kuixu/alphafold

Install alphafold on the local machine, get out of docker.

alphafold's Issues

let pip3 install requirements without versions

Regarding pip3 install of requirement.txt
I removed versions and let pip install the latest version. The next pip3 did not work:

af2) [satyshur@KAFKA alphafold]$ pip3 install --upgrade "jax[cuda111]" -f
https://storage.googleapis.com/jax-releases/jax_releases.html
&& pip3 install jaxlib==0.1.70+cuda111 -f
https://storage.googleapis.com/jax-releases/jax_releases.html
Looking in links: https://storage.googleapis.com/jax-releases/jax_releases.html
Requirement already satisfied: jax[cuda111] in /home/satyshur/.conda/envs/af2/lib/python3.8/site-packages (0.4.13)
Collecting jax[cuda111]
Downloading https://storage.googleapis.com/jax-releases/jax/jax-0.4.21-py3-none-any.whl (1.7 MB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.7/1.7 MB 25.9 MB/s eta 0:00:00
WARNING: jax 0.4.21 does not provide the extra 'cuda111'
Requirement already satisfied: ml-dtypes>=0.2.0 in /home/satyshur/.conda/envs/af2/lib/python3.8/site-packages (from jax[cuda111]) (0.2.0)
Requirement already satisfied: numpy>=1.22 in /home/satyshur/.conda/envs/af2/lib/python3.8/site-packages (from jax[cuda111]) (1.22.3)
Requirement already satisfied: opt-einsum in /home/satyshur/.conda/envs/af2/lib/python3.8/site-packages (from jax[cuda111]) (3.3.0)
Requirement already satisfied: scipy>=1.9 in /home/satyshur/.conda/envs/af2/lib/python3.8/site-packages (from jax[cuda111]) (1.10.1)
Requirement already satisfied: importlib-metadata>=4.6 in /home/satyshur/.conda/envs/af2/lib/python3.8/site-packages (from jax[cuda111]) (7.0.0)
Requirement already satisfied: zipp>=0.5 in /home/satyshur/.conda/envs/af2/lib/python3.8/site-packages (from importlib-metadata>=4.6->jax[cuda111]) (3.17.0)
INFO: pip is looking at multiple versions of jax to determine which version is compatible with other requirements. This could take a while.
WARNING: jax 0.4.21 does not provide the extra 'cuda111'
Downloading https://storage.googleapis.com/jax-releases/jax/jax-0.4.20-py3-none-any.whl (1.7 MB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.7/1.7 MB 44.3 MB/s eta 0:00:00
WARNING: jax 0.4.20 does not provide the extra 'cuda111'
WARNING: jax 0.4.20 does not provide the extra 'cuda111'
Downloading https://storage.googleapis.com/jax-releases/jax/jax-0.4.19-py3-none-any.whl (1.7 MB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.7/1.7 MB 37.5 MB/s eta 0:00:00
WARNING: jax 0.4.19 does not provide the extra 'cuda111'
WARNING: jax 0.4.19 does not provide the extra 'cuda111'
Downloading https://storage.googleapis.com/jax-releases/jax/jax-0.4.18-py3-none-any.whl (1.7 MB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.7/1.7 MB 28.7 MB/s eta 0:00:00
WARNING: jax 0.4.18 does not provide the extra 'cuda111'
WARNING: jax 0.4.18 does not provide the extra 'cuda111'
Downloading https://storage.googleapis.com/jax-releases/jax/jax-0.4.17-py3-none-any.whl (1.7 MB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.7/1.7 MB 22.1 MB/s eta 0:00:00
WARNING: jax 0.4.17 does not provide the extra 'cuda111'
WARNING: jax 0.4.17 does not provide the extra 'cuda111'
Downloading https://storage.googleapis.com/jax-releases/jax/jax-0.4.16-py3-none-any.whl (1.6 MB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.6/1.6 MB 38.7 MB/s eta 0:00:00
WARNING: jax 0.4.16 does not provide the extra 'cuda111'
WARNING: jax 0.4.16 does not provide the extra 'cuda111'
Downloading https://storage.googleapis.com/jax-releases/jax/jax-0.4.14.tar.gz (1.3 MB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.3/1.3 MB 31.9 MB/s eta 0:00:00
Installing build dependencies ... done
Getting requirements to build wheel ... done
Preparing metadata (pyproject.toml) ... done
WARNING: jax 0.4.14 does not provide the extra 'cuda111'
WARNING: jax 0.4.14 does not provide the extra 'cuda111'
WARNING: jax 0.4.13 does not provide the extra 'cuda111'
Looking in links: https://storage.googleapis.com/jax-releases/jax_releases.html
ERROR: Ignored the following yanked versions: 0.1.63, 0.4.0
ERROR: Could not find a version that satisfies the requirement jaxlib==0.1.70+cuda111 (from versions: 0.1.32, 0.1.40, 0.1.41, 0.1.42, 0.1.43, 0.1.44, 0.1.46, 0.1.50, 0.1.51, 0.1.52, 0.1.55, 0.1.56, 0.1.57, 0.1.58, 0.1.59, 0.1.60, 0.1.61, 0.1.62, 0.1.63, 0.1.64, 0.1.65, 0.1.66, 0.1.67, 0.1.68, 0.1.69, 0.1.70, 0.1.71, 0.1.72, 0.1.73, 0.1.74, 0.1.75, 0.1.76, 0.3.0, 0.3.2, 0.3.5, 0.3.7, 0.3.8, 0.3.10, 0.3.14, 0.3.15, 0.3.18, 0.3.20, 0.3.22, 0.3.24, 0.3.25, 0.4.1, 0.4.2, 0.4.3, 0.4.4, 0.4.6, 0.4.7, 0.4.9, 0.4.10, 0.4.11, 0.4.12, 0.4.13)
ERROR: No matching distribution found for jaxlib==0.1.70+cuda111

indeed, the html does not have any matching jaxlib==0.1.70+cuda111

database/pdb_mmcif/raw/*/*.cif': No such file or directory

Hi there,

I started downloading all data into my designated folder with sufficient room. While I am able to download params, bfd, mgnify, pdb70, and uniclust30, I got error message for downloading mmcif.

$ ./download_pdb70.sh /database
02/02 09:21:07 [NOTICE] Downloading 1 item(s)
[#b9fa7b 1.8GiB/19GiB(9%) CN:1 DL:2.7MiB ETA:1h47m45s]

but

$ ./download_pdb_mmcif.sh /database
Running rsync to fetch all mmCIF files (note that the rsync progress estimate might be inaccurate)...
Unzipping all mmCIF files...
Flattening all mmCIF files...
mv: cannot stat '/home5/alphafold_database/pdb_mmcif/raw/*/*.cif': No such file or directory

I also noticed that I am unable to download uniref90. Is this might be the firewall issue on my end? My university did put firewall on any ftp source.

Please help.

Thank you!

David

can't find file to patch at input line 5

patch -p0 < /mnt/e/total_scripts/ref_pkgs/bioinfor/protein/conda_alphafold/alphafold/docker/openmm.patch
can't find file to patch at input line 5
Perhaps you used the wrong -p or --strip option?
The text leading up to this was:

|Index: simtk/openmm/app/topology.py
|===================================================================
|--- simtk.orig/openmm/app/topology.py
|+++ simtk/openmm/app/topology.py

File to patch:

FileNotFoundError: [Errno 2] No such file or directory: '../AlphaLink2/alphafold/all_data/pdb_mmcif/mmcif_files/4fxg.cif'

How to deal with this problem? The file folder "mmcif_files" and file "obsolete.dat" are both newly updated.

Only 1 GPU is used

Hi, I could successfully install and run your version of alphafold, however, I notice only 1 GPU is used, even though I have 8x RTX 3090 cards.
When it reaches certain stage of relaxation, all the CPUs are used.

I tried to put "export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7" in exp/run_local.sh, but still only 1 GPU is used.
Any suggestion is greatly appreciated!

Running output

Hi, I have run as guide (see below).
python3 run_alphafold.py --fasta_paths=m.fasta --max_template_date=2015-11-10

However, I only see a folder msas/ in output dir, with both mgnify_hits.sto and uniref90_hits.sto inside folder msas/.

During running, there is log below.

I0808 12:36:13.795397 139856370325312 tpu_client.py:54] Starting the local TPU driver.
I0808 12:36:13.796061 139856370325312 xla_bridge.py:214] Unable to initialize backend 'tpu_driver': Not found: Unable to find driver in registry given worker: local://
I0808 12:36:14.556874 139856370325312 xla_bridge.py:214] Unable to initialize backend 'tpu': Invalid argument: TpuPlatform is not available.
I0808 12:36:19.394575 139856370325312 run_alphafold.py:293] Have 5 models: ['model_1', 'model_2', 'model_3', 'model_4', 'model_5']
I0808 12:36:19.394828 139856370325312 run_alphafold.py:306] Using random seed 8198044181325425710 for the data pipeline
I0808 12:36:19.395596 139856370325312 pipeline.py:136] loading uniref90
I0808 12:36:19.511542 139856370325312 pipeline.py:147] loading mgnify
I0808 12:36:19.513229 139856370325312 pipeline.py:153] query mgnify
I0808 12:36:19.513805 139856370325312 hhblits.py:128] Launching subprocess "hhblits -i m.fasta -cpu 4 -oa3m /tmp/tmpe4g0qjk_/output.a3m -o /dev/null -n 3 -e 0.001 -maxseq 1000000 -realign_max 100000 -maxfilt 100000 -min_prefilter_hits 1000 -d /data01/xukui/alphafold/bfd/bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt -d /data01/xukui/alphafold/uniclust30/uniclust30_2018_08/uniclust30_2018_08"
I0808 12:36:19.571469 139856370325312 utils.py:36] Started HHblits query

I see -o /dev/null. Where should I change that option? Thank you!

Setting working directory in install_on_local.sh

The script install_on_local.sh sets the variable work_path to $(PWD). In bash, this tries to run the command PWD, which usually doesn't exist. I believe, it should be $PWD, ${PWD} or $(pwd).

alphafold/install_on_local.sh

Line 24 in 21530fa

work_path=$(PWD)

Also, ideally, some quotes should probably be added to support people running this in paths with spaces, eg:

# work_path=/path/to/alphafold-code
work_path="$PWD"
# update openmm 
a="$(which python)"
cd "$(dirname "$(dirname "$a")")/lib/python3.8/site-packages"
patch -p0 < "$work_path/docker/openmm.patch"

stereo_chemical_props.txt is not found

The file stereo_chemical_props.txt, which is read by alphafold/common/residue_constants.py is not found. This is because that path is presumed to be in the current directory, which would only by the case if the program is run from the source directory.

That path should probably be joined to FLAGS.data_dir, but FLAGS.data_dir is not passed into the function that needs it.

Unable to initialize backend 'gpu'

Hello,I have follow you readme to install the anaconda environment,and it can run with cpu ,but it can not run with gpu ,and in my devices have a nvidia RTX TITAN GPU with 24G momeory ,when I whatever use bash python3 run_alphafold.py --fasta_paths=T1050.fasta --max_template_date=2020-05-14# or simply exp/run_local.sh T1050.fasta it warning with I0820 16:00:20.270564 140257858221888 xla_bridge.py:212] Unable to initialize backend 'tpu_driver': Not found: Unable to find driver in registry given worker: local://
I0820 16:00:20.281177 140257858221888 xla_bridge.py:212] Unable to initialize backend 'gpu': Not found: Could not find registered platform with name: "cuda". Available platform names are: Interpreter Host
I0820 16:00:20.281787 140257858221888 xla_bridge.py:212] Unable to initialize backend 'tpu': Invalid argument: TpuPlatform is not available.
W0820 16:00:20.282057 140257858221888 xla_bridge.py:215] No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)
I feel it like run only with cpu ,and it only use 315MiB GPU Momory when I try to input nvidia-smi to look how much GPU memory use.

ValueError: Could not find CIFs in /data01/xukui/alphafold/pdb_mmcif/mmcif_files

Hello, I'm getting this error. Wondering is there a step I missed in setup. Can I download these mmcif files from somewhere? I cannot see them in your repository structure!

Many thanks,
Linda

E0930 14:56:15.892669 139744909522752 templates.py:860] Could not find CIFs in /data01/xukui/alphafold/pdb_mmcif/mmcif_files
Traceback (most recent call last):
  File "run_alphafold.py", line 338, in <module>
    app.run(main)
  File "/home/linda/programs/anaconda3/envs/af2/lib/python3.8/site-packages/absl/app.py", line 312, in run
    _run_main(main, args)
  File "/home/linda/programs/anaconda3/envs/af2/lib/python3.8/site-packages/absl/app.py", line 258, in _run_main
    sys.exit(main(argv))
  File "run_alphafold.py", line 265, in main
    template_featurizer = templates.TemplateHitFeaturizer(
  File "/home/linda/alphafold/alphafold/data/templates.py", line 861, in __init__
    raise ValueError(f'Could not find CIFs in {self._mmcif_dir}')
ValueError: Could not find CIFs in /data01/xukui/alphafold/pdb_mmcif/mmcif_files

jaxlib.xla_extension.XlaRuntimeError

My system is AMD 3955W, 128 GB RAM, RTX A4000 16GB, Windows 10 WSL2, Ubuntu 20.04.

When I tried to predict a 156 a.a sequence, I got an error message "jaxlib.xla_extension.XlaRuntimeError: INTERNAL: RET_CHECK failure (external/org_tensorflow/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc:641) dnn != nullptr".
Below is the log. Can someone help me to troubleshoot it?

(alphafold) whelanlab@CHEM-AG007D:~/alphafold$ exp/run_local.sh R1.fasta
/home/whelanlab/localcolabfold/colabfold-conda/lib/python3.9/site-packages/haiku/_src/data_structures.py:37: FutureWarning: jax.tree_structure is deprecated, and will be removed in a future release. Use jax.tree_util.tree_structure instead.
PyTreeDef = type(jax.tree_structure(None))
/home/whelanlab/localcolabfold/colabfold-conda/lib/python3.9/site-packages/absl/flags/_validators.py:203: UserWarning: Flag --output_dir has a non-None defau lt value; therefore, mark_flag_as_required will pass even if flag is not specified in the command line!
warnings.warn(
/home/whelanlab/localcolabfold/colabfold-conda/lib/python3.9/site-packages/absl/flags/_validators.py:203: UserWarning: Flag --model_names has a non-None defa ult value; therefore, mark_flag_as_required will pass even if flag is not specified in the command line!
warnings.warn(
/home/whelanlab/localcolabfold/colabfold-conda/lib/python3.9/site-packages/absl/flags/_validators.py:203: UserWarning: Flag --data_dir has a non-None default value; therefore, mark_flag_as_required will pass even if flag is not specified in the command line!
warnings.warn(
/home/whelanlab/localcolabfold/colabfold-conda/lib/python3.9/site-packages/absl/flags/_validators.py:203: UserWarning: Flag --preset has a non-None default v alue; therefore, mark_flag_as_required will pass even if flag is not specified in the command line!
warnings.warn(
/home/whelanlab/localcolabfold/colabfold-conda/lib/python3.9/site-packages/absl/flags/_validators.py:203: UserWarning: Flag --uniref90_database_path has a no n-None default value; therefore, mark_flag_as_required will pass even if flag is not specified in the command line!
warnings.warn(
/home/whelanlab/localcolabfold/colabfold-conda/lib/python3.9/site-packages/absl/flags/_validators.py:203: UserWarning: Flag --mgnify_database_path has a non- None default value; therefore, mark_flag_as_required will pass even if flag is not specified in the command line!
warnings.warn(
/home/whelanlab/localcolabfold/colabfold-conda/lib/python3.9/site-packages/absl/flags/_validators.py:203: UserWarning: Flag --uniclust30_database_path has a non-None default value; therefore, mark_flag_as_required will pass even if flag is not specified in the command line!
warnings.warn(
/home/whelanlab/localcolabfold/colabfold-conda/lib/python3.9/site-packages/absl/flags/_validators.py:203: UserWarning: Flag --bfd_database_path has a non-Non e default value; therefore, mark_flag_as_required will pass even if flag is not specified in the command line!
warnings.warn(
/home/whelanlab/localcolabfold/colabfold-conda/lib/python3.9/site-packages/absl/flags/_validators.py:203: UserWarning: Flag --pdb70_database_path has a non-N one default value; therefore, mark_flag_as_required will pass even if flag is not specified in the command line!
warnings.warn(
/home/whelanlab/localcolabfold/colabfold-conda/lib/python3.9/site-packages/absl/flags/_validators.py:203: UserWarning: Flag --template_mmcif_dir has a non-No ne default value; therefore, mark_flag_as_required will pass even if flag is not specified in the command line!
warnings.warn(
/home/whelanlab/localcolabfold/colabfold-conda/lib/python3.9/site-packages/absl/flags/_validators.py:203: UserWarning: Flag --max_template_date has a non-Non e default value; therefore, mark_flag_as_required will pass even if flag is not specified in the command line!
warnings.warn(
/home/whelanlab/localcolabfold/colabfold-conda/lib/python3.9/site-packages/absl/flags/validators.py:203: UserWarning: Flag --obsolete_pdbs_path has a non-No ne default value; therefore, mark_flag_as_required will pass even if flag is not specified in the command line!
warnings.warn(
I0320 18:45:41.543587 139960428058432 templates.py:880] Using precomputed obsolete pdbs /mnt/d/alphafold/database/pdb_mmcif/obsolete.dat.
I0320 18:45:43.169499 139960428058432 xla_bridge.py:166] Remote TPU is not linked into jax; skipping remote TPU.
I0320 18:45:43.169672 139960428058432 xla_bridge.py:413] Unable to initialize backend 'tpu_driver': Could not initialize backend 'tpu_driver'
I0320 18:45:43.511162 139960428058432 xla_bridge.py:413] Unable to initialize backend 'rocm': NOT_FOUND: Could not find registered platform with name: "rocm" . Available platform names are: Interpreter Host CUDA
I0320 18:45:43.511674 139960428058432 xla_bridge.py:413] Unable to initialize backend 'tpu': module 'jaxlib.xla_extension' has no attribute 'get_tpu_client'
I0320 18:45:43.511768 139960428058432 xla_bridge.py:413] Unable to initialize backend 'plugin': xla_extension has no attributes named get_plugin_device_clien t. Compile TensorFlow with //tensorflow/compiler/xla/python:enable_plugin_device set to true (defaults to false) to enable this.
I0320 18:45:51.545218 139960428058432 run_alphafold.py:293] Have 5 models: ['model_1', 'model_2', 'model_3', 'model_4', 'model_5']
I0320 18:45:51.545373 139960428058432 run_alphafold.py:306] Using random seed 4477881255564421494 for the data pipeline
I0320 18:45:51.553825 139960428058432 pipeline.py:130] query uniref90
I0320 18:45:51.554164 139960428058432 jackhmmer.py:119] Launching subprocess "jackhmmer -o /dev/null -A /tmp/tmpydz_0bbz/output.sto --noali --F1 0.0005 --F2 5e-05 --F3 5e-07 --incE 0.0001 -E 0.0001 --cpu 8 -N 1 R1.fasta /mnt/d/alphafold/database/uniref90/uniref90.fasta"
I0320 18:45:51.564941 139960428058432 utils.py:36] Started Jackhmmer (uniref90.fasta) query
I0320 19:45:55.761877 139960428058432 utils.py:40] Finished Jackhmmer (uniref90.fasta) query in 3604.197 seconds
I0320 19:45:55.808635 139960428058432 pipeline.py:141] query mgnify
I0320 19:45:55.809039 139960428058432 jackhmmer.py:119] Launching subprocess "jackhmmer -o /dev/null -A /tmp/tmp5t31w7bf/output.sto --noali --F1 0.0005 --F2 5e-05 --F3 5e-07 --incE 0.0001 -E 0.0001 --cpu 8 -N 1 R1.fasta /mnt/d/alphafold/database/mgnify/mgy_clusters.fa"
I0320 19:45:55.817553 139960428058432 utils.py:36] Started Jackhmmer (mgy_clusters.fa) query
I0320 20:44:21.103180 139960428058432 utils.py:40] Finished Jackhmmer (mgy_clusters.fa) query in 3505.285 seconds
I0320 20:44:21.107893 139960428058432 pipeline.py:153] query mgnify
I0320 20:44:21.108175 139960428058432 hhblits.py:128] Launching subprocess "hhblits -i R1.fasta -cpu 4 -oa3m /tmp/tmpasn145w/output.a3m -o /dev/null -n 3 -e 0.001 -maxseq 1000000 -realign_max 100000 -maxfilt 100000 -min_prefilter_hits 1000 -d /mnt/d/alphafold/database/bfd/bfd_metaclust_clu_complete_id30_c9 0_final_seq.sorted_opt -d /mnt/d/alphafold/database/uniclust30/uniclust30_2018_08/uniclust30_2018_08"
I0320 20:44:21.117413 139960428058432 utils.py:36] Started HHblits query
I0321 02:22:28.651200 139960428058432 utils.py:40] Finished HHblits query in 20287.534 seconds
I0321 02:22:28.785565 139960428058432 hhsearch.py:76] Launching subprocess "hhsearch -i /tmp/tmp5es22b99/query.a3m -o /tmp/tmp5es22b99/output.hhr -maxseq 1000000 -d /mnt/d/alphafold/database/pdb70/pdb70"
I0321 02:22:28.794022 139960428058432 utils.py:36] Started HHsearch query
I0321 02:54:02.204876 139960428058432 utils.py:40] Finished HHsearch query in 1893.411 seconds
I0321 02:54:02.476578 139960428058432 templates.py:892] Searching for template for: None
W0321 02:54:02.476918 139960428058432 templates.py:139] Template structure not in release dates dict: 1ivz
I0321 02:54:02.477041 139960428058432 templates.py:753] Reading PDB entry from /mnt/d/alphafold/database/pdb_mmcif/mmcif_files/1ivz.cif. Query: ATVPFMVPFT LNFTITNLQYEEDMRHPGSRKFNATERELQGLLKPLFRNSSLEYLYSGCRLASLRPEKDSSATAVDAICTHRPDPEDLGLDRERLYWELSNLTNGIQELGPYTLDRNSLYVNGFTHRSSMPTTSTPGTSTVDVGTSGTPSSSPSPT, templa te: SSSSQHFNLNFTITNLPYSQDIAQPSTTKYQQTKRSIENALNQLFRNSSIKSYFSDCQVLAFRSVSNNNNHTGVDSLCNFSPLARRVDRVAIYEEFLRMTHNGTQLLNFTLDRKSVFVDSGP
I0321 02:54:04.145149 139960428058432 templates.py:278] Found an exact template match 1ivz_A.
W0321 02:54:04.168012 139960428058432 templates.py:139] Template structure not in release dates dict: 2e7v
I0321 02:54:04.168263 139960428058432 templates.py:753] Reading PDB entry from /mnt/d/alphafold/database/pdb_mmcif/mmcif_files/2e7v.cif. Query: ATVPFMVPFT LNFTITNLQYEEDMRHPGSRKFNATERELQGLLKPLFRNSSLEYLYSGCRLASLRPEKDSSATAVDAICTHRPDPEDLGLDRERLYWELSNLTNGIQELGPYTLDRNSLYVNGFTHRSSMPTTSTPGTSTVDVGTSGTPSSSPSPT, templa te: KAYFYHSSFQILNVEYTEALNSPATHEYRTLSERIEAMITDEFRGSSLKSEFIRTHVVKLRKEGTGVVADVVMKFRSSKRNNRKVMKTRIQSVLRRLSSSGNLEIAPSNEI
I0321 02:54:04.227076 139960428058432 templates.py:278] Found an exact template match 2e7v_A.
W0321 02:54:04.230510 139960428058432 templates.py:139] Template structure not in release dates dict: 6bsb
I0321 02:54:04.230618 139960428058432 templates.py:753] Reading PDB entry from /mnt/d/alphafold/database/pdb_mmcif/mmcif_files/6bsb.cif. Query: ATVPFMVPFT LNFTITNLQYEEDMRHPGSRKFNATERELQGLLKPLFRNSSLEYLYSGCRLASLRPEKDSSATAVDAICTHRPDPEDLGLDRERLYWELSNLTNGIQELGPYTLDRNSLYVNGFTHRSSMPTTSTPGTSTVDVGTSGTPSSSPSPT, templa te: FFFLSFHISNLQFNSSLEDPSTDYYQELQRDISEMFLQIYKQGGFLGLSNIKFRPG
I0321 02:54:04.350127 139960428058432 templates.py:278] Found an exact template match 6bsb_A.
W0321 02:54:04.353108 139960428058432 templates.py:139] Template structure not in release dates dict: 6bsc
I0321 02:54:04.353233 139960428058432 templates.py:753] Reading PDB entry from /mnt/d/alphafold/database/pdb_mmcif/mmcif_files/6bsc.cif. Query: ATVPFMVPFT LNFTITNLQYEEDMRHPGSRKFNATERELQGLLKPLFRNSSLEYLYSGCRLASLRPEKDSSATAVDAICTHRPDPEDLGLDRERLYWELSNLTNGIQELGPYTLDRNSLYVNGFTHRSSMPTTSTPGTSTVDVGTSGTPSSSPSPT, templa te: FFFLSFHISNLQFNSSLEDPSTDYYQELQRDISEMFLQIYKQGGFLGLSNIKFRPG
I0321 02:54:04.616760 139960428058432 templates.py:278] Found an exact template match 6bsc_A.
W0321 02:54:04.619688 139960428058432 templates.py:139] Template structure not in release dates dict: 2acm
I0321 02:54:04.619805 139960428058432 templates.py:753] Reading PDB entry from /mnt/d/alphafold/database/pdb_mmcif/mmcif_files/2acm.cif. Query: ATVPFMVPFT LNFTITNLQYEEDMRHPGSRKFNATERELQGLLKPLFRNSSLEYLYSGCRLASLRPEKDSSATAVDAICTHRPDPEDLGLDRERLYWELSNLTNGIQELGPYTLDRNSLYVNGFTHRSSMPTTSTPGTSTVDVGTSGTPSSSPSPT, templa te: SFFFLSFHISNLQFNSSLEDPSTDYYQELQRDISEMFLQIYKQGGFLGLSNIKFRPG
I0321 02:54:05.585400 139960428058432 templates.py:278] Found an exact template match 2acm_A.
W0321 02:54:05.598183 139960428058432 templates.py:139] Template structure not in release dates dict: 5yl9
I0321 02:54:05.598412 139960428058432 templates.py:753] Reading PDB entry from /mnt/d/alphafold/database/pdb_mmcif/mmcif_files/5yl9.cif. Query: ATVPFMVPFT LNFTITNLQYEEDMRHPGSRKFNATERELQGLLKPLFRNSSLEYLYSGCRLASLRPEKDSSATAVDAICTHRPDPEDLGLDRERLYWELSNLTNGIQELGPYTLDRNSLYVNGFTHRSSMPTTSTPGTSTVDVGTSGTPSSSPSPT, templa te: VVEQYNQTILNLTSEISTLENKSAELNYTVQKLQTLIDNI
I0321 02:54:05.712731 139960428058432 templates.py:278] Found an exact template match 5yl9_B.
W0321 02:54:05.715018 139960428058432 templates.py:139] Template structure not in release dates dict: 1gvp
I0321 02:54:05.715114 139960428058432 templates.py:753] Reading PDB entry from /mnt/d/alphafold/database/pdb_mmcif/mmcif_files/1gvp.cif. Query: ATVPFMVPFT LNFTITNLQYEEDMRHPGSRKFNATERELQGLLKPLFRNSSLEYLYSGCRLASLRPEKDSSATAVDAICTHRPDPEDLGLDRERLYWELSNLTNGIQELGPYTLDRNSLYVNGFTHRSSMPTTSTPGTSTVDVGTSGTPSSSPSPT, templa te: APGLYTVHLSSFKVGQFGS
I0321 02:54:05.777992 139960428058432 templates.py:278] Found an exact template match 1gvp_A.
W0321 02:54:05.780884 139960428058432 templates.py:139] Template structure not in release dates dict: 6s8s
I0321 02:54:05.780977 139960428058432 templates.py:753] Reading PDB entry from /mnt/d/alphafold/database/pdb_mmcif/mmcif_files/6s8s.cif. Query: ATVPFMVPFT LNFTITNLQYEEDMRHPGSRKFNATERELQGLLKPLFRNSSLEYLYSGCRLASLRPEKDSSATAVDAICTHRPDPEDLGLDRERLYWELSNLTNGIQELGPYTLDRNSLYVNGFTHRSSMPTTSTPGTSTVDVGTSGTPSSSPSPT, templa te: FDFEGNLALFDKAAVFEEIDTY
I0321 02:54:06.046435 139960428058432 templates.py:278] Found an exact template match 6s8s_B.
W0321 02:54:06.049705 139960428058432 templates.py:139] Template structure not in release dates dict: 1ae3
I0321 02:54:06.049842 139960428058432 templates.py:753] Reading PDB entry from /mnt/d/alphafold/database/pdb_mmcif/mmcif_files/1ae3.cif. Query: ATVPFMVPFT LNFTITNLQYEEDMRHPGSRKFNATERELQGLLKPLFRNSSLEYLYSGCRLASLRPEKDSSATAVDAICTHRPDPEDLGLDRERLYWELSNLTNGIQELGPYTLDRNSLYVNGFTHRSSMPTTSTPGTSTVDVGTSGTPSSSPSPT, templa te: APGLYTVHLSSFKVGQFG
I0321 02:54:06.119893 139960428058432 templates.py:278] Found an exact template match 1ae3_A.
W0321 02:54:06.122766 139960428058432 templates.py:139] Template structure not in release dates dict: 4v23
I0321 02:54:06.122855 139960428058432 templates.py:753] Reading PDB entry from /mnt/d/alphafold/database/pdb_mmcif/mmcif_files/4v23.cif. Query: ATVPFMVPFT LNFTITNLQYEEDMRHPGSRKFNATERELQGLLKPLFRNSSLEYLYSGCRLASLRPEKDSSATAVDAICTHRPDPEDLGLDRERLYWELSNLTNGIQELGPYTLDRNSLYVNGFTHRSSMPTTSTPGTSTVDVGTSGTPSSSPSPT, templa te: AFKYIKPQSQFIVDLGAY
I0321 02:54:06.287062 139960428058432 templates.py:278] Found an exact template match 4v23_A.
I0321 02:54:06.426746 139960428058432 run_alphafold.py:185] Running model model_1
WARNING:tensorflow:From /home/whelanlab/alphafold/alphafold/model/tf/input_pipeline.py:151: calling map_fn (from tensorflow.python.ops.map_fn) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Use fn_output_signature instead
W0321 02:54:06.770174 139960428058432 deprecation.py:528] From /home/whelanlab/alphafold/alphafold/model/tf/input_pipeline.py:151: calling map_fn (from te nsorflow.python.ops.map_fn) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Use fn_output_signature instead
2023-03-21 02:54:08.766418: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_G ROWTH environment variable is set. Original config value was 0.
I0321 02:54:09.424343 139960428058432 model.py:132] Running predict with shape(feat) = {'aatype': (4, 156), 'residue_index': (4, 156), 'seq_length': (4,), 'template_aatype': (4, 4, 156), 'template_all_atom_masks': (4, 4, 156, 37), 'template_all_atom_positions': (4, 4, 156, 37, 3), 'template_sum_probs': (4, 4, 1), 'is_distillation': (4,), 'seq_mask': (4, 156), 'msa_mask': (4, 508, 156), 'msa_row_mask': (4, 508), 'random_crop_to_size_seed': (4, 2), 'template_m ask': (4, 4), 'template_pseudo_beta': (4, 4, 156, 3), 'template_pseudo_beta_mask': (4, 4, 156), 'atom14_atom_exists': (4, 156, 14), 'residx_atom14_to_atom 37': (4, 156, 14), 'residx_atom37_to_atom14': (4, 156, 37), 'atom37_atom_exists': (4, 156, 37), 'extra_msa': (4, 5120, 156), 'extra_msa_mask': (4, 5120, 1 56), 'extra_msa_row_mask': (4, 5120), 'bert_mask': (4, 508, 156), 'true_msa': (4, 508, 156), 'extra_has_deletion': (4, 5120, 156), 'extra_deletion_value': (4, 5120, 156), 'msa_feat': (4, 508, 156, 49), 'target_feat': (4, 156, 22)}
2023-03-21 02:54:09.995058: E external/org_tensorflow/tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:417] Loaded runtime CuDNN library: 8.2.1 bu t source was compiled with: 8.6.0. CuDNN library needs to have matching major version and equal or higher minor version. If using a binary install, upgra de your CuDNN library. If building from sources, make sure the library loaded at runtime is compatible with the version specified during compile configur ation.
2023-03-21 02:54:09.997237: E external/org_tensorflow/tensorflow/compiler/xla/status_macros.cc:54] INTERNAL: RET_CHECK failure (external/org_tensorflow/te nsorflow/compiler/xla/service/gpu/gpu_compiler.cc:641) dnn != nullptr
*** Begin stack trace ***

    _PyObject_MakeTpCall

    _PyEval_EvalFrameDefault
    _PyFunction_Vectorcall
    _PyObject_Call
    _PyEval_EvalFrameDefault

    _PyFunction_Vectorcall
    _PyEval_EvalFrameDefault
    _PyFunction_Vectorcall
    _PyEval_EvalFrameDefault

    _PyFunction_Vectorcall
    _PyObject_Call
    _PyEval_EvalFrameDefault

    _PyFunction_Vectorcall
    _PyEval_EvalFrameDefault


    _PyEval_EvalFrameDefault

    _PyFunction_Vectorcall
    _PyObject_Call
    _PyEval_EvalFrameDefault

    _PyFunction_Vectorcall
    _PyObject_Call
    _PyEval_EvalFrameDefault

    _PyFunction_Vectorcall
    PyObject_Call

    _PyObject_Call
    _PyEval_EvalFrameDefault

    _PyFunction_Vectorcall
    _PyObject_Call
    _PyEval_EvalFrameDefault

    _PyFunction_Vectorcall


    _PyObject_Call
    _PyEval_EvalFrameDefault
    _PyFunction_Vectorcall
    _PyEval_EvalFrameDefault
    _PyFunction_Vectorcall
    _PyEval_EvalFrameDefault

    _PyFunction_Vectorcall
    _PyEval_EvalFrameDefault
    _PyFunction_Vectorcall
    _PyEval_EvalFrameDefault
    _PyFunction_Vectorcall
    _PyEval_EvalFrameDefault

    _PyFunction_Vectorcall
    _PyEval_EvalFrameDefault

    _PyFunction_Vectorcall
    _PyObject_Call
    _PyEval_EvalFrameDefault
    _PyFunction_Vectorcall
    _PyEval_EvalFrameDefault
    _PyFunction_Vectorcall
    _PyEval_EvalFrameDefault


    _PyEval_EvalFrameDefault

    _PyFunction_Vectorcall
    _PyEval_EvalFrameDefault
    _PyFunction_Vectorcall
    _PyEval_EvalFrameDefault
    _PyFunction_Vectorcall
    _PyEval_EvalFrameDefault
    _PyFunction_Vectorcall
    _PyEval_EvalFrameDefault

    _PyFunction_Vectorcall
    _PyEval_EvalFrameDefault
    _PyFunction_Vectorcall
    _PyEval_EvalFrameDefault
    _PyFunction_Vectorcall
    _PyEval_EvalFrameDefault

    _PyFunction_Vectorcall
    _PyEval_EvalFrameDefault

    _PyEval_EvalCodeWithName
    PyEval_EvalCodeEx
    PyEval_EvalCode



    PyRun_SimpleFileExFlags
    Py_RunMain
    Py_BytesMain
    __libc_start_main

*** End stack trace ***

Traceback (most recent call last):
File "/home/whelanlab/alphafold/run_alphafold.py", line 338, in
app.run(main)
File "/home/whelanlab/localcolabfold/colabfold-conda/lib/python3.9/site-packages/absl/app.py", line 312, in run
_run_main(main, args)
File "/home/whelanlab/localcolabfold/colabfold-conda/lib/python3.9/site-packages/absl/app.py", line 258, in _run_main
sys.exit(main(argv))
File "/home/whelanlab/alphafold/run_alphafold.py", line 310, in main
predict_structure(
File "/home/whelanlab/alphafold/run_alphafold.py", line 192, in predict_structure
prediction_result = model_runner.predict(processed_feature_dict)
File "/home/whelanlab/alphafold/alphafold/model/model.py", line 134, in predict
result = self.apply(self.params, jax.random.PRNGKey(0), feat)
File "/home/whelanlab/localcolabfold/colabfold-conda/lib/python3.9/site-packages/jax/_src/random.py", line 136, in PRNGKey
key = prng.seed_with_impl(impl, seed)
File "/home/whelanlab/localcolabfold/colabfold-conda/lib/python3.9/site-packages/jax/_src/prng.py", line 269, in seed_with_impl
return random_seed(seed, impl=impl)
File "/home/whelanlab/localcolabfold/colabfold-conda/lib/python3.9/site-packages/jax/_src/prng.py", line 581, in random_seed
return random_seed_p.bind(seeds_arr, impl=impl)
File "/home/whelanlab/localcolabfold/colabfold-conda/lib/python3.9/site-packages/jax/_src/core.py", line 360, in bind
return self.bind_with_trace(find_top_trace(args), args, params)
File "/home/whelanlab/localcolabfold/colabfold-conda/lib/python3.9/site-packages/jax/_src/core.py", line 363, in bind_with_trace
out = trace.process_primitive(self, map(trace.full_raise, args), params)
File "/home/whelanlab/localcolabfold/colabfold-conda/lib/python3.9/site-packages/jax/_src/core.py", line 807, in process_primitive
return primitive.impl(*tracers, **params)
File "/home/whelanlab/localcolabfold/colabfold-conda/lib/python3.9/site-packages/jax/_src/prng.py", line 593, in random_seed_impl
base_arr = random_seed_impl_base(seeds, impl=impl)
File "/home/whelanlab/localcolabfold/colabfold-conda/lib/python3.9/site-packages/jax/_src/prng.py", line 598, in random_seed_impl_base
return seed(seeds)
File "/home/whelanlab/localcolabfold/colabfold-conda/lib/python3.9/site-packages/jax/_src/prng.py", line 833, in threefry_seed
lax.shift_right_logical(seed, lax_internal._const(seed, 32)))
File "/home/whelanlab/localcolabfold/colabfold-conda/lib/python3.9/site-packages/jax/_src/lax/lax.py", line 513, in shift_right_logical
return shift_right_logical_p.bind(x, y)
File "/home/whelanlab/localcolabfold/colabfold-conda/lib/python3.9/site-packages/jax/_src/core.py", line 360, in bind
return self.bind_with_trace(find_top_trace(args), args, params)
File "/home/whelanlab/localcolabfold/colabfold-conda/lib/python3.9/site-packages/jax/_src/core.py", line 363, in bind_with_trace
out = trace.process_primitive(self, map(trace.full_raise, args), params)
File "/home/whelanlab/localcolabfold/colabfold-conda/lib/python3.9/site-packages/jax/_src/core.py", line 807, in process_primitive
return primitive.impl(*tracers, **params)
File "/home/whelanlab/localcolabfold/colabfold-conda/lib/python3.9/site-packages/jax/_src/dispatch.py", line 122, in apply_primitive
compiled_fun = xla_primitive_callable(prim, *unsafe_map(arg_spec, args),
File "/home/whelanlab/localcolabfold/colabfold-conda/lib/python3.9/site-packages/jax/_src/util.py", line 254, in wrapper
return cached(config._trace_context(), *args, **kwargs)
File "/home/whelanlab/localcolabfold/colabfold-conda/lib/python3.9/site-packages/jax/_src/util.py", line 247, in cached
return f(*args, **kwargs)
File "/home/whelanlab/localcolabfold/colabfold-conda/lib/python3.9/site-packages/jax/_src/dispatch.py", line 201, in xla_primitive_callable
compiled = _xla_callable_uncached(lu.wrap_init(prim_fun), device, None,
File "/home/whelanlab/localcolabfold/colabfold-conda/lib/python3.9/site-packages/jax/_src/dispatch.py", line 357, in _xla_callable_uncached
return computation.compile(_allow_propagation_to_outputs=allow_prop).unsafe_call
File "/home/whelanlab/localcolabfold/colabfold-conda/lib/python3.9/site-packages/jax/_src/interpreters/pxla.py", line 3373, in compile
executable = self._compile_unloaded(
File "/home/whelanlab/localcolabfold/colabfold-conda/lib/python3.9/site-packages/jax/_src/interpreters/pxla.py", line 3344, in _compile_unloaded
return UnloadedMeshExecutable.from_hlo(
File "/home/whelanlab/localcolabfold/colabfold-conda/lib/python3.9/site-packages/jax/_src/interpreters/pxla.py", line 3626, in from_hlo
xla_executable = dispatch.compile_or_get_cached(
File "/home/whelanlab/localcolabfold/colabfold-conda/lib/python3.9/site-packages/jax/_src/dispatch.py", line 1091, in compile_or_get_cached
return backend_compile(backend, serialized_computation, compile_options,
File "/home/whelanlab/localcolabfold/colabfold-conda/lib/python3.9/site-packages/jax/_src/profiler.py", line 314, in wrapper
return func(*args, **kwargs)
File "/home/whelanlab/localcolabfold/colabfold-conda/lib/python3.9/site-packages/jax/_src/dispatch.py", line 1036, in backend_compile
return backend.compile(built_c, compile_options=options)
jaxlib.xla_extension.XlaRuntimeError: INTERNAL: RET_CHECK failure (external/org_tensorflow/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc:641) dnn != nullptr

Issue in installing the software

Dear All,
I am trying to install the software on my system (ubuntu) and when I execute the script for installing the software (install_on_local.sh) I get following error;
"""
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source.

UnsatisfiableError: The following specifications were found to be incompatible with each other:

Output in format: Requested package -> Available versions

Package _openmp_mutex conflicts for:
hhsuite==3.3.0 -> _openmp_mutex[version='>=4.5']
python=3.7 -> libgcc-ng[version='>=7.5.0'] -> _openmp_mutex[version='>=4.5']
kalign2 -> libgcc-ng[version='>=9.3.0'] -> _openmp_mutex[version='>=4.5']
hmmer -> libgcc-ng[version='>=9.3.0'] -> _openmp_mutex[version='>=4.5']

Package libstdcxx-ng conflicts for:
python=3.7 -> libstdcxx-ng[version='>=7.2.0|>=7.3.0']
hmmer -> libstdcxx-ng[version='>=4.9|>=7.3.0|>=7.5.0|>=9.3.0']
hhsuite==3.3.0 -> libstdcxx-ng[version='>=7.5.0|>=9.3.0']
hhsuite==3.3.0 -> python[version='>=3.9,<3.10.0a0'] -> libstdcxx-ng[version='>=7.2.0|>=7.3.0']

Package libgcc-ng conflicts for:
hhsuite==3.3.0 -> perl[version='>=5.26.2,<5.26.3.0a0'] -> libgcc-ng[version='>=7.2.0|>=7.3.0']
hhsuite==3.3.0 -> libgcc-ng[version='>=7.5.0|>=9.3.0']The following specifications were found to be incompatible with your system:

• feature:/linux-64::__glibc==2.31=0
• hhsuite==3.3.0 -> libgcc-ng[version='>=9.3.0'] -> __glibc[version='>=2.17']
• hmmer -> libgcc-ng[version='>=9.3.0'] -> __glibc[version='>=2.17']
• kalign2 -> libgcc-ng[version='>=9.3.0'] -> __glibc[version='>=2.17']
• python=3.7 -> libgcc-ng[version='>=7.5.0'] -> __glibc[version='>=2.17']

Your installed version is: 2.31

Collecting package metadata (current_repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source.
Collecting package metadata (repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
Solving environment: -
Found conflicts! Looking for incompatible packages.
"""
I mostly appreciate to suggest me what the issue comes from and how I can solve it?
Thanks so much
Maryam

Could not find CIFs

Hi, I can't run alphafold deamon after installation (see below).
$ python3 run_alphafold.py --fasta_paths=T1050.fasta --max_template_date=2020-05-14
/opt/conda3/envs/af2/lib/python3.8/site-packages/absl/flags/_validators.py:203: UserWarning: Flag --output_dir has a non-None default value; therefore, mark_flag_as_required will pass even if flag is not specified in the command line!
warnings.warn(
/opt/conda3/envs/af2/lib/python3.8/site-packages/absl/flags/_validators.py:203: UserWarning: Flag --model_names has a non-None default value; therefore, mark_flag_as_required will pass even if flag is not specified in the command line!
warnings.warn(
/opt/conda3/envs/af2/lib/python3.8/site-packages/absl/flags/_validators.py:203: UserWarning: Flag --data_dir has a non-None default value; therefore, mark_flag_as_required will pass even if flag is not specified in the command line!
warnings.warn(
/opt/conda3/envs/af2/lib/python3.8/site-packages/absl/flags/_validators.py:203: UserWarning: Flag --preset has a non-None default value; therefore, mark_flag_as_required will pass even if flag is not specified in the command line!
warnings.warn(
/opt/conda3/envs/af2/lib/python3.8/site-packages/absl/flags/_validators.py:203: UserWarning: Flag --uniref90_database_path has a non-None default value; therefore, mark_flag_as_required will pass even if flag is not specified in the command line!
warnings.warn(
/opt/conda3/envs/af2/lib/python3.8/site-packages/absl/flags/_validators.py:203: UserWarning: Flag --mgnify_database_path has a non-None default value; therefore, mark_flag_as_required will pass even if flag is not specified in the command line!
warnings.warn(
/opt/conda3/envs/af2/lib/python3.8/site-packages/absl/flags/_validators.py:203: UserWarning: Flag --uniclust30_database_path has a non-None default value; therefore, mark_flag_as_required will pass even if flag is not specified in the command line!
warnings.warn(
/opt/conda3/envs/af2/lib/python3.8/site-packages/absl/flags/_validators.py:203: UserWarning: Flag --bfd_database_path has a non-None default value; therefore, mark_flag_as_required will pass even if flag is not specified in the command line!
warnings.warn(
/opt/conda3/envs/af2/lib/python3.8/site-packages/absl/flags/_validators.py:203: UserWarning: Flag --pdb70_database_path has a non-None default value; therefore, mark_flag_as_required will pass even if flag is not specified in the command line!
warnings.warn(
/opt/conda3/envs/af2/lib/python3.8/site-packages/absl/flags/_validators.py:203: UserWarning: Flag --template_mmcif_dir has a non-None default value; therefore, mark_flag_as_required will pass even if flag is not specified in the command line!
warnings.warn(
/opt/conda3/envs/af2/lib/python3.8/site-packages/absl/flags/_validators.py:203: UserWarning: Flag --max_template_date has a non-None default value; therefore, mark_flag_as_required will pass even if flag is not specified in the command line!
warnings.warn(
/opt/conda3/envs/af2/lib/python3.8/site-packages/absl/flags/_validators.py:203: UserWarning: Flag --obsolete_pdbs_path has a non-None default value; therefore, mark_flag_as_required will pass even if flag is not specified in the command line!
warnings.warn(
E0808 08:51:38.610640 140055556486976 templates.py:860] Could not find CIFs in /data01/xukui/alphafold/pdb_mmcif/mmcif_files
Traceback (most recent call last):
File "run_alphafold.py", line 338, in
app.run(main)
File "/opt/conda3/envs/af2/lib/python3.8/site-packages/absl/app.py", line 312, in run
_run_main(main, args)
File "/opt/conda3/envs/af2/lib/python3.8/site-packages/absl/app.py", line 258, in _run_main
sys.exit(main(argv))
File "run_alphafold.py", line 265, in main
template_featurizer = templates.TemplateHitFeaturizer(
File "/data2/sw/af-kuixu/alphafold/alphafold/data/templates.py", line 861, in init
raise ValueError(f'Could not find CIFs in {self._mmcif_dir}')
ValueError: Could not find CIFs in /data01/xukui/alphafold/pdb_mmcif/mmcif_files

Did I miss anything to change the default path /data01/xukui/alphafold/pdb_mmcif/mmcif_files to my own path? Thank you!

Could not find HHBlits database

Traceback (most recent call last):
  File "run_alphafold.py", line 338, in <module>
    app.run(main)
  File "/home/yulab/anaconda3/envs/af2/lib/python3.8/site-packages/absl/app.py", line 312, in run
    _run_main(main, args)
  File "/home/yulab/anaconda3/envs/af2/lib/python3.8/site-packages/absl/app.py", line 258, in _run_main
    sys.exit(main(argv))
  File "run_alphafold.py", line 273, in main
    data_pipeline = pipeline.DataPipeline(
  File "/home/yulab/software/alphafold/alphafold/data/pipeline.py", line 101, in __init__
    self.hhblits_bfd_uniclust_runner = hhblits.HHBlits(
  File "/home/yulab/software/alphafold/alphafold/data/tools/hhblits.py", line 83, in __init__
    raise ValueError(f'Could not find HHBlits database {database_path}')
ValueError: Could not find HHBlits database /data1/AF2_data/uniclust30/UniRef30_2020_02

in file run_alphafold.py, change line 77 to the following code will fix it.

    DOWNLOAD_DIR, 'uniclust30', 'UniRef30_2020_02', 'UniRef30_2020_02')

pip3 install requirement are not compatible

This must be an old installation since I tried to follow the directions explicitly and got this message:

ERROR: Cannot install -r ./requirements.txt (line 11), -r ./requirements.txt (line 12), -r ./requirements.txt (line 2), -r ./requirements.txt (line 3), -r ./requirements.txt (line 4), -r ./requirements.txt (line 8), chex, numpy==1.19.5 and tensorflow because these package versions have conflicting dependencies.

requiremnets.txt says
absl-py==0.13.0
biopython==1.79
chex==0.0.7
dm-haiku==0.0.4
dm-tree==0.1.6
docker==5.0.0
immutabledict==2.0.0
jax==0.2.14
ml-collections==0.1.0
numpy==1.19.5
scipy==1.7.0
tensorflow==2.5.1
The conflict is caused by:
The user requested numpy==1.19.5
biopython 1.79 depends on numpy
chex 0.0.7 depends on numpy>=1.18.0
dm-haiku 0.0.4 depends on numpy>=1.18.0
jax 0.2.14 depends on numpy>=1.12
scipy 1.7.0 depends on numpy<1.23.0 and >=1.16.5
tensorflow 2.5.1 depends on numpy~=1.19.2
h5py 3.1.0 depends on numpy>=1.17.5; python_version == "3.8"
jaxlib 0.4.13 depends on numpy>=1.21
The user requested numpy==1.19.5
biopython 1.79 depends on numpy
chex 0.0.7 depends on numpy>=1.18.0
dm-haiku 0.0.4 depends on numpy>=1.18.0
jax 0.2.14 depends on numpy>=1.12
scipy 1.7.0 depends on numpy<1.23.0 and >=1.16.5
tensorflow 2.5.1 depends on numpy~=1.19.2
h5py 3.1.0 depends on numpy>=1.17.5; python_version == "3.8"
jaxlib 0.4.12 depends on numpy>=1.21
The user requested numpy==1.19.5
biopython 1.79 depends on numpy
chex 0.0.7 depends on numpy>=1.18.0
dm-haiku 0.0.4 depends on numpy>=1.18.0
jax 0.2.14 depends on numpy>=1.12
scipy 1.7.0 depends on numpy<1.23.0 and >=1.16.5
tensorflow 2.5.1 depends on numpy~=1.19.2
h5py 3.1.0 depends on numpy>=1.17.5; python_version == "3.8"
jaxlib 0.4.11 depends on numpy>=1.21
The user requested numpy==1.19.5
biopython 1.79 depends on numpy
chex 0.0.7 depends on numpy>=1.18.0
dm-haiku 0.0.4 depends on numpy>=1.18.0
jax 0.2.14 depends on numpy>=1.12
scipy 1.7.0 depends on numpy<1.23.0 and >=1.16.5
tensorflow 2.5.1 depends on numpy~=1.19.2
h5py 3.1.0 depends on numpy>=1.17.5; python_version == "3.8"
jaxlib 0.4.10 depends on numpy>=1.21
The user requested numpy==1.19.5
biopython 1.79 depends on numpy
chex 0.0.7 depends on numpy>=1.18.0
dm-haiku 0.0.4 depends on numpy>=1.18.0
jax 0.2.14 depends on numpy>=1.12
scipy 1.7.0 depends on numpy<1.23.0 and >=1.16.5
tensorflow 2.5.1 depends on numpy~=1.19.2
h5py 3.1.0 depends on numpy>=1.17.5; python_version == "3.8"
jaxlib 0.4.9 depends on numpy>=1.21
The user requested numpy==1.19.5
biopython 1.79 depends on numpy
chex 0.0.7 depends on numpy>=1.18.0
dm-haiku 0.0.4 depends on numpy>=1.18.0
jax 0.2.14 depends on numpy>=1.12
scipy 1.7.0 depends on numpy<1.23.0 and >=1.16.5
tensorflow 2.5.1 depends on numpy~=1.19.2
h5py 3.1.0 depends on numpy>=1.17.5; python_version == "3.8"
jaxlib 0.4.7 depends on numpy>=1.21
The user requested numpy==1.19.5
biopython 1.79 depends on numpy
chex 0.0.7 depends on numpy>=1.18.0
dm-haiku 0.0.4 depends on numpy>=1.18.0
jax 0.2.14 depends on numpy>=1.12
scipy 1.7.0 depends on numpy<1.23.0 and >=1.16.5
tensorflow 2.5.1 depends on numpy~=1.19.2
h5py 3.1.0 depends on numpy>=1.17.5; python_version == "3.8"
jaxlib 0.4.6 depends on numpy>=1.20
The user requested numpy==1.19.5
biopython 1.79 depends on numpy
chex 0.0.7 depends on numpy>=1.18.0
dm-haiku 0.0.4 depends on numpy>=1.18.0
jax 0.2.14 depends on numpy>=1.12
scipy 1.7.0 depends on numpy<1.23.0 and >=1.16.5
tensorflow 2.5.1 depends on numpy~=1.19.2
h5py 3.1.0 depends on numpy>=1.17.5; python_version == "3.8"
jaxlib 0.4.4 depends on numpy>=1.20
The user requested numpy==1.19.5
biopython 1.79 depends on numpy
chex 0.0.7 depends on numpy>=1.18.0
dm-haiku 0.0.4 depends on numpy>=1.18.0
jax 0.2.14 depends on numpy>=1.12
scipy 1.7.0 depends on numpy<1.23.0 and >=1.16.5
tensorflow 2.5.1 depends on numpy~=1.19.2
h5py 3.1.0 depends on numpy>=1.17.5; python_version == "3.8"
jaxlib 0.4.3 depends on numpy>=1.20
The user requested numpy==1.19.5
biopython 1.79 depends on numpy
chex 0.0.7 depends on numpy>=1.18.0
dm-haiku 0.0.4 depends on numpy>=1.18.0
jax 0.2.14 depends on numpy>=1.12
scipy 1.7.0 depends on numpy<1.23.0 and >=1.16.5
tensorflow 2.5.1 depends on numpy~=1.19.2
h5py 3.1.0 depends on numpy>=1.17.5; python_version == "3.8"
jaxlib 0.4.2 depends on numpy>=1.20
The user requested numpy==1.19.5
biopython 1.79 depends on numpy
chex 0.0.7 depends on numpy>=1.18.0
dm-haiku 0.0.4 depends on numpy>=1.18.0
jax 0.2.14 depends on numpy>=1.12
scipy 1.7.0 depends on numpy<1.23.0 and >=1.16.5
tensorflow 2.5.1 depends on numpy~=1.19.2
h5py 3.1.0 depends on numpy>=1.17.5; python_version == "3.8"
jaxlib 0.4.1 depends on numpy>=1.20

Can this be updated to let pip decide to resolve dependencies?

--preset reduced_dbs not available flag

Hi! Thank you for your hard work.

Is there any particular reason reduced_dbs is not provided as a --preset flag option?

Wrong order of conda packages?

Hi,
thanks for your docker-less solution!

I always got Unsatisfiable Errors while installing bioconda. After installing conda-forge (and the others) it work!

Again, thanks!

Core dumpled

I am getting the following error:

`I0826 14:47:31.789251 139693760276288 run_alphafold.py:185] Running model model_1
WARNING:tensorflow:From /home/zapata/alphafold/alphafold/model/tf/input_pipeline.py:151: calling map_fn (from tensorflow.python.ops.map_fn) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Use fn_output_signature instead
W0826 14:47:32.193996 139693760276288 deprecation.py:528] From /home/zapata/alphafold/alphafold/model/tf/input_pipeline.py:151: calling map_fn (from tensorflow.python.ops.map_fn) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Use fn_output_signature instead
I0826 14:47:37.243475 139693760276288 model.py:132] Running predict with shape(feat) = {'aatype': (4, 485), 'residue_index': (4, 485), 'seq_length': (4,), 'template_aatype': (4, 4, 485), 'template_all_atom_masks': (4, 4, 485, 37), 'template_all_atom_positions': (4, 4, 485, 37, 3), 'template_sum_probs': (4, 4, 1), 'is_distillation': (4,), 'seq_mask': (4, 485), 'msa_mask': (4, 508, 485), 'msa_row_mask': (4, 508), 'random_crop_to_size_seed': (4, 2), 'template_mask': (4, 4), 'template_pseudo_beta': (4, 4, 485, 3), 'template_pseudo_beta_mask': (4, 4, 485), 'atom14_atom_exists': (4, 485, 14), 'residx_atom14_to_atom37': (4, 485, 14), 'residx_atom37_to_atom14': (4, 485, 37), 'atom37_atom_exists': (4, 485, 37), 'extra_msa': (4, 5120, 485), 'extra_msa_mask': (4, 5120, 485), 'extra_msa_row_mask': (4, 5120), 'bert_mask': (4, 508, 485), 'true_msa': (4, 508, 485), 'extra_has_deletion': (4, 5120, 485), 'extra_deletion_value': (4, 5120, 485), 'msa_feat': (4, 508, 485, 49), 'target_feat': (4, 485, 22)}
2021-08-26 14:48:24.665668: E external/org_tensorflow/tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_NOT_INITIALIZED
2021-08-26 14:48:24.665708: F external/org_tensorflow/tensorflow/compiler/xla/service/gpu/gemm_algorithm_picker.cc:113] Check failed: stream->parent()->GetBlasGemmAlgorithms(&algorithms)
Fatal Python error: Aborted

Current thread 0x00007f0cfcf56740 (most recent call first):
File "/home/zapata/anaconda3/envs/af2/lib/python3.8/site-packages/jax/interpreters/xla.py", line 385 in backend_compile
File "/home/zapata/anaconda3/envs/af2/lib/python3.8/site-packages/jax/interpreters/xla.py", line 81 in compile_or_get_cached
File "/home/zapata/anaconda3/envs/af2/lib/python3.8/site-packages/jax/interpreters/xla.py", line 772 in _xla_callable
File "/home/zapata/anaconda3/envs/af2/lib/python3.8/site-packages/jax/linear_util.py", line 262 in memoized_fun
File "/home/zapata/anaconda3/envs/af2/lib/python3.8/site-packages/jax/interpreters/xla.py", line 619 in _xla_call_impl
File "/home/zapata/anaconda3/envs/af2/lib/python3.8/site-packages/jax/core.py", line 613 in process_call
File "/home/zapata/anaconda3/envs/af2/lib/python3.8/site-packages/jax/core.py", line 1617 in process
File "/home/zapata/anaconda3/envs/af2/lib/python3.8/site-packages/jax/core.py", line 1605 in call_bind
File "/home/zapata/anaconda3/envs/af2/lib/python3.8/site-packages/jax/core.py", line 1614 in bind
File "/home/zapata/anaconda3/envs/af2/lib/python3.8/site-packages/jax/_src/api.py", line 405 in cache_miss
File "/home/zapata/anaconda3/envs/af2/lib/python3.8/site-packages/jax/_src/traceback_util.py", line 162 in reraise_with_filtered_traceback
File "/home/zapata/alphafold/alphafold/model/model.py", line 134 in predict
File "run_alphafold.py", line 192 in predict_structure
File "run_alphafold.py", line 310 in main
File "/home/zapata/anaconda3/envs/af2/lib/python3.8/site-packages/absl/app.py", line 258 in _run_main
File "/home/zapata/anaconda3/envs/af2/lib/python3.8/site-packages/absl/app.py", line 312 in run
File "run_alphafold.py", line 338 in
Aborted (core dumped)
`
I have nvidia455 and cuda 11.11. I created an environment as indicated but It does not run.

download_pdb_mmcif.sh has the rsyncs commented-out, then fails

I ran download_all.sh. When it got to the script download_pdb_mmcif.sh, it failed with this error:
mv: cannot stat ‘.../alphafold/pdb_mmcif/raw/*/*.cif’: No such file or directory

This seems to be because both of the rsync commands are commented out, and therefore the raw directory doesn't exist.

Should one or both of the rsync commands be uncommented?

Thanks,
Matthew