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{bio}[foss/2023a] AlphaFold v2.3.2, dm-haiku v0.0.12, tensorstore v0.1.65 w/ CUDA v12.1.1 #19942
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Rename Kalign-3.4.0-GCCcore-12.3.0.eb to k/Kalign/Kalign-3.4.0-GCCcor…
ThomasHoffmann77 3478d12
Rename k/Kalign/Kalign-3.4.0-GCCcore-12.3.0.eb to easybuild/eeasyconf…
ThomasHoffmann77 4b2d49e
Delete easybuild/eeasyconfigs/k/Kalign directory
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Rename dm-haiku-0.0.11-foss-2023a-CUDA-12.1.1.eb to easybuild/eeasyco…
ThomasHoffmann77 66f01b3
Rename dm-haiku-0.0.11-foss-2023a-CUDA-12.1.1.eb_ to dm-haiku-0.0.11-…
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Rename easybuild/easyconfigs/t/tensorstore-0.1.53-foss-2023a.eb to ea…
ThomasHoffmann77 76bb632
Delete easybuild/easyconfigs/d/dm-haiku/dm-haiku-0.0.11-foss-2023a-CU…
ThomasHoffmann77 b2651fc
update dm-haiku to 0.0.12; @akesandgren suggestions
ThomasHoffmann77 572eb15
sanity_check_commands order
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Update dm-haiku-0.0.12-foss-2023a-CUDA-12.1.1.eb
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Delete easybuild/easyconfigs/d/dm-haiku/dm-haiku-0.0.11-foss-2023a-CU…
ThomasHoffmann77 34926eb
Delete easybuild/easyconfigs/a/AlphaFold/AlphaFold-2.3.2_BioPythonSCO…
ThomasHoffmann77 4fee5a7
revert TF to ver 2.13.0; uniref30 default version env var
ThomasHoffmann77 5c938cf
update tensorstore to 0.1.65
ThomasHoffmann77 69898e1
Merge branch 'easybuilders:develop' into 20240220124705_new_pr_AlphaF…
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Delete easybuild/easyconfigs/t/tensorstore/tensorstore-0.1.53-foss-20…
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Delete easybuild/easyconfigs/a/AlphaFold/AlphaFold-2.3.2_data-dep-pat…
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use standalone dependency for Optax 0.2.2 as dependency for dm-haiku …
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167 changes: 167 additions & 0 deletions
167
easybuild/easyconfigs/a/AlphaFold/AlphaFold-2.3.2-foss-2023a-CUDA-12.1.1.eb
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easyblock = 'PythonBundle' | ||
|
||
name = 'AlphaFold' | ||
version = '2.3.2' | ||
versionsuffix = '-CUDA-%(cudaver)s' | ||
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||
homepage = 'https://deepmind.com/research/case-studies/alphafold' | ||
description = "AlphaFold can predict protein structures with atomic accuracy even where no similar structure is known" | ||
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toolchain = {'name': 'foss', 'version': '2023a'} | ||
|
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builddependencies = [ | ||
('poetry', '1.5.1') | ||
] | ||
|
||
dependencies = [ | ||
('Python', '3.11.3'), | ||
('CUDA', '12.1.1', '', SYSTEM), | ||
('SciPy-bundle', '2023.07'), | ||
('PyYAML', '6.0'), | ||
('TensorFlow', '2.13.0'), # doesn't require TF-gpu | ||
('Biopython', '1.83'), | ||
('HH-suite', '3.3.0'), | ||
('HMMER', '3.4'), | ||
('Kalign', '3.4.0'), | ||
('jax', '0.4.25', versionsuffix), # also provides absl-py # requirement is ==0.3.25! | ||
('UCX-CUDA', '1.14.1', versionsuffix), | ||
('cuDNN', '8.9.2.26', versionsuffix, SYSTEM), | ||
('NCCL', '2.18.3', versionsuffix), | ||
('OpenMM', '8.0.0', versionsuffix), | ||
('dm-tree', '0.1.8'), | ||
('dm-haiku', '0.0.12', versionsuffix), | ||
] | ||
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# commit to use for downloading stereo_chemical_props.txt and copy to alphafold/common, | ||
# see docker/Dockerfile in AlphaFold repository | ||
local_scp_commit = '7102c6' | ||
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components = [ | ||
('stereo_chemical_props.txt', local_scp_commit, { | ||
'easyblock': 'Binary', | ||
'source_urls': [ | ||
'https://git.scicore.unibas.ch/schwede/openstructure/-/raw/%s/modules/mol/alg/src/' % local_scp_commit, | ||
], | ||
'sources': [ | ||
{ | ||
'download_filename': 'stereo_chemical_props.txt', | ||
'filename': 'stereo_chemical_props-%s.txt' % local_scp_commit, | ||
'extract_cmd': "cp %s ./stereo_chemical_props.txt", | ||
} | ||
], | ||
'checksums': [ | ||
'24510899eeb49167cffedec8fa45363a4d08279c0c637a403b452f7d0ac09451', # stereo_chemical_props-7102c6.txt | ||
] | ||
}) | ||
] | ||
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use_pip = True | ||
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exts_list = [ | ||
('PDBFixer', '1.9', { | ||
'source_urls': ['https://github.com/openmm/pdbfixer/archive/refs/tags/'], | ||
'sources': [{'download_filename': '%(version)s.tar.gz', 'filename': SOURCE_TAR_GZ}], | ||
'checksums': ['88b9a77e50655f89d0eb2075093773e82c27a4cef842cb7d735c877b20cd39fb'], | ||
}), | ||
('tabulate', '0.9.0', { | ||
'checksums': ['0095b12bf5966de529c0feb1fa08671671b3368eec77d7ef7ab114be2c068b3c'], | ||
}), | ||
('websocket-client', '1.5.1', { | ||
'modulename': 'websocket', | ||
'checksums': ['3f09e6d8230892547132177f575a4e3e73cfdf06526e20cc02aa1c3b47184d40'], | ||
}), | ||
('docker', '7.0.0', { | ||
'checksums': ['323736fb92cd9418fc5e7133bc953e11a9da04f4483f828b527db553f1e7e5a3'], | ||
}), | ||
('immutabledict', '4.1.0', { | ||
'checksums': ['93d100ccd2cd09a1fd3f136b9328c6e59529ba341de8bb499437f6819159fe8a'], | ||
}), | ||
('contextlib2', '21.6.0', { | ||
'checksums': ['ab1e2bfe1d01d968e1b7e8d9023bc51ef3509bba217bb730cee3827e1ee82869'], | ||
}), | ||
('ml_collections', '0.1.1', { | ||
'preinstallopts': "touch requirements.txt && touch requirements-test.txt && ", | ||
'checksums': ['3fefcc72ec433aa1e5d32307a3e474bbb67f405be814ea52a2166bfc9dbe68cc'], | ||
}), | ||
(name, version, { | ||
'patches': [ | ||
'AlphaFold-2.0.0_fix-packages.patch', | ||
'AlphaFold-2.3.2_data-dep-paths-shebang-UniRef30.patch', | ||
'AlphaFold-2.0.0_n-cpu.patch', | ||
'AlphaFold-2.0.1_setup_rm_tfcpu.patch', | ||
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'AlphaFold-2.3.2_use_openmm_8.0.0.patch', | ||
'AlphaFold-2.3.2_BioPythonPDBData.patch', | ||
], | ||
'source_urls': ['https://github.com/deepmind/alphafold/archive/refs/tags/'], | ||
'sources': [{'download_filename': 'v%(version)s.tar.gz', 'filename': SOURCE_TAR_GZ}], | ||
'checksums': [ | ||
{'AlphaFold-2.3.2.tar.gz': '4ea8005ba1b573fa1585e4c29b7d188c5cbfa59b4e4761c9f0c15c9db9584a8e'}, | ||
{'AlphaFold-2.0.0_fix-packages.patch': '826d2d1a5d6ac52c51a60ba210e1947d5631a1e2d76f8815305b5d23f74458db'}, | ||
{'AlphaFold-2.3.2_data-dep-paths-shebang-UniRef30.patch': | ||
'58cd0ce4094afe76909649abe68034c4fbdb500967f5c818f49b530356dc012b'}, | ||
{'AlphaFold-2.0.0_n-cpu.patch': 'dfda4dd5f9aba19fe2b6eb9a0ec583d12dcefdfee8ab8803fc57ad48d582db04'}, | ||
{'AlphaFold-2.0.1_setup_rm_tfcpu.patch': | ||
'1a2e4e843bd9a4d15ee39e6c37cc63ba281311cc7a0a5610f0e43b52ef93faac'}, | ||
{'AlphaFold-2.3.2_use_openmm_8.0.0.patch': | ||
'bbef940c0c959040aaf3984ec47777a229c164517b54616a2688d58fae636d84'}, | ||
{'AlphaFold-2.3.2_BioPythonPDBData.patch': | ||
'e4483a525ae5c4dc5a5f633bed8cf5337c329e64b603ab7b684a9d18cd26a22f'}, | ||
], | ||
}), | ||
] | ||
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local_pylibdir = '%(installdir)s/lib/python%(pyshortver)s/site-packages' | ||
local_link_scp = 'ln -s %%(installdir)s/stereo_chemical_props.txt %s/alphafold/common' % local_pylibdir | ||
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postinstallcmds = [ | ||
'cp %(builddir)s/AlphaFold/alphafold-%(version)s/run_alphafold*.py %(installdir)s/bin', | ||
'cp -rpP %(builddir)s/AlphaFold/alphafold-%(version)s/scripts %(installdir)s', | ||
'cd %(installdir)s/bin && ln -s run_alphafold.py alphafold', | ||
'chmod a+x %(installdir)s/bin/run_alphafold.py', | ||
local_link_scp, | ||
] | ||
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sanity_check_paths = { | ||
'files': ['bin/alphafold', 'bin/pdbfixer', 'bin/run_alphafold.py', 'stereo_chemical_props.txt'], | ||
'dirs': ['lib/python%(pyshortver)s/site-packages', 'scripts'], | ||
} | ||
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sanity_check_commands = [ | ||
"pdbfixer --help", | ||
"python -m openmm.testInstallation", | ||
"python -c 'import alphafold'", | ||
"alphafold --help 2>&1 | grep 'Full AlphaFold protein structure prediction script'", | ||
"python %(installdir)s/bin/run_alphafold_test.py", | ||
] | ||
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sanity_pip_check = True | ||
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# these allow to make predictions on proteins that would typically be too long to fit into GPU memory; | ||
# see https://github.com/deepmind/alphafold/blob/main/docker/run_docker.py | ||
modextravars = { | ||
# these allow to make predictions on proteins that would typically be too long to fit into GPU memory; | ||
# see https://github.com/deepmind/alphafold/blob/main/docker/run_docker.py | ||
'TF_FORCE_UNIFIED_MEMORY': '1', | ||
# jaxlib 0.4.1: https://jax.readthedocs.io/en/latest/changelog.html#jaxlib-0-4-1-dec-13-2022 | ||
# "The behavior of XLA_PYTHON_CLIENT_MEM_FRACTION=.XX has been changed to allocate XX% of the total GPU memory | ||
# instead of the previous behavior of using currently available GPU memory to calculate preallocation. Please refer | ||
# to GPU memory allocation for more details." | ||
# https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html | ||
'XLA_PYTHON_CLIENT_MEM_FRACTION': '2.5', | ||
# | ||
# Download with $EBROOTALPHAFOLD/scripts/download_all_data.sh /path/to/AlphaFold_DBs/$EBVERSIONALPHAFOLD | ||
'ALPHAFOLD_DATA_DIR': '/path/to/AlphaFold_DBs/%(versions)s', # please adapt | ||
# Adapt in order to use a different version of UniRef30 by default, | ||
# e.g., v2023_02 from https://wwwuser.gwdg.de/~compbiol/uniclust/2023_02/UniRef30_2023_02_hhsuite.tar.gz: | ||
'ALPHAFOLD_UNIREF30_VER': '2021_03', | ||
'OPENMM_RELAX': 'CUDA' # unset or set to 'CPU' in order not to run the energy minimization on GPU; PR#189 | ||
} | ||
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postinstallmsgs = [ | ||
"A newer version of UniRef30 (2023_02) is available at: " | ||
"https://wwwuser.gwdg.de/~compbiol/uniclust/2023_02/UniRef30_2023_02_hhsuite.tar.gz. " | ||
"Untar to $ALPHAFOLD_DATA_DIR/uniref30/ and set the default version accordingly by changing " | ||
"modextravars:ALPHAFOLD_UNIREF30_VER." | ||
] | ||
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moduleclass = 'bio' |
14 changes: 14 additions & 0 deletions
14
easybuild/easyconfigs/a/AlphaFold/AlphaFold-2.3.2_BioPythonPDBData.patch
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# Thomas Hoffmann, EMBL Heidelberg, structures-it@embl.de, 2024/10 | ||
# BioPython 1.83 does not provide protein_letters_3to1 in Bio.Data.SCOPdata but in Bio.Data.PDBData (and Bio.Data.IUPACData) | ||
diff -ru -ru alphafold-2.3.2/alphafold/data/mmcif_parsing.py alphafold-2.3.2_BioPythonSCOPData/alphafold/data/mmcif_parsing.py | ||
--- alphafold-2.3.2/alphafold/data/mmcif_parsing.py 2024-02-19 09:55:16.359778490 +0100 | ||
+++ alphafold-2.3.2_BioPythonSCOPData/alphafold/data/mmcif_parsing.py 2023-03-27 13:50:49.000000000 +0200 | ||
@@ -21,7 +21,7 @@ | ||
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from absl import logging | ||
from Bio import PDB | ||
-from Bio.Data import SCOPData | ||
+from Bio.Data import PDBData as SCOPData | ||
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# Type aliases: | ||
ChainId = str |
164 changes: 164 additions & 0 deletions
164
easybuild/easyconfigs/a/AlphaFold/AlphaFold-2.3.2_data-dep-paths-shebang-UniRef30.patch
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pick up on $ALPHAFOLD_DATA_DIR to specify location to downloaded data | ||
(see https://github.com/deepmind/alphafold/blob/main/docker/run_docker.py); | ||
pick up on HH-suite, HHMER, Kalign dependencies provided via EasyBuild | ||
author: Kenneth Hoste (HPC-UGent) | ||
update 2.0.1 -> 2.1.0/2.1.2/2.3.0/2.3.2: Thomas Hoffmann (EMBL); | ||
uniref30 version env. variable (THEMBL) | ||
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diff -ru alphafold-2.3.2/run_alphafold.py alphafold-2.3.2_data-dep-paths-shebang-UniRef30/run_alphafold.py | ||
--- alphafold-2.3.2/run_alphafold.py 2023-03-27 13:50:49.000000000 +0200 | ||
+++ alphafold-2.3.2_data-dep-paths-shebang-UniRef30/run_alphafold.py 2024-10-11 11:34:06.330278962 +0200 | ||
@@ -1,3 +1,4 @@ | ||
+#!/usr/bin/env python | ||
# Copyright 2021 DeepMind Technologies Limited | ||
# | ||
# Licensed under the Apache License, Version 2.0 (the "License"); | ||
@@ -42,6 +43,48 @@ | ||
import numpy as np | ||
|
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# Internal import (7716). | ||
+use_reduced_dbs = any("--db_preset=reduced_dbs" in s for s in sys.argv[1:]) | ||
+use_monomer_preset = not any("--model_preset=multimer" in s for s in sys.argv[1:]) | ||
+ | ||
+data_dir = os.getenv('ALPHAFOLD_DATA_DIR') | ||
+use_gpu_relax = os.getenv('OPENMM_RELAX')=='CUDA' | ||
+uniref30_ver = os.getenv('ALPHAFOLD_UNIREF30_VER') | ||
+if not uniref30_ver: uniref30_ver = '2021_03' | ||
+ | ||
+if data_dir: | ||
+ mgnify_database_path = os.path.join(data_dir, 'mgnify', 'mgy_clusters_2022_05.fa') | ||
+ uniref90_database_path = os.path.join(data_dir, 'uniref90', 'uniref90.fasta') | ||
+ template_mmcif_dir = os.path.join(data_dir, 'pdb_mmcif', 'mmcif_files') | ||
+ obsolete_pdbs_path = os.path.join(data_dir, 'pdb_mmcif', 'obsolete.dat') | ||
+ if use_monomer_preset: | ||
+ pdb_seqres_database_path = None | ||
+ uniprot_database_path = None | ||
+ pdb70_database_path = os.path.join(data_dir, 'pdb70', 'pdb70') | ||
+ else: | ||
+ pdb_seqres_database_path = os.path.join(data_dir, 'pdb_seqres', 'pdb_seqres.txt') | ||
+ uniprot_database_path = os.path.join(data_dir, 'uniprot', 'uniprot.fasta') | ||
+ pdb70_database_path = None | ||
+ if use_reduced_dbs: | ||
+ small_bfd_database_path = os.path.join(data_dir, 'small_bfd','bfd-first_non_consensus_sequences.fasta') | ||
+ uniref30_database_path = None | ||
+ bfd_database_path = None | ||
+ else: | ||
+ small_bfd_database_path = None | ||
+ bfd_database_path = os.path.join(data_dir, 'bfd', 'bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt') | ||
+ uniref30_database_path = os.path.join(data_dir, 'uniref30', 'UniRef30_%s' % uniref30_ver) | ||
+else: | ||
+ sys.stderr.write("$ALPHAFOLD_DATA_DIR is not defined!") | ||
+ uniref90_database_path = None | ||
+ mgnify_database_path = None | ||
+ bfd_database_path = None | ||
+ uniref30_database_path = None | ||
+ pdb70_database_path = None | ||
+ template_mmcif_dir = None | ||
+ obsolete_pdbs_path = None | ||
+ small_bfd_database_path = None | ||
+ uniprot_database_path = None | ||
+ pdb_seqres_database_path = None | ||
+ use_gpu_relax = None | ||
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logging.set_verbosity(logging.INFO) | ||
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@@ -59,7 +102,7 @@ | ||
'separated by commas. All FASTA paths must have a unique basename as the ' | ||
'basename is used to name the output directories for each prediction.') | ||
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-flags.DEFINE_string('data_dir', None, 'Path to directory of supporting data.') | ||
+flags.DEFINE_string('data_dir', data_dir, 'Path to directory of supporting data.') | ||
flags.DEFINE_string('output_dir', None, 'Path to a directory that will ' | ||
'store the results.') | ||
flags.DEFINE_string('jackhmmer_binary_path', shutil.which('jackhmmer'), | ||
@@ -71,32 +114,32 @@ | ||
flags.DEFINE_string('hmmsearch_binary_path', shutil.which('hmmsearch'), | ||
'Path to the hmmsearch executable.') | ||
flags.DEFINE_string('hmmbuild_binary_path', shutil.which('hmmbuild'), | ||
- 'Path to the hmmbuild executable.') | ||
+ 'Path to the hmmbuild executable.') | ||
flags.DEFINE_string('kalign_binary_path', shutil.which('kalign'), | ||
- 'Path to the Kalign executable.') | ||
-flags.DEFINE_string('uniref90_database_path', None, 'Path to the Uniref90 ' | ||
- 'database for use by JackHMMER.') | ||
-flags.DEFINE_string('mgnify_database_path', None, 'Path to the MGnify ' | ||
- 'database for use by JackHMMER.') | ||
-flags.DEFINE_string('bfd_database_path', None, 'Path to the BFD ' | ||
- 'database for use by HHblits.') | ||
-flags.DEFINE_string('small_bfd_database_path', None, 'Path to the small ' | ||
- 'version of BFD used with the "reduced_dbs" preset.') | ||
-flags.DEFINE_string('uniref30_database_path', None, 'Path to the UniRef30 ' | ||
- 'database for use by HHblits.') | ||
-flags.DEFINE_string('uniprot_database_path', None, 'Path to the Uniprot ' | ||
- 'database for use by JackHMMer.') | ||
-flags.DEFINE_string('pdb70_database_path', None, 'Path to the PDB70 ' | ||
- 'database for use by HHsearch.') | ||
-flags.DEFINE_string('pdb_seqres_database_path', None, 'Path to the PDB ' | ||
- 'seqres database for use by hmmsearch.') | ||
-flags.DEFINE_string('template_mmcif_dir', None, 'Path to a directory with ' | ||
- 'template mmCIF structures, each named <pdb_id>.cif') | ||
+ 'Path to the Kalign executable.') | ||
+flags.DEFINE_string('uniref90_database_path', uniref90_database_path, 'Path to the Uniref90 ' | ||
+ 'database for use by JackHMMER.') | ||
+flags.DEFINE_string('mgnify_database_path', mgnify_database_path, 'Path to the MGnify ' | ||
+ 'database for use by JackHMMER.') | ||
+flags.DEFINE_string('bfd_database_path', bfd_database_path, 'Path to the BFD ' | ||
+ 'database for use by HHblits.') | ||
+flags.DEFINE_string('small_bfd_database_path', small_bfd_database_path, 'Path to the small ' | ||
+ 'version of BFD used with the "reduced_dbs" preset.') | ||
+flags.DEFINE_string('uniref30_database_path', uniref30_database_path, 'Path to the UniRef30 ' | ||
+ 'database for use by HHblits.') | ||
+flags.DEFINE_string('uniprot_database_path', uniprot_database_path, 'Path to the Uniprot ' | ||
+ 'database for use by JackHMMer.') | ||
+flags.DEFINE_string('pdb70_database_path', pdb70_database_path, 'Path to the PDB70 ' | ||
+ 'database for use by HHsearch.') | ||
+flags.DEFINE_string('pdb_seqres_database_path', pdb_seqres_database_path, 'Path to the PDB ' | ||
+ 'seqres database for use by hmmsearch.') | ||
+flags.DEFINE_string('template_mmcif_dir', template_mmcif_dir, 'Path to a directory with ' | ||
+ 'template mmCIF structures, each named <pdb_id>.cif') | ||
flags.DEFINE_string('max_template_date', None, 'Maximum template release date ' | ||
- 'to consider. Important if folding historical test sets.') | ||
-flags.DEFINE_string('obsolete_pdbs_path', None, 'Path to file containing a ' | ||
- 'mapping from obsolete PDB IDs to the PDB IDs of their ' | ||
- 'replacements.') | ||
+ 'to consider. Important if folding historical test sets.') | ||
+flags.DEFINE_string('obsolete_pdbs_path', obsolete_pdbs_path, 'Path to file containing a ' | ||
+ 'mapping from obsolete PDB IDs to the PDB IDs of their ' | ||
+ 'replacements.') | ||
flags.DEFINE_enum('db_preset', 'full_dbs', | ||
['full_dbs', 'reduced_dbs'], | ||
'Choose preset MSA database configuration - ' | ||
@@ -137,7 +180,7 @@ | ||
'distracting stereochemical violations but might help ' | ||
'in case you are having issues with the relaxation ' | ||
'stage.') | ||
-flags.DEFINE_boolean('use_gpu_relax', None, 'Whether to relax on GPU. ' | ||
+flags.DEFINE_boolean('use_gpu_relax', use_gpu_relax, 'Whether to relax on GPU. ' | ||
'Relax on GPU can be much faster than CPU, so it is ' | ||
'recommended to enable if possible. GPUs must be available' | ||
' if this setting is enabled.') | ||
@@ -334,6 +377,10 @@ | ||
'sure it is installed on your system.') | ||
|
||
use_small_bfd = FLAGS.db_preset == 'reduced_dbs' | ||
+ if use_small_bfd and data_dir: | ||
+ bfd_database_path = None | ||
+ uniref30_database_path = None | ||
+ | ||
_check_flag('small_bfd_database_path', 'db_preset', | ||
should_be_set=use_small_bfd) | ||
_check_flag('bfd_database_path', 'db_preset', | ||
@@ -456,13 +503,7 @@ | ||
flags.mark_flags_as_required([ | ||
'fasta_paths', | ||
'output_dir', | ||
- 'data_dir', | ||
- 'uniref90_database_path', | ||
- 'mgnify_database_path', | ||
- 'template_mmcif_dir', | ||
'max_template_date', | ||
- 'obsolete_pdbs_path', | ||
- 'use_gpu_relax', | ||
]) | ||
|
||
app.run(main) |
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2.15.1 might be better?
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CPU-only TensorFlow is 2.13.0 in
2023a
generation, so this is fine (people can always swap to 2.15.1, but I doubt it really matters for AlphaFold)