |
|_| |_|| / | | | | | | | |_| |_|| ~ |
| | | \ | | | | | | | | | |___, |
| | | . \ | | | | | | | | | | |
|__| |__|\_||____||__|__||____| |__| |____/

Trinity-v2.15.1

#
#
# Required:
#
# --seqType <string> :type of reads: ('fa' or 'fq')
#
# --max_memory <string> :suggested max memory to use by Trinity where limiting can be enabled. (jellyfish, sorting, etc)
# provided in Gb of RAM, ie. '--max_memory 10G'
#
# If paired reads:
# --left <string> :left reads, one or more file names (separated by commas, no spaces)
# --right <string> :right reads, one or more file names (separated by commas, no spaces)
#
# Or, if unpaired reads:
# --single <string> :single reads, one or more file names, comma-delimited (note, if single file contains pairs, can use flag: --run_as_paired )
#
# Or,
# --samples_file <string> tab-delimited text file indicating biological replicate relationships.
# ex.
# cond_A cond_A_rep1 A_rep1_left.fq A_rep1_right.fq
# cond_A cond_A_rep2 A_rep2_left.fq A_rep2_right.fq
# cond_B cond_B_rep1 B_rep1_left.fq B_rep1_right.fq
# cond_B cond_B_rep2 B_rep2_left.fq B_rep2_right.fq
#
# # if single-end instead of paired-end, then leave the 4th column above empty.
#
####################################
## Misc: #########################
#
# --SS_lib_type <string> :Strand-specific RNA-Seq read orientation.
# if paired: RF or FR,
# if single: F or R. (dUTP method = RF)
# See web documentation.
#
# --CPU <int> :number of CPUs to use, default: 2
# --min_contig_length <int> :minimum assembled contig length to report
# (def=200, must be >= 100)
#
# --long_reads <string> :fasta file containing error-corrected or circular consensus (CCS) pac bio reads
# (** note: experimental parameter **, this functionality continues to be under development)
#
# --genome_guided_bam <string> :genome guided mode, provide path to coordinate-sorted bam file.
# (see genome-guided param section under --show_full_usage_info)
#
# --long_reads_bam <string> :long reads to include for genome-guided Trinity
# (bam file consists of error-corrected or circular consensus (CCS) pac bio read aligned to the genome)
#
# --jaccard_clip :option, set if you have paired reads and
# you expect high gene density with UTR
# overlap (use FASTQ input file format
# for reads).
# (note: jaccard_clip is an expensive
# operation, so avoid using it unless
# necessary due to finding excessive fusion
# transcripts w/o it.)
#
# --trimmomatic :run Trimmomatic to quality trim reads
# see '--quality_trimming_params' under full usage info for tailored settings.
#
# --output <string> :name of directory for output (will be
# created if it doesn't already exist)
# default( your current working directory: "/home/cft07037/trinity_out_dir"
# note: must include 'trinity' in the name as a safety precaution! )
#
# --full_cleanup :only retain the Trinity fasta file, rename as ${output_dir}.Trinity.fasta
#
# --cite :show the Trinity literature citation
#
# --verbose :provide additional job status info during the run.
#
# --version :reports Trinity version (Trinity-v2.15.1) and exits.
#
# --show_full_usage_info :show the many many more options available for running Trinity (expert usage).

#
# --no_super_reads :turn off super-reads mode
#
# --prep :Only prepare files (high I/O usage) and stop before kmer counting.
#
# --no_cleanup :retain all intermediate input files.
#
# --no_version_check :dont run a network check to determine if software updates are available.
#
# --no_symlink :dont symlink, just copy files instead (sets env var NO_SYMLINK=TRUE)
#
# --monitoring :use collectl to monitor all steps of Trinity
# --monitor_sec <int> : number of seconds for each interval of runtime monitoring (default: 60)
#
# --no_distributed_trinity_exec :do not run Trinity phase 2 (assembly of partitioned reads), and stop after generating command list.
#
# --workdir <string> :where Trinity phase-2 assembly computation takes place (defaults to --output setting).
# (can set this to a node-local drive or RAM disk)
#
####################################################
# Inchworm and K-mer counting-related options: #####
#
# --min_kmer_cov <int> :min count for K-mers to be assembled by
# Inchworm (default: 1)
# --inchworm_cpu <int> :number of CPUs to use for Inchworm, default is min(6, --CPU option)
#
# --no_run_inchworm :stop after running jellyfish, before inchworm. (phase 1, read clustering only)
#
###################################
# Chrysalis-related options: ######
#
# --max_reads_per_graph <int> :maximum number of reads to anchor within
# a single graph (default: 200000)
# --min_glue <int> :min number of reads needed to glue two inchworm contigs
# together. (default: 2)
#
# --max_chrysalis_cluster_size <int> :max number of Inchworm contigs to be included in a single Chrysalis cluster. (default: 25)
#
# --no_bowtie :dont run bowtie to use pair info in chrysalis clustering.
#
# --no_run_chrysalis :stop after running inchworm, before chrysalis. (phase 1, read clustering only)
#
#####################################
### Butterfly-related options: ####
#
# --bfly_algorithm <string> : assembly algorithm to use. Options: ORIGINAL PASAFLY
#
# --bfly_opts <string> :additional parameters to pass through to butterfly
# (see butterfly options: java -jar Butterfly.jar ).
# (note: only for expert or experimental use. Commonly used parameters are exposed through this Trinity menu here).
#
#
# Butterfly read-pair grouping settings (used to define 'pair paths'):
#
# --group_pairs_distance <int> :maximum length expected between fragment pairs (default: 500)
# (reads outside this distance are treated as single-end)
#
# ///////////////////////////////////////////////
# Butterfly default reconstruction mode settings.
#
# --path_reinforcement_distance <int> :minimum overlap of reads with growing transcript
# path (default: PE: 25, SE: 25)
# Set to 1 for the most lenient path extension requirements.
#
#
# /////////////////////////////////////////
# Butterfly transcript reduction settings:
#
# --no_path_merging : all final transcript candidates are output (including SNP variations, however, some SNPs may be unphased)
#
# By default, alternative transcript candidates are merged (in reality, discarded) if they are found to be too similar, according to the following logic:
#
# (identity=(numberOfMatches/shorterLen) > 98.0% or if we have <= 2 mismatches) and if we have internal gap lengths <= 10
#
# with parameters as:
#
# --min_per_id_same_path <int> default: 98 min percent identity for two paths to be merged into single paths
# --max_diffs_same_path <int> default: 2 max allowed differences encountered between path sequences to combine them
# --max_internal_gap_same_path <int> default: 10 maximum number of internal consecutive gap characters allowed for paths to be merged into single paths.
#
# If, in a comparison between two alternative transcripts, they are found too similar, the transcript with the greatest cumulative
# compatible read (pair-path) support is retained, and the other is discarded.
#
#
# //////////////////////////////////////////////
# Butterfly Java and parallel execution settings.
#
# --bflyHeapSpaceMax <string> :java max heap space setting for butterfly
# (default: 10G) => yields command
# 'java -Xmx10G -jar Butterfly.jar ... $bfly_opts'
# --bflyHeapSpaceInit <string> :java initial heap space settings for
# butterfly (default: 1G) => yields command
# 'java -Xms1G -jar Butterfly.jar ... $bfly_opts'
# --bflyGCThreads <int> :threads for garbage collection
# (default: 2))
# --bflyCPU <int> :CPUs to use (default will be normal
# number of CPUs; e.g., 2)
# --bflyCalculateCPU :Calculate CPUs based on 80% of max_memory
# divided by maxbflyHeapSpaceMax
#
# --bfly_jar <string> : /path/to/Butterfly.jar, otherwise default
# Trinity-installed version is used.
#
#
################################################################################
#### Quality Trimming Options ####
#
# --quality_trimming_params <string> defaults to: "ILLUMINACLIP:/apps/eb/Trinity/2.15.1-foss-2022a/trinityrnaseq-v2.15.1/trinity-plugins/Trimmomatic/adapters/TruSeq3-PE.fa:2:30:10 SLIDINGWINDOW:4:5 LEADING:5 TRAILING:5 MINLEN:25"
#
################################################################################
#### In silico Read Normalization Options ###
#
# --normalize_max_read_cov <int> defaults to 200
# --normalize_by_read_set run normalization separate for each pair of fastq files,
# then one final normalization that combines the individual normalized reads.
# Consider using this if RAM limitations are a consideration.
#
# --just_normalize_reads stop after performing read normalization
#
# --no_normalize_reads :Do *not* run in silico normalization of reads. Defaults to max. read coverage of 200.
# see '--normalize_max_read_cov' under full usage info for tailored settings.
# (Note, as of Sept 21, 2016, normalization is on by default)
# (*Turning off normalization is not recommended for most applications)
#
# --no_parallel_norm_stats :Do not try to run the high-mem normalization stats generator in parallel for paired-end fastqs.
#
###############################################################################
#### Genome-guided de novo assembly
#
# * required:
#
# --genome_guided_max_intron <int> :maximum allowed intron length (also maximum fragment span on genome)
#
# * optional:
#
# --genome_guided_min_coverage <int> :minimum read coverage for identifying and expressed region of the genome. (default: 1)
#
# --genome_guided_min_reads_per_partition <int> :default min of 10 reads per partition
#
#
#######################################################################
# Trinity phase 2 (parallel assembly of read clusters) Options: #######
#
# --grid_exec <string> :your command-line utility for submitting jobs to the grid.
# This should be a command line tool that accepts a single parameter:
# ${your_submission_tool} /path/to/file/containing/commands.txt
# and this submission tool should exit(0) upon successful
# completion of all commands.
#
# --grid_node_CPU <int> number of threads for each parallel process to leverage. (default: 1)
#
# --grid_node_max_memory <string> max memory targeted for each grid node. (default: 1G)
#
# The --grid_node_CPU and --grid_node_max_memory are applied as
# the --CPU and --max_memory parameters for the Trinity jobs run in
# Trinity Phase 2 (assembly of read clusters)
#
# --FORCE ignore failed commands from earlier run, continue on.
# (Note, this should only be used after you've
# already dealt with these failed commands directly as needed)
#
########################################################################
# Singularity-related options
#
# --singularity_img <string> :path to a Trinity singularity image to use
#
# --singularity_extra_params <string> :additional parameters to include for the singularity command execution
#
#

#
#
###############################################################################
#
# *Note, a typical Trinity command might be:
#
# Trinity --seqType fq --max_memory 50G --left reads_1.fq --right reads_2.fq --CPU 6
#
# (if you have multiple samples, use --samples_file ... see above for details)
#
# and for Genome-guided Trinity, provide a coordinate-sorted bam:
#
# Trinity --genome_guided_bam rnaseq_alignments.csorted.bam --max_memory 50G
# --genome_guided_max_intron 10000 --CPU 6
#
# see: /apps/eb/Trinity/2.15.1-foss-2022a/trinityrnaseq-v2.15.1/sample_data/test_Trinity_Assembly/
# for sample data and 'runMe.sh' for example Trinity execution
#
# For more details, visit: http://trinityrnaseq.github.io
#
###############################################################################
</pre>
[[#top|Back to Top]]

== Installation==

Sources are downloaded from [https://trinityrnaseq.github.io Trinity]

==System==
64-bit Linux

[[#top|Back to Top]]

Trinity-HpcGridRunner

2026-05-11T19:28:26Z

Shtsai:

==Description==
Instruction on how to modify a Trinity script to run in conjunction with HpcGridRunner
==Running Program==
Step 1: Create normal Trinity script

* Note: normal Trinity jobs should be run utilizing /lscratch, however, this method running with HpcGridRunner does not benefit from /lscratch so this example will not have any /lscratch components in it. For more information on running normal Trinity jobs with /lscratch, please see [[Trinity-Sapelo2#Utilizing /lscratch in Trinity Job Submission Script|Utilizing /lscratch in Trinity Job Submission Script]].
* For more information on creating a Trinity job (without utilizing /lscratch) please see [[Trinity-Sapelo2#Running Program|Running Trinity Jobs]].

Step 2: add a line to load HpcGridRunner module AND add the --grid_exec flag in your Trinity command (see how to format --grid exec below)

* Your grid_exec flag should look exactly like the one below, where the only part you'll change is the '''location to your config.conf''' file after --grid_conf. Note the location of the quotation marks as they are necessary.
<pre class="gscript">
#!/bin/bash
#SBATCH --job-name=Trinity_HpcGridRunner
#SBATCH --partition=batch
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=8
#SBATCH --mem=200G
#SBATCH --time=48:00:00
#SBATCH --output=log.%j.out
#SBATCH --error=log.%j.err

cd $SLURM_SUBMIT_DIR

ml Trinity/2.15.1-foss-2022a
ml HpcGridRunner/1.0.2

Trinity --seqType <string> --max_memory 100G \
--CPU 8 \
--left reads.left.fq.gz \
--right reads.right.fq.gz \
--output /scratch/cft07037/trinity_tests/testing/${SLURM_JOB_ID}/outputs/trinity/ \
--full_cleanup \
--grid_exec "/apps/eb/HpcGridRunner/1.0.2/hpc_cmds_GridRunner.pl --grid_conf /scratch/path/to/your/configfile/config.conf -c"

</pre>
Step 3: create config.conf file<pre class="gscript">
# grid type:
grid=SLURM

# template for a grid submission:
cmd=sbatch -p batch --mem 80gb -n 1 --cpus-per-task 8 -t 01:00:00

##########################################################################################
# settings below configure the Trinity job submission system, not tied to the grid itself.
##########################################################################################

# number of grid submissions to be maintained at steady state by the Trinity submission system
max_nodes=5

# number of commands that are batched into a single grid submission job. These run sequentially, not concurrently, so you only need to request enough cpus-per-task for the threads of a single command
cmds_per_node=3
</pre>

* The main things to change in your config.conf file are the max_nodes (how many individual grid jobs will be allowed to run at the same time) and the cmds_per_node (number of commands per grid job), which together determine how the grid will run on the cluster. You can also alter the cmd, which is the command that will be used to submit the individual grid jobs.
* The number of grid jobs that will be submitted depends on how many recursive Trinity commands you have and what variables you choose for max_nodes and cmds_per_node.
** Increasing the number of cmds_per_node and max_nodes will lower the total time it takes for all of the recursive Trinity commands to finish running, however it is important to note that though you may put a large number for max_nodes, the amount actually used will be determined by SLURM and ultimately depends on what resources are currently available and how many jobs you already have running. Similarly, setting a large number for the variable cmds_per_node can make the overall job complete more quickly, but then those individual jobs (submitted by the cmd variable) may need more memory.

==Other Resources==

https://github.com/trinityrnaseq/trinityrnaseq/wiki/Running-Trinity#optional-adapting-trinity-to-a-computing-grid-for-massively-parallel-processing-of-embarrassingly-parallel-steps

Trinity-HpcGridRunner

2026-05-11T19:27:06Z

Shtsai:

==Description==
Instruction on how to modify a Trinity script to run in conjunction with HpcGridRunner
==Running Program==
Step 1: Create normal Trinity script

* Note: normal Trinity jobs should be run utilizing /lscratch, however, this method running with HpcGridRunner does not benefit from /lscratch so this example will not have any /lscratch components in it. For more information on running normal Trinity jobs with /lscratch, please see [[Trinity-Sapelo2#Utilizing /lscratch in Trinity Job Submission Script|Utilizing /lscratch in Trinity Job Submission Script]].
* For more information on creating a Trinity job (without utilizing /lscratch) please see [[Trinity-Sapelo2#Trinity v2.15.1 Software Module on Sapelo2|here]].

Step 2: add a line to load HpcGridRunner module AND add the --grid_exec flag in your Trinity command (see how to format --grid exec below)

* Your grid_exec flag should look exactly like the one below, where the only part you'll change is the '''location to your config.conf''' file after --grid_conf. Note the location of the quotation marks as they are necessary.
<pre class="gscript">
#!/bin/bash
#SBATCH --job-name=Trinity_HpcGridRunner
#SBATCH --partition=batch
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=8
#SBATCH --mem=200G
#SBATCH --time=48:00:00
#SBATCH --output=log.%j.out
#SBATCH --error=log.%j.err

cd $SLURM_SUBMIT_DIR

ml Trinity/2.15.1-foss-2022a
ml HpcGridRunner/1.0.2

Trinity --seqType <string> --max_memory 100G \
--CPU 8 \
--left reads.left.fq.gz \
--right reads.right.fq.gz \
--output /scratch/cft07037/trinity_tests/testing/${SLURM_JOB_ID}/outputs/trinity/ \
--full_cleanup \
--grid_exec "/apps/eb/HpcGridRunner/1.0.2/hpc_cmds_GridRunner.pl --grid_conf /scratch/path/to/your/configfile/config.conf -c"

</pre>
Step 3: create config.conf file<pre class="gscript">
# grid type:
grid=SLURM

# template for a grid submission:
cmd=sbatch -p batch --mem 80gb -n 1 --cpus-per-task 8 -t 01:00:00

##########################################################################################
# settings below configure the Trinity job submission system, not tied to the grid itself.
##########################################################################################

# number of grid submissions to be maintained at steady state by the Trinity submission system
max_nodes=5

# number of commands that are batched into a single grid submission job. These run sequentially, not concurrently, so you only need to request enough cpus-per-task for the threads of a single command
cmds_per_node=3
</pre>

* The main things to change in your config.conf file are the max_nodes (how many individual grid jobs will be allowed to run at the same time) and the cmds_per_node (number of commands per grid job), which together determine how the grid will run on the cluster. You can also alter the cmd, which is the command that will be used to submit the individual grid jobs.
* The number of grid jobs that will be submitted depends on how many recursive Trinity commands you have and what variables you choose for max_nodes and cmds_per_node.
** Increasing the number of cmds_per_node and max_nodes will lower the total time it takes for all of the recursive Trinity commands to finish running, however it is important to note that though you may put a large number for max_nodes, the amount actually used will be determined by SLURM and ultimately depends on what resources are currently available and how many jobs you already have running. Similarly, setting a large number for the variable cmds_per_node can make the overall job complete more quickly, but then those individual jobs (submitted by the cmd variable) may need more memory.

==Other Resources==

https://github.com/trinityrnaseq/trinityrnaseq/wiki/Running-Trinity#optional-adapting-trinity-to-a-computing-grid-for-massively-parallel-processing-of-embarrassingly-parallel-steps

Trinity-HpcGridRunner

2026-05-11T18:59:35Z

Shtsai:

==Description==
Instruction on how to modify a Trinity script to run in conjunction with HpcGridRunner
==Running Program==
Step 1: Create normal Trinity script

* Note: normal Trinity jobs should be run utilizing /lscratch, however, this method running with HpcGridRunner does not benefit from /lscratch so this example will not have any /lscratch components in it. For more information on running normal Trinity jobs with /lscratch, please see [[Trinity-Sapelo2#Utilizing /lscratch in Trinity Job Submission Script|here]].
* For more information on creating a Trinity job (without utilizing /lscratch) please see [[Trinity-Sapelo2#Trinity v2.15.1 Software Module on Sapelo2|here]].

Step 2: add a line to load HpcGridRunner module AND add the --grid_exec flag in your Trinity command (see how to format --grid exec below)

* Your grid_exec flag should look exactly like the one below, where the only part you'll change is the '''location to your config.conf''' file after --grid_conf. Note the location of the quotation marks as they are necessary.
<pre class="gscript">
#!/bin/bash
#SBATCH --job-name=Trinity_HpcGridRunner
#SBATCH --partition=batch
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=8
#SBATCH --mem=200G
#SBATCH --time=48:00:00
#SBATCH --output=log.%j.out
#SBATCH --error=log.%j.err

cd $SLURM_SUBMIT_DIR

ml Trinity/2.15.1-foss-2022a
ml HpcGridRunner/1.0.2

Trinity --seqType <string> --max_memory 100G \
--CPU 8 \
--left reads.left.fq.gz \
--right reads.right.fq.gz \
--output /scratch/cft07037/trinity_tests/testing/${SLURM_JOB_ID}/outputs/trinity/ \
--full_cleanup \
--grid_exec "/apps/eb/HpcGridRunner/1.0.2/hpc_cmds_GridRunner.pl --grid_conf /scratch/path/to/your/configfile/config.conf -c"

</pre>
Step 3: create config.conf file<pre class="gscript">
# grid type:
grid=SLURM

# template for a grid submission:
cmd=sbatch -p batch --mem 80gb -n 1 --cpus-per-task 8 -t 01:00:00

##########################################################################################
# settings below configure the Trinity job submission system, not tied to the grid itself.
##########################################################################################

# number of grid submissions to be maintained at steady state by the Trinity submission system
max_nodes=5

# number of commands that are batched into a single grid submission job. These run sequentially, not concurrently, so you only need to request enough cpus-per-task for the threads of a single command
cmds_per_node=3
</pre>

* The main things to change in your config.conf file are the max_nodes (how many individual grid jobs will be allowed to run at the same time) and the cmds_per_node (number of commands per grid job), which together determine how the grid will run on the cluster. You can also alter the cmd, which is the command that will be used to submit the individual grid jobs.
* The number of grid jobs that will be submitted depends on how many recursive Trinity commands you have and what variables you choose for max_nodes and cmds_per_node.
** Increasing the number of cmds_per_node and max_nodes will lower the total time it takes for all of the recursive Trinity commands to finish running, however it is important to note that though you may put a large number for max_nodes, the amount actually used will be determined by SLURM and ultimately depends on what resources are currently available and how many jobs you already have running. Similarly, setting a large number for the variable cmds_per_node can make the overall job complete more quickly, but then those individual jobs (submitted by the cmd variable) may need more memory.

==Other Resources==

https://github.com/trinityrnaseq/trinityrnaseq/wiki/Running-Trinity#optional-adapting-trinity-to-a-computing-grid-for-massively-parallel-processing-of-embarrassingly-parallel-steps

Software installed on Rocky 8

2026-05-11T18:57:17Z

Shtsai:

As part of our August 29-31,2023 maintenance window, the GACRC will be upgrading the Sapelo2 cluster operating system from CentOS 7 to Rocky 8.

Because this is a major OS update, we need to recompile all the applications and ensure that they work with the new version of OS.

Below is a list of the modules already installed on the Rocky 8 system. More software packages continue to be installed. If the software you need is not in this list yet, please feel free to let us know, if you would like us to install it centrally on the updated cluster.

All singularity containers available in /apps/singularity-images on the current Sapelo2 (CentOS 7) will continue to be available after the maintenance.

== List of software already installed centrally on the Rocky 8 system (new Sapelo2, available after the maintenance) ==
3-NA/201008-foss-2021b-Python-2.7.18
ABySS/2.3.5-foss-2021b
ACTC/1.1-GCCcore-11.2.0
AMIXTURE/1.3.0
AGAT/0.9.2-GCC-11.2.0
AGAT/1.1.0
AMOS/3.1.0-foss-2021b
ANTLR/2.7.7-GCCcore-11.2.0-Java-11
ANTs/2.4.4-foss-2021b
APR-util/1.6.1-GCCcore-11.2.0
APR/1.7.0-GCCcore-11.2.0
ART/2016.06.05-GCC-11.2.0
ASE/3.22.1-foss-2021b
ASTRAL/5.7.8-Java-1.8.0_241
ATK/2.36.0-GCCcore-11.2.0
ATK/2.38.0-GCCcore-11.3.0
AUGUSTUS/3.4.0-foss-2021b
AUGUSTUS/3.5.0-foss-2022a
AlignGraph/2023-03-02-GCC-8.3.0
AlphaFold/2.3.4-foss-2022a-CUA-11.7.0-ColabFold
Amber/22.0-foss-2021b-AmberTools-22.3-CUA-11.4.1
Anaconda3/2022.10
Archive-Zip/1.68-GCCcore-11.2.0
Armadillo/11.4.3-foss-2021b
Armadillo/11.4.3-foss-2022a
Arrow/8.0.0-foss-2021b
Autoconf/2.69-GCCcore-8.3.0
Autoconf/2.69-GCCcore-10.2.0
Autoconf/2.71-GCCcore-11.2.0
Autoconf/2.71-GCCcore-11.3.0
Autoconf/2.71-GCCcore-12.2.0
Automake/1.16.1-GCCcore-8.3.0
Automake/1.16.2-GCCcore-10.2.0
Automake/1.16.4-GCCcore-11.2.0
Automake/1.16.5-GCCcore-11.3.0
Automake/1.16.5-GCCcore-12.2.0
Autotools/20180311-GCCcore-8.3.0
Autotools/20200321-GCCcore-10.2.0
Autotools/20210726-GCCcore-11.2.0
Autotools/20220317-GCCcore-11.3.0
Autotools/20220317-GCCcore-12.2.0
BBMap/38.98-GCC-11.2.0
BCFtools/1.14-GCC-11.2.0
BCFtools/1.15.1-GCC-11.3.0
BEOPS/2.4.41-foss-2021b
BETools/2.30.0-GCC-11.2.0
BLAST+/2.2.31
BLAST+/2.12.0-gompi-2021b
BLAST+/2.13.0-gompi-2021b
BLAST+/2.13.0-gompi-2022a
BLAST/2.2.26-Linux_x86_64
BLAT/3.5-GCC-11.2.0
BLAT/3.7-GCC-11.3.0
BLIS/0.8.1-GCC-11.2.0
BLIS/0.9.0-GCC-11.3.0
BRAKER/2.1.6-foss-2021b
BRIG/0.95-gompi-2021b-Java-1.8.0_241
BUSCO/5.4.3-foss-2021b
BWA/0.7.17-GCCcore-11.2.0
BamTools/2.5.2-GCC-11.2.0
BamTools/2.5.2-GCC-11.3.0
BamUtil/1.0.15-foss-2021b
Bandage/0.9.0-GCCcore-11.2.0
BayeScan/2.1-foss-2019b
Bazel/3.7.2-GCCcore-11.2.0
Bazel/4.2.2-GCCcore-11.2.0
Bazel/5.1.1-GCCcore-11.3.0
Beagle/5.4.22Jul22.46e-Java-11
Beast/2.7.1-foss-2021b-CUA-11.4.1
BigFT/1.9.1-foss-2021b
Bio-B-HTS/3.01-GCC-11.2.0
BioPerl/1.7.8-GCCcore-11.2.0
BioPerl/1.7.8-GCCcore-11.3.0
Biopython/1.79-foss-2021b
Biopython/1.79-foss-2022a
Biopython/1.81-foss-2021b
Bismark/0.23.1-foss-2021b
Bison/3.3.2-GCCcore-8.3.0
Bison/3.3.2
Bison/3.7.1-GCCcore-10.2.0
Bison/3.7.6-GCCcore-11.2.0
Bison/3.8.2-GCCcore-11.3.0
Bison/3.8.2-GCCcore-12.2.0
Bison/3.8.2
Blosc/1.21.3-GCCcore-11.2.0
Boost.MPI/1.79.0-gompi-2022a
Boost.Python/1.77.0-GCC-11.2.0
Boost/1.75.0-GCC-11.2.0
Boost/1.77.0-GCC-11.2.0
Boost/1.79.0-GCC-11.3.0
Bottleneck/1.3.7-foss-2022a
Bowtie/1.3.1-GCC-11.2.0
Bowtie/1.3.1-GCC-11.3.0
Bowtie2/2.4.1-GCC-8.3.0
Bowtie2/2.4.4-GCC-11.2.0
Bowtie2/2.4.5-GCC-11.2.0
Bowtie2/2.4.5-GCC-11.3.0
Bracken/2.7-GCCcore-11.2.0
Brotli/1.0.9-GCCcore-11.2.0
Brotli/1.0.9-GCCcore-11.3.0
C-HIT/4.8.1-GCC-11.2.0
C-HIT/4.8.1-GCC-11.3.0
CFITSIO/3.49-GCCcore-11.2.0
CGAL/4.14.3-gompi-2021b
CGmapTools/0.1.2-foss-2021b
CMake/3.12.1
CMake/3.21.1-GCCcore-11.2.0
CMake/3.22.1-GCCcore-11.2.0
CMake/3.23.1-GCCcore-11.3.0
CMake/3.24.3-GCCcore-11.3.0
CUA/11.3.1
CUA/11.4.1
CUA/11.7.0
CUA/12.0.0
CapnProto/0.9.1-GCCcore-11.2.0
CellRanger/7.0.0
Centrifuge/1.0.4-gompi-2021b
Cereal/1.3.0
CheMPS2/1.8.11-foss-2021b
Check/0.15.2-GCCcore-11.2.0
CheckM/1.1.3-foss-2021b
Chimera/1.16-linux_x86_64
Circos/0.69-9-GCCcore-11.2.0
Clang/13.0.1-GCCcore-11.2.0
Clustal-Omega/1.2.4-GCC-11.2.0
ClustalW2/2.1-GCC-11.2.0
Consed/29.0-foss-2021b
Consed/29.0-foss-2022a
CoordgenLibs/3.0.1-gompi-2021b
CppUnit/1.15.1-GCCcore-11.2.0
Cufflinks/20190706-GCC-11.2.0
Cython/0.27.3-GCCcore-11.3.0-Python-2.7.18
Cytoscape/3.9.1-Java-11
B/18.1.32-GCCcore-8.3.0
B/18.1.40-GCCcore-10.2.0
B/18.1.40-GCCcore-11.2.0
B/18.1.40-GCCcore-11.3.0
B/18.1.40-GCCcore-12.2.0
B-mysql/4.050-GCC-11.2.0
B_File/1.857-GCCcore-11.2.0
B_File/1.858-GCCcore-11.3.0
Bus/1.13.18-GCCcore-11.2.0
Bus/1.14.0-GCCcore-11.3.0
IAMON/2.0.13-GCC-11.2.0
IAMON/2.0.15-GCC-11.2.0
IAMON/2.1.0-GCC-11.3.0
ataWarrior/5.5.0
endroPy/4.5.2-GCCcore-11.2.0
eve-Size/0.83-GCCcore-11.2.0-Perl-5.34.0
ockQ/1.0-foss-2022a
oxygen/1.9.1-GCCcore-11.2.0
oxygen/1.9.4-GCCcore-11.3.0
ELPA/2021.11.001-foss-2021b
EMBOSS/6.6.0-foss-2021b
ETE/3.1.2-foss-2021b
EasyBuild/4.6.0
Eigen/3.3.9-GCCcore-11.2.0
Eigen/3.4.0-GCCcore-11.2.0
Eigen/3.4.0-GCCcore-11.3.0
EnTAP/0.10.8-beta-foss-2021b
Exonerate/2.4.0-GCC-11.2.0
Exonerate/2.4.0-GCC-11.3.0
FASTX-Toolkit/0.0.14-GCC-11.2.0
FFTW.MPI/3.3.10-gompi-2022a
FFTW/3.3.8-gompi-2019b
FFTW/3.3.10-GCC-11.3.0
FFTW/3.3.10-gompi-2021b
FFmpeg/4.3.2-GCCcore-11.2.0
FLAC/1.3.3-GCCcore-11.2.0
FLAC/1.3.4-GCCcore-11.3.0
FLASH/2.2.00-GCC-11.2.0
FLTK/1.3.7-GCCcore-11.2.0
FSL/6.0.5.1-foss-2021b
FTGL/2.4.0-GCCcore-11.3.0
FastANI/1.33-GCC-11.2.0
FastME/2.1.6.1-GCC-11.2.0
FastQC/0.11.9-Java-11
FastTree/2.1.11-GCCcore-11.2.0
Flask/2.0.2-GCCcore-11.2.0
Flask/2.2.2-GCCcore-11.3.0
FlexiBLAS/3.0.4-GCC-11.2.0
FlexiBLAS/3.2.0-GCC-11.3.0
Flye/2.9.1-GCC-11.2.0
FragGeneScan/1.31-GCCcore-11.2.0
FreeImage/3.18.0-GCCcore-11.2.0
FreeSurfer/7.4.1-foss-2021b
FriBidi/1.0.10-GCCcore-11.2.0
FriBidi/1.0.12-GCCcore-11.3.0
GATK/4.3.0.0-GCCcore-11.2.0-Java-11
GATK/4.3.0.0-GCCcore-11.2.0-Java-13.0.2
GATK/4.3.0.0-GCCcore-11.3.0-Java-11
GCC/8.3.0
GCC/10.2.0
GCC/11.2.0
GCC/11.3.0
GCC/12.2.0
GCCcore/8.3.0
GCCcore/10.2.0
GCCcore/11.2.0
GCCcore/11.3.0
GCCcore/12.2.0
G/2.75-GCCcore-11.2.0
GAL/3.3.2-foss-2021b
GAL/3.5.0-foss-2022a
GRCopy/2.3-GCCcore-11.2.0
GRCopy/2.3-GCCcore-11.3.0
GEM/1.5.1-foss-2022a
GEOS/3.9.1-GCC-11.2.0
GEOS/3.10.3-GCC-11.3.0
GLPK/5.0-GCCcore-11.2.0
GLPK/5.0-GCCcore-11.3.0
GLib/2.69.1-GCCcore-11.2.0
GLib/2.72.1-GCCcore-11.3.0
GLibmm/2.66.4-GCCcore-11.2.0
GMAP-GSNAP/2021-21-17-GCC-11.2.0
GMAP-GSNAP/2023-02-17-GCC-11.3.0
GMP/6.1.2-GCCcore-8.3.0
GMP/6.2.1-GCCcore-11.2.0
GMP/6.2.1-GCCcore-11.3.0
GObject-Introspection/1.68.0-GCCcore-11.2.0
GObject-Introspection/1.72.0-GCCcore-11.3.0
GROMACS/2021.5-foss-2021b-CUA-11.4.1-PLUME-2.8.0
GSL/2.7-GCC-11.2.0
GSL/2.7-GCC-11.3.0
GST-plugins-base/1.18.5-GCC-11.2.0
GStreamer/1.18.5-GCC-11.2.0
GTK+/3.24.23-GCCcore-11.3.0
GTK2/2.24.33-GCCcore-11.3.0
GTK3/3.24.31-GCCcore-11.2.0
GTS/0.7.6-GCCcore-11.2.0
Gdk-Pixbuf/2.42.6-GCCcore-11.2.0
Gdk-Pixbuf/2.42.8-GCCcore-11.3.0
GeneMark-ET/4.71-GCCcore-11.2.0
GeneMark-ET/4.71-GCCcore-11.3.0
GeneMarkS-T/5.1-GCCcore-11.2.0
Genome/05012020-foss-2021b
Genome/05012020-foss-2022a
GenomeThreader/1.7.3-Linux_x86_64-64bit
GenomeTools/1.6.2-GCC-11.2.0
GffCompare/0.12.6-GCC-11.2.0
Ghostscript/9.54.0-GCCcore-11.2.0
Ghostscript/9.56.1-GCCcore-11.3.0
GitPython/3.1.24-GCCcore-11.2.0
GlimmerHMM/3.0.4c-GCC-11.2.0
Globus-CLI/3.6.0-GCCcore-11.2.0
Go/1.17.6
Grace/5.1.25-foss-2021b
Graphviz/2.50.0-GCCcore-11.2.0
Guile/1.8.8-GCCcore-8.3.0
Guile/3.0.7-GCCcore-11.2.0
HF/4.2.15-GCCcore-11.2.0
HF/4.2.15-GCCcore-11.3.0
HF5/1.10.8-gompi-2021b
HF5/1.10.8-gompi-2022a
HF5/1.12.1-gompi-2021b
HF5/1.12.2-gompi-2022a
HEC-RAS/6.10
HH-suite/3.3.0-gompi-2021b
HH-suite/3.3.0-gompi-2022a
HISAT2/2.2.1-gompi-2021b
HISAT2/2.2.1-gompi-2022a
HMMER/3.3.2-gompi-2021b
HMMER/3.3.2-gompi-2022a
HPL/2.3-foss-2021b
HTSeq/2.0.2-foss-2021b
HTSlib/1.9-GCC-11.2.0
HTSlib/1.14-GCC-11.2.0
HTSlib/1.15.1-GCC-11.3.0
HarfBuzz/2.8.2-GCCcore-11.2.0
HarfBuzz/4.2.1-GCCcore-11.3.0
Homer/4.11-foss-2021b
HyPhy/2.5.33-gompi-2021b
HybPiper/2.1.6-conda
ICU/69.1-GCCcore-11.2.0
ICU/71.1-GCCcore-11.3.0
IBA-U/1.1.3-GCC-11.2.0
IGV/2.16.1-Java-11
IPython/7.26.0-GCCcore-11.2.0
IQ-TREE/2.2.1-gompi-2021b
ISA-L/2.30.0-GCCcore-11.2.0
ImageMagick/7.1.0-4-GCCcore-11.2.0
ImageMagick/7.1.0-37-GCCcore-11.3.0
Infernal/1.1.4-foss-2021b
Infernal/1.1.4-foss-2022a
InterProScan/5.62-94.0-foss-2021b
JAGS/4.3.0-foss-2021b
JUnit/4.12-Java-1.8.0_241
JasPer/2.0.33-GCCcore-11.2.0
JasPer/2.0.33-GCCcore-11.3.0
Java/1.7.0_80
Java/1.8.0_241
Java/11.0.16
Java/13.0.2
Java/15.0.1
Java/17.0.6
Jellyfish/2.3.0-GCC-11.2.0
Jellyfish/2.3.0-GCC-11.3.0
JsonCpp/1.9.4-GCCcore-11.2.0
JsonCpp/1.9.5-GCCcore-11.3.0
Judy/1.0.5-GCCcore-11.2.0
Judy/1.0.5-GCCcore-11.3.0
Julia/1.8.2-linux-x86_64
Kalign/3.3.2-GCCcore-11.2.0
Kalign/3.3.5-GCCcore-11.3.0
Kent_tools/422-GCC-11.2.0
KerasTuner/1.3.5-foss-2022a
KronaTools/2.8.1-GCCcore-11.2.0
LAME/3.100-GCCcore-11.2.0
LAME/3.100-GCCcore-11.3.0
LAMMPS/23Jun2022-foss-2021b-kokkos
LAPACK/3.10.1-GCC-11.2.0
LASTZ/1.04.15-foss-2021b
LLVM/12.0.1-GCCcore-11.2.0
LLVM/14.0.3-GCCcore-11.3.0
LMB/0.9.29-GCCcore-11.2.0
LMB/0.9.29-GCCcore-11.3.0
LS2/2.3-GCCcore-11.2.0
LTR_retriever/2.9.0-foss-2021b-RepeatMasker-4.1.4
LTR_retriever/2.9.0-foss-2021b
LZO/2.10-GCCcore-11.2.0
LZO/2.10-GCCcore-11.3.0
LibTIFF/4.3.0-GCCcore-11.2.0
LibTIFF/4.3.0-GCCcore-11.3.0
LightGBM/3.3.5-foss-2021b-CUA-11.4.1
LightGBM/3.3.5-foss-2021b
LittleCMS/2.12-GCCcore-11.2.0
LittleCMS/2.13.1-GCCcore-11.3.0
LoFreq/2.1.3.1-foss-2021b-Python-2.7.18
Lua/5.4.3-GCCcore-11.2.0
M4/1.4.18-GCCcore-8.3.0
M4/1.4.18-GCCcore-10.2.0
M4/1.4.18
M4/1.4.19-GCCcore-11.2.0
M4/1.4.19-GCCcore-11.3.0
M4/1.4.19-GCCcore-12.2.0
M4/1.4.19
MACS2/2.2.7.1-foss-2021b
MAFFT/7.490-GCC-11.2.0-with-extensions
MAFFT/7.490-gompi-2021b-with-extensions
MAKER/3.01.04-foss-2022a
MCL/14.137-GCCcore-11.2.0
MEGAN/6.24.23-Java-17
MEME/5.4.1-gompi-2021b
MEME/5.5.0-gompi-2021b
METIS/5.1.0-GCCcore-11.2.0
METIS/5.1.0-GCCcore-11.3.0
MMseqs2/13-45111-gompi-2021b
MMseqs2/14-7e284-gompi-2021b
MMseqs2/14-7e284-gompi-2022a
MPACK/3.0.0-foss-2021b
MPFR/4.1.0-GCCcore-11.2.0
MPFR/4.1.0-GCCcore-11.3.0
MUMmer/4.0.0beta2-GCCcore-11.2.0
MUMmer/4.0.0rc1-GCCcore-11.2.0
MUSCLE/3.8.31-GCCcore-11.2.0
Make-Lastz-Chains/0.9.1
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Python/2.7.18-GCCcore-11.3.0
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Python/3.10.4-GCCcore-11.3.0-bare
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R/4.2.1-foss-2021b
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dask/2022.1.0-foss-2021b
deepTools/3.5.1-foss-2021b
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elfutils/0.185-GCCcore-11.2.0
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expat/2.4.9-GCCcore-12.2.0
expecttest/0.1.3-GCCcore-11.2.0
fastStructure/1.0-foss-2022a-Python-2.7.18
fastahack/1.0.0-GCCcore-11.2.0
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fermi-lite/20190320-GCCcore-11.2.0
file/5.41-GCCcore-11.2.0
file/5.43-GCCcore-11.3.0
filevercmp/20191210-GCCcore-11.2.0
flatbuffers-python/2.0-GCCcore-11.2.0
flatbuffers-python/2.0-GCCcore-11.3.0
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flex/2.6.4
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fontconfig/2.14.0-GCCcore-11.3.0
foss/2019b
foss/2021b
foss/2022a
freebayes/1.3.6-foss-2021b-R-4.1.2
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gaussian/09-AM-SSE4a
gaussian/09-Intel-SSE4_2
gaussian/16-AVX
gaussian/16-AVX2
gaussian/16-SSE4
gc/7.6.12-GCCcore-8.3.0
gc/8.2.0-GCCcore-11.2.0
gettext/0.19.8.1
gettext/0.21-GCCcore-11.2.0
gettext/0.21-GCCcore-11.3.0
gettext/0.21
gettext/0.21.1
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git/2.33.1-GCCcore-11.2.0-nodocs
git/2.36.0-GCCcore-11.3.0-nodocs
glew/2.2.0-GCCcore-11.3.0-egl
gnuplot/5.4.2-GCCcore-11.2.0
gompi/2019b
gompi/2021b
gompi/2022a
googletest/1.11.0-GCCcore-11.3.0
gperf/3.1-GCCcore-11.2.0
gperf/3.1-GCCcore-11.3.0
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groff/1.22.4-GCCcore-8.3.0
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groff/1.22.4-GCCcore-11.2.0
groff/1.22.4-GCCcore-11.3.0
groff/1.22.4-GCCcore-12.2.0
gzip/1.10-GCCcore-11.2.0
gzip/1.12-GCCcore-11.3.0
h5py/3.6.0-foss-2021b
h5py/3.7.0-foss-2022a
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hwloc/2.7.1-GCCcore-11.3.0
hwloc/2.8.0-GCCcore-12.2.0
hypothesis/4.57.1-GCCcore-11.2.0-Python-2.7.18
hypothesis/4.57.1-GCCcore-11.3.0-Python-2.7.18
hypothesis/6.14.6-GCCcore-11.2.0
hypothesis/6.46.7-GCCcore-11.3.0
iccifort/2019.5.281
igv-reports/1.7.0-GCC-11.2.0-Python-3.9.6
iimpi/2021b
imageio/2.13.5-foss-2021b
imkl-FFTW/2021.4.0-iimpi-2021b
imkl/2021.4.0
impi/2021.4.0-intel-compilers-2021.4.0
intel-compilers/2021.4.0
intel-compilers/2022.2.1
intel/2021b
intervaltree/0.1-GCCcore-11.2.0
intltool/0.51.0-GCCcore-11.2.0
intltool/0.51.0-GCCcore-11.3.0
jax/0.3.23-foss-2021b-CUA-11.4.1
jax/0.3.25-foss-2022a-CUA-11.7.0
jbigkit/2.1-GCCcore-11.2.0
jbigkit/2.1-GCCcore-11.3.0
jemalloc/5.2.1-GCCcore-11.2.0
jemalloc/5.2.1-GCCcore-11.3.0
jemalloc/5.3.0-GCCcore-11.3.0
kallisto/0.48.0-gompi-2021b
kallisto/0.48.0-gompi-2022a
kim-api/2.3.0-GCCcore-11.2.0
kineto/0.4.0-GCC-11.2.0
libGLU/9.0.2-GCCcore-11.2.0
libGLU/9.0.2-GCCcore-11.3.0
libaio/0.3.112-GCCcore-11.2.0
libaio/0.3.112-GCCcore-11.3.0
libarchive/3.5.1-GCCcore-11.2.0
libarchive/3.6.1-GCCcore-11.3.0
libcerf/1.17-GCCcore-11.2.0
libdeflate/1.8-GCCcore-11.2.0
libdeflate/1.10-GCCcore-11.3.0
libdrm/2.4.107-GCCcore-11.2.0
libdrm/2.4.110-GCCcore-11.3.0
libepoxy/1.5.8-GCCcore-11.2.0
libepoxy/1.5.10-GCCcore-11.3.0
libevent/2.1.12-GCCcore-10.2.0
libevent/2.1.12-GCCcore-11.2.0
libevent/2.1.12-GCCcore-11.3.0
libevent/2.1.12-GCCcore-12.2.0
libfabric/1.11.0-GCCcore-10.2.0
libfabric/1.13.2-GCCcore-11.2.0
libfabric/1.15.1-GCCcore-11.3.0
libfabric/1.16.1-GCCcore-12.2.0
libffi/3.2.1-GCCcore-8.3.0
libffi/3.4.2-GCCcore-11.2.0
libffi/3.4.2-GCCcore-11.3.0
libgd/2.3.3-GCCcore-11.2.0
libgeotiff/1.7.0-GCCcore-11.2.0
libgeotiff/1.7.1-GCCcore-11.3.0
libgit2/1.1.1-GCCcore-11.2.0
libgit2/1.4.3-GCCcore-11.3.0
libglvnd/1.3.3-GCCcore-11.2.0
libglvnd/1.4.0-GCCcore-11.3.0
libgtextutils/0.7-GCCcore-11.2.0
libharu/2.3.0-foss-2021b
libiconv/1.16-GCCcore-11.2.0
libiconv/1.17-GCCcore-11.3.0
libidn2/2.3.2-GCCcore-11.3.0
libjpeg-turbo/2.0.6-GCCcore-11.2.0
libjpeg-turbo/2.1.3-GCCcore-11.3.0
libmatheval/1.1.11-GCCcore-8.3.0
libogg/1.3.5-GCCcore-11.2.0
libogg/1.3.5-GCCcore-11.3.0
libopus/1.3.1-GCCcore-11.3.0
libpciaccess/0.14-GCCcore-8.3.0
libpciaccess/0.16-GCCcore-10.2.0
libpciaccess/0.16-GCCcore-11.2.0
libpciaccess/0.16-GCCcore-11.3.0
libpciaccess/0.17-GCCcore-12.2.0
libpng/1.6.37-GCCcore-11.2.0
libpng/1.6.37-GCCcore-11.3.0
libreadline/8.0-GCCcore-8.3.0
libreadline/8.0-GCCcore-10.2.0
libreadline/8.1-GCCcore-11.2.0
libreadline/8.1.2-GCCcore-11.3.0
libreadline/8.2-GCCcore-12.2.0
libsigc++/3.4.0-GCCcore-11.2.0
libsndfile/1.0.31-GCCcore-11.2.0
libsndfile/1.1.0-GCCcore-11.3.0
libsodium/1.0.18-GCCcore-11.2.0
libtirpc/1.3.2-GCCcore-11.2.0
libtirpc/1.3.2-GCCcore-11.3.0
libtool/2.4.6-GCCcore-8.3.0
libtool/2.4.6-GCCcore-10.2.0
libtool/2.4.6-GCCcore-11.2.0
libtool/2.4.7-GCCcore-11.3.0
libtool/2.4.7-GCCcore-12.2.0
libunistring/0.9.10-GCCcore-8.3.0
libunistring/1.0-GCCcore-11.2.0
libunwind/1.5.0-GCCcore-11.2.0
libunwind/1.6.2-GCCcore-11.3.0
libuv/1.37.0-GCCcore-11.3.0
libvorbis/1.3.7-GCCcore-11.2.0
libvorbis/1.3.7-GCCcore-11.3.0
libwebp/1.2.0-GCCcore-11.2.0
libxc/5.1.6-GCC-11.2.0
libxc/5.2.3-GCC-11.2.0
libxml++/2.42.1-GCC-11.2.0
libxml2/2.9.9-GCCcore-8.3.0
libxml2/2.9.10-GCCcore-10.2.0
libxml2/2.9.10-GCCcore-11.2.0
libxml2/2.9.13-GCCcore-11.3.0
libxml2/2.10.3-GCCcore-12.2.0
libxslt/1.1.34-GCCcore-11.2.0
libxslt/1.1.34-GCCcore-11.3.0
libyaml/0.2.5-GCCcore-11.2.0
libyaml/0.2.5-GCCcore-11.3.0
lpsolve/5.5.2.11-GCC-11.2.0
lpsolve/5.5.2.11-GCC-11.3.0
lxml/4.6.3-GCCcore-11.2.0
lz4/1.9.3-GCCcore-11.2.0
lz4/1.9.3-GCCcore-11.3.0
maeparser/1.3.0-gompi-2021b
magma/2.6.2-foss-2021b-CUA-11.4.1
make/4.3-GCCcore-11.2.0
make/4.3-GCCcore-11.3.0
makeinfo/6.7-GCCcore-8.3.0-minimal
makeinfo/6.7-GCCcore-10.2.0-minimal
matlab/R2022b
matlab/R2023a
matplotlib/2.2.5-foss-2021b-Python-2.7.18
matplotlib/2.2.5-foss-2022a-Python-2.7.18
matplotlib/3.4.3-foss-2021b
matplotlib/3.5.2-foss-2021b
matplotlib/3.5.2-foss-2022a
methylpy/1.4.6-foss-2021b
minimap2/2.22-GCCcore-11.2.0
minimap2/2.24-GCCcore-11.2.0
mm-common/1.0.5-GCCcore-11.2.0
molmod/1.4.8-foss-2021b
motif/2.3.8-GCCcore-11.2.0
multichoose/1.0.3-GCCcore-11.2.0
ncbi-vdb/2.10.9-gompi-2021b
ncbi-vdb/2.11.2-gompi-2021b
ncbi-vdb/3.0.2-gompi-2022a
ncurses/6.0
ncurses/6.1-GCCcore-8.3.0
ncurses/6.1
ncurses/6.2-GCCcore-10.2.0
ncurses/6.2-GCCcore-11.2.0
ncurses/6.2
ncurses/6.3-GCCcore-11.3.0
ncurses/6.3-GCCcore-12.2.0
ncurses/6.3
netCF-Fortran/4.5.3-gompi-2021b
netCF/4.8.1-gompi-2021b
netCF/4.9.0-gompi-2022a
netMHCpan/4.1b
nettle/3.7.3-GCCcore-11.2.0
nettle/3.8-GCCcore-11.3.0
networkx/2.6.3-foss-2021b
networkx/2.8.4-foss-2022a
nlohmann_json/3.10.5-GCCcore-11.3.0
nodejs/14.17.6-GCCcore-11.2.0
nodejs/16.15.1-GCCcore-11.3.0
nsync/1.24.0-GCCcore-11.2.0
nsync/1.25.0-GCCcore-11.3.0
numactl/2.0.12-GCCcore-8.3.0
numactl/2.0.13-GCCcore-10.2.0
numactl/2.0.14-GCCcore-11.2.0
numactl/2.0.14-GCCcore-11.3.0
numactl/2.0.16-GCCcore-12.2.0
numexpr/2.8.4-foss-2021b
pandasql/0.7.3-foss-2021b-Python-3.9.6
parallel/20210722-GCCcore-11.2.0
parallel/20221122-GCCcore-11.2.0
pblat/2.5-GCCcore-8.3.0
picard/2.25.1-Java-11
picard/2.27.5-Java-15
pigz/2.6-GCCcore-11.2.0
pixman/0.40.0-GCCcore-11.2.0
pixman/0.40.0-GCCcore-11.3.0
pkg-config/0.29.2-GCCcore-10.2.0
pkg-config/0.29.2-GCCcore-11.2.0
pkg-config/0.29.2-GCCcore-11.3.0
pkgconf/1.8.0-GCCcore-11.2.0
pkgconf/1.8.0-GCCcore-11.3.0
pkgconf/1.8.0
pkgconf/1.9.3-GCCcore-12.2.0
pkgconfig/1.5.5-GCCcore-11.2.0-python
pkgconfig/1.5.5-GCCcore-11.3.0-python
plotly.py/5.4.0-GCCcore-11.2.0
poppler/22.11.0-GCC-11.2.0
pplacer/1.1.alpha19
pretty-yaml/21.10.1-GCCcore-11.2.0
prodigal/2.6.3-GCCcore-11.2.0
prodigal/2.6.3-GCCcore-11.3.0
protobuf-python/3.17.3-GCCcore-11.2.0
protobuf-python/3.19.4-GCCcore-11.3.0
protobuf/3.17.3-GCCcore-11.2.0
protobuf/3.19.4-GCCcore-11.3.0
psutil/5.9.4-GCCcore-11.2.0
purge_dups/1.2.5-foss-2021b
pyBigWig/0.3.18-foss-2021b
pybedtools/0.8.2-GCC-11.2.0
pybind11/2.7.1-GCCcore-11.2.0-Python-2.7.18
pybind11/2.7.1-GCCcore-11.2.0
pybind11/2.7.1-GCCcore-11.3.0-Python-2.7.18
pybind11/2.9.2-GCCcore-11.3.0
pytest-xdist/2.5.0-GCCcore-11.2.0
pytest-xdist/2.5.0-GCCcore-11.3.0
pytest/7.1.3-GCCcore-11.2.0
python-isal/0.11.1-GCCcore-11.2.0
python-isal/1.1.0-GCCcore-11.2.0
re2c/2.2-GCCcore-11.2.0
rjags/4-12-foss-2021b-R-4.2.1
scikit-bio/0.5.7-foss-2021b
scikit-build/0.11.1-GCCcore-11.2.0
scikit-image/0.19.1-foss-2021b
scikit-learn/1.0.1-foss-2021b
scikit-learn/1.1.2-foss-2022a
scikit-optimize/0.9.0-foss-2021b
seqtk/1.3-GCC-11.2.0
smithwaterman/20160702-GCCcore-11.2.0
snappy/1.1.9-GCCcore-11.2.0
snappy/1.1.9-GCCcore-11.3.0
spaln/2.4.12-GCC-11.2.0
sparsehash/2.0.4-GCCcore-11.2.0
spglib-python/1.16.3-foss-2021b
statsmodels/0.13.1-foss-2021b
tRNAscan-SE/2.0.12-foss-2021b
tRNAscan-SE/2.0.12-GCC-11.2.0
tabixpp/1.1.0-GCC-11.2.0
tbb/2019_U9-GCCcore-8.3.0
tbb/2020.3-GCCcore-11.2.0
tbb/2021.5.0-GCCcore-11.3.0
tcsh/6.24.01-GCCcore-11.2.0
tensorboard/2.8.0-foss-2021b
time/1.9-GCCcore-11.2.0
tmux/3.3a-GCCcore-11.3.0
tqdm/4.62.3-GCCcore-11.2.0
typing-extensions/3.10.0.2-GCCcore-11.2.0
ucsc/434
ucsc/443
utf8proc/2.6.1-GCCcore-11.2.0
utf8proc/2.7.0-GCCcore-11.3.0
util-linux/2.37-GCCcore-11.2.0
util-linux/2.38-GCCcore-11.3.0
vcflib/1.0.3-foss-2021b-R-4.1.2
wget/1.21.3-GCCcore-11.3.0
x264/20210613-GCCcore-11.2.0
x265/3.5-GCCcore-11.2.0
xorg-macros/1.19.2-GCCcore-8.3.0
xorg-macros/1.19.2-GCCcore-10.2.0
xorg-macros/1.19.3-GCCcore-11.2.0
xorg-macros/1.19.3-GCCcore-11.3.0
xorg-macros/1.19.3-GCCcore-12.2.0
xprop/1.2.5-GCCcore-11.2.0
xxd/8.2.4220-GCCcore-11.2.0
yaff/1.6.0-foss-2021b
zlib/1.2.11-GCCcore-8.3.0
zlib/1.2.11-GCCcore-10.2.0
zlib/1.2.11-GCCcore-11.2.0
zlib/1.2.11
zlib/1.2.12-GCCcore-11.3.0
zlib/1.2.12-GCCcore-12.2.0
zlib/1.2.12
zstd/1.5.0-GCCcore-11.2.0
zstd/1.5.2-GCCcore-11.3.0

Code Compilation on Sapelo2

2026-05-11T15:59:17Z

Shtsai: Added header for column 1 and table caption

[[Category:Sapelo2]]

==Where should I compile my code?==



Please DO NOT compile source code on the login node. Instead, compile your code in an interactive session started with the <code>interact</code> command.

Code compilation can be done in an interactive session. To start an interactive session, first login into Sapelo2 and from there issue the <code>'''[[Running Jobs on Sapelo2#How to open an interactive session|interact]]'''</code> command
<pre class="gcommand">
interact
</pre>
If you plan to run the code on an AMD node, you can start an interactive session on an AMD node to compile the code. To start an interactive on an AMD node, use the command
<pre class="gcommand">
interact --constraint AMD
</pre>

If you plan to run the code on an Intel node, you can start an interactive session on an Intel node to compile the code. To start an interactive on an Intel node, use the command
<pre class="gcommand">
interact --constraint Intel
</pre>

For detailed information on how to access the compute node interactively for code compilation, please see [[Running Jobs on Sapelo2]].

----

==Compilers==

A number of Fortran and C/C++ compilers, as well as Java and scripting languages such as Perl and Python, are available on Sapelo2.

=== Summary of main Fortran and C/C++ compilers installed ===
{| width="100%" border="1" cellspacing="0" cellpadding="2" align="center" class="wikitable unsortable"
|+Table showing various compilers for a given language
|-
! scope="col" |Language
! scope="col" | NVHPC
! scope="col" | Intel
! scope="col" | GNU
! scope="col" | OpenMPI
! scope="col" | File extension
|-

| Fortran77 || nvfortran
| ifort || gfortran
| mpif77|| .f
|-
| Fortran90 || nvfortran
| ifort || gfortran|| mpif90|| .f90
|-
| Fortran95 || nvfortran
| ifort || gfortran || mpifort || .f95
|-
|Fortran2003
|nvfortran
|ifort
|gfortran
|mpifort
|.f
|-
| C || nvc || icc || gcc || mpicc || .c
|-
| C++ || nvcc || icpc || g++ || mpicxx || .C, .cpp, .cc
|-

|}

The various compiler suites are provided by their environment modules.

=== GNU compiler suites ===
The following command will show all the modules that provide GCC compiler suites:
<pre class="gcommand">
module spider GCC
</pre>

Sample partial output of this command:
<pre class="gcomment">
[shtsai@d2-13 ~]$ module spider GCC
-----------------------------------------------------------------------------------------------------------------
GCC:
-----------------------------------------------------------------------------------------------------------------
Description:
The GNU Compiler Collection includes front ends for C, C++, Objective-C, Fortran, Java, and Ada, as well as libraries for these languages (libstdc++, libgcj,...).

Versions:
GCC/11.2.0
GCC/11.3.0
GCC/12.3.0
GCC/13.2.0
GCC/13.3.0
Other possible modules matches:
GCCcore
-----------------------------------------------------------------------------------------------------------------
</pre>

This output indicates that the following versions of GCC compilers are available:

*Version 11.2.0, with binutils 2.37, provided by the GCC/11.2.0 module, includes C, C++, and Fortran compilers.
*Version 11.3.0, with binutils 2.38, provided by the GCC/11.3.0 module, includes C, C++, and Fortran compilers.
*Version 12.3.0, with binutils 2.40, provided by the GCC/12.3.0 module, includes C, C++, and Fortran compilers.
*Version 13.2.0, with binutils 2.40, provided by the GCC/13.2.0 module, includes C, C++, and Fortran compilers.
*Version 13.3.0, with binutils 2.42, provided by the GCC/13.3.0 module, includes C, C++, and Fortran compilers.

We suggest that you run the<code> module spider GCC</code> command to check an updated list of GCC compilers available on the cluster.

=== Intel compiler suites ===
The following command will show all the modules that provide Intel compiler suites:
<pre class="gcommand">
module spider intel-compilers
</pre>

Sample output of this command
<pre class="gcomment">
[shtsai@d2-13 ~]$ ml spider intel-compilers

-----------------------------------------------------------------------------------------------------------------
intel-compilers:
-----------------------------------------------------------------------------------------------------------------
Description:
Intel C, C++ & Fortran compilers (classic and oneAPI)

Versions:
intel-compilers/2021.4.0
intel-compilers/2022.1.0
intel-compilers/2023.1.0
intel-compilers/2023.2.1
intel-compilers/2024.2.0
-----------------------------------------------------------------------------------------------------------------
</pre>

This output indicates that the following versions of the Intel compiler suites are available:

*Version 2021.4.0, provided by the intel-compilers/2021.4.0 module.
*Version 2022.1.0, provided by the intel-compilers/2022.1.0 module.
*Version 2023.1.0, provided by the intel-compilers/2023.1.0 module.
*Version 2023.2.1, provided by the intel-compilers/2023.2.1 module.
*Version 2024.2.0, provided by the intel-compilers/2024.2.0 module.

We suggest that you run the <code>module spider intel-compilers</code> or <code>module spider iccifort</code> command to check an updated list of Intel compilers available on the cluster.

=== LLVM compiler suites ===
The following command will show all the modules that provide LLVM compilers:
<pre class="gcommand">
module spider LLVM
</pre>
Sample output of this command
<pre class="gcomment">
[shtsai@d2-13 ~]$ module spider LLVM
------------------------------------------------------------------------------------------------------------------------------------
LLVM:
------------------------------------------------------------------------------------------------------------------------------------
Description:
The LLVM Core libraries provide a modern source- and target-independent optimizer, along with code generation support for many popular CPUs (as well as some less common ones!) These libraries are built around a well specified
code representation known as the LLVM intermediate representation ("LLVM IR"). The LLVM Core libraries are well documented, and it is particularly easy to invent your own language (or port an existing compiler) to use LLVM as an
optimizer and code generator.

Versions:
LLVM/14.0.3-GCCcore-11.3.0
LLVM/14.0.6-GCCcore-12.3.0-llvmlite
LLVM/14.0.6-GCCcore-13.3.0-llvmlite
LLVM/16.0.6-GCCcore-12.3.0
LLVM/16.0.6-GCCcore-13.2.0
LLVM/18.1.8-GCCcore-13.3.0-minimal

-----------------------------------------------------------------------------------------------------------------------------------
</pre>

=== How to load a compiler module ===

To use any of the compiler suite, please first load the corresponding module. For example, to use the GNU 12.3.0 compiler suite, load the module with<pre class="gcommand">
module load GCC/12.3.0
</pre>
Once this module is loaded the gcc, g++, and gfortran for GCC v. 12.3.0 will be available in your path.

Please note that you can only have one compiler module loaded at a time.

=== Some commonly used compiler options ===

====Intel compiler suite====
{| class="wikitable unsortable" width="100%" border="1" cellspacing="0" cellpadding="2" align="center"
|-
! scope="col" |Option
! scope="col" |Description
|-

| -O0||Specifies no optimization, recommended for code debugging
|-
| -O2 ||Enables optimizations for speed. This is the generally recommended optimization level.
|-
| -O3||Performs -O2 optimizations and more aggressive loop transformations. Use with care.
|-
| -fast ||Chooses generally good optimization options for the platform. Type pgcc -fast -help to see the equivalent options.
|-
| -Mbounds ||Performs runtime array bound check, recommended for code debugging
|}

====GNU compiler suite====
{| class="wikitable unsortable" width="100%" border="1" cellspacing="0" cellpadding="2" align="center"
|-
! scope="col" |Option
! scope="col" | Description
|-

| -O0||Specifies no optimization, recommended for code debugging
|-
| -O2 ||Enables optimizations for speed. This is the generally recommended optimization level.
|-
| -O3||Performs -O2 optimizations and more aggressive loop transformations. Use with care.
|-
| -std=
|Determine the language standard. This option is currently only supported when compiling C or C++.
|-
| -fopenmp
|Enable handling of OpenMP directives "#pragma omp" in C/C++ and "!$omp" in Fortran.
|-
| -fopenacc
| Enable handling of OpenACC directives "#pragma acc" in C/C++ and "!$acc" in Fortran.
|-
|<nowiki>-Wpedantic</nowiki>
|Issue all the warnings demanded by strict ISO C and ISO C++; reject all programs that use forbidden extensions, and some other programs that do not follow ISO C and ISO C++.
|-
| -Wall
|This enables all the warnings about constructions that some users consider questionable.
|}

----
[[#top|Back to Top]]

== Compiler Toolchains==
On Sapelo2 we use the [https://easybuild.readthedocs.io/en/latest/ EasyBuild] framework to install software applications. The EasyBuild toolchains are also available for users to compile their own code. Each toolchain provides a compiler suite and some basic libraries, such as MPI, BLAS, LAPACK, FFTW, etc.

More information about compiler toolchains, please [[Available Toolchains and Toolchain Compatibility]].

===foss toolchains===
Most software applications are installed with the '''foss''' toolchain, where '''foss''' is short for “Free and Open Source Software”.

The foss toolchain consists of:

*binutils (https://www.gnu.org/software/binutils/)
*the GNU Compiler Collection (GCC, https://gcc.gnu.org/), i.e. gcc (C), g++ (C++) and gfortran (Fortran)
*the Open MPI library (https://www.open-mpi.org/)
*the OpenBLAS (http://www.openblas.net/) + LAPACK (http://netlib.org/lapack) libraries
*the ScaLAPACK (http://netlib.org/scalapack) library is also included
*the FFTW library (http://fftw.org/)

You can check the foss toolchain modules that are installed on the cluster with the command
<pre class="gcommand">
module spider foss
</pre>

When you load a foss toolchain, all it components will be loaded. For example:
<pre class="gcommand">
[shtsai@d2-13 ~]$ module list
No modules loaded
[shtsai@d2-13 ~]$ module load foss/2023a
[shtsai@d2-13 ~]$ module list

Currently Loaded Modules:
1) GCCcore/12.3.0 7) libxml2/2.11.4-GCCcore-12.3.0 13) libfabric/1.18.0-GCCcore-12.3.0 19) FFTW/3.3.10-GCC-12.3.0
2) zlib/1.2.13-GCCcore-12.3.0 8) libpciaccess/0.17-GCCcore-12.3.0 14) PMIx/4.2.4-GCCcore-12.3.0 20) gompi/2023a
3) binutils/2.40-GCCcore-12.3.0 9) hwloc/2.9.1-GCCcore-12.3.0 15) UCC/1.2.0-GCCcore-12.3.0 21) FFTW.MPI/3.3.10-gompi-2023a
4) GCC/12.3.0 10) OpenSSL/1.1 16) OpenMPI/4.1.5-GCC-12.3.0 22) ScaLAPACK/2.2.0-gompi-2023a-fb
5) numactl/2.0.16-GCCcore-12.3.0 11) libevent/2.1.12-GCCcore-12.3.0 17) OpenBLAS/0.3.23-GCC-12.3.0 23) foss/2023a
6) XZ/5.4.2-GCCcore-12.3.0 12) UCX/1.14.1-GCCcore-12.3.0 18) FlexiBLAS/3.3.1-GCC-12.3.0

</pre>

===intel toolchains===
The intel toolchain consists of

*the Intel compiler suite
*the Intel MPI libraries
*the Intel Math Kernel Libraries (MKL)

You can check the intel toolchain modules that are installed on the cluster with the command
<pre class="gcommand">
module spider intel
</pre>

When you load an intel toolchain, all it components will be loaded. For example:
<pre class="gcommand">
[shtsai@d2-13 ~]$ module list
No modules loaded
[shtsai@d2-13 ~]$ module load intel/2023a
[shtsai@d2-13 ~]$ module list

Currently Loaded Modules:
1) GCCcore/12.3.0 4) intel-compilers/2023.1.0 7) impi/2021.9.0-intel-compilers-2023.1.0 10) imkl-FFTW/2023.1.0-iimpi-2023a
2) zlib/1.2.13-GCCcore-12.3.0 5) numactl/2.0.16-GCCcore-12.3.0 8) imkl/2023.1.0 11) intel/2023a
3) binutils/2.40-GCCcore-12.3.0 6) UCX/1.14.1-GCCcore-12.3.0 9) iimpi/2023a

</pre>





----
[[#top|Back to Top]]

== Linking with libraries==
Some library packages are installed along with some compiler toolchains. Examples of these are OpenBLAS, MKL, FFTW, etc. Other libraries are installed as a separate module, for example, Boost and GSL.

If you want to compile a code that uses a library that is not included with compiler toolchain, you will have to load a library module that uses a[[Available Toolchains and Toolchain Compatibility | compatible]] toolchain. For example, if you want to compile your code with GCC 12.3.0 (or with the foss/2023a toolchain), and you need to use GSL, you can load the GSL/2.7-GCC-12.3.0 module.

Also note that when you load a module for a library or an application, the full path to its installation directory will be stored in an environment variable called '''EBROOT''NAME''''', where ''NAME'' is the name of the application or library. For example, when you load a GSL module, the directory where the GSL libraries are installed will be in an environment variable called EBROOTGSL.

For example:

<pre class="gcommand">
[shtsai@d2-13 ~]$ module list
No modules loaded
[shtsai@d2-13 ~]$ module load GCC/12.3.0
[shtsai@d2-13 ~]$ echo $EBROOTGCC
/apps/eb/GCCcore/12.3.0
[shtsai@d2-13 ~]$ echo $EBROOTGSL

[shtsai@d2-13 ~]$ module load GSL/2.7-GCC-12.3.0
[shtsai@d2-13 ~]$ echo $EBROOTGSL
/apps/eb/GSL/2.7-GCC-12.3.0
[shtsai@d2-13 ~]$
</pre>

As shown in the example above, when you load an GSL module, an environment variable called '''EBROOTGSL''' is defined, and it points to the installation path for GSL.

When you compile your code, you can add the compiler option:

<code> -I${EBROOTGSL}/include </code>

and the linker option

<code> -L${EBROOTGSL}/lib -lgsl -lgslcblas </code>

'''Example of program compilation that uses GCC 12.3.0 and GSL v. 2.7:'''

<pre class="gcommand">
module load GSL/2.7-GCC-12.3.0

gcc -O program.c -I${EBROOTGSL}/include -L${EBROOTGSL}/lib -lgsl -lgslcblas -Wl,-rpath=${EBROOTGSL}/lib
</pre>

Users can include the compilation option e.g. '''-Wl,-rpath=${EBROOTGSL}/lib''' to include the library directory in the '''runtime path'''. If this option is not included, then at runtime the user has to load the GSL module again, in order to define the environment variable LD_LIBRARY_PATH.
----
[[#top|Back to Top]]

Software installed on Rocky 9

2026-05-11T15:09:40Z

Shtsai: changed section heading for WAG.

As part of our July 29-31,2025 maintenance window, the GACRC will be upgrading the Sapelo2 cluster operating system from Rocky 8 to Rocky 9.

Because this is a major OS update, we need to recompile all the applications and ensure that they work with the new version of OS.

Below is a list of the modules already installed on the Rocky 9 system. More software packages continue to be installed. If the software you need is not in this list yet, please feel free to let us know, if you would like us to install it centrally on the updated cluster.

All singularity containers available in /apps/singularity-images on the current Sapelo2 (Rocky 8) will continue to be available after the maintenance.

== List of software already installed centrally on the Rocky 9 system (new Sapelo2, available after the maintenance) ==
ABySS/2.3.7-foss-2023a
ADMIXTURE/1.3.0
AFNI/25.1.01-foss-2024a
AGAT/1.4.0-GCC-12.3.0
AMOS/3.1.0-foss-2023a
ANIcalculator/1.0-GCCcore-11.3.0
ANSYS/2025R1
ANTLR/2.7.7-GCCcore-12.3.0-Java-11
ANTLR/2.7.7-GCCcore-13.3.0-Java-17
APR-util/1.6.3-GCCcore-13.3.0
APR/1.7.4-GCCcore-13.3.0
ASE/3.22.1-foss-2022a
ASE/3.23.0-gfbf-2024a
ASTRAL/5.7.8-Java-1.8.0_241
ATK/2.38.0-GCCcore-11.3.0
ATK/2.38.0-GCCcore-12.3.0
ATK/2.38.0-GCCcore-13.2.0
AUGUSTUS/3.5.0-foss-2022a
AUGUSTUS/3.5.0-foss-2023a
AUGUSTUS/3.5.0-foss-2024a
AUGUSTUS/3.5.0-20240612-foss-2023a
Abseil/20230125.3-GCCcore-12.3.0
Abseil/20240116.1-GCCcore-13.2.0
Abseil/20240722.0-GCCcore-13.3.0
Albumentations/1.3.0-foss-2022a
Albumentations/1.4.0-foss-2023a
AlphaFold/2.3.2-foss-2023a-CUDA-12.1.1
AlphaPulldown/2.0.3-foss-2023a-CUDA-12.1.1
Amber/22.4-foss-2022a-AmberTools-22.5-CUDA-11.7.0
Amber/24.3-foss-2022a-AmberTools-24.10-CUDA-12.1.1
Ancestry_HMM/1.0.2-foss-2024a
Archive-Zip/1.68-GCCcore-12.3.0
Archive-Zip/1.68-GCCcore-13.3.0
Armadillo/11.4.3-foss-2022a
Armadillo/12.6.2-foss-2023a
Armadillo/12.8.0-foss-2023b
Armadillo/14.0.3-foss-2024a
Arrow/14.0.1-gfbf-2023a
Arrow/16.1.0-gfbf-2023b
Arrow/17.0.0-gfbf-2024a
Autoconf/2.71-GCCcore-11.3.0
Autoconf/2.71-GCCcore-12.3.0
Autoconf/2.71-GCCcore-13.2.0
Autoconf/2.71
Autoconf/2.72-GCCcore-13.3.0
Automake/1.16.5-GCCcore-11.3.0
Automake/1.16.5-GCCcore-12.3.0
Automake/1.16.5-GCCcore-13.2.0
Automake/1.16.5-GCCcore-13.3.0
Automake/1.16.5
Autotools/20220317-GCCcore-11.3.0
Autotools/20220317-GCCcore-12.3.0
Autotools/20220317-GCCcore-13.2.0
Autotools/20220317
Autotools/20231222-GCCcore-13.3.0
BBMap/39.01-GCC-11.3.0
BBMap/39.01-GCC-12.3.0
BBMap/39.19-GCC-13.3.0
BCFtools/1.15.1-GCC-11.3.0
BCFtools/1.18-GCC-12.3.0
BCFtools/1.21-GCC-13.3.0
BEDOPS/2.4.41-foss-2023a
BEDTools/2.31.0-GCC-12.3.0
BEDTools/2.31.1-GCC-13.3.0
BLAST+/2.13.0-gompi-2022a
BLAST+/2.14.1-gompi-2023a
BLAST+/2.16.0-gompi-2024a
BLAT/3.7-GCC-11.3.0
BLAT/3.7-GCC-12.3.0
BLIS/0.9.0-GCC-11.3.0
BLIS/0.9.0-GCC-12.3.0
BLIS/0.9.0-GCC-13.2.0
BLIS/1.0-GCC-13.3.0
BRAKER/3.0.8-foss-2023a
BUSCO/5.8.3-foss-2023a
BWA/0.7.17-GCCcore-11.3.0
BWA/0.7.17-GCCcore-12.3.0
BWA/0.7.18-GCCcore-13.2.0
BWA/0.7.18-GCCcore-13.3.0
BamTools/2.5.2-GCC-11.3.0
BamTools/2.5.2-GCC-12.3.0
BamTools/2.5.2-GCC-13.3.0
Bandage/0.9.0-GCCcore-12.3.0
Bazel/6.1.0-GCCcore-12.3.0
Bazel/6.3.1-GCCcore-12.3.0
Beagle/5.4.22Jul22.46e-Java-11
Beast/2.7.7-GCC-12.3.0-CUDA-12.1.1
BeautifulSoup/4.12.2-GCCcore-12.3.0
BeautifulSoup/4.12.2-GCCcore-13.2.0
BeautifulSoup/4.12.3-GCCcore-13.3.0
Bio-DB-HTS/3.01-GCC-12.3.0
Bio-DB-HTS/3.01-GCC-13.3.0
Bio-SearchIO-hmmer/1.7.3-GCC-12.3.0
BioPerl/1.7.2-GCCcore-13.3.0
BioPerl/1.7.8-GCCcore-11.3.0
BioPerl/1.7.8-GCCcore-12.3.0
BioPerl/1.7.8-GCCcore-13.2.0
BioPerl/1.7.8-GCCcore-13.3.0
Biopython/1.79-foss-2022a
Biopython/1.83-foss-2023a
Biopython/1.84-foss-2023b
Biopython/1.84-foss-2024a
Bismark/0.24.2-GCC-12.3.0
Bison/3.8.2-GCCcore-11.3.0
Bison/3.8.2-GCCcore-12.3.0
Bison/3.8.2-GCCcore-13.2.0
Bison/3.8.2-GCCcore-13.3.0
Bison/3.8.2
Blosc/1.21.5-GCCcore-12.3.0
Blosc/1.21.6-GCCcore-13.3.0
Blosc2/2.17.0-GCCcore-13.3.0
Boltz/2.1.1
Boost.MPI/1.79.0-gompi-2022a
Boost.MPI/1.82.0-gompi-2023a
Boost.Python/1.82.0-GCC-12.3.0
Boost.Python/1.85.0-GCC-13.3.0
Boost/1.75.0-GCC-12.3.0
Boost/1.79.0-GCC-11.3.0
Boost/1.82.0-GCC-12.3.0
Boost/1.83.0-GCC-13.2.0
Boost/1.85.0-GCC-13.3.0
Bowtie/1.3.1-GCC-11.3.0
Bowtie/1.3.1-GCC-12.3.0
Bowtie2/2.4.5-GCC-11.3.0
Bowtie2/2.5.1-GCC-12.3.0
Bowtie2/2.5.2-GCC-11.3.0
Bracken/3.1-GCCcore-12.3.0
BrainNetViewer/20191031
Brotli-python/1.1.0-GCCcore-13.3.0
Brotli/1.0.9-GCCcore-11.3.0
Brotli/1.0.9-GCCcore-12.3.0
Brotli/1.1.0-GCCcore-13.2.0
Brotli/1.1.0-GCCcore-13.3.0
Brunsli/0.1-GCCcore-12.3.0
Brunsli/0.1-GCCcore-13.2.0
Brunsli/0.1-GCCcore-13.3.0
CD-HIT/4.8.1-GCC-11.3.0
CD-HIT/4.8.1-GCC-12.3.0
CDBtools/0.99-GCC-12.3.0
CDO/2.4.4-gompi-2024a
CFITSIO/4.3.0-GCCcore-12.3.0
CFITSIO/4.3.1-GCCcore-13.2.0
CFITSIO/4.4.1-GCCcore-13.3.0
CGAL/5.6.1-GCCcore-13.3.0
CMSeq/1.0.4-foss-2024a
CMake/3.18.4
CMake/3.23.1-GCCcore-11.3.0
CMake/3.24.3-GCCcore-11.3.0
CMake/3.26.3-GCCcore-12.3.0
CMake/3.27.6-GCCcore-13.2.0
CMake/3.29.3-GCCcore-13.3.0
CUDA/11.7.0
CUDA/12.1.1
CUDA/12.4.0
CUDA/12.6.0
CUnit/2.1-3-GCCcore-12.3.0
CVXPY/1.4.2-foss-2023a
CapnProto/0.10.2-GCCcore-11.3.0
CapnProto/1.0.1.1-GCCcore-13.2.0
CapnProto/1.1.0-GCCcore-13.3.0
Cartopy/0.22.0-foss-2023a
Catch2/2.13.9-GCCcore-12.3.0
Catch2/2.13.9-GCCcore-13.2.0
Catch2/2.13.10-GCCcore-13.3.0
Cbc/2.10.12-foss-2024a
Cereal/1.3.0
Cereal/1.3.2
Cgl/0.60.8-foss-2024a
CharLS/2.4.2-GCCcore-13.3.0
CheMPS2/1.8.12-foss-2023a
CheMPS2/1.8.12-foss-2023b
CheMPS2/1.8.12-foss-2024a
Check/0.15.2-GCCcore-13.3.0
CheckM-Database/2015_01_16
CheckM/1.2.2-foss-2022a
Circlator/1.5.5-foss-2023a
Clang/13.0.1-GCCcore-11.3.0
Clang/16.0.6-GCCcore-12.3.0-CUDA-12.1.1
Clang/16.0.6-GCCcore-12.3.0
Clang/18.1.8-GCCcore-13.3.0-CUDA-12.6.0
Clang/18.1.8-GCCcore-13.3.0
Clarabel.rs/0.7.1-gfbf-2023a
Clp/1.17.10-foss-2024a
Clustal-Omega/1.2.4-GCC-12.3.0
ClustalW2/2.1-GCC-12.3.0
CodingQuarry/2.0-foss-2023a
CoinUtils/2.11.12-GCC-13.3.0
Compress-Raw-Zlib/2.213-GCCcore-12.3.0
Compress-Raw-Zlib/2.213-GCCcore-13.3.0
Consed/29.0-foss-2022a
CoordgenLibs/3.0.2-gompi-2023a
CoverM/0.4.0
CppUnit/1.15.1-GCCcore-12.3.0
Cufflinks/20190706-GCC-12.3.0
Cython/0.29.37-GCCcore-13.3.0-Python-2.7.18
Cython/3.0.7-GCCcore-12.3.0
Cython/3.0.8-GCCcore-12.3.0
Cython/3.0.10-GCCcore-13.2.0
Cython/3.0.10-GCCcore-13.3.0
DB/18.1.40-GCCcore-11.3.0
DB/18.1.40-GCCcore-12.3.0
DB/18.1.40-GCCcore-13.2.0
DB/18.1.40-GCCcore-13.3.0
DBD-mysql/4.050-GCC-12.3.0
DB_File/1.858-GCCcore-11.3.0
DB_File/1.859-GCCcore-12.3.0
DB_File/1.859-GCCcore-13.2.0
DB_File/1.859-GCCcore-13.3.0
DBus/1.14.0-GCCcore-11.3.0
DBus/1.15.4-GCCcore-12.3.0
DBus/1.15.8-GCCcore-13.2.0
DBus/1.15.8-GCCcore-13.3.0
DIAMOND/2.1.0-GCC-11.3.0
DIAMOND/2.1.8-GCC-12.3.0
DIAMOND/2.1.9-GCC-13.2.0
DIAMOND/2.1.11-GCC-13.3.0
Delly/1.3.3-GCC-13.3.0
DendroPy/4.5.2-GCCcore-11.3.0
DendroPy/4.6.1-GCCcore-12.3.0
Doxygen/1.9.4-GCCcore-11.3.0
Doxygen/1.9.7-GCCcore-12.3.0
Doxygen/1.9.8-GCCcore-13.2.0
Doxygen/1.11.0-GCCcore-13.3.0
ELPA/2024.05.001-foss-2024a
EMBOSS/6.6.0-foss-2023a
ESMF/8.6.0-foss-2023a
ETE/3.1.3-foss-2023a
EVidenceModeler/2.1.0-foss-2023a
EasyBuild/4.7.2
EasyBuild/4.8.0
EasyBuild/4.9.4
Eigen/3.4.0-GCCcore-11.3.0
Eigen/3.4.0-GCCcore-12.3.0
Eigen/3.4.0-GCCcore-13.2.0
Eigen/3.4.0-GCCcore-13.3.0
Exonerate/2.4.0-GCC-12.3.0
Exonerate/2.4.0-GCC-13.3.0
FASTA/36.3.8i-GCC-12.3.0
FASTX-Toolkit/0.0.14-GCC-11.3.0
FFTW.MPI/3.3.10-gompi-2022a
FFTW.MPI/3.3.10-gompi-2023a
FFTW.MPI/3.3.10-gompi-2023b
FFTW.MPI/3.3.10-gompi-2024a
FFTW/3.3.10-GCC-11.3.0
FFTW/3.3.10-GCC-12.3.0
FFTW/3.3.10-GCC-13.2.0
FFTW/3.3.10-GCC-13.3.0
FFmpeg/4.4.2-GCCcore-11.3.0
FFmpeg/6.0-GCCcore-12.3.0
FFmpeg/6.0-GCCcore-13.2.0
FFmpeg/7.0.2-GCCcore-13.3.0
FLAC/1.3.4-GCCcore-11.3.0
FLAC/1.4.2-GCCcore-12.3.0
FLAC/1.4.3-GCCcore-13.2.0
FLAC/1.4.3-GCCcore-13.3.0
FLASH/2.2.00-GCCcore-13.3.0
FastANI/1.33-GCC-11.3.0
FastANI/1.34-GCC-13.2.0
FastK/1.1.0-GCC-12.3.0
FastME/2.1.6.3-GCC-12.3.0
FastQC/0.11.9-Java-11
FastQC/0.12.1-Java-11
FastTree/2.1.11-GCCcore-11.3.0
FastTree/2.1.11-GCCcore-12.3.0
FastTree/2.1.11-GCCcore-13.2.0
Fastaq/3.17.0-GCC-12.3.0
Filtlong/0.2.1-GCC-12.3.0
Fiona/1.8.21-foss-2022a
Fiona/1.9.5-foss-2023a
Fiona/1.10.1-foss-2024a
Flask/2.2.2-GCCcore-11.3.0
Flask/2.3.3-GCCcore-12.3.0
Flask/3.0.3-GCCcore-13.3.0
FlexiBLAS/3.2.0-GCC-11.3.0
FlexiBLAS/3.3.1-GCC-12.3.0
FlexiBLAS/3.3.1-GCC-13.2.0
FlexiBLAS/3.4.4-GCC-13.3.0
Flye/2.9.5-GCC-13.3.0
FreeSurfer/7.4.1-GCCcore-12.3.0
FreeXL/2.0.0-GCCcore-12.3.0
FriBidi/1.0.12-GCCcore-11.3.0
FriBidi/1.0.12-GCCcore-12.3.0
FriBidi/1.0.13-GCCcore-13.2.0
FriBidi/1.0.15-GCCcore-13.3.0
GATK/4.3.0.0-GCCcore-11.3.0-Java-11
GATK/4.3.0.0-GCCcore-12.3.0-Java-11
GATK/4.4.0.0-GCCcore-12.3.0-Java-17
GATK/4.6.0.0-GCCcore-13.2.0-Java-17
GCC/11.3.0
GCC/12.3.0
GCC/13.2.0
GCC/13.3.0
GCCcore/11.3.0
GCCcore/12.3.0
GCCcore/13.2.0
GCCcore/13.3.0
GDAL/3.5.0-foss-2022a
GDAL/3.7.1-foss-2023a
GDAL/3.9.0-foss-2023b
GDAL/3.10.0-foss-2024a
GDGraph/1.56-GCCcore-11.3.0
GDRCopy/2.3-GCCcore-11.3.0
GDRCopy/2.3.1-GCCcore-12.3.0
GDRCopy/2.4-GCCcore-13.2.0
GDRCopy/2.4.1-GCCcore-13.3.0
GEM/1.5.1-foss-2023a
GEMMA/0.98.5-gfbf-2023b
GEOS/3.10.3-GCC-11.3.0
GEOS/3.12.0-GCC-12.3.0
GEOS/3.12.1-GCC-13.2.0
GEOS/3.12.2-GCC-13.3.0
GLM/1.0.1-GCCcore-13.3.0
GLPK/5.0-GCCcore-11.3.0
GLPK/5.0-GCCcore-12.3.0
GLPK/5.0-GCCcore-13.2.0
GLPK/5.0-GCCcore-13.3.0
GLib/2.72.1-GCCcore-11.3.0
GLib/2.77.1-GCCcore-12.3.0
GLib/2.78.1-GCCcore-13.2.0
GLib/2.80.4-GCCcore-13.3.0
GLibmm/2.77.0-GCCcore-12.3.0
GMAP-GSNAP/2023-02-17-GCC-11.3.0
GMAP-GSNAP/2023-04-20-GCC-12.3.0
GMAP-GSNAP/2024-09-18-GCC-13.3.0
GMP/6.2.1-GCCcore-11.3.0
GMP/6.2.1-GCCcore-12.3.0
GMP/6.3.0-GCCcore-13.2.0
GMP/6.3.0-GCCcore-13.3.0
GOATOOLS/1.4.5-foss-2023a
GOATOOLS/1.4.12-foss-2024a
GObject-Introspection/1.72.0-GCCcore-11.3.0
GObject-Introspection/1.76.1-GCCcore-12.3.0
GObject-Introspection/1.78.1-GCCcore-13.2.0
GObject-Introspection/1.80.1-GCCcore-13.3.0
GRASS/8.4.0-foss-2023a
GROMACS/2024.4-foss-2023b-CUDA-12.4.0-PLUMED-2.9.2
GSL/2.7-GCC-11.3.0
GSL/2.7-GCC-12.3.0
GSL/2.7-GCC-13.2.0
GSL/2.8-GCC-13.3.0
GST-plugins-bad/1.20.2-GCC-11.3.0
GST-plugins-bad/1.22.5-GCC-12.3.0
GST-plugins-base/1.20.2-GCC-11.3.0
GST-plugins-base/1.22.5-GCC-12.3.0
GStreamer/1.20.2-GCC-11.3.0
GStreamer/1.22.5-GCC-12.3.0
GStreamer/1.24.8-GCC-13.3.0
GTDB-Tk/2.3.2-foss-2022a
GTK2/2.24.33-GCCcore-11.3.0
GTK3/3.24.37-GCCcore-12.3.0
GTK3/3.24.39-GCCcore-13.2.0
GTK4/4.7.0-GCC-11.3.0
GTK4/4.13.1-GCC-12.3.0
GTS/0.7.6-GCCcore-12.3.0
GTS/0.7.6-GCCcore-13.3.0
Gdk-Pixbuf/2.42.8-GCCcore-11.3.0
Gdk-Pixbuf/2.42.10-GCCcore-12.3.0
Gdk-Pixbuf/2.42.10-GCCcore-13.2.0
Gdk-Pixbuf/2.42.11-GCCcore-13.3.0
GeneMark-ET/4.72-GCCcore-12.3.0
GeneMark-ETP/1.0.0-GCC-12.3.0
Genome/05012020-foss-2022a
GenomeThreader/1.7.3-Linux_x86_64-64bit
GenomeTools/1.6.2-GCC-11.3.0
GetOrganelle/1.7.7.1-foss-2023a
Ghostscript/9.56.1-GCCcore-11.3.0
Ghostscript/10.01.2-GCCcore-12.3.0
Ghostscript/10.02.1-GCCcore-13.2.0
Ghostscript/10.03.1-GCCcore-13.3.0
GitPython/3.1.40-GCCcore-12.3.0
GitPython/3.1.43-GCCcore-13.3.0
Giza/1.4.1-GCCcore-13.2.0
GlimmerHMM/3.0.4c-GCC-12.3.0
GlobalArrays/5.8.2-intel-2023a
GlobalArrays/5.8.2-intel-2024a
GnuTLS/3.7.8-GCCcore-12.3.0
Go/1.22.1
Graphene/1.10.8-GCCcore-11.3.0
Graphene/1.10.8-GCCcore-12.3.0
Graphviz/8.1.0-GCCcore-12.3.0
Graphviz/12.2.0-GCCcore-13.3.0
Greenlet/2.0.2-GCCcore-11.3.0
Greenlet/3.0.3-GCCcore-13.2.0
Greenlet/3.1.1-GCCcore-13.3.0
Guile/3.0.9-GCCcore-12.3.0
Gurobi/11.0.2-GCCcore-12.3.0
HDF/4.2.15-GCCcore-11.3.0
HDF/4.2.16-2-GCCcore-12.3.0
HDF/4.2.16-2-GCCcore-13.2.0
HDF/4.3.0-GCCcore-13.3.0
HDF5/1.12.2-gompi-2022a
HDF5/1.14.0-gompi-2023a
HDF5/1.14.0-iimpi-2023a
HDF5/1.14.3-gompi-2023b
HDF5/1.14.5-gompi-2024a
HH-suite/3.3.0-gompi-2023a
HISAT2/2.2.1-gompi-2022a
HISAT2/2.2.1-gompi-2023a
HMMER/3.3.2-gompi-2022a
HMMER/3.4-gompi-2023a
HMMER/3.4-gompi-2023b
HMMER/3.4-gompi-2024a
HTSeq/2.0.7-foss-2023a
HTSlib/1.15.1-GCC-11.3.0
HTSlib/1.18-GCC-12.3.0
HTSlib/1.19.1-GCC-13.2.0
HTSlib/1.21-GCC-13.3.0
HarfBuzz/4.2.1-GCCcore-11.3.0
HarfBuzz/5.3.1-GCCcore-12.3.0
HarfBuzz/8.2.2-GCCcore-13.2.0
HarfBuzz/9.0.0-GCCcore-13.3.0
Highway/1.0.4-GCCcore-12.3.0
Homer/5.1-foss-2023a-R-4.3.2
Hybpiper/2.3.2-foss-2024a
Hypre/2.29.0-foss-2023a
Hypre/2.32.0-foss-2024a
ICU/71.1-GCCcore-11.3.0
ICU/73.2-GCCcore-12.3.0
ICU/74.1-GCCcore-13.2.0
ICU/75.1-GCCcore-13.3.0
IDBA-UD/1.1.3-GCCcore-12.3.0
IGV/2.19.1-Java-21
IPython/8.14.0-GCCcore-12.3.0
IPython/8.17.2-GCCcore-13.2.0
IPython/8.27.0-GCCcore-13.3.0
IPython/8.28.0-GCCcore-13.3.0
IQ-TREE/2.3.6-gompi-2023a
IRkernel/1.3.2-gfbf-2023a-R-4.3.2
ISA-L/2.30.0-GCCcore-12.3.0
ISA-L/2.31.0-GCCcore-13.2.0
ISA-L/2.31.0-GCCcore-13.3.0
ImageMagick/7.1.0-37-GCCcore-11.3.0
ImageMagick/7.1.1-15-GCCcore-12.3.0
ImageMagick/7.1.1-34-GCCcore-13.2.0
ImageMagick/7.1.1-38-GCCcore-13.3.0
Imath/3.1.5-GCCcore-11.3.0
Imath/3.1.7-GCCcore-12.3.0
Imath/3.1.9-GCCcore-13.2.0
Imath/3.1.11-GCCcore-13.3.0
Infernal/1.1.4-gompi-2022a
Infernal/1.1.5-foss-2023a
Infernal/1.1.5-gompi-2023a
InterProScan/5.73-104.0-GCC-13.3.0
IsoQuant/3.5.0-foss-2023a
IsoQuant/3.7.0-foss-2023a
IsoSeq/4.3.0
JAGS/4.3.2-foss-2023a
JAGS/4.3.2-foss-2024a
Jansson/2.14-GCC-12.3.0
JasPer/2.0.33-GCCcore-11.3.0
JasPer/4.0.0-GCCcore-12.3.0
JasPer/4.0.0-GCCcore-13.2.0
JasPer/4.2.4-GCCcore-13.3.0
Java/1.8.0_241
Java/1.8.0_311 (1.8)
Java/11.0.20 (11)
Java/17.0.6 (17)
Java/21.0.2
Java/21.0.5 (D:21)
Jellyfish/2.3.0-GCC-11.3.0
Jellyfish/2.3.1-GCC-12.3.0
JsonCpp/1.9.5-GCCcore-12.3.0
JsonCpp/1.9.5-GCCcore-13.3.0
Judy/1.0.5-GCCcore-11.3.0
Judy/1.0.5-GCCcore-12.3.0
Judy/1.0.5-GCCcore-13.3.0
Juicebox/2.20.00
Juicer/1.6-foss-2022a-CUDA-11.7.0-juicebox-1.9.9
Julia/1.11.3-linux-x86_64
JupyterLab/4.0.5-GCCcore-12.3.0
JupyterLab/4.2.0-GCCcore-13.2.0
JupyterLab/4.2.5-GCCcore-13.3.0
JupyterNotebook/7.0.2-GCCcore-12.3.0
JupyterNotebook/7.2.0-GCCcore-13.2.0
JupyterNotebook/7.2.3-GCCcore-13.3.0
KMC/3.2.4-GCC-13.3.0
Kaleido/0.2.1-GCCcore-12.3.0
Kaleido/0.2.1-GCCcore-13.3.0
Kalign/3.4.0-GCCcore-12.3.0
Kent_tools/442-GCC-11.3.0
Kent_tools/468-GCC-12.3.0
Kraken2/2.1.3-gompi-2023a
LAME/3.100-GCCcore-11.3.0
LAME/3.100-GCCcore-12.3.0
LAME/3.100-GCCcore-13.2.0
LAME/3.100-GCCcore-13.3.0
LAMMPS/29Aug2024-foss-2023b-kokkos
LASTZ/1.04.22-GCC-12.3.0
LDC/1.24.0-x86_64
LDC/1.30.0-GCCcore-11.3.0
LERC/4.0.0-GCCcore-12.3.0
LERC/4.0.0-GCCcore-13.2.0
LERC/4.0.0-GCCcore-13.3.0
LLVM/14.0.3-GCCcore-11.3.0
LLVM/14.0.6-GCCcore-12.3.0-llvmlite
LLVM/14.0.6-GCCcore-13.3.0-llvmlite
LLVM/16.0.6-GCCcore-12.3.0
LLVM/16.0.6-GCCcore-13.2.0
LLVM/18.1.8-GCCcore-13.3.0-minimal
LMDB/0.9.29-GCCcore-11.3.0
LMDB/0.9.31-GCCcore-12.3.0
LMDB/0.9.31-GCCcore-13.2.0
LMDB/0.9.31-GCCcore-13.3.0
LSD2/2.4.1-GCCcore-12.3.0
LTR_retriever/2.9.0-foss-2022a
LZO/2.10-GCCcore-11.3.0
LZO/2.10-GCCcore-12.3.0
LZO/2.10-GCCcore-13.3.0
Leptonica/1.85.0-GCCcore-13.3.0
LevelDB/1.22-GCCcore-12.3.0
LibSoup/3.6.1-GCC-12.3.0
LibTIFF/4.3.0-GCCcore-11.3.0
LibTIFF/4.5.0-GCCcore-12.3.0
LibTIFF/4.6.0-GCCcore-13.2.0
LibTIFF/4.6.0-GCCcore-13.3.0
Libint/2.7.2-GCC-12.3.0-lmax-6-cp2k
LiftOn/1.0.5-foss-2023a
LightGBM/4.5.0-foss-2023a-CUDA-12.1.1
LittleCMS/2.13.1-GCCcore-11.3.0
LittleCMS/2.15-GCCcore-12.3.0
LittleCMS/2.15-GCCcore-13.2.0
LittleCMS/2.16-GCCcore-13.3.0
Lua/5.4.4-GCCcore-11.3.0
Lua/5.4.6-GCCcore-12.3.0
Lua/5.4.7-GCCcore-13.3.0
M4/1.4.18
M4/1.4.19-GCCcore-11.3.0
M4/1.4.19-GCCcore-12.3.0
M4/1.4.19-GCCcore-13.2.0
M4/1.4.19-GCCcore-13.3.0
M4/1.4.19
MACS2/2.2.9.1-foss-2023a
MACS3/3.0.1-gfbf-2023a
MAFFT/7.505-GCC-11.3.0-with-extensions
MAFFT/7.520-GCC-12.3.0-with-extensions
MAFFT/7.526-GCC-13.2.0-with-extensions
MAFFT/7.526-GCC-13.3.0-with-extensions
MCL/22.282-GCCcore-12.3.0
MCR/R2018a
MCR/R2023a
MCR/R2023b.9
MDI/1.4.29-gompi-2023b
MDTraj/1.9.7-foss-2022a
MDTraj/1.9.9-gfbf-2023a
MEGA/10.0.5
MEGA/11.0.13
MEGAHIT/1.2.9-GCCcore-13.3.0
MEME/5.5.7-gompi-2023b
METABOLIC/4.0-foss-2022a
METIS/5.1.0-GCCcore-11.3.0
METIS/5.1.0-GCCcore-12.3.0
METIS/5.1.0-GCCcore-13.2.0
METIS/5.1.0-GCCcore-13.3.0
MMseqs2/14-7e284-gompi-2022a
MMseqs2/14-7e284-gompi-2023a
MPC/1.3.1-GCCcore-12.3.0
MPC/1.3.1-GCCcore-13.2.0
MPC/1.3.1-GCCcore-13.3.0
MPFR/4.1.0-GCCcore-11.3.0
MPFR/4.2.0-GCCcore-12.3.0
MPFR/4.2.1-GCCcore-13.2.0
MPFR/4.2.1-GCCcore-13.3.0
MSTmap/20240122-GCC-12.3.0
MUMPS/5.6.1-foss-2023a-metis
MUMPS/5.7.2-foss-2024a-metis
MUMmer/4.0.0rc1-GCCcore-11.3.0
MUMmer/4.0.0rc1-GCCcore-12.3.0
MUSCLE/5.1.0-GCCcore-12.3.0
Mako/1.2.0-GCCcore-11.3.0
Mako/1.2.4-GCCcore-12.3.0
Mako/1.2.4-GCCcore-13.2.0
Mako/1.3.5-GCCcore-13.3.0
Mamba/23.11.0-0
MariaDB/10.9.3-GCC-11.3.0
MariaDB/11.6.0-GCC-12.3.0
Markdown/3.6-GCCcore-13.2.0
Mash/2.3-GCC-11.3.0
Mash/2.3-GCC-13.2.0
Mash/2.3-GCC-13.3.0
Maven/3.6.3
Maven/3.9.7
Medusa/1.6-foss-2023a-Python-3.11.3-Java-11
Mesa/22.0.3-GCCcore-11.3.0
Mesa/23.1.4-GCCcore-12.3.0
Mesa/23.1.9-GCCcore-13.2.0
Mesa/24.1.3-GCCcore-13.3.0
Meson/0.58.2-GCCcore-12.3.0
Meson/0.62.1-GCCcore-11.3.0
Meson/0.64.0-GCCcore-12.3.0
Meson/1.1.1-GCCcore-12.3.0
Meson/1.2.3-GCCcore-13.2.0
Meson/1.3.1-GCCcore-12.3.0
Meson/1.4.0-GCCcore-13.3.0
MetaBAT/2.17-GCC-13.3.0
MetaEuk/6-GCC-11.3.0
MetaEuk/6-GCC-12.3.0
Micromamba/2.3.0
Miniconda3/23.5.2-0
Miniforge3/24.11.3-0
MitoFinder/1.4.2-foss-2023a-Python-2.7.18
Molden/7.3-GCCcore-13.3.0
Monocle3/1.3.1-foss-2023a-R-4.3.2
MrBayes/3.2.7-gompi-2023a
MultiQC/1.28-foss-2024a
NAMD/2.14-foss-2023a-mpi
NASM/2.15.05-GCCcore-11.3.0
NASM/2.16.01-GCCcore-12.3.0
NASM/2.16.01-GCCcore-13.2.0
NASM/2.16.03-GCCcore-13.3.0
NCCL/2.12.12-GCCcore-11.3.0-CUDA-11.7.0
NCCL/2.18.3-GCCcore-11.3.0-CUDA-12.1.1
NCCL/2.18.3-GCCcore-12.3.0-CUDA-12.1.1
NCCL/2.20.5-GCCcore-13.2.0-CUDA-12.4.0
NCCL/2.22.3-GCCcore-13.3.0-CUDA-12.6.0
NCO/5.1.9-foss-2023a
NGS/3.0.1-GCCcore-12.3.0-Java-11
NLTK/3.8.1-foss-2023a
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NLopt/2.7.1-GCCcore-12.3.0
NLopt/2.7.1-GCCcore-13.2.0
NLopt/2.7.1-GCCcore-13.3.0
NSPR/4.34-GCCcore-11.3.0
NSPR/4.35-GCCcore-12.3.0
NSPR/4.35-GCCcore-13.2.0
NSPR/4.35-GCCcore-13.3.0
NSS/3.79-GCCcore-11.3.0
NSS/3.89.1-GCCcore-12.3.0
NSS/3.94-GCCcore-13.2.0
NSS/3.104-GCCcore-13.3.0
NanoPlot/1.43.0-foss-2023a
NextDenovo/2.5.2-20240510-GCCcore-12.3.0
Nextflow/24.10.2
Nextflow/25.04.2
Nextflow/25.04.4
NiBabel/4.0.2-foss-2022a
NiBabel/5.2.0-gfbf-2023a
NiBabel/5.3.2-gfbf-2023b
Ninja/1.10.2-GCCcore-11.3.0
Ninja/1.10.2-GCCcore-12.3.0
Ninja/1.11.1-GCCcore-12.3.0
Ninja/1.11.1-GCCcore-13.2.0
Ninja/1.12.1-GCCcore-13.3.0
ONNX/1.15.0-gfbf-2023a
ONNX/1.17.0-gfbf-2023b
ORCA/6.0.1-gompi-2023b-avx2
OpenBLAS/0.3.20-GCC-11.3.0
OpenBLAS/0.3.23-GCC-12.3.0
OpenBLAS/0.3.24-GCC-13.2.0
OpenBLAS/0.3.27-GCC-13.3.0
OpenBabel/3.1.1-gompi-2023a
OpenCV/4.6.0-foss-2022a-contrib
OpenCV/4.8.1-foss-2023a-contrib
OpenCV/4.8.1-foss-2023a-CUDA-12.1.1-contrib
OpenEXR/3.1.5-GCCcore-11.3.0
OpenEXR/3.1.7-GCCcore-12.3.0
OpenEXR/3.2.0-GCCcore-13.2.0
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OpenJPEG/2.5.0-GCCcore-11.3.0
OpenJPEG/2.5.0-GCCcore-12.3.0
OpenJPEG/2.5.0-GCCcore-13.2.0
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OpenMM/8.0.0-foss-2023a-CUDA-12.1.1
OpenMM/8.0.0-foss-2023a
OpenMMTools/0.23.1-foss-2023a
OpenMPI/4.1.4-GCC-11.3.0
OpenMPI/4.1.5-GCC-12.3.0
OpenMPI/4.1.6-GCC-13.2.0
OpenMPI/5.0.3-GCC-13.3.0
OpenMolcas/23.06-intel-2023a
OpenPGM/5.2.122-GCCcore-12.3.0
OpenPGM/5.2.122-GCCcore-13.2.0
OpenPGM/5.2.122-GCCcore-13.3.0
OpenSSL/1.1
OpenSSL/3
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Optax/0.2.2-gfbf-2023a
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Optuna/3.5.0-foss-2023a
Optuna/3.6.1-foss-2023b
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Osi/0.108.11-GCC-13.3.0
PAML/4.9j-GCCcore-11.3.0
PAML/4.10.5-GCCcore-11.3.0
PASA/2.5.3-foss-2023a
PCAngsd/1.2-gfbf-2023a
PCRE/8.45-GCCcore-11.3.0
PCRE/8.45-GCCcore-12.3.0
PCRE/8.45-GCCcore-13.2.0
PCRE/8.45-GCCcore-13.3.0
PCRE2/10.40-GCCcore-11.3.0
PCRE2/10.42-GCCcore-12.3.0
PCRE2/10.42-GCCcore-13.2.0
PCRE2/10.43-GCCcore-13.3.0
PDAL/2.8.2-foss-2023a
PDM/2.12.4-GCCcore-12.3.0
PETSc/3.20.3-foss-2023a
PHYLIP/3.697-GCC-12.3.0
PICRUSt2/2.6.2-foss-2023a
PLUMED/2.9.2-foss-2023b
PLY/3.11-GCCcore-12.3.0
PLY/3.11-GCCcore-13.3.0
PMIx/4.1.2-GCCcore-11.3.0
PMIx/4.2.4-GCCcore-12.3.0
PMIx/4.2.6-GCCcore-13.2.0
PMIx/5.0.2-GCCcore-13.3.0
PRANK/170427-GCC-12.3.0
PROJ/9.0.0-GCCcore-11.3.0
PROJ/9.2.0-GCCcore-12.3.0
PROJ/9.3.1-GCCcore-13.2.0
PROJ/9.4.1-GCCcore-13.3.0
PRRTE/3.0.5-GCCcore-13.3.0
PSI4/1.9.1-foss-2023a
Pandoc/3.6.2
Pango/1.50.7-GCCcore-11.3.0
Pango/1.50.14-GCCcore-12.3.0
Pango/1.51.0-GCCcore-13.2.0
Pango/1.54.0-GCCcore-13.3.0
ParMETIS/4.0.3-gompi-2023a
ParaView/5.11.2-foss-2023a
Parallel-Hashmap/1.3.12-GCCcore-12.3.0
Parsnp/2.1.1-foss-2023b
Perl-bundle-CPAN/5.36.1-GCCcore-12.3.0
Perl-bundle-CPAN/5.38.0-GCCcore-13.2.0
Perl-bundle-CPAN/5.38.2-GCCcore-13.3.0
Perl/5.34.1-GCCcore-11.3.0-minimal
Perl/5.34.1-GCCcore-11.3.0
Perl/5.36.1-GCCcore-12.3.0
Perl/5.38.0-GCCcore-13.2.0
Perl/5.38.0
Perl/5.38.2-GCCcore-13.3.0
PhiPack/2016.06.14-GCC-13.2.0
Phobius/1.01-GCCcore-12.3.0-Perl-5.36.1
Phobius/1.01-GCCcore-13.2.0-Perl-5.38.0
Phobius/1.01-GCCcore-13.3.0-Perl-5.38.2
Phrap/0.990329-foss-2022a
Phred/0.071220.c-foss-2022a
PhyloNet/3.8.3-Java-1.8.0_241
Pillow-SIMD/9.5.0-GCCcore-12.3.0
Pillow-SIMD/10.4.0-GCCcore-13.3.0
Pillow/9.1.1-GCCcore-11.3.0
Pillow/10.0.0-GCCcore-12.3.0
Pillow/10.2.0-GCCcore-13.2.0
Pillow/10.4.0-GCCcore-13.3.0
Pint/0.23-GCCcore-12.3.0
Pint/0.24-GCCcore-13.2.0
Pint/0.24.4-GCCcore-13.3.0
PnetCDF/1.12.3-gompi-2022a
Porechop/0.2.4-20240119-GCCcore-12.3.0
PostgreSQL/16.1-GCCcore-12.3.0
PostgreSQL/16.1-GCCcore-13.2.0
PostgreSQL/16.4-GCCcore-13.3.0
ProtHint/2.6.0-foss-2023a
Proteinortho/6.3.2-gompi-2023a
PuLP/2.8.0-foss-2024a
PyBioLib/1.1.988-GCCcore-11.3.0
PyBioLib/1.1.2250-GCCcore-12.3.0
PyCairo/1.21.0-GCCcore-11.3.0
PyCairo/1.25.0-GCCcore-12.3.0
PyCairo/1.25.1-GCCcore-13.2.0
PyGObject/3.42.1-GCCcore-11.3.0
PyGObject/3.46.0-GCCcore-12.3.0
PyHMMER/0.10.6-gompi-2023a
PyHMMER/0.10.15-gompi-2023b
PyOpenCL/2023.1.4-foss-2023a-CUDA-12.1.1
PyQt-builder/1.15.4-GCCcore-12.3.0
PyQt-builder/1.18.1-GCCcore-13.3.0
PyQt5/5.15.10-GCCcore-12.3.0
PyQt5/5.15.11-GCCcore-13.3.0
PyRosetta/4.release-384-gompi-2022a
PyRosetta/4.release-387-gompi-2023a
PyTables/3.10.2-foss-2024a
PyTorch-Geometric/2.1.0-foss-2022a-PyTorch-1.12.0
PyTorch-Lightning/2.2.1-foss-2023a-CUDA-12.1.1
PyTorch/1.12.0-foss-2022a
PyTorch/2.1.2-foss-2023a-CUDA-12.1.1
PyTorch/2.1.2-foss-2023a
PyTorch/2.1.2-foss-2023b
PyWavelets/1.8.0-foss-2024a
PyYAML/6.0-GCCcore-11.3.0
PyYAML/6.0-GCCcore-12.3.0
PyYAML/6.0.1-GCCcore-13.2.0
PyYAML/6.0.2-GCCcore-13.3.0
PyZMQ/25.1.1-GCCcore-12.3.0
PyZMQ/25.1.2-GCCcore-13.2.0
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netCDF-Fortran/4.6.1-gompi-2024a
netCDF/4.9.0-gompi-2022a
netCDF/4.9.2-gompi-2023a
netCDF/4.9.2-gompi-2023b
netCDF/4.9.2-gompi-2024a
netcdf4-python/1.6.4-foss-2023a
netcdf4-python/1.7.1.post2-foss-2024a
nettle/3.8-GCCcore-11.3.0
nettle/3.9.1-GCCcore-12.3.0
nettle/3.9.1-GCCcore-13.2.0
nettle/3.10-GCCcore-13.3.0
networkx/2.6.3-foss-2023a
networkx/2.8.4-foss-2022a
networkx/3.1-gfbf-2023a
networkx/3.2.1-gfbf-2023b
networkx/3.4.2-gfbf-2024a
newick_utils/1.6
nghttp2/1.58.0-GCC-12.3.0
nghttp3/1.3.0-GCCcore-12.3.0
ngsutils/0.5.9-GCC-13.3.0-Python-2.7.18
ngtcp2/1.2.0-GCC-12.3.0
nlohmann_json/3.10.5-GCCcore-11.3.0
nlohmann_json/3.11.2-GCCcore-12.3.0
nlohmann_json/3.11.3-GCCcore-13.2.0
nlohmann_json/3.11.3-GCCcore-13.3.0
nodejs/16.15.1-GCCcore-11.3.0
nodejs/18.17.1-GCCcore-12.3.0
nodejs/20.9.0-GCCcore-13.2.0
nodejs/20.13.1-GCCcore-13.3.0
nsync/1.26.0-GCCcore-12.3.0
numactl/2.0.14-GCCcore-11.3.0
numactl/2.0.16-GCCcore-12.3.0
numactl/2.0.16-GCCcore-13.2.0
numactl/2.0.18-GCCcore-13.3.0
numba/0.56.4-foss-2022a
numba/0.58.1-foss-2023a
numba/0.60.0-foss-2024a
numexpr/2.9.0-foss-2023a
numpy/1.16.6-foss-2023a-Python-2.7.18
nvtop/2.0.2-GCCcore-11.3.0
ont-fast5-api/4.1.2-foss-2023a
openpyxl/3.1.2-GCCcore-12.3.0
openpyxl/3.1.5-GCCcore-13.3.0
orfipy/0.0.4
p11-kit/0.25.3-GCCcore-12.3.0
p7zip/17.05-GCCcore-13.3.0
pairtools/1.1.2-gfbf-2024a
parallel-fastq-dump/0.6.7-gompi-2022a
parallel-fastq-dump/0.6.7-gompi-2023a
parallel/20230722-GCCcore-12.3.0
parallel/20240322-GCCcore-13.2.0
parallel/20240722-GCCcore-13.3.0
parasail/2.6.2-GCC-12.3.0
patchelf/0.15.0-GCCcore-11.3.0
patchelf/0.18.0-GCCcore-12.3.0
patchelf/0.18.0-GCCcore-13.2.0
patchelf/0.18.0-GCCcore-13.3.0
pblat/2.5.1-foss-2023a
phyx/1.3-foss-2022a
picard/2.25.1-Java-11
picard/3.3.0-Java-17
pigz/2.8-GCCcore-12.3.0
pigz/2.8-GCCcore-13.3.0
pixman/0.40.0-GCCcore-11.3.0
pixman/0.42.2-GCCcore-12.3.0
pixman/0.42.2-GCCcore-13.2.0
pixman/0.43.4-GCCcore-13.3.0
pkg-config/0.29.2-GCCcore-11.3.0
pkg-config/0.29.2-GCCcore-12.3.0
pkgconf/1.8.0-GCCcore-11.3.0
pkgconf/1.8.0
pkgconf/1.9.5-GCCcore-12.3.0
pkgconf/2.0.3-GCCcore-13.2.0
pkgconf/2.2.0-GCCcore-13.3.0
pkgconfig/1.5.5-GCCcore-11.3.0-python
pkgconfig/1.5.5-GCCcore-12.3.0-python
plotly.py/5.12.0-GCCcore-11.3.0
plotly.py/5.16.0-GCCcore-12.3.0
plotly.py/5.18.0-GCCcore-13.2.0
plotly.py/5.24.1-GCCcore-13.3.0
pocl/1.8-GCC-11.3.0
pocl/4.0-GCC-12.3.0-CUDA-12.1.1
pocl/4.0-GCC-12.3.0
poetry/1.5.1-GCCcore-12.3.0
poetry/1.6.1-GCCcore-13.2.0
poetry/1.7.1-GCCcore-12.3.0
poetry/1.8.3-GCCcore-13.3.0
poppler/23.09.0-GCC-12.3.0
pplacer/1.1.alpha19
pretty-yaml/24.7.0-GCCcore-12.3.0
prodigal/2.6.3-GCCcore-11.3.0
prodigal/2.6.3-GCCcore-12.3.0
prokka/1.14.5-gompi-2023a
protobuf-python/3.19.4-GCCcore-11.3.0
protobuf-python/4.24.0-GCCcore-12.3.0
protobuf-python/4.25.3-GCCcore-13.2.0
protobuf/3.19.4-GCCcore-11.3.0
protobuf/24.0-GCCcore-12.3.0
protobuf/25.3-GCCcore-13.2.0
psutil/5.9.8-GCCcore-12.3.0
psutil/6.0.0-GCCcore-13.3.0
psutil/6.1.0-GCCcore-13.2.0
psycopg/3.1.18-GCCcore-13.2.0
psycopg/3.2.3-GCCcore-13.3.0
psycopg2/2.9.9-GCCcore-12.3.0
pugixml/1.14-GCCcore-12.3.0
py-cpuinfo/9.0.0-GCCcore-12.3.0
py-cpuinfo/9.0.0-GCCcore-13.2.0
py-cpuinfo/9.0.0-GCCcore-13.3.0
pyBigWig/0.3.22-gfbf-2023a
pybedtools/0.9.1-foss-2023a
pybind11/2.9.2-GCCcore-11.3.0
pybind11/2.11.1-GCCcore-12.3.0
pybind11/2.11.1-GCCcore-13.2.0
pybind11/2.12.0-GCC-13.3.0
pydantic/1.10.4-GCCcore-11.3.0
pydantic/2.5.3-GCCcore-12.3.0
pydantic/2.6.4-GCCcore-13.2.0
pydantic/2.9.1-GCCcore-13.3.0
pydicom/2.3.0-GCCcore-11.3.0
pydicom/2.4.4-GCCcore-12.3.0
pydicom/3.0.1-GCCcore-13.2.0
pydot/2.0.0-GCCcore-12.3.0
pydot/3.0.3-GCCcore-13.3.0
pyfaidx/0.8.1.1-GCCcore-12.3.0
pyfaidx/0.8.1.2-GCCcore-13.3.0
pyproj/3.4.0-GCCcore-11.3.0
pyproj/3.6.0-GCCcore-12.3.0
pyproj/3.7.0-GCCcore-13.3.0
pyspoa/0.2.1-GCC-12.3.0
pyspoa/0.2.1-GCC-13.2.0
pytest-flakefinder/1.1.0-GCCcore-12.3.0
pytest-flakefinder/1.1.0-GCCcore-13.2.0
pytest-rerunfailures/12.0-GCCcore-12.3.0
pytest-rerunfailures/14.0-GCCcore-13.2.0
pytest-shard/0.1.2-GCCcore-12.3.0
pytest-shard/0.1.2-GCCcore-13.2.0
pytest-xdist/3.3.1-GCCcore-12.3.0
pytest/4.6.11-GCCcore-12.3.0-Python-2.7.18
pytest/7.4.2-GCCcore-12.3.0
pytest/7.4.2-GCCcore-13.2.0
pytest/8.3.3-GCCcore-13.3.0
python-isal/1.1.0-GCCcore-12.3.0
python-isal/1.7.0-GCCcore-13.3.0
python-parasail/1.3.4-foss-2023a
python-xxhash/3.4.1-GCCcore-12.3.0
rasterio/1.4.3-foss-2024a
re2c/2.2-GCCcore-11.3.0
re2c/3.1-GCCcore-12.3.0
re2c/3.1-GCCcore-13.2.0
re2c/3.1-GCCcore-13.3.0
redis-py/4.5.1-foss-2022a
redis-py/5.0.1-GCCcore-12.3.0
redis-py/5.0.9-GCCcore-13.2.0
redis-py/5.1.1-GCCcore-13.3.0
rjags/4-15-foss-2023a-R-4.3.2
ruamel.yaml/0.17.32-GCCcore-12.3.0
ruamel.yaml/0.18.6-GCCcore-13.2.0
ruamel.yaml/0.18.6-GCCcore-13.3.0
samclip/0.4.0-GCCcore-13.3.0
scanpy/1.9.8-foss-2023a
schrodinger/2025-1
scikit-build-core/0.5.0-GCCcore-12.3.0
scikit-build-core/0.9.3-GCCcore-12.3.0
scikit-build-core/0.9.3-GCCcore-13.2.0
scikit-build-core/0.10.6-GCCcore-13.3.0
scikit-build/0.17.6-GCCcore-12.3.0
scikit-build/0.17.6-GCCcore-13.2.0
scikit-build/0.17.6-GCCcore-13.3.0
scikit-image/0.19.3-foss-2022a
scikit-image/0.22.0-foss-2023a
scikit-image/0.25.0-foss-2024a
scikit-learn/1.1.2-foss-2022a
scikit-learn/1.3.1-gfbf-2023a
scikit-learn/1.3.2-gfbf-2023b
scikit-learn/1.4.0-gfbf-2023b
scikit-learn/1.4.2-gfbf-2023a
scikit-learn/1.5.2-gfbf-2024a
scikit-optimize/0.10.2-foss-2023a
seqtk/1.4-GCC-13.3.0
setuptools-rust/1.6.0-GCCcore-12.3.0
setuptools-rust/1.8.0-GCCcore-13.2.0
setuptools-rust/1.9.0-GCCcore-13.3.0
smithwaterman/20160702-GCCcore-12.3.0
smithwaterman/20160702-GCCcore-13.3.0
snakemake/8.27.0-foss-2024a
snappy/1.1.9-GCCcore-11.3.0
snappy/1.1.10-GCCcore-12.3.0
snappy/1.1.10-GCCcore-13.2.0
snappy/1.2.1-GCCcore-13.3.0
snpEff/5.2c-GCCcore-12.3.0-Java-11
spaln/2.4.13f-GCC-12.3.0
spaln/3.0.6b-GCC-12.3.0
sparsehash/2.0.4-GCCcore-12.3.0
spglib-python/2.0.0-foss-2022a
spglib-python/2.5.0-gfbf-2024a
spglib/2.5.0-GCCcore-12.3.0
spoa/4.1.0-GCC-12.3.0
spoa/4.1.0-GCC-13.2.0
statsmodels/0.14.1-gfbf-2023a
statsmodels/0.14.4-gfbf-2024a
swalign/0.2-GCCcore-13.3.0-Python-2.7.18
sympy/1.12-gfbf-2023a
sympy/1.12-gfbf-2023b
sympy/1.13.3-gfbf-2024a
tRNAscan-SE/2.0.12-foss-2023a
tabix/0.2.6-GCCcore-12.3.0
tabix/0.2.6-GCCcore-13.3.0
tabixpp/1.1.2-GCC-12.3.0
tabixpp/1.1.2-GCC-13.3.0
tantan/50-GCC-12.3.0
tbb/2021.5.0-GCCcore-11.3.0
tbb/2021.11.0-GCCcore-12.3.0
tbb/2021.13.0-GCCcore-13.2.0
tbl2asn/20230713-GCCcore-12.3.0
tcsh/6.24.01-GCCcore-11.3.0
tcsh/6.24.10-GCCcore-12.3.0
tcsh/6.24.13-GCCcore-13.3.0
tensorboard/2.15.1-gfbf-2023a
tensorboard/2.18.0-gfbf-2023b
tensorboardX/2.6.2.2-foss-2023a
tensorstore/0.1.65-foss-2023a
tesseract/5.5.0-GCCcore-13.3.0
texlive/20230313-GCC-12.3.0
tiktoken/0.9.0-GCCcore-13.3.0
time/1.9-GCCcore-11.3.0
time/1.9-GCCcore-13.3.0
tmux/3.4-GCCcore-13.3.0
tokenizers/0.15.2-GCCcore-12.3.0
tokenizers/0.19.1-GCCcore-13.2.0
torchvision/0.16.0-foss-2023a-CUDA-12.1.1
tornado/6.3.2-GCCcore-12.3.0
tornado/6.4-GCCcore-13.2.0
tornado/6.4.1-GCCcore-13.3.0
tqdm/4.64.0-GCCcore-11.3.0
tqdm/4.66.1-GCCcore-12.3.0
tqdm/4.66.2-GCCcore-13.2.0
tqdm/4.66.5-GCCcore-13.3.0
trimAl/1.4.1-GCCcore-12.3.0
trimAl/1.5.0-GCCcore-13.3.0
typing-extensions/4.3.0-GCCcore-11.3.0
typing-extensions/4.9.0-GCCcore-12.3.0
typing-extensions/4.10.0-GCCcore-13.2.0
typing-extensions/4.11.0-GCCcore-13.3.0
ucsc/443
umap-learn/0.5.5-foss-2023a
unifdef/2.12-GCCcore-12.3.0
unimap/0.1-GCCcore-11.3.0
utf8proc/2.8.0-GCCcore-12.3.0
utf8proc/2.9.0-GCCcore-13.2.0
utf8proc/2.9.0-GCCcore-13.3.0
util-linux/2.38-GCCcore-11.3.0
util-linux/2.39-GCCcore-12.3.0
util-linux/2.39-GCCcore-13.2.0
util-linux/2.40-GCCcore-13.3.0
vcflib/1.0.9-gfbf-2023a-R-4.3.2
vcflib/1.0.9-gfbf-2024a-R-4.4.2
versioningit/3.1.2-GCCcore-13.3.0
vg/1.65.0
virtualenv/20.23.1-GCCcore-12.3.0
virtualenv/20.24.6-GCCcore-13.2.0
virtualenv/20.26.2-GCCcore-13.3.0
wandb/0.16.1-GCC-12.3.0
wget/1.21.3-GCCcore-11.3.0
wget/1.24.5-GCCcore-12.3.0
wpebackend-fdo/1.15.90-GCCcore-12.3.0
wrapt/1.16.0-gfbf-2024a
wxPython/4.2.1-foss-2023a
wxWidgets/3.2.2.1-GCC-12.3.0
x264/20220620-GCCcore-11.3.0
x264/20230226-GCCcore-12.3.0
x264/20231019-GCCcore-13.2.0
x264/20240513-GCCcore-13.3.0
x265/3.5-GCCcore-11.3.0
x265/3.5-GCCcore-12.3.0
x265/3.5-GCCcore-13.2.0
x265/3.6-GCCcore-13.3.0
xarray/2024.11.0-gfbf-2024a
xorg-macros/1.19.3-GCCcore-11.3.0
xorg-macros/1.20.0-GCCcore-12.3.0
xorg-macros/1.20.0-GCCcore-13.2.0
xorg-macros/1.20.1-GCCcore-13.3.0
xprop/1.2.8-GCCcore-13.3.0
xproto/7.0.31-GCCcore-11.3.0
xproto/7.0.31-GCCcore-13.3.0
xxHash/0.8.2-GCCcore-12.3.0
xxd/9.0.2112-GCCcore-12.3.0
xxd/9.1.0307-GCCcore-13.2.0
xxd/9.1.1275-GCCcore-13.3.0
zarr/2.18.4-foss-2024a
zlib/1.2.11
zlib/1.2.12-GCCcore-11.3.0
zlib/1.2.12
zlib/1.2.13-GCCcore-12.3.0
zlib/1.2.13-GCCcore-13.2.0
zlib/1.2.13
zlib/1.3.1-GCCcore-13.3.0
zlib/1.3.1
zstd/1.5.2-GCCcore-11.3.0
zstd/1.5.5-GCCcore-12.3.0
zstd/1.5.5-GCCcore-13.2.0
zstd/1.5.6-GCCcore-13.3.0

Software installed on Rocky 8

2026-05-11T15:02:23Z

Shtsai: Changed the heading type

Consulting

2026-05-06T18:51:47Z

Shtsai:

==Perl and UNIX-shell scripting==

Automating and simplifying tasks with command-scripts is a routine procedure in the Unix/Linux environment. We regularly help users develop scripts to suit their specific needs.

==Code Development==

We provide assistance with code compilation and debugging in serial and parallel environments.

==Computational Physics==

Computational physics methods are sometimes useful and applicable to problems in other fields. We provide some
consultation for computational physics algorithm selection and implementation.

==Bioinformatics==

From the installation of programs and data (e.g., genetic databases), to helping users get started, bioinformatics support is a key support area.

==General Usage==

GACRC staff are available to provide consultation in order to facilitate and optimize the use of GACRC’s resources. We are available to meet with small groups of users, and to provide guest lectures for courses that use GACRC resources.

==People==

===Administrators===

*'''Guy Cormier, Ph.D.''' <br />Director, UGA Georgia Advanced Computing Resource Center<br />Associate Research Scientist, Institute of Bioinformatics

===GACRC Consultants===





*'''Isaiah Davis''' <br /> Software Support Associate
*'''Zhuofei Hou, Ph.D.''' <br /> High Performance Computing Facilitator
*'''Chelsea Ware''' <br /> Software Support Associate
*'''Shan-Ho Tsai, Ph.D.''' <br /> Consulting Group Lead and High Performance Computing Consultant
*'''Jordan Utley, M.S.''' <br /> Bioinformatics Support Specialist



===GACRC IT Staff===

*'''Paul Brunk''' <br /> Principal Unix System Administrator
*'''Bruce Carpenter''' <br /> Linux System Administrator
*'''Jonathan "Jon" Gibson''' <br /> Linux System Administrator
*'''Keyton Stanier''' <br /> Linux System Administrator
*'''Kris Lamoureux''' <br /> Linux System Administrator


===Emeritus===
*'''Greg Derda, Ph.D.''' <br /> Operations Manager Extraordinaire

==Office Hours==
GACRC staff are located in the Computing Services Building (formerly called Statistics Building), rooms 101 to 108. Our office is open Monday through Friday from 9am until 5pm. You are welcome to stop by and ask questions, or you can schedule an appointment using the [https://uga.teamdynamix.com/TDClient/Requests/ServiceDet?ID=25844 GACRC General Support].

==Directions==
The Computing Services Building is located at 101 Cedar Street, Athens, GA 30602, on the corner of Cedar Street and East Campus Rd. As you enter the building from the entrance on Cedar Street, on the East Campus Rd. end of the building, there are stairs leading down to the first floor. Our offices are on your left, as you come down those stairs.

Policies

2026-05-06T18:43:16Z

Shtsai:

==Introduction to GACRC Policies==

The following policies are subject to revision, especially as the GACRC grows in scope and services. Your comments and questions will be useful to our policy formulation and refinement and are actively solicited by the GACRC Advisory Committee.

The GACRC computational infrastructure, including its servers, clusters, data stores, and other related devices are for the exclusive use of authorized users only.

Anyone using these systems expressly consents to abide by the policies of the University of Georgia and the Georgia Advanced Computing Resource Center and, accordingly, is subject to account termination and/or immediate disconnection from GACRC resources.

==GACRC Resource Usage==

The computational resources of the Georgia Advanced Computing Resource Center are to be used in direct support of research programs at the University of Georgia. Support is also provided for classes that teach computational methods, and provide training for high performance computing. The GACRC reserves the right to restrict access to its resources for course work if such work is deemed to present a negative impact to authorized research activities.

GACRC policies supplement UGA’s Policies on the Use of Computers, found at: [https://policy.uga.edu/policies#/programs/BJWfhwRra?group=Information%20Technology%20&bc=true&bcCurrent=Information%20Technology%20&bcItemType=programs&bc=true&bcCurrent=Acceptable%20Use%20Policy&bcGroup=Information%20Technology%20&bcItemType=programs UGA’s Policies on the Use of Computers]

==GACRC Eligibility and Access ==

Access to and use of the computing facilities managed by the Georgia Advanced Computing Resource Center are limited to persons affiliated with the University of Georgia or associated with research projects sponsored by UGA.

Affiliation in this context means faculty, research staff and supervised students of the University of Georgia. Faculty includes persons holding permanent or temporary appointments as well as adjunct faculty, instructors and visiting faculty while in residence at the University. It also includes those persons with faculty status such as research associates, research scientists, post-doctoral researchers and academic and service professionals. Staff includes all those non-faculty persons employed directly by the University in a research-support role. Graduate and undergraduate students who are members of faculty research labs are eligible for accounts as well. For directly affiliated users, accounts on the GACRC computers will remain active as long as the individuals hold the above status.

Access by non-UGA researchers and their students, affiliated to higher-education institutions or non-profit research organizations, for work on research projects conducted in collaboration with UGA Faculty is possible under the guidelines established by the Office of the Vice President for Research and the Office of International Education. A request for access can be forwarded to the GACRC by the UGA Faculty, providing details of the collaboration on a joint research project. Such Affiliate users will be considered part of the UGA Faculty’s group and will be under the Faculty’s responsibility. Affiliate users’ access will be granted for a fixed period of time, according to the expected length of the collaborative project. Renewal of affiliate accounts will be required annually.

All accounts will remain active no more than 30 days following a status change (i.e., leaving the university). Graduate instructional accounts will only remain active for the duration of the semester in which they are actually needed. HOME and PROJECT directories will be archived for at least 90 days, but no longer than 180 days after an account becomes inactive.

Requests for access by individuals other than those listed above should be directed to the Director of the Center for consideration with the GACRC Advisory Committee.

Access will be granted to a specific GACRC resource after appropriate training is undertaken with GACRC staff. Existing access to other GACRC resources is not a sufficient criteria for access to a new resource. No exceptions will be given to the training requirement.

==GACRC Identity Management==

Below are described the procedures for validating the identity of account users.

===UGA Users and Faculty Lab Groups===

*A UGA Faculty must first establish a GACRC group account using the instructions provided on the GACRC website (http://gacrc.uga.edu/accounts). The UGA Faculty can choose or not to obtain a GACRC user account affiliated with his/her group account.

*User accounts will only be created at the request of a UGA Faculty member, using the instructions provided on the GACRC website (http://gacrc.uga.edu/accounts).

*Upon acceptance of the application, the user will be notified via e-mail. The applicant’s UGA MyID and password will be used to log into the requested GACRC resources.

===Affiliate Users===

*A recognized Affiliate user must be sponsored by a UGA Faculty member through an established GACRC group account. The UGA Faculty involved in an established collaboration with the Affiliate user, must apply on behalf of the applicant by contacting the GACRC staff.

*A request will be made by the GACRC to EITS to allocate to the Affiliate a UGA MyID.

*Upon acceptance of the Affiliate user application, the Affiliate will be notified via e-mail. The Affiliate’s UGA MyID and password will be used to log into the requested GACRC resources.

===Protection of Passwords===

As described in UGA’s Password Policy, an account holder must never divulge their MyID and password to a third party. Only authorized account holders may access the resources of the GACRC. If a third party is found to be using an account holder’s login with or without the permission of the account holder, the account holder’s access privileges may be revoked at the sole discretion of the GACRC Manager or Director. Enforcement of this policy is under the responsibility of the Office of the Vice President for Information Technology’s Division of Information Security.

More information is found at the following EITS website:

[https://policy.uga.edu/policies#/programs/HJe8T3sH6?group=Information%20Technology%20&bc=true&bcCurrent=Information%20Technology%20&bcItemType=programs&bc=true&bcCurrent=Password%20Policy&bcGroup=Information%20Technology%20&bcItemType=programs UGA Policy Website]

==GACRC Storage Usage ==

===Some working definitions===

'''Snapshot''' - Copies of files that are stored on the same storage system as the original files. Snapshots are primarily used to recover files that have been accidentally deleted or corrupted within the recent past. Users are able to manage the file recovery tasks. Snapshots are not maintained beyond a defined rotation schedule, i.e., some number of hourly, daily, weekly, and monthly snapshots are kept on the storage system.

'''Backup''' - Copies of files and/or snapshots kept on a storage system (disk/tape) other than the one that the original files reside on. Backups are primarily used to recover files following a catastrophic failure of the original file or storage system. Backups require administrators to perform file system recovery tasks. Like snapshots, backups have a defined rotation schedule.

'''Archive''' - Copies of files that are not currently being accessed, on a resilient storage system dedicated to reliable long-term storage. Archives can be tape-based or disk-based, and typically part of a disaster recovery plan. The files may be copies of original data which is stored elsewhere (individual groups having their own copies), or the archive storage system may be fed by a dedicated "backup" storage system.

'''Active Projects''' – Projects that have on-going computational work being performed with files that are regularly created, accessed or modified.

===Policy Statement for SCRATCH File System===

The SCRATCH file system resides on a high-performance storage device and is to be used uniquely for temporary storage of files in use by actively running compute jobs. Files are to be removed from SCRATCH when a job completes, e.g. can be copied to the PROJECT file system. The SCRATCH file system is not backed up in any way and no snapshots are taken. The SCRATCH filesystem is mounted under /scratch on all the compute nodes, login nodes and data transfer nodes.

Any file that is not accessed or modified by a compute job in a time period of at least 30 days will be automatically deleted off the SCRATCH file system. Measures circumventing this policy will be monitored and actively discouraged.

There is no storage size quota for SCRATCH usage. Space is only limited by the physical size of the scratch space being used. If usage across the entire file system is more than 80% of total capacity, the GACRC will take additional measures to reduce usage to a more suitable level. Amongst possible actions, the GACRC may request/force users to clean up their SCRATCH directories or reduce temporarily the 30 day limit to a lower limit.

===Policy Statement for WORK File System===

The WORK file system resides on a high-performance storage device and is to be used for storing files that are frequently used by the group for computation. The WORK file system is '''NOT''' subject to the 30-day purge policy. The filesystem usage is controlled using a quota on the size and number of files that can be stored in a lab group's WORK area. Initially each group is given a 500GB and 100,000-file quota. The WORK file system is not backed up in any way and no snapshots are taken. The WORK filesystem is mounted under /work on all the compute nodes, login nodes and data transfer nodes. Each lab group has a directory under the /work directory.

The WORK file system is '''NOT''' subject to the 30-day purge policy. But if there is sufficient space consumption on the storage appliance, we reserve the right to ask users to clean up their WORK area. If the users do not respond in a timely fashion we will purge files beginning with the oldest ones. Please do not use the WORK area to store files long term.

===Policy Statement for HOME File System===

The HOME file system resides on a high-performance storage device and is used for long-term storage of files, typically programs and scripts, needed for analysis on the GACRC computing cluster.

All users have 200GB allocated for their HOME usage.

HOME directories will have daily, weekly and up to 3 monthly snapshots kept on the same storage unit to protect against accidental file deletion. HOME directories will also be backed-up on a different storage unit to protect against filesystem or hardware issues.

Users are strongly encouraged to make their own copies of critical files, while accepting any risks associated with HOME usage.

Snapshot retention, data purge and quota allocation policies are subject to change based on available storage capacity, users’ demand, equipment condition and availability, as well as any other conditions that might affect the provision of the HOME service.

===Policy Statement for PROJECT File System===

The PROJECT filesystem resides on lower-performance/higher-capacity storage devices.

This filesystem is accessible only from the GACRC’s transfer (xfer) nodes. PROJECT will not be accessible on Sapelo2's compute nodes.

This filesystem is to be used by groups for storage of active projects using Sapelo2. PROJECT should not be seen as a long-term repository, as it is not designed as such. Once a project is completed, data should be moved from the PROJECT space to user-managed storage, freeing up capacity for the next active project.

This filesystem is not for backing-up non-GACRC-related material, such as personal files (music, photos), office documents, and the like. If found, this material is subject to immediate deletion.

Access to the PROJECT filesystem is not supported through NFS to a destination outside of the Boyd Data Center, or through the use of the Samba or CIFS protocols. Transfer protocols available through the data transfer nodes are secure ftp, scp, rsync, GridFTP, amongst others.

Each group can request a PROJECT volume with an initial 1TB allocation, accessible by all users ascribed to the group, where the sharing of files will be enabled. Users are encouraged to consider their PROJECT space as the primary area to transfer compute job outputs. Additional space can be requested by a Faculty on behalf of his/her group.

The GACRC reserves the right to establish a cost-recovery rate for PROJECT storage beyond the initial 1TB allocation. Appropriate communications will take place in such an event.

PROJECT directories will have daily, weekly and monthly snapshots kept on the same storage unit to protect against accidental file deletion. PROJECT directories will also be backed-up on a different storage unit to protect against filesystem or hardware issues.

Users are strongly encouraged to make their own copies of critical files, while accepting any risks associated with PROJECT usage.

Snapshot retention, data purge and quota allocation policies are subject to change based on available storage capacity, users’ demand, equipment condition and availability, as well as any other conditions that might affect the provision of the PROJECT service.

==GACRC Software Policy==

The GACRC maintains a collection of program libraries and software packages to support research computing activities across diverse research domains. While a user can install a software package in their own environment, for the sake of general access across groups, and an appropriate deployment with current libraries, compilers and other dependencies, we strongly recommend that GACRC staff be asked to perform the installation or upgrade.

Any software that requires a signed license or contract, even if it is a click-through agreement, must absolutely be reviewed and handled by the Office of Legal Affairs before being signed by an appropriate signature authority. After the license or contract is accepted and the software is made available, GACRC users must fully comply and use the software in a way that does not violate any terms of the license or contract. Further information on licensing issues can be found at the following EITS website:


As a matter of policy, the GACRC will not purchase any commercial software for the use of a single group or a small number of groups. Commercial software currently purchased and maintained by the GACRC are of general interest and applicability to the whole UGA research community. The GACRC will however install and maintain a group-purchased commercial software, which complies with the above comments on licenses and contracts.

==Security==

To minimize disruption of service, protect data integrity, conserve facility resources and maximize the effectiveness of staff support, the GACRC maintains strict security requirements for access to GACRC resources. Over time, the enforcement of these requirements will become increasingly strict, with the goal of preventing any access to the GACRC resources by any person or any device that is not in strict compliance with these requirements.

===Operating Systems===

Any computer accessing the GACRC for any purpose must run a currently supported operating system, updated to the latest version and update (patch) levels.

===Anti-Virus Software===

Any computer accessing the GACRC for any purpose must meet minimum levels of anti-virus protection. Any computer used by an account holder must have anti-virus software from a source approved by UGA’s Office of Information Security must have that virus protection activated, and must have automatic updates activated.

More information can be found at the following EITS website:

[https://policy.uga.edu/policies#/programs/BySr6vy8a?q=%20Minimum%20Security%20Standards%20Policy&&limit=20&skip=0&bc=true&bcCurrent=Minimum%20Security%20Standards%20Policy&bcItemType=programs Minimum Security Standards Policy]

===Suspiciously Behaving Software===

Any software that behaves in a suspicious manner may at any time be terminated and/or deleted from GACRC resources at the sole discretion of the GACRC’s systems administrator(s), manager, Director, or EITS information security staff.

===Suspiciously Behaving Networks and Devices===

Any connection from any device to the GACRC may be terminated at any time, if the device or the connection or a network to which the device is attached appears to be not in compliance with UGA’s security requirements, is behaving suspiciously, or if a threat emerges requiring termination for intrusion prevention at the sole discretion of the GACRC’s systems administrator(s), manager, Director, or EITS information security staff.

===Account Holder Responsibility===

The account holder is responsible for diligently monitoring their account and compliance with the GACRC’s operating system, intrusion and virus protection standards. The account holder will be duly notified if GACRC personnel determine that minimum security requirements are not met. Specific actions will be requested of the account holder and compliance to these will be expected in a timely fashion. An account holder’s privileges to use GACRC facilities may be terminated by the GACRC Manager or Director at any time, without notice if, in the opinion of either, the account holder is reluctant or averse to practicing diligence in meeting the GACRC’s minimum requirements for intrusion and/or anti-viral protection.

==Resolving Disagreements about Revocation of Privileges or Provisioning Resources==

The Director of the Georgia Advanced Computing Resource Center has full authority to revoke a user's privileges or deny the request of a new resource allocation. The decision to revoke a user's privileges will be based on, but not limited to, abuses of the UGA Policies on the Use of Computers and/or abuses of the UGA Password Policy.

If an account holder is denied a request for provisioning of GACRC resources or resource privileges are revoked, the account holder’s Department Head may appeal to the Vice President for Research and the Vice President for Information Technology. Their decision will be informed by the Director of the GACRC, the Chief Technology Officer as well as the Associate Chief Information Officer for Information Security. The decision of the Vice President for Research and Vice President for Information Technology is final.

==System Maintenance and Downtime==

===Planned Maintenance===

The GACRC instituted monthly maintenance windows in order to perform maintenance operations requiring system operations to be reduced or interrupted.

The schedule will be as follows:

*The last Wednesday of each month from 10AM to 4PM will be reserved for partial cluster maintenance.

*Twice a year, a two-day shut-down of GACRC services will be scheduled for more complex maintenance operations. These will occur on the last Tuesday and Wednesday of the months of January and July.

These maintenance windows represent periods when the GACRC may choose to drain the queues of running jobs and suspend access to the Sapelo2 cluster, as well as storage devices for maintenance purposes. Interruptions will be kept as brief as possible.

The GACRC will notify all users at least 10 days in advance that a maintenance window will be in effect. The notification will describe the nature and extent (partial or full) of the interruptions of cluster and or storage services. In case a maintenance window has to be extended due to unavoidable technical reasons, adequate communications will be made to all users.

The impact of the outages will vary, and the GACRC will do its best to preserve pending and running jobs, which is often very doable. Nevertheless, users will need to plan their job submissions around the maintenance windows.

===Unplanned Maintenance and System Outage===

From time to time, hardware, software, and/or environmental factors may cause a system or subsystem to malfunction, causing disruption to service. Also, there may be circumstances or events related to possible security issues or intrusions which will cause GACRC staff to take systems offline while the nature of the apparent breach is analyzed and appropriate action is taken.

Whenever possible, account holders will be notified by e-mail of these outages in advance, but that may not always be possible. Account holders will be notified by e-mail if the disruption should last more than 30 minutes.

GACRC staff will strive to preserve the work and/or prevent disruption of jobs in process during such outages. However, there may be circumstances which cause disruption of jobs and loss of data. Users are encouraged to implement methods in their code which minimize the effect of unplanned interruption of a job’s execution, such as checkpoints. Users are also strongly encouraged to maintain copies of files of importance.

==Regulatory Compliance==

The GACRC as an infrastructure and service provider does NOT currently warrant that its practices or facilities meet government-mandated requirements for the storage and protection of sensitive, private or classified information. Users may not store such information on GACRC facilities. In other words, data that falls under HIPAA, FERPA, FISMA or similar regulatory requirements, may not be stored, computed against or otherwise transacted through, or with, GACRC infrastructure.

The GACRC and its users must comply with all existing Federal export control regulations for services and infrastructure. Research groups must agree to NOT install or use any software or data that falls under Export Control regulations. More information on the subject of Export Control is available at the following OVPR website:

http://research.uga.edu/export-control/

Copyrighted materials are prohibited without proper authorization. Additionally, illegal content is prohibited.

Non-compliance with any such Federal requirements might impact GACRC operations or delivery of services and could place the GACRC and UGA at risk. If a research group is found to be in non-compliance, then account access will be immediately suspended, while an investigation by EITS’s Information Security division is instigated.

===Disclosure===

Research groups that are involved in activities that store protected data on GACRC infrastructure must contact immediately the GACRC Director in order to address the issue. Depending on circumstances, accommodations might be possible for such activities.

===Research Data Compliance===

Research Data Management is a critical factor in both obtaining federal funding from the NSF, NIH, DoD and other federal agencies, and in the conduct of funded research. Responsibility in maintaining and preserving research data, as detailed in data management plans submitted to Federal funding agencies, is entirely placed upon the research faculty, post docs, and graduate students conducting the research. The GACRC will help by providing information and assistance, but will not be responsible to ensure compliance with a project’s data management plan.

During the phase of proposal writing, arrangements can be discussed and agreed upon as to the GACRC playing an active role, and ensuring the provision of specific services towards the compliance of a data management plan. Depending on the complexity or the nature of the proposed services, the GACRC might require the purchase of specific hardware/software and/or the availability of a %FTE salary and benefits.

More information on data management plans can be found [http://guides.libs.uga.edu/c.php?g=349946&p=2363161 here].

Maker-Sapelo2

2026-05-06T18:34:30Z

Shtsai:

[[Category:Sapelo2]][[Category:Software]][[Category:Bioinformatics]]
== Category ==
Bioinformatics

== Program On ==

Sapelo2

== Version ==

3.01.04

== Author / Distributor ==

[https://github.com/Yandell-Lab/maker Yandell-lab]

== Description ==

More details from http://gmod.org/wiki/MAKER_Tutorial#What_does_MAKER_do.3F

MAKER is an easy-to-use genome annotation pipeline designed to be usable by small research groups with little bioinformatics experience; however, MAKER is also designed to be scalable and is appropriate for projects of any size even including use by large sequence centers. MAKER can be used for de novo annotation of newly sequenced genomes, for updating existing annotations to reflect new evidence, or just to combine annotations, evidence, and quality control statistics for use in other GMOD programs.

== Running Program ==

Also refer to [[Running Jobs on Sapelo2]]

Version 3.01.04 is installed at /apps/eb/Maker/3.01.04-foss-2022a

Example to prepare the configuration files.
<pre class="gscript">
module load MAKER/3.01.04-foss-2022a
maker -CTL
</pre>

Command "maker -CTL" will generate the configuration files '''maker_bopts.ctl''', '''maker_evm.ctl''', '''maker_exe.ctl''', and '''maker_opts.ctl''' in your working folder. You need to configure those configuration files before you run maker. Examples can be found at [http://weatherby.genetics.utah.edu/MAKER/wiki/index.php/MAKER_Tutorial_for_WGS_Assembly_and_Annotation_Winter_School_2018#Running_MAKER_with_example_data Running_MAKER_with_example_data].

To use an MPI-enabled version:
<pre class="gscript">
module load MAKER/3.01.04-foss-2022a-MPI
</pre>

=== Running maker in a MPI job ===

Here is an example of submission script (sub.sh) for running maker in a MPI job of 60 MPI processes running on 5 nodes:

<pre class="gscript">
#!/bin/bash
#SBATCH --partition=batch
#SBATCH --job-name=maker-mpi
#SBATCH --nodes=1
#SBATCH --ntasks=16
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=1G
#SBATCH --time=24:00:00
#SBATCH --output=log.o%j
#SBATCH --export=NONE

export OMPI_MCA_mpi_warn_on_fork=0
unset SLURM_EXPORT_ENV

cd $SLURM_SUBMIT_DIR

ml MAKER/3.01.04-foss-2022a-MPI

srun $EBROOTMAKER/bin/maker
</pre>

=== Running maker in a memory-sharing threaded job ===

The maker command provides the -cpus option to specify how many threads to use for BLAST+.

Here is an example of submission script (sub.sh) for running maker in a threaded job of 16 threads running on 1 node:

<pre class="gscript">
#!/bin/bash
#SBATCH --partition=batch
#SBATCH --job-name=Maker
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=16
#SBATCH --mem=20G
#SBATCH --time=8:00:00
#SBATCH --output=log.o%j
#SBATCH --export=NONE

cd $SLURM_SUBMIT_DIR

ml MAKER/3.01.04-foss-2022a

maker -cpus 16
</pre>

=== Running maker in a single-core job ===

Here is an example of submission script (sub.sh) for running maker in a single-core job:

<pre class="gscript">
#!/bin/bash
#SBATCH --partition=batch
#SBATCH --job-name=Maker
#SBATCH --ntasks=1
#SBATCH --mem=10G
#SBATCH --time=8:00:00
#SBATCH --output=log.o%j
#SBATCH --export=NONE

cd $SLURM_SUBMIT_DIR

ml MAKER/3.01.04-foss-2022a

maker
</pre>

Then submit the job by:
<pre class="gcommand">
sbatch ./sub.sh
</pre>

== Documentation ==

Maker wiki site is at https://weatherby.genetics.utah.edu/MAKER/wiki/index.php/Main_Page

<pre class="gcommand">
ml MAKER/3.01.04-foss-2022a
maker -h

MAKER version 3.01.04

Usage:

maker [options] <maker_opts> <maker_bopts> <maker_exe>

Description:

MAKER is a program that produces gene annotations in GFF3 format using
evidence such as EST alignments and protein homology. MAKER can be used to
produce gene annotations for new genomes as well as update annotations
from existing genome databases.

The three input arguments are control files that specify how MAKER should
behave. All options for MAKER should be set in the control files, but a
few can also be set on the command line. Command line options provide a
convenient machanism to override commonly altered control file values.
MAKER will automatically search for the control files in the current
working directory if they are not specified on the command line.

Input files listed in the control options files must be in fasta format
unless otherwise specified. Please see MAKER documentation to learn more
about control file configuration. MAKER will automatically try and
locate the user control files in the current working directory if these
arguments are not supplied when initializing MAKER.

It is important to note that MAKER does not try and recalculated data that
it has already calculated. For example, if you run an analysis twice on
the same dataset you will notice that MAKER does not rerun any of the
BLAST analyses, but instead uses the blast analyses stored from the
previous run. To force MAKER to rerun all analyses, use the -f flag.

MAKER also supports parallelization via MPI on computer clusters. Just
launch MAKER via mpiexec (i.e. mpiexec -n 40 maker). MPI support must be
configured during the MAKER installation process for this to work though

Options:

-genome|g <file> Overrides the genome file path in the control files

-RM_off|R Turns all repeat masking options off.

-datastore/ Forcably turn on/off MAKER's two deep directory
nodatastore structure for output. Always on by default.

-old_struct Use the old directory styles (MAKER 2.26 and lower)

-base <string> Set the base name MAKER uses to save output files.
MAKER uses the input genome file name by default.

-tries|t <integer> Run contigs up to the specified number of tries.

-cpus|c <integer> Tells how many cpus to use for BLAST analysis.
Note: this is for BLAST and not for MPI!

-force|f Forces MAKER to delete old files before running again.
This will require all blast analyses to be rerun.

-again|a recaculate all annotations and output files even if no
settings have changed. Does not delete old analyses.

-quiet|q Regular quiet. Only a handlful of status messages.

-qq Even more quiet. There are no status messages.

-dsindex Quickly generate datastore index file. Note that this
will not check if run settings have changed on contigs

-nolock Turn off file locks. May be usful on some file systems,
but can cause race conditions if running in parallel.

-TMP Specify temporary directory to use.

-CTL Generate empty control files in the current directory.

-OPTS Generates just the maker_opts.ctl file.

-BOPTS Generates just the maker_bopts.ctl file.

-EXE Generates just the maker_exe.ctl file.

-MWAS <option> Easy way to control mwas_server for web-based GUI

options: STOP
START
RESTART

-version Prints the MAKER version.

-help|? Prints this usage statement.

</pre>

[[#top|Back to Top]]
== Installation ==

Source download from https://github.com/Yandell-Lab/maker

== System ==
64-bit Linux

Software Template

2026-05-06T17:26:18Z

Shtsai:

<pre>
[[Category:Sapelo2]][[Category:Software]][[Category:Bioinformatics]]
== Category ==
Bioinformatics
== Program On ==
Sapelo2
== Version ==

== Author / Distributor ==

== Description ==

== Running Program ==

Also refer to [[Running Jobs on zcluster]]

== Documentation ==
== Installation ==
== System ==
64-bit Linux
</pre>