AlphaFold-Sapelo2

Program On

Sapelo2

Version

2.0.0, 2.0.1

Author / Distributor

Please see https://github.com/deepmind/alphafold

Description

From https://github.com/deepmind/alphafold: "This package provides an implementation of the inference pipeline of AlphaFold v2.0. This is a completely new model that was entered in CASP14 and published in Nature. "

Running Program

Also refer to Running Jobs on Sapelo2

For more information on Environment Modules on Sapelo2 please see the Lmod page.

Version 2.0.0, installed as a conda environment in /apps/gb/AlphaFold/2.0.0/

To use this version of AlphaFold, please first load the module with

ml AlphaFold/2.0.0_conda

Once you load the module, an environmental variable called EBROOTALPHAFOLD is exported. It stores the AlphaFold installation path on the cluster, i.e., /apps/gb/AlphaFold/2.0.0. The bash script run_alphafold.sh in installed in EBROOTALPHAFOLD/alphafold, and the 2.2TB of database files are in /apps/db/AlphaFold (this is the directory that you need to use for the -d option of run_alphafold.sh).

Note: This program does not work on the nodes with K20Xm GPU devices, because the CPUs on those nodes do not support AVX. If you run this program on the gpu_p partition, please request a K40 or a P100 GPU device.

Version 2.0.1, installed with EasyBuild in /apps/eb/AlphaFold/2.0.1-fosscuda-2020b/

To use this version of AlphaFold, please first load the module with

ml AlphaFold/2.0.1-fosscuda-2020b

Once you load the module, an environmental variable called EBROOTALPHAFOLD is exported. It stores the AlphaFold installation path on the cluster, i.e., /apps/eb/AlphaFold/2.0.1-fosscuda-2020b. The python script run_alphafold.py in installed in EBROOTALPHAFOLD/bin, and the 2.2TB of database files are in /apps/db/AlphaFold.

Note: This program does not work on the nodes with K20Xm GPU devices, because the CPUs on those nodes do not support AVX. If you run this program on the gpu_p partition, please request a K40 or a P100 GPU device.

Sample job submission script (sub.sh) to run AlphaFold 2.0.0 using run_alphafold.sh in a batch job (without GPU):

#!/bin/bash
#SBATCH --job-name=alphafoldjobname       
#SBATCH --partition=batch            
#SBATCH --ntasks=1                  	
#SBATCH --cpus-per-task=4        
#SBATCH --mem=20gb                    
#SBATCH --time=120:00:00           
#SBATCH --output=%x.%j.out     
#SBATCH --error=%x.%j.err          
#SBATCH --mail-user=username@uga.edu  
#SBATCH --mail-type=ALL   

cd $SLURM_SUBMIT_DIR

ml AlphaFold/2.0.0_conda

bash $EBROOTALPHAFOLD/alphafold/run_alphafold.sh -d /apps/db/AlphaFold [options]

An example of the required options to use are

bash $EBROOTALPHAFOLD/alphafold/run_alphafold.sh -d /apps/db/AlphaFold -o ./test/ -m model_1 -f ./query.fasta -t 2020-05-14

Sample job submission script (sub.sh) to run AlphaFold 2.0.0 using run_alphafold.sh in a batch job (with GPU):

#!/bin/bash
#SBATCH --job-name=alphafoldjobname    
#SBATCH --partition=gpu_p         
#SBATCH --ntasks=1                  	
#SBATCH --cpus-per-task=4
#SBATCH --gres=gpu:K40:1
#SBATCH --mem=40gb                    
#SBATCH --time=120:00:00           
#SBATCH --output=%x.%j.out     
#SBATCH --error=%x.%j.err          
#SBATCH --mail-user=username@uga.edu  
#SBATCH --mail-type=ALL   

cd $SLURM_SUBMIT_DIR

ml AlphaFold/2.0.0_conda

bash $EBROOTALPHAFOLD/alphafold/run_alphafold.sh -d /apps/db/AlphaFold [options]

where $EBROOTALPHAFOLD is the environmental variable that stores the AlphaFold installation path on the cluster; [options] need to be replaced by the options (command and arguments) you want to use. Other parameters of the job, such as the maximum wall clock time, maximum memory, the number of cores per node, and the job name need to be modified appropriately as well. You can also request a P100 device, using #SBATCH --gres=gpu:P100:1 if you submit the job to the gpu_p partition.

Example of job submission

sbatch sub.sh

Documentation

Details and references are at https://github.com/deepmind/alphafold.

ml AlphaFold/2.0.0_conda

bash $EBROOTALPHAFOLD/alphafold/run_alphafold.sh -h

Usage: /apps/gb/AlphaFold/2.0.0_conda/alphafold/run_alphafold.sh <OPTIONS>
Required Parameters:
-d <data_dir>     Path to directory of supporting data
-o <output_dir>   Path to a directory that will store the results.
-m <model_names>  Names of models to use (a comma separated list)
-f <fasta_path>   Path to a FASTA file containing one sequence
-t <max_template_date> Maximum template release date to consider (ISO-8601 format - i.e. YYYY-MM-DD). Important if folding historical test sets
Optional Parameters:
-b <benchmark>    Run multiple JAX model evaluations to obtain a timing that excludes the compilation time, which should be more indicative of the time required for inferencing many
    proteins (default: 'False')
-g <use_gpu>      Enable NVIDIA runtime to run with GPUs (default: 'True')
-a <gpu_devices>  Comma separated list of devices to pass to 'CUDA_VISIBLE_DEVICES' (default: 'all')
-p <preset>       Choose preset model configuration - no ensembling (full_dbs) or 8 model ensemblings (casp14) (default: 'full_dbs')

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Installation

Version 2.0.0: Installed using a conda environment following the steps in the dockerfile available at https://github.com/deepmind/alphafold. The run_alphafold.sh bash script was obtained from https://github.com/kalininalab/alphafold_non_docker and some documentation related to this script is available at that URL.

Version 2.0.1: Installed using EasyBuild.

The database files are installed in /apps/db/AlphaFold/

System

64-bit Linux

AlphaFold-Sapelo2

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