3dem / model-angelo Goto Github PK
View Code? Open in Web Editor NEWAutomatic atomic model building program for cryo-EM maps
License: MIT License
model-angelo's People
Contributors
Stargazers
Watchers
Forkers
truatpasteurdotfr qitsweauca rrw1007 kimdn datnoor jowodo bastiaanbergman clil16 asclepiusinformatica danny0559 guillawme kiri2002 yanjordan assmanng sunhaifeng123 the-lay seanpm2001model-angelo's Issues
GNN model ref failed
hey,
I wanted to try to build a model in my recent map, but GNN module failed to run. Ca tracing worked within 6 minutes or so, but GNN stopped when starting round 1.
Could anybody tell me how to fix this. Below you can find a log file of a job that was started and failed.
model_angelo.log
Thanks in advance
Incorrect _label_seq_id numbering
I've attached the result of a test run, building 7uxa from the deposited FASTA and map, and noticed what I think is a bug in the sequence numbering. If you look at residues 107-112 (_auth_seq_id numbering) of chain Db, ModelAngelo has missed one residue (sequence should be QGRQRL, modelled sequence is QGRRL). While the _auth_seq_id skips a number at the break (going from 109-111), the _label_seq_id does not. If I understand the mmCIF specification correctly, this is wrong - _label_seq_id should reflect the authoritative numbering derived from the real-world sequence, where _auth_seq_id is the more flexible label that follow whatever system the authors choose. In ChimeraX (and probably in other viewers, but I haven't checked) this leads to the chain being treated as continuous at this point.
Upshot: I believe the residues with assigned sequence need to be mapped back to the original FASTA sequence for the purpose of assigning _label_seq_id.
Did not download weights because the flag -w or --download-weights was not specified
Dear Developer,
I followed your instruction to install the "model_angelo" for my "personal use", I noticed at the final step of my installation, it said that:
Finished processing dependencies for model-angelo==0.0.1
Did not download weights because the flag -w or --download-weights was not specified
I checked with the command "model_angelo build -h", and it produced some feedback, seems intalled successfully~
just want to double check to make sure that I do not miss the "weights" file~
I also noticed that the installation command for personal use is:
source install_script.sh
While the Installing for a shared computational environment is:
source install_script.sh --download-weights
Best,
Henry
RuntimeError: CUDA out of memory. Can it be avoided, at least for tests
I'm developing on a laptop...so my GPU is under specs.
I wonder if there is a way to prevent out of memory errors, event if that sacrifices results. I'm now interested it running it and registering the output and so on and less in getting the best results possible.
Error I'm getting is:
RuntimeError: CUDA out of memory. Tried to allocate 1024.00 MiB (GPU 0; 5.94 GiB total capacity; 3.90 GiB already allocated; 457.88 MiB free; 4.86 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try
Volume is a 200x200x200. Runinng without sequence.
install issue for windows
I followed the install instruction and when I run the install_script.sh it did show already installed. However, when I tried the last step with
"model_angelo build -h", it is not working. Appreciate any help.
Very slow weights download speed
Hi,
I wanted to reinstall model-Angelo on the new workstation and download the weights. However, the download speed is rather low (150-300 kbps). The speed is the same on Google colab as well, so it isn't related to my service provider. Was it capped by the LMB servers, or is the demand so high? Nevertheless, I just wanted to report that.
Thanks for this amazing tool,
Best,
Dawid
What would be the recomended way to update model angelo
I'm asking in case there is a "shorted method" than removing the installation and starting again
I guess models could be in a different folder?
model_angelo setup.py installed in editable mode + git pull?
key error
Hi Kiarash,
I got the following error when trying to run the program with a simple map and sequence file("model_angelo build -v PostProcess/job099/postprocess_masked.mrc -f hS.fasta -o model_angelo/"):
2022-12-01 at 13:13:10 | ERROR | Error in ModelAngelo
Traceback (most recent call last):
File "/home/xuewuzhang/miniconda3/envs/model_angelo/bin/model_angelo", line 33, in
sys.exit(load_entry_point('model-angelo==0.2.2', 'console_scripts', 'model_angelo')())
\u2502 \u2502 \u2514 <function importlib_load_entry_point at 0x7f4d69444160>
\u2502 \u2514
\u2514 <module 'sys' (built-in)>
File "/home/xuewuzhang/miniconda3/envs/model_angelo/lib/python3.9/site-packages/model_angelo-0.2.2-py3.9.egg/model_angelo/main.py", line 51, in main
args.func(args)
\u2502 \u2502 \u2514 Namespace(volume_path='PostProcess/job099/postprocess_masked.mrc', fasta_path='./hS.fasta', output_dir='model_angelo', ma...
\u2502 \u2514 <function main at 0x7f4c36565940>
\u2514 Namespace(volume_path='PostProcess/job099/postprocess_masked.mrc', fasta_path='./hS.fasta', output_dir='model_angelo', ma...
File "/home/xuewuzhang/miniconda3/envs/model_angelo/lib/python3.9/site-packages/model_angelo-0.2.2-py3.9.egg/model_angelo/apps/build.py", line 225, in main
gnn_output = gnn_infer(gnn_infer_args)
\u2502 \u2514 {'num_rounds': 3, 'crop_length': 200, 'repeat_per_residue': 3, 'esm_model': 'esm1b_t33_650M_UR50S', 'aggressive_pruning': Tru...
\u2514 <function infer at 0x7f4c365658b0>
File "/home/xuewuzhang/miniconda3/envs/model_angelo/lib/python3.9/site-packages/model_angelo-0.2.2-py3.9.egg/model_angelo/gnn/inference.py", line 243, in infer
protein = get_lm_embeddings_for_protein(lang_model, batch_converter, protein)
\u2502 \u2502 \u2502 \u2514 Protein(atom_positions=None, atom14_positions=None, aatype=None, atom_mask=None, atom14_mask=None, residue_index=None, chain_...
\u2502 \u2502 \u2514 <esm.data.BatchConverter object at 0x7f4c206bd070>
\u2502 \u2514 ProteinBertModel(
\u2502 (embed_tokens): Embedding(33, 1280, padding_idx=1)
\u2502 (layers): ModuleList(
\u2502 (0): TransformerLayer(
\u2502 ...
\u2514 <function get_lm_embeddings_for_protein at 0x7f4c372daca0>
File "/home/xuewuzhang/miniconda3/envs/model_angelo/lib/python3.9/site-packages/model_angelo-0.2.2-py3.9.egg/model_angelo/data/generate_complete_prot_files.py", line 32, in get_lm_embeddings_for_protein
[result[s]["representations"][33].cpu().numpy() for s in seq_names],
\u2502 \u2514 ['0']
\u2514 {}
File "/home/xuewuzhang/miniconda3/envs/model_angelo/lib/python3.9/site-packages/model_angelo-0.2.2-py3.9.egg/model_angelo/data/generate_complete_prot_files.py", line 32, in
[result[s]["representations"][33].cpu().numpy() for s in seq_names],
\u2502 \u2502 \u2514 '0'
\u2502 \u2514 '0'
\u2514 {}
KeyError: '0'
I am not sure whether my installation was corrupt, or there is a bug in the package. Can you please help?
Thanks,
Xuewu
KeyError when downloading original_no_seq weights
Hi Model-Angelo Team,
I am following your instructions for setting up a shared install on our cluster. (I am trying in my own space first before doing a central install.)
Model-Angelo seems to install correctly and download the original bundle of weights successfully, but when it comes to downloading the original_no_seq bundle I get the following error:
Setting up bundle with name: original_no_seq for the first time.
Traceback (most recent call last):
File "/gpfs2/well/rescomp/users/iub031/src/model-angelo/model_angelo/utils/setup_weights.py", line 17, in <module>
model_dst = download_and_install_model(args.bundle_name)
File "/well/rescomp/users/iub031/conda/skylake/envs/model_angelo/lib/python3.9/site-packages/model_angelo-0.0.1-py3.9.egg/model_angelo/utils/torch_utils.py", line 456, in download_and_install_model
bundle_name_to_link[bundle_name],
KeyError: 'original_no_seq'
This occurs when sourcing the install script with the --download-weights option.
Please could you help?
Divide by zero error
Hi, I encountered an error when running during the GNN model refinement. /home/roger/.conda/envs/model_angelo/lib/python3.9/site-packages/model_angelo-0.2.2-py3.9.egg/model_angelo/utils/aa_probs_to_hmm.py:88: RuntimeWarning: divide by zero encountered in log
negative_log_prob = - np.log(aa_probs). I'm not sure what the issue is. The map was generated using the latest cryosparc version and is ~3.0A. Not an issue with the installation since it works fine for another map file. This was wwith sequence provided. Both maps had same sequence and had been processed with cryosparc.
ModelAngelo failed with ValueError
I run the following command and failed.
model_angelo build -v 7o6q.mrc -f 7o6q.fasta -o 7o6qData: https://drive.google.com/drive/folders/172nIVa9Upw5kJLkK6ABXEkxYwVLEHHgA?usp=share_link
Log: model_angelo.log
GNN model refinement problem _ SBGrid and cluster
Good afternoon,
Very genuine question. I would like to run ModelAngelo through SBGrid on a cluster. I use this command :
salloc -p gpu -n 1 -c 1 --gres=gpu:1 srun model_angelo build -v EMmap.mrc -f sequence.fasta -o ModelAngelo_test1
The first step Initial C-alpha prediction is fast and easy, but the program seems to run endlessly without any progress during the first round of GNN model refinement.
The log file does not give much information and finishes with :
2023-01-20 at 15:29:50 | INFO | Model prediction done, took 511.72 seconds for 512 sliding windows
2023-01-20 at 15:29:50 | INFO | Average time is 999.458 ms
2023-01-20 at 15:29:50 | INFO | Starting Cα grid to points...
2023-01-20 at 15:29:51 | INFO | Have 322 Cα points before pruning and 154 after pruning
2023-01-20 at 15:29:51 | INFO | Finished inference!
2023-01-20 at 15:29:51 | INFO | GNN model refinement round 1 with args: {'num_rounds': 3, 'crop_length': 200, 'repeat_per_residue': 3, 'esm_model': 'esm1b_t33_650M_UR50S', 'aggressive_pruning': True, 'seq_attention_batch_size': 200, 'map': 'PATH/EMmap.mrc', 'fasta':'sequence.fasta', 'struct': 'PATH/see_alpha_output/see_alpha_output_ca.cif', 'output_dir': 'PATH/gnn_output_round_1', 'model_dir': '/programs/share/modelangelo/0.2.2/hub/checkpoints/model_angelo/original/gnn', 'device': 'cuda:0'}
2023-01-20 at 15:30:25 | INFO | Loaded module from step: 301201
Do you have a suggestion to understand why the refinement does not work?
Thank you very much
RNA only does not run
Hello -
I was testing my model-angelo v.1.0.1 installation using this RNA only cryo-EM map:
https://www.rcsb.org/structure/6ues
https://www.emdataresource.org/EMD-20755
But, with this as input, the job won't start. Instead, it displays the help message.
Mike
$ model_angelo build -v emd_20755.mrc -rf rcsb_pdb_6UES.fasta
usage: model_angelo build [-h] --volume-path VOLUME_PATH --protein-fasta PROTEIN_FASTA [--rna-fasta RNA_FASTA]
[--dna-fasta DNA_FASTA] [--output-dir OUTPUT_DIR] [--mask-path MASK_PATH] [--device DEVICE]
[--config-path CONFIG_PATH] [--model-bundle-name MODEL_BUNDLE_NAME]
[--model-bundle-path MODEL_BUNDLE_PATH] [--keep-intermediate-results]
model_angelo build: error: the following arguments are required: --protein-fasta/--fasta-path/--f/--pf/-f/-pf
Let's "source" not "bash" the install script
In our cluster, we don't put source /home1/software/packages/miniconda3/etc/profile.d/conda.sh in ~/.bashrc to avoid conflicts.
In this case,
source /home1/software/packages/miniconda3/etc/profile.d/conda.sh
bash install_script.sh --download-weightsdoes not work because we "Can't execute conda activate from bash script".
conda activate model-angelo silently failed and installed Torch etc into the base environment. I didn't notice the issue until pip failed.
source install_script.sh --download-weights
did the trick.
(The messed up base environment was rolled back by conda install --revision X as explained here.)
RFE: warnings for size of the weight files ?
Hi,
maybe one could inform the users about the size requirements for the software and weight files?
conda:
[truadm@abiy model-angelo]$ du -sh /esmmc/model-angelo-miniconda3/envs/model_angelo/
5.5G /esmmc/model-angelo-miniconda3/envs/model_angelo/
weights:
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2.58G/2.58G [02:15<00:00, 20.5MB/s]
Bundle original successfully installed.
Setting up language model bundle with name: esm1b_t33_650M_UR50S for the first time.
50%|██████████████████████████████████████████████████████████████████████████████████▊ | 3.66G/7.29G [02:55<02:46, 23.4MB/s]
...
Cheers,
Tru
ModelAngelo output is the wrong or incomplet sequence
Hello, when I run the ModelAngelo command as in the guide, providing the fasta sequence, the output is only half of the sequence for the non-raw .cif or a wrong sequence but complete map occupation for the raw.cif. Why is that?
Thank you in advance,
David.
Issue with RTX3080
Initially the default pytorch isn't compatible with an RTX3080 GPU. So I edited the install.sh script:
Line 45 for my system should have read:
conda install pytorch torchvision torchaudio pytorch-cuda=11.7 cudatoolkit=11.7 -c pytorch-nightly -c nvidia
After doing this it works fine. Before the version of PyTorch wasn't compatible with the GPU.
Just a heads up for anyone else with a 3000 series card.
Error after Initial C-alpha prediction
Hi,
I'm trying to run Model Angelo and get the following error after Initial C-alpha prediction:
2023-01-10 at 14:20:29 | INFO | GNN model refinement round 1 with args: {'num_rounds': 3, 'crop_length': 200, 'repeat_per_residue': 3, 'esm_model': 'esm1b_t33_650M_UR50S', 'aggressive_pruning': True, 'seq_attention_batch_size': 200, 'map': 'X.mrc', 'fasta': X1.fasta', 'struct': 'X/output/see_alpha_output/see_alpha_output_ca.cif', 'output_dir': 'X/output/gnn_output_round_1', 'model_dir': '/data/model_angelo_weights/hub/checkpoints/model_angelo/original/gnn', 'device': 'cuda:0'}
2023-01-10 at 14:20:32 | INFO | Loaded module from step: 301201
2023-01-10 at 14:20:41 | ERROR | Error in ModelAngelo
Traceback (most recent call last):
File "/home/tafurpet/miniconda3/envs/model_angelo/bin/model_angelo", line 33, in
sys.exit(load_entry_point('model-angelo==0.2.2', 'console_scripts', 'model_angelo')())
│ │ └ <function importlib_load_entry_point at 0x7fcf95889160>
│ └
└ <module 'sys' (built-in)>
File "/home/tafurpet/miniconda3/envs/model_angelo/lib/python3.9/site-packages/model_angelo-0.2.2-py3.9.egg/model_angelo/main.py", line 51, in main
args.func(args)
│ │ └ Namespace(volume_path='/X.mrc', fasta_path='/data/loewith/tafurp...
│ └ <function main at 0x7fce5b046ca0>
└ Namespace(volume_path='X.mrc', fasta_path='/data/loewith/tafurp...
File "/home/tafurpet/miniconda3/envs/model_angelo/lib/python3.9/site-packages/model_angelo-0.2.2-py3.9.egg/model_angelo/apps/build.py", line 225, in main
gnn_output = gnn_infer(gnn_infer_args)
│ └ {'num_rounds': 3, 'crop_length': 200, 'repeat_per_residue': 3, 'esm_model': 'esm1b_t33_650M_UR50S', 'aggressive_pruning': Tru...
└ <function infer at 0x7fce5b046c10>
File "/home/tafurpet/miniconda3/envs/model_angelo/lib/python3.9/site-packages/model_angelo-0.2.2-py3.9.egg/model_angelo/gnn/inference.py", line 243, in infer
protein = get_lm_embeddings_for_protein(lang_model, batch_converter, protein)
│ │ │ └ Protein(atom_positions=None, atom14_positions=None, aatype=None, atom_mask=None, atom14_mask=None, residue_index=None, chain_...
│ │ └ <esm.data.BatchConverter object at 0x7fce4c300190>
│ └ ProteinBertModel(
│ (embed_tokens): Embedding(33, 1280, padding_idx=1)
│ (layers): ModuleList(
│ (0): TransformerLayer(
│ ...
└ <function get_lm_embeddings_for_protein at 0x7fce5be48940>
File "/home/tafurpet/miniconda3/envs/model_angelo/lib/python3.9/site-packages/model_angelo-0.2.2-py3.9.egg/model_angelo/data/generate_complete_prot_files.py", line 32, in get_lm_embeddings_for_protein
[result[s]["representations"][33].cpu().numpy() for s in seq_names],
│ └ ['0']
└ {}
File "/home/tafurpet/miniconda3/envs/model_angelo/lib/python3.9/site-packages/model_angelo-0.2.2-py3.9.egg/model_angelo/data/generate_complete_prot_files.py", line 32, in
[result[s]["representations"][33].cpu().numpy() for s in seq_names],
│ │ └ '0'
│ └ '0'
└ {}
KeyError: '0'
(END)
Unfortunately I lack the programming skills to understand the error message. I followed the standalone installation instructions and downloaded the weights.
I would greatly appreciate any help.
Thanks in advance!
Very long calculation time on symmetric map
Hi Model Angelo DevTeam,
I tried to test model angelo on a < 3Å of us, but the calculation takes very, very long.
The Ca tracing was ok, with 3:31:18h, but for GNN model refinement 1/3 it took 105 hours. I am now in GNN 2/3 for 30h.
So far, the intermediate results look very nice.
The map was created with O symmetry, and the fasta file contains 24 times the same protein. I am not sure, if this is the reason for the long calculation time (or the limitation of our machine - but it uses only a single CPU and a single GPU [2080 Ti]).
Is there an option to increase the speed of highly symmetric protein structures?
Best
Christian
Modelangelo eval_per_resid | Attribute Error
On Ubuntu 20.04 and CentOS I have reproducibly gotten an attribute error when running ModelAngelo's eval_per_resid option. On both devices if I run the following, I get the same error.
model_angelo eval_per_resid --predicted-structure run1.cif --target-structure myfile.cif --output-file out.txt
Traceback (most recent call last):
File "/programs/x86_64-linux//modelangelo/0.2.2/bin/model_angelo", line 33, in <module>
sys.exit(load_entry_point('model-angelo==0.2.2', 'console_scripts', 'model_angelo')())
File "/programs/x86_64-linux/modelangelo/0.2.2/miniconda3/lib/python3.9/site-packages/model_angelo/__main__.py", line 51, in main
args.func(args)
File "/programs/x86_64-linux/modelangelo/0.2.2/miniconda3/lib/python3.9/site-packages/model_angelo/apps/eval_per_resid.py", line 286, in main
) = get_residue_fit_report(
File "/programs/x86_64-linux/modelangelo/0.2.2/miniconda3/lib/python3.9/site-packages/model_angelo/apps/eval_per_resid.py", line 36, in get_residue_fit_report
target_correspondence.astype(np.int32),
AttributeError: 'list' object has no attribute 'astype'
I have tried changing the "cif" files and the output file (directory, filename, and extension).
Zero pad grid inputs smaller than 64
Assertion Error: standardize_mrc did not get any inputs
Hi there,
I've been working on installing model angelo and noted a few things.
For context, I'm working on installing it on a cluster using mamba. I found that while doing this, some of the code in the residue_constants.py file within python3.9/site-packages/model_angelo-0.2.3-py3.9.egg/model_angelo/utils was trying to use deprecated Numpy code, I have since altered the code based on the information provided by Numpy (see attached).
residue_constants.txt
This was the first issue, and was reasonably easy to fix. Following this fix, I was able to start the program using "model_angelo -h", though when I actually try to run the build_no_seq command with a specified --volume-path, I receive the following error:
2023-04-05 at 12:31:23 | INFO | ModelAngelo with args: {'volume_path': '/stor1/data/model_angelo_data/test/cryosparc_p163_J37_005_volume_map_sharp.mrc', 'output_dir': '/stor1/data/model_angelo_data/test/test-output', 'mask_path': None, 'device': 'cuda:>
2023-04-05 at 12:31:23 | INFO | Input volume preprocessing with args: {'target_voxel_size': 1.5, 'crop_z': 0, 'bfactor_to_apply': 0, 'auto_mask': False, 'input_path': '/stor1/data/model_angelo_data/test/cryosparc_p163_J37_005_volume_map_sharp.mrc', 'ou>
2023-04-05 at 12:31:23 | ERROR | Error in ModelAngelo
Traceback (most recent call last):
File "/apps/mamba/envs/model_angelo/bin/model_angelo", line 33, in
sys.exit(load_entry_point('model-angelo==0.2.3', 'console_scripts', 'model_angelo')())
│ │ └ <function importlib_load_entry_point at 0x7fc12cf080d0>
│ └
└ <module 'sys' (built-in)>
File "/apps/mamba/envs/model_angelo/lib/python3.9/site-packages/model_angelo-0.2.3-py3.9.egg/model_angelo/main.py", line 51, in main
args.func(args)
│ │ └ Namespace(volume_path='/stor1/data/model_angelo_data/test/cryosparc_p163_J37_005_volume_map_sharp.mrc', output_dir='/stor1/da...
│ └ <function main at 0x7fbe5e1b7160>
└ Namespace(volume_path='/stor1/data/model_angelo_data/test/cryosparc_p163_J37_005_volume_map_sharp.mrc', output_dir='/stor1/da...
File "/apps/mamba/envs/model_angelo/lib/python3.9/site-packages/model_angelo-0.2.3-py3.9.egg/model_angelo/apps/build_no_seq.py", line 164, in main
assert (
AssertionError: standardize_mrc did not get any inputs: /stor1/data/model_angelo_data/test/cryosparc_p163_J37_005_volume_map_sharp.mrc
I'm not really sure what I need to do here. I'm guessing its an issue of file format or something to do with my syntax in terms of defining the .mrc file's path, but regardless I am very confused. Any assistance would be greatly appreciated.
Thanks,
Shane
Cuda - the memory issue
Dear colleagues,
I faced the following error running modelangelo with map; no protein sequence; mask
Any ideas?
thanks in advance.
Sincerely,
Dmitry
=================================================================
20932: 100%|█████████▉| 2.05G/2.05G [38:53<00:00, 923kB/s]
20933: 100%|█████████▉| 2.05G/2.05G [38:53<00:00, 957kB/s]
20934: 100%|█████████▉| 2.05G/2.05G [38:53<00:00, 985kB/s]
20935: 100%|█████████▉| 2.05G/2.05G [38:53<00:00, 970kB/s]
20936: 100%|██████████| 2.05G/2.05G [38:53<00:00, 944kB/s]
20937: 2022-10-25 at 10:25:29 | INFO | ModelAngelo with args: {'volume_path': 'Runs/007501_ProtCryoSparc3DHomogeneousRefine/extra/cryosparc_P30_J158_007_volume_map.mrc', 'output_dir': 'Runs/007658_ProtModelAngelo/extra', 'mask_path': 'Runs/007626_ProtImportMask/extra/cryosparc_P30_J158_007_volume_mask_refine.mrc', 'device': 'cuda:0', 'config_path': None, 'model_bundle_name': 'original_no_seq', 'model_bundle_path': None, 'pipeline_control': False, 'func': <function main at 0x7f461f121ee0>}
20938: 2022-10-25 at 10:25:29 | INFO | Input volume preprocessing with args: {'target_voxel_size': 1.5, 'crop_z': 0, 'bfactor_to_apply': 0, 'auto_mask': False, 'input_path': 'Runs/007501_ProtCryoSparc3DHomogeneousRefine/extra/cryosparc_P30_J158_007_volume_map.mrc', 'output_path': 'Runs/007658_ProtModelAngelo/extra'}
20939: 2022-10-25 at 10:25:34 | INFO | Initial C-alpha prediction with args: {'model_checkpoint': 'chkpt.torch', 'bfactor': 0, 'batch_size': 4, 'stride': 16, 'dont_mask_input': True, 'threshold': 0.05, 'save_real_coordinates': False, 'save_cryo_em_grid': False, 'do_nucleotides': False, 'save_backbone_trace': False, 'save_ca_grid': False, 'crop': 6, 'log_dir': '/home/user/Data/Software/scipion3/software/em/modelangelomodels-0.1/hub/checkpoints/model_angelo/original_no_seq/c_alpha', 'map_path': 'Runs/007658_ProtModelAngelo/extra/cryosparc_P30_J158_007_volume_map_fixed.mrc', 'output_path': 'Runs/007658_ProtModelAngelo/extra/see_alpha_output', 'mask_path': 'Runs/007626_ProtImportMask/extra/cryosparc_P30_J158_007_volume_mask_refine.mrc', 'device': 'cuda:0', 'auto_mask': False}
20940: 2022-10-25 at 10:25:38 | INFO | Using model file /home/user/Data/Software/scipion3/software/em/modelangelomodels-0.1/hub/checkpoints/model_angelo/original_no_seq/c_alpha/model.py
20941: 2022-10-25 at 10:25:38 | INFO | Using checkpoint file /home/user/Data/Software/scipion3/software/em/modelangelomodels-0.1/hub/checkpoints/model_angelo/original_no_seq/c_alpha/chkpt.torch
20942: 2022-10-25 at 10:25:46 | INFO | Input structure has shape: (368, 368, 368)
20943: 2022-10-25 at 10:25:46 | INFO | Running with these arguments:
20944: 2022-10-25 at 10:25:46 | INFO | {'model_checkpoint': 'chkpt.torch', 'bfactor': 0, 'batch_size': 4, 'stride': 16, 'dont_mask_input': True, 'threshold': 0.05, 'save_real_coordinates': False, 'save_cryo_em_grid': False, 'do_nucleotides': False, 'save_backbone_trace': False, 'save_ca_grid': False, 'crop': 6, 'log_dir': '/home/user/Data/Software/scipion3/software/em/modelangelomodels-0.1/hub/checkpoints/model_angelo/original_no_seq/c_alpha', 'map_path': 'Runs/007658_ProtModelAngelo/extra/cryosparc_P30_J158_007_volume_map_fixed.mrc', 'output_path': 'Runs/007658_ProtModelAngelo/extra/see_alpha_output', 'mask_path': 'Runs/007626_ProtImportMask/extra/cryosparc_P30_J158_007_volume_mask_refine.mrc', 'device': 'cuda:0', 'auto_mask': False}
20945: 2022-10-25 at 10:25:46 | INFO | Model has these arguments:
20946: 2022-10-25 at 10:25:46 | INFO | Namespace(dataset_list='/ssd/see-alpha-phosphorus-unmasked/train.txt', log_dir='/ssd/train_std', valid_dataset_list='/ssd/see-alpha-phosphorus-unmasked/test.txt', validation_ratio=500, checkpoint_ratio=10000, num_steps=400000, box_size=64, batch_size=2, accumulate_grad_steps=1, lr=0.0001, use_cosine_annealing=True, use_focal_loss=True, use_tversky_loss=True, use_dice_loss=False, use_weighted_loss=True, use_backbone_trace_loss=True, debug=False, dont_load=False, image_ratio=100, clip_grad_norm=10.0, weight_decay=0.0, max_noise=0.5, dont_use_data_augmentation=False, positional_encoding_dim=0, use_global_normalization=False, match_model=True)
20947: 2022-10-25 at 12:44:18 | INFO | Model prediction done, took 8311.91 seconds for 8000 sliding windows
20948: 2022-10-25 at 12:44:18 | INFO | Average time is 1038.989 ms
20949: 2022-10-25 at 12:44:19 | INFO | Starting Cα grid to points...
20950: 2022-10-25 at 12:44:23 | INFO | Have 61453 Cα points before pruning and 50669 after pruning
20951: 2022-10-25 at 12:44:30 | INFO | Finished inference!
20952: 2022-10-25 at 12:44:30 | INFO | GNN model refinement round 1 with args: {'num_rounds': 3, 'crop_length': 200, 'repeat_per_residue': 3, 'esm_model': 'esm1b_t33_650M_UR50S', 'aggressive_pruning': False, 'seq_attention_batch_size': 200, 'map': 'Runs/007658_ProtModelAngelo/extra/cryosparc_P30_J158_007_volume_map_fixed.mrc', 'struct': 'Runs/007658_ProtModelAngelo/extra/see_alpha_output/see_alpha_output_ca.cif', 'output_dir': 'Runs/007658_ProtModelAngelo/extra/gnn_output_round_1', 'model_dir': '/home/user/Data/Software/scipion3/software/em/modelangelomodels-0.1/hub/checkpoints/model_angelo/original_no_seq/gnn', 'device': 'cuda:0'}
20953: 2022-10-25 at 12:44:33 | INFO | Loaded module from step: 529999
20954: 2022-10-25 at 12:44:36 | ERROR | Error in ModelAngelo
20955: Traceback (most recent call last):
20956:
20957: File "/home/user/Data/Software/miniconda/envs/modelangelo-git/bin/model_angelo", line 33, in
20958: sys.exit(load_entry_point('model-angelo', 'console_scripts', 'model_angelo')())
20959: │ │ └ <function importlib_load_entry_point at 0x7f472ca5b280>
20960: │ └
20961: └ <module 'sys' (built-in)>
20962:
20963: File "/home/user/Data/Software/scipion3/software/em/modelangelo-git/model-angelo/model_angelo/main.py", line 51, in main
20964: args.func(args)
20965: │ │ └ Namespace(volume_path='Runs/007501_ProtCryoSparc3DHomogeneousRefine/extra/cryosparc_P30_J158_007_volume_map.mrc', output_dir=...
20966: │ └ <function main at 0x7f461f121ee0>
20967: └ Namespace(volume_path='Runs/007501_ProtCryoSparc3DHomogeneousRefine/extra/cryosparc_P30_J158_007_volume_map.mrc', output_dir=...
20968:
20969: > File "/home/user/Data/Software/scipion3/software/em/modelangelo-git/model-angelo/model_angelo/apps/build_no_seq.py", line 205, in main
20970: gnn_output = gnn_no_seq_infer(gnn_infer_args)
20971: │ └ {'num_rounds': 3, 'crop_length': 200, 'repeat_per_residue': 3, 'esm_model': 'esm1b_t33_650M_UR50S', 'aggressive_pruning': Fal...
20972: └ <function infer at 0x7f461f121e50>
20973:
20974: File "/home/user/Data/Software/scipion3/software/em/modelangelo-git/model-angelo/model_angelo/gnn/inference_no_seq.py", line 263, in infer
20975: collated_results = init_empty_collate_results(
20976: └ <function init_empty_collate_results at 0x7f461f121ca0>
20977:
20978: File "/home/user/Data/Software/scipion3/software/em/modelangelo-git/model-angelo/model_angelo/gnn/inference_no_seq.py", line 112, in init_empty_collate_results
20979: result["edge_counts"] = torch.zeros(num_residues, num_residues, device=device)
20980: │ │ │ │ │ └ device(type='cuda', index=0)
20981: │ │ │ │ └ 50669
20982: │ │ │ └ 50669
20983: │ │ └ <built-in method zeros of type object at 0x7f46f3643200>
20984: │ └ <module 'torch' from '/home/user/Data/Software/miniconda/envs/modelangelo-git/lib/python3.9/site-packages/torch/init.py'>
20985: └ {'counts': tensor([0., 0., 0., ..., 0., 0., 0.], device='cuda:0')}
20986:
20987: RuntimeError: CUDA out of memory. Tried to allocate 9.56 GiB (GPU 0; 10.76 GiB total capacity; 536.01 MiB already allocated; 9.15 GiB free; 622.00 MiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
20988: Traceback (most recent call last):
20989: File "/home/user/Data/Software/miniconda/envs/scipion3/lib/python3.8/site-packages/pyworkflow/protocol/protocol.py", line 202, in run
20990: self._run()
20991: File "/home/user/Data/Software/miniconda/envs/scipion3/lib/python3.8/site-packages/pyworkflow/protocol/protocol.py", line 253, in _run
20992: resultFiles = self._runFunc()
20993: File "/home/user/Data/Software/miniconda/envs/scipion3/lib/python3.8/site-packages/pyworkflow/protocol/protocol.py", line 249, in _runFunc
20994: return self._func(*self._args)
20995: File "/home/user/Data/Software/miniconda/envs/scipion3/lib/python3.8/site-packages/modelangelo/protocols/protocol_model_angelo.py", line 148, in predictStep
20996: raise ChildProcessError("Model angelo has failed: %s. See error log for more details." % line) from None
20997: ChildProcessError: Model angelo has failed: RuntimeError: CUDA out of memory. Tried to allocate 9.56 GiB (GPU 0; 10.76 GiB total capacity; 536.01 MiB already allocated; 9.15 GiB free; 622.00 MiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF. See error log for more details.
20998: Protocol failed: Model angelo has failed: RuntimeError: CUDA out of memory. Tried to allocate 9.56 GiB (GPU 0; 10.76 GiB total capacity; 536.01 MiB already allocated; 9.15 GiB free; 622.00 MiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF. See error log for more details.c
ModelAngelo not using GPUs (2080 Ti, CUDA 11.4)
Hi everyone,
I installed modelangelo on our server (as described in README), and noticed that Ca building took ~5 hours, and GPU utilization was 0 at that time.
After that, I ran python manually, and checked torch availability:
>>> import torch
torch.cuda.is_available()
>>> torch.cuda.is_available()
False
>>> which seems to be the reason.
CUDA, however, seems to be working fine on the server:
$ nvcc --vesrion
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2021 NVIDIA Corporation
Built on Mon_Oct_11_21:27:02_PDT_2021
Cuda compilation tools, release 11.4, V11.4.152
Build cuda_11.4.r11.4/compiler.30521435_0(also for other packages, e.g. relion/cryosparc/you name it).
Could you please point me to what I should do to fix that?
RuntimeError: shape '[16, 20]' is invalid for input of size 240
I'm trying to determine a structure of an unknown contaminant protein by cryoEM single particle analysis. I wanted to generate a main-chain model of my density map from cryoSPARC. However, I get the following error (on both flipped unflipped density maps in .mrc format) during GNN model refinement Round 2/3:
RuntimeError: shape '[16, 20]' is invalid for input of size 240
I am running modelAngelo with 8 CPUs, 1 GPU, and 60 GB of RAM on a cluster (submitted with Sbatch), using the following script (SBATCH submission lines excluded):
module load Anaconda3/
module load CUDA/11.1.1-GCC-10.2.0
source activate model_angelo
model_angelo build_no_seq -v /path/to/mrc.mrc
-o output/path
source deactivate
I have previously had success (no error) running this same script on a lower resolution density map (the output was junk because map was low resolution, but no error in the log).
Full error traceback attached from model_angelo.log
model_angelo_error.txt
Use canonical pyHMMER way to make HMM files
See here: althonos/pyhmmer#40 (comment)
Run extremely slow on 1080Ti
It took about 8hrs for 50% of the run to finish. Incredibly slow. Installation initially had failed for pytorch so removed the env and recreated. Started fresh and it all went smoothly the next time. No errors.
model_angelo build -h provides the appropriate help output
Command provided includes map, fasta file and output. Speed doesn't change if I provide the device number or not.
Computer specs
OS - Linuxmint 20.1 (Ulyssa)
Cuda - 11.7
Driver version - 515.43.04
I've also tried the installation on a 3090 machine also (runs redhat, driver version 515,76, cuda 11.7). Same result.
Return error exit code
Hi, it's me again. When modelangelo fails it actually does nor return any exit code and therefore programs calling modelangelo fail to detect the error.
Would it be possible to implement this?
Thanks.
a weird problem "class 'urllib.error.URLError'"
Hi Model-angelo experts,
When I used the model_angelo to predict the protein sequence with a MRC map only, some error had appeared as following that
"File "/data/wangning/anaconda3/envs/model_angelo/lib/python3.9/urllib/request.py", line 494, in _call_chain
result = func(*args)
│ └ (<urllib.request.Request object at 0x7fb18f8d4730>,)
└ <bound method HTTPSHandler.https_open of <urllib.request.HTTPSHandler object at 0x7fb18f8bb430>>
File "/data/wangning/anaconda3/envs/model_angelo/lib/python3.9/urllib/request.py", line 1389, in https_open
return self.do_open(http.client.HTTPSConnection, req,
│ │ │ │ │ └ <urllib.request.Request object at 0x7fb18f8d4730>
│ │ │ │ └ <class 'http.client.HTTPSConnection'>
│ │ │ └ <module 'http.client' from '/data/wangning/anaconda3/envs/model_angelo/lib/python3.9/http/client.py'>
│ │ └ <module 'http' from '/data/wangning/anaconda3/envs/model_angelo/lib/python3.9/http/init.py'>
│ └ <function AbstractHTTPHandler.do_open at 0x7fb2375c1d30>
└ <urllib.request.HTTPSHandler object at 0x7fb18f8bb430>
File "/data/wangning/anaconda3/envs/model_angelo/lib/python3.9/urllib/request.py", line 1349, in do_open
raise URLError(err)
└ <class 'urllib.error.URLError'>
urllib.error.URLError: <urlopen error [Errno 111] Connection refused> " Does It seem that model_angelo try to connect the internet? I don't understand this error. I used GPU 2080Ti and CUDA11.5 for this. Please help me. Thanks in advance!
Best regards,
Ning
Model cannot be placed within the source directory
If I make weights directory inside the source directory, setuptools complains:
error: Multiple top-level packages discovered in a flat-layout: ['weights', 'model_angelo'].
To avoid accidental inclusion of unwanted files or directories,
setuptools will not proceed with this build.
If you are trying to create a single distribution with multiple packages
on purpose, you should not rely on automatic discovery.
Instead, consider the following options:
1. set up custom discovery (`find` directive with `include` or `exclude`)
2. use a `src-layout`
3. explicitly set `py_modules` or `packages` with a list of names
To find more information, look for "package discovery" on setuptools docs.
This point should be mentioned in the documentation.
The following patch to setup.py enabled such a use case but I am not sure if this has unwanted side effects.
diff --git a/setup.py b/setup.py
index 6c9e328..34da75a 100644
--- a/setup.py
+++ b/setup.py
@@ -7,7 +7,7 @@ Setup module for ModelAngelo
import os
import sys
-from setuptools import setup
+from setuptools import find_packages, setup
sys.path.insert(0, f"{os.path.dirname(__file__)}/model_angelo")
@@ -22,6 +22,7 @@ setup(
"model_angelo = model_angelo.__main__:main",
],
},
+ packages=find_packages(),
package_data={'': ['utils/stereo_chemical_props.txt']},
version=model_angelo.__version__,
)Modelangelo v1.0.1 failing if the fasta contains unknown residues "X"
Hey,
I have a fasta file where certain residues are unknown and therefor represented with X
such as (1 at the start and 1 at place 105):
>chain 'CM'
XPFKRFVEIGRVALVNYGKDYGRLVVIVDVVDQNRALVDAPDMVRCQINFKRLSLTDIKIDIKRVPKKTTLIKAMEEADVKNKWENSSWGKKLIVQKRRASLNDXDRFKVMLAKIKRGGAIRQELAKLKKTAAA
When trying to build against these fasta sequences you get an internal assertion error:
click here for the log file
2023-06-15 at 17:54:09 | INFO | ModelAngelo with args: {'volume_path': '../sharpened.mrc', 'protein_fasta': '../fasta_files/proteins.fa', 'rna_fasta': '../fasta_files/rna.fa', 'dna_fasta': None, 'output_dir': '20230615_fasta', 'mask_path': None, 'device': '0', 'config_path': None, 'model_bundle_name': 'nucleotides', 'model_bundle_path': None, 'keep_intermediate_results': False, 'pipeline_control': False, 'func': <function main at 0x7f302c6b9430>}
2023-06-15 at 17:54:09 | INFO | Initial C-alpha prediction with args: {'model_checkpoint': 'chkpt.torch', 'bfactor': 0, 'batch_size': 4, 'box_size': 64, 'stride': 16, 'dont_mask_input': True, 'threshold': 0.05, 'save_real_coordinates': False, 'save_cryo_em_grid': False, 'do_nucleotides': True, 'save_backbone_trace': False, 'save_output_grid': False, 'crop': 6, 'log_dir': '/data/public/model_angelo_weights/hub/checkpoints/model_angelo_v1.0/nucleotides/c_alpha', 'map_path': '../sharpened.mrc', 'output_path': '20230615_fasta/see_alpha_output', 'mask_path': None, 'device': '0', 'auto_mask': False}
2023-06-15 at 17:54:09 | INFO | Using model file /data/public/model_angelo_weights/hub/checkpoints/model_angelo_v1.0/nucleotides/c_alpha/model.py
2023-06-15 at 17:54:09 | INFO | Using checkpoint file /data/public/model_angelo_weights/hub/checkpoints/model_angelo_v1.0/nucleotides/c_alpha/chkpt.torch
2023-06-15 at 17:54:10 | INFO | Input structure has shape: (162, 162, 162)
2023-06-15 at 17:54:10 | INFO | Running with these arguments:
2023-06-15 at 17:54:10 | INFO | {'model_checkpoint': 'chkpt.torch', 'bfactor': 0, 'batch_size': 4, 'box_size': 64, 'stride': 16, 'dont_mask_input': True, 'threshold': 0.05, 'save_real_coordinates': False, 'save_cryo_em_grid': False, 'do_nucleotides': True, 'save_backbone_trace': False, 'save_output_grid': False, 'crop': 6, 'log_dir': '/data/public/model_angelo_weights/hub/checkpoints/model_angelo_v1.0/nucleotides/c_alpha', 'map_path': '../sharpened.mrc', 'output_path': '20230615_fasta/see_alpha_output', 'mask_path': None, 'device': '0', 'auto_mask': False}
2023-06-15 at 18:01:55 | INFO | Model prediction done, took 465.11 seconds for 343 sliding windows
2023-06-15 at 18:01:55 | INFO | Average time is 1356.012 ms
2023-06-15 at 18:01:55 | INFO | Starting Cα grid to points...
2023-06-15 at 18:01:56 | INFO | Have 17015 Cα points before pruning and 7629 after pruning
2023-06-15 at 18:01:57 | INFO | Starting P grid to points...
2023-06-15 at 18:01:58 | INFO | Have 10785 P points before pruning and 4260 after pruning
2023-06-15 at 18:01:59 | INFO | Finished inference!
2023-06-15 at 18:01:59 | INFO | GNN model refinement round 1 with args: {'num_rounds': 3, 'crop_length': 200, 'repeat_per_residue': 1, 'esm_model': 'esm1b_t33_650M_UR50S', 'aggressive_pruning': True, 'seq_attention_batch_size': 200, 'fp16': False, 'batch_size': 1, 'voxel_size': 1.0, 'map': '../sharpened.mrc', 'protein_fasta': '../fasta_files/proteins.fa', 'rna_fasta': '../fasta_files/rna.fa', 'dna_fasta': None, 'struct': '20230615_fasta/see_alpha_output/see_alpha_merged_output.cif', 'output_dir': '20230615_fasta/gnn_output_round_1', 'model_dir': '/data/public/model_angelo_weights/hub/checkpoints/model_angelo_v1.0/nucleotides/gnn', 'device': '0', 'write_hmm_profiles': False, 'refine': False}
2023-06-15 at 18:01:59 | INFO | Loaded module from step: 483863
2023-06-15 at 18:02:49 | ERROR | Error in ModelAngelo
Traceback (most recent call last):
File "/opt/apps/miniconda3/envs/model_angelo/bin/model_angelo", line 33, in <module>
sys.exit(load_entry_point('model-angelo==1.0.1', 'console_scripts', 'model_angelo')())
│ │ └ <function importlib_load_entry_point at 0x7f30f12630d0>
│ └ <built-in function exit>
└ <module 'sys' (built-in)>
File "/opt/apps/miniconda3/envs/model_angelo/lib/python3.9/site-packages/model_angelo-1.0.1-py3.9.egg/model_angelo/__main__.py", line 52, in main
args.func(args)
│ │ └ Namespace(volume_path='../sharpened.mrc', protein_fasta='../fasta_files/proteins.fa', rna_fasta='../fasta_files/rna.fa', dna_...
│ └ <function main at 0x7f302c6b9430>
└ Namespace(volume_path='../sharpened.mrc', protein_fasta='../fasta_files/proteins.fa', rna_fasta='../fasta_files/rna.fa', dna_...
> File "/opt/apps/miniconda3/envs/model_angelo/lib/python3.9/site-packages/model_angelo-1.0.1-py3.9.egg/model_angelo/apps/build.py", line 241, in main
gnn_output = gnn_infer(gnn_infer_args)
│ └ {'num_rounds': 3, 'crop_length': 200, 'repeat_per_residue': 1, 'esm_model': 'esm1b_t33_650M_UR50S', 'aggressive_pruning': Tru...
└ <function infer at 0x7f302d433d30>
File "/opt/apps/miniconda3/envs/model_angelo/lib/python3.9/site-packages/model_angelo-1.0.1-py3.9.egg/model_angelo/gnn/inference.py", line 92, in infer
protein = get_lm_embeddings_for_protein(lang_model, batch_converter, protein)
│ │ │ └ Protein(atom_positions=None, atomc_positions=None, aatype=None, atom_mask=None, atomc_mask=None, residue_index=None, chain_in...
│ │ └ <esm.data.BatchConverter object at 0x7f301e126fd0>
│ └ ProteinBertModel(
│ (embed_tokens): Embedding(33, 1280, padding_idx=1)
│ (layers): ModuleList(
│ (0): TransformerLayer(
│ ...
└ <function get_lm_embeddings_for_protein at 0x7f302d433e50>
File "/opt/apps/miniconda3/envs/model_angelo/lib/python3.9/site-packages/model_angelo-1.0.1-py3.9.egg/model_angelo/data/generate_complete_prot_files.py", line 34, in get_lm_embeddings_for_protein
protein_with_lm = add_lm_embeddings_to_protein(protein, lm_embeddings)
│ │ └ array([[ 8.0417655e-04, 3.0484083e-01, 6.0511094e-01, ...,
│ │ -2.1142796e-01, -2.8297421e-01, -9.1318183e-02],
│ │ ...
│ └ Protein(atom_positions=None, atomc_positions=None, aatype=None, atom_mask=None, atomc_mask=None, residue_index=None, chain_in...
└ <function add_lm_embeddings_to_protein at 0x7f302d43d670>
File "/opt/apps/miniconda3/envs/model_angelo/lib/python3.9/site-packages/model_angelo-1.0.1-py3.9.egg/model_angelo/utils/protein.py", line 897, in add_lm_embeddings_to_protein
assert len(lm_embeddings) == input_protein.unified_seq_len
│ │ └ 5488
│ └ Protein(atom_positions=None, atomc_positions=None, aatype=None, atom_mask=None, atomc_mask=None, residue_index=None, chain_in...
└ array([[ 8.0417655e-04, 3.0484083e-01, 6.0511094e-01, ...,
-2.1142796e-01, -2.8297421e-01, -9.1318183e-02],
...
AssertionError: assert len(lm_embeddings) == input_protein.unified_seq_len
If I (just) remove the "X" from the fasta sequence it seems to at least build a model without issue (still have to check if it is reasonable for my complete complex).
What would be the best way of dealing with these unknown residues, just delete them, replace them with glycines, or something else?
Also, it would probably be nice to catch this issue before the start of the C-alpha prediction
the model is shown as lines; IndexError: arrays used as indices must be of integer (or boolean) type
Hello Kiarash, @jamaliki
After running model-angelo with a fasta file, the final result (see_alpha_output_ca.cif) is shown as direct lines and looks more like a hedgehog.
The results in the other folders (output_ca_points_before_pruning.cif) look like that
As for the gnn_output_round_ folders - the results are looking somewhat better
But if I want to see only the alpha-helices and hide atoms, I see this
How could you interpret the results?
Did something go wrong?
Please see the model-angelo log below. It seems that it has an error.
Is there a way to fix it and the model itself?
model_angelo.log
Kind regards,
Dmitry
Some questions about format of Input
I find that the most format EM maps is .map, but this software needs .mrc. Is the software supports .maps format?
Successful? model build (output.cif written) despite run ending in error
Hi, I have installed this on a 4xA5000 system using default "personal use" parameters as specified in the readme. Install went fine.
I tested building without sequence file provided with a 3A ribosome map and a 2.5A soluble protein map. 2.5A soluble protein map went well, while 3A ribosome map only modelled a small section of the density. In the output given during the run, no errors or warnings were given. However in the logFile, both runs ended with following error message
2022-11-02 at 23:47:17 | INFO | Loaded module from step: 529999
2022-11-03 at 00:04:23 | ERROR | Error in ModelAngelo
Traceback (most recent call last):
File "/home/administrator/miniconda3/envs/model_angelo/bin/model_angelo", line 33, in <module>
sys.exit(load_entry_point('model-angelo==0.2', 'console_scripts', 'model_angelo')())
│ │ └ <function importlib_load_entry_point at 0x7fd5ce1aa0d0>
│ └ <built-in function exit>
└ <module 'sys' (built-in)>
File "/home/administrator/miniconda3/envs/model_angelo/lib/python3.9/site-packages/model_angelo-0.2-py3.9.egg/model_angelo/__main__.py", line 51, in main
args.func(args)
│ │ └ Namespace(volume_path='run_class001.mrc', output_dir='output', mask_path=None, device='cpu', config_path=None, model_bundle_n...
│ └ <function main at 0x7fd50b2ccca0>
└ Namespace(volume_path='run_class001.mrc', output_dir='output', mask_path=None, device='cpu', config_path=None, model_bundle_n...
> File "/home/administrator/miniconda3/envs/model_angelo/lib/python3.9/site-packages/model_angelo-0.2-py3.9.egg/model_angelo/apps/build_no_seq.py", line 230, in main
if not parsed_args.keep_intermediate_output:
└ Namespace(volume_path='run_class001.mrc', output_dir='output', mask_path=None, device='cpu', config_path=None, model_bundle_n...
AttributeError: 'Namespace' object has no attribute 'keep_intermediate_output'
I hope this is the right place to report this and I am not doing something dumb.
FASTA filename extension
My calculation failed in the below lines of model_angelo.gnn.inference.infer(), because I used a FASTA file with '.fa' extension.
model-angelo/model_angelo/gnn/inference.py
Lines 263 to 264 in 8b57c9d
According to Wikipedia, a FASTA format filename extension for a amino-acid sequence can be {.fasta, .fa, .faa, .mpfa}, so I think at least these extensions should be accepted. Or an appropriate FASTA format checker should be implemented?
(It will be also helpful if the file format/extension check is performed at the very beginning of the entire program.)
Thank you.
The output.cif is in wrong protein sequence
I have done the model building by model-angelo with a fasta sequence, but when I open the output data in pymol, the protein sequence is wrong and it is the repeat of ANDCGHK instead of what I uploaded. Why is this situation?
Issue using model-angelo on mac with Apple M2 chip
Hello,
I encountered an issue when trying to test model-angelo with model_angelo build -h on my macbook. I got the following error:
error: command '/var/folders/nz/j6p8yfhx1mv_0grj5xl4650h0000gp/T/abs_810lo85vyi/croot/python-split_1678271120546/_build_env/bin/llvm-ar' failed: No such file or directory
[end of output]
note: This error originates from a subprocess, and is likely not a problem with pip.
ERROR: Failed building wheel for pyhmmer
Running setup.py clean for pyhmmer
Failed to build pyhmmer
ERROR: Could not build wheels for pyhmmer, which is required to install pyproject.toml-based projects
Is there a way to work around this issue to get model-angelo running on this computer?
Thank you!
Issue with refine module
Hello there!
I was trying Model Angelo's refine module with my .mrc and pdb file. I am getting error:
File "/home/******/.conda/envs/model_angelo/lib/python3.10/site-packages/model_angelo-1.0.1-py3.10.egg/model_angelo/gnn/inference.py", line 60, in infer
voxel_size = args.voxel_size
└ {'num_rounds': 3, 'crop_length': 200, 'repeat_per_residue': 3, 'esm_model': 'esm1b_t33_650M_UR50S', 'aggressive_pruning': Tru...
AttributeError: 'Args' object has no attribute 'voxel_size'
I have tried with different map and model files and still facing same error.
Please suggest something for this error.
Is there a mismatch in the names when downloading models?
I'm trying to integrate model angelo in Scipion.
Here are some comments:
1.- From a python program is almost impossible to use "source". source is a builtin shell command not initialized in out context. There is this more compatible command . (dot operator) but it does not support [[ ]] .
2.- So I moved to a step by step installation, more suitable for us running all commands inside install_script.sh
I'm currently trapped at
python model_angelo/utils/setup_weights.py --bundle-name original_no_seq ....
Ok, I see there is a commit 2 hours ago fixing it!!
Installation not possible with ftp links
Hi,
I work for a corporate Pharma that wishes to evaluate model-angelo.
In attempting to install the package I've found that the code tries to download files via ftp.
Specifically the code is here:
torch_utils.py: f"ftp://ftp.mrc-lmb.cam.ac.uk/pub/scheres/modelangelo/{bundle_name}.zip"
ftp has been considered outmoded by the world for some time. To the extent that even chrome no longer supports retrieval of files from any ftp sites.
Our corporation (and I expect most all), have banned all internal and external use of ftp sites due to the insecure nature of the protocol.
I am therefore writing this only to request that you please consider moving (or asking the powers that be), to provide more modern and secure methods of file transfer. sftp, https etc.
Having to disassemble and manually install the application (downloading these files on a private open connection), due to a lack of being able to source the materials commercially is quite frustrating.
While I thank you for making your code available to the public and understand that you have no obligation whatsoever to change, I just wanted to mention this as I run across this problem often and I always ask the maintainers of such repositories to consider seeking better alternatives.
Thanks for your time.
Cheers,
Rick.
numpy 1.24 no attribute int
model-angelo version: 0.2.3
When executing the help command I get the following error. This only happens with numpy 1.24. When downgrading to numpy 1.19 another error appears. I could circumvent this error by appending a restriction of numpy to the requirements file:
echo -e "\nnumpy<1.24" >> requirements.txt
While this resolved the issue for me, I think this is rather a quick dirty fix than a sustainable solution of the problem.
Error:
$ model_angelo build --help
Traceback (most recent call last):
File "/home/apps/conda/miniconda3/envs/modelangelo-0.2.3/bin/model_angelo", line 33, in <module>
sys.exit(load_entry_point('model-angelo==0.2.3', 'console_scripts', 'model_angelo')())
File "/home/apps/conda/miniconda3/envs/modelangelo-0.2.3/lib/python3.9/site-packages/model_angelo-0.2.3-py3.9.egg/model_angelo/__main__.py", line 23, in main
import model_angelo.apps.build
File "/home/apps/conda/miniconda3/envs/modelangelo-0.2.3/lib/python3.9/site-packages/model_angelo-0.2.3-py3.9.egg/model_angelo/apps/build.py", line 19, in <module>
from model_angelo.c_alpha.inference import infer as c_alpha_infer
File "/home/apps/conda/miniconda3/envs/modelangelo-0.2.3/lib/python3.9/site-packages/model_angelo-0.2.3-py3.9.egg/model_angelo/c_alpha/inference.py", line 14, in <module>
from model_angelo.data.dataset_preprocess import decompress_data
File "/home/apps/conda/miniconda3/envs/modelangelo-0.2.3/lib/python3.9/site-packages/model_angelo-0.2.3-py3.9.egg/model_angelo/data/dataset_preprocess.py", line 14, in <module>
from model_angelo.utils.fasta_utils import read_fasta
File "/home/apps/conda/miniconda3/envs/modelangelo-0.2.3/lib/python3.9/site-packages/model_angelo-0.2.3-py3.9.egg/model_angelo/utils/fasta_utils.py", line 10, in <module>
from model_angelo.utils.residue_constants import index_to_restype_1
File "/home/apps/conda/miniconda3/envs/modelangelo-0.2.3/lib/python3.9/site-packages/model_angelo-0.2.3-py3.9.egg/model_angelo/utils/residue_constants.py", line 1094, in <module>
restype_atom37_to_rigid_group = np.zeros([21, 37], dtype=np.int)
File "/home/apps/conda/miniconda3/envs/modelangelo-0.2.3/lib/python3.9/site-packages/numpy/__init__.py", line 305, in __getattr__
raise AttributeError(__former_attrs__[attr])
AttributeError: module 'numpy' has no attribute 'int'.
`np.int` was a deprecated alias for the builtin `int`. To avoid this error in existing code, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
Dependency resolution issues on fresh install
Trying to install Model-Angelo into a fairly barebones Ubuntu 22.04 environment (in a cloud Docker instance, if that makes a difference) leaves me in conda package resolution hell (see stderr output below). I suspect one way to avert this would be to put specific version numbers for each package on the conda install line in install_script.sh... could you offer any advice?
Building graph of deps: 0%| | 0/31 [00:00<?, ?it/s]
Examining readline: 0%| | 0/31 [00:00<?, ?it/s]
Examining torchvision: 3%|████ | 1/31 [00:00<00:00, 169.54it/s]
Examining _libgcc_mutex: 6%|████████ | 2/31 [00:25<06:12, 12.85s/it]
Examining _libgcc_mutex: 10%|████████████ | 3/31 [00:25<03:59, 8.56s/it]
Examining @/linux-64::__cuda==11.4=0: 10%|██████████▊ | 3/31 [00:25<03:59, 8.56s/it]
Examining ca-certificates: 13%|███████████████▊ | 4/31 [00:25<03:51, 8.56s/it]
Examining libuuid: 16%|█████████████████████▏ | 5/31 [00:25<03:42, 8.56s/it]
Examining ld_impl_linux-64: 19%|███████████████████████▌ | 6/31 [00:25<03:34, 8.56s/it]
Examining @/linux-64::__unix==0=0: 23%|█████████████████████████▉ | 7/31 [00:25<03:25, 8.56s/it]
Examining torchaudio: 26%|█████████████████████████████████ | 8/31 [00:25<03:16, 8.56s/it]
Examining pytorch-cuda=11.7: 29%|███████████████████████████████████▏ | 9/31 [00:34<03:08, 8.56s/it]
Examining pytorch-cuda=11.7: 32%|██████████████████████████████████████▋ | 10/31 [00:34<01:01, 2.95s/it]
Examining @/linux-64::__linux==5.4.241=0: 32%|██████████████████████████████████▌ | 10/31 [00:34<01:01, 2.95s/it]
Examining @/linux-64::__archspec==1=x86_64: 35%|█████████████████████████████████████▎ | 11/31 [00:34<00:58, 2.95s/it]
Examining python=3.10: 39%|████████████████████████████████████████████████▊ | 12/31 [00:34<00:55, 2.95s/it]
Examining tzdata: 42%|██████████████████████████████████████████████████████▉ | 13/31 [00:34<00:53, 2.95s/it]
Examining tzdata: 45%|███████████████████████████████████████████████████████████▏ | 14/31 [00:34<00:31, 1.83s/it]
Examining libnsl: 45%|███████████████████████████████████████████████████████████▏ | 14/31 [00:34<00:31, 1.83s/it]
Examining libzlib: 48%|██████████████████████████████████████████████████████████████▉ | 15/31 [00:34<00:29, 1.83s/it]
Examining bzip2: 52%|████████████████████████████████████████████████████████████████████▏ | 16/31 [00:34<00:27, 1.83s/it]
Examining libgcc-ng: 55%|██████████████████████████████████████████████████████████████████████▏ | 17/31 [00:34<00:25, 1.83s/it]
Examining pip: 58%|█████████████████████████████████████████████████████████████████████████████▊ | 18/31 [00:34<00:23, 1.83s/it]
Examining _openmp_mutex: 61%|████████████████████████████████████████████████████████████████████████████ | 19/31 [00:36<00:21, 1.83s/it]
Examining _openmp_mutex: 65%|████████████████████████████████████████████████████████████████████████████████ | 20/31 [00:36<00:12, 1.14s/it]
Examining @/linux-64::__glibc==2.35=0: 65%|██████████████████████████████████████████████████████████████████████▉ | 20/31 [00:36<00:12, 1.14s/it]
Examining libsqlite: 68%|██████████████████████████████████████████████████████████████████████████████████████▋ | 21/31 [00:36<00:11, 1.14s/it]
Examining openssl: 71%|████████████████████████████████████████████████████████████████████████████████████████████▎ | 22/31 [00:36<00:10, 1.14s/it]
Examining pytorch: 74%|████████████████████████████████████████████████████████████████████████████████████████████████▍ | 23/31 [00:36<00:09, 1.14s/it]
Examining tk: 77%|████████████████████████████████████████████████████████████████████████████████████████████████████████▌ | 24/31 [00:50<00:08, 1.14s/it]
Examining tk: 81%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▊ | 25/31 [00:50<00:10, 1.71s/it]
Examining libffi: 81%|█████████████████████████████████████████████████████████████████████████████████████████████████████████▋ | 25/31 [00:50<00:10, 1.71s/it]
Examining libgomp: 84%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████ | 26/31 [00:50<00:08, 1.71s/it]
Examining ncurses: 87%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏ | 27/31 [00:50<00:06, 1.71s/it]
Examining xz: 90%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉ | 28/31 [00:50<00:05, 1.71s/it]
Examining setuptools: 94%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊ | 29/31 [00:50<00:03, 1.71s/it]
Examining wheel: 97%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋ | 30/31 [00:51<00:01, 1.71s/it]
Examining wheel: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 31/31 [00:51<00:00, 1.16s/it]
Determining conflicts: 0%| | 0/31 [00:00<?, ?it/s]
Examining conflict for readline pytorch torchvision torchaudio pip python setuptools wheel: 0%| | 0/31 [00:00<?, ?it/s]
Examining conflict for readline bzip2 openssl libuuid pytorch torchvision libgcc-ng tk torchaudio libffi ncurses libsqlite python _openmp_mutex xz libnsl libzlib: 3%| | 1/31 [00:09<04:
Examining conflict for readline bzip2 openssl libuuid pytorch torchvision libgcc-ng tk torchaudio libffi ncurses libsqlite python _openmp_mutex xz libnsl libzlib: 6%| | 2/31 [00:09<02:
Examining conflict for readline torchvision libuuid torchaudio python libnsl libzlib bzip2 libgcc-ng pip libsqlite openssl pytorch tk libffi ncurses xz setuptools wheel: 6%| | 2/31 [00
Examining conflict for readline torchvision libuuid torchaudio python libnsl libzlib bzip2 libgcc-ng pip libsqlite openssl pytorch tk libffi ncurses xz setuptools wheel: 10%| | 3/31 [00
Examining conflict for readline pytorch torchvision torchaudio ncurses pip python setuptools wheel: 10%|████▊ | 3/31 [00:27<02:57, 6.35s/it]
Examining conflict for readline pytorch torchvision torchaudio ncurses pip python setuptools wheel: 13%|██████▍ | 4/31 [00:27<03:18, 7.36s/it]
Examining conflict for readline bzip2 openssl libuuid pytorch torchvision libgcc-ng tk libffi libgomp _libgcc_mutex libsqlite python ncurses _openmp_mutex xz libnsl libzlib: 13%|▏| 4/31
Examining conflict for readline bzip2 openssl libuuid pytorch torchvision libgcc-ng tk libffi libgomp _libgcc_mutex libsqlite python ncurses _openmp_mutex xz libnsl libzlib: 16%|▏| 5/31
Examining conflict for readline pytorch torchvision torchaudio libffi ncurses pip python setuptools wheel: 16%|██████▉ | 5/31 [00:43<03:26, 7.95s/it]
Examining conflict for readline pytorch torchvision torchaudio libffi ncurses pip python setuptools wheel: 19%|████████▎ | 6/31 [00:43<03:15, 7.84s/it]
Examining conflict for readline bzip2 openssl libuuid pytorch torchvision libgcc-ng tk torchaudio libffi ncurses __glibc python xz libnsl: 19%|██▏ | 6/31 [00:52<03:15, 7.84s/it]
Examining conflict for readline bzip2 openssl libuuid pytorch torchvision libgcc-ng tk torchaudio libffi ncurses __glibc python xz libnsl: 23%|██▍ | 7/31 [00:52<03:17, 8.24s/it]
Examining conflict for torchaudio pytorch torchvision: 23%|█████████████████████▍ | 7/31 [01:01<03:17, 8.24s/it]
Examining conflict for torchaudio pytorch torchvision: 26%|████████████████████████▌ | 8/31 [01:01<03:13, 8.42s/it]
Examining conflict for openssl pytorch torchvision torchaudio pip python setuptools wheel: 26%|███████████████▏ | 8/31 [01:05<03:13, 8.42s/it]
Examining conflict for openssl pytorch torchvision torchaudio pip python setuptools wheel: 29%|█████████████████▏ | 9/31 [01:05<02:37, 7.16s/it]
Examining conflict for openssl ca-certificates pytorch torchvision pip python setuptools wheel: 29%|███████████████▋ | 9/31 [01:15<02:37, 7.16s/it]
Examining conflict for openssl ca-certificates pytorch torchvision pip python setuptools wheel: 32%|█████████████████ | 10/31 [01:15<02:42, 7.75s/it]
Examining conflict for pytorch torchvision torchaudio pip python setuptools wheel: 32%|█████████████████████▎ | 10/31 [01:22<02:42, 7.75s/it]
Examining conflict for pytorch torchvision torchaudio pip python setuptools wheel: 35%|███████████████████████▍ | 11/31 [01:22<02:36, 7.80s/it]
Examining conflict for pytorch-cuda torchaudio pytorch torchvision: 35%|████████████████████████████▋ | 11/31 [01:31<02:36, 7.80s/it]
Examining conflict for pytorch-cuda torchaudio pytorch torchvision: 39%|███████████████████████████████▎ | 12/31 [01:31<02:35, 8.17s/it]
Examining conflict for setuptools pytorch torchvision torchaudio pip python libnsl wheel: 39%|██████████████████████▊ | 12/31 [01:41<02:35, 8.17s/it]
Examining conflict for setuptools pytorch torchvision torchaudio pip python libnsl wheel: 42%|████████████████████████▋ | 13/31 [01:41<02:31, 8.42s/it]
Examining conflict for ld_impl_linux-64 pytorch torchvision torchaudio pip python setuptools wheel: 42%|████████████████████▌ | 13/31 [01:50<02:31, 8.42s/it]
Examining conflict for ld_impl_linux-64 pytorch torchvision torchaudio pip python setuptools wheel: 45%|██████████████████████▏ | 14/31 [01:50<02:26, 8.63s/it]
Examining conflict for libuuid pytorch torchvision torchaudio pip python setuptools wheel: 45%|██████████████████████████▏ | 14/31 [01:59<02:26, 8.63s/it]
Examining conflict for libuuid pytorch torchvision torchaudio pip python setuptools wheel: 48%|████████████████████████████ | 15/31 [01:59<02:20, 8.76s/it]
Examining conflict for pytorch torchvision tk torchaudio pip python setuptools wheel: 48%|██████████████████████████████▍ | 15/31 [02:08<02:20, 8.76s/it]
Examining conflict for pytorch torchvision tk torchaudio pip python setuptools wheel: 52%|████████████████████████████████▌ | 16/31 [02:08<02:12, 8.85s/it]
Examining conflict for pytorch torchvision: 52%|██████████████████████████████████████████████████████▏ | 16/31 [02:17<02:12, 8.85s/it]
Examining conflict for pytorch torchvision: 55%|█████████████████████████████████████████████████████████▌ | 17/31 [02:17<02:05, 8.95s/it]
Examining conflict for pytorch torchvision torchaudio pip python xz setuptools wheel: 55%|██████████████████████████████████▌ | 17/31 [02:30<02:05, 8.95s/it]
Examining conflict for pytorch torchvision torchaudio pip python xz setuptools wheel: 58%|████████████████████████████████████▌ | 18/31 [02:30<02:14, 10.32s/it]
Examining conflict for pytorch torchvision torchaudio libffi pip python setuptools wheel: 58%|██████████████████████████████████▎ | 18/31 [02:40<02:14, 10.32s/it]
Examining conflict for pytorch torchvision torchaudio libffi pip python setuptools wheel: 61%|████████████████████████████████████▏ | 19/31 [02:40<01:59, 9.99s/it]
Examining conflict for pip setuptools wheel torchvision: 61%|████████████████████████████████████████████████████████▍ | 19/31 [02:49<01:59, 9.99s/it]
Examining conflict for pip setuptools wheel torchvision: 65%|███████████████████████████████████████████████████████████▎ | 20/31 [02:49<01:47, 9.76s/it]
Examining conflict for pytorch torchvision torchaudio pip python setuptools wheel libsqlite: 65%|████████████████████████████████████▏ | 20/31 [02:52<01:47, 9.76s/it]
Examining conflict for pytorch torchvision torchaudio pip python setuptools wheel libsqlite: 68%|█████████████████████████████████████▉ | 21/31 [02:52<01:18, 7.88s/it]
Examining conflict for bzip2 pytorch torchvision torchaudio pip python setuptools wheel: 68%|████████████████████████████████████████▋ | 21/31 [03:02<01:18, 7.88s/it]
Examining conflict for bzip2 pytorch torchvision torchaudio pip python setuptools wheel: 71%|██████████████████████████████████████████▌ | 22/31 [03:02<01:14, 8.28s/it]
Examining conflict for pytorch torchvision tk torchaudio pip libsqlite python setuptools wheel libzlib: 71%|███████████████████████████████▉ | 22/31 [03:11<01:14, 8.28s/it]
Examining conflict for pytorch torchvision tk torchaudio pip libsqlite python setuptools wheel libzlib: 74%|█████████████████████████████████▍ | 23/31 [03:11<01:08, 8.56s/it]
Examining conflict for pytorch torchvision pip python setuptools wheel: 74%|█████████████████████████████████████████████████████████▏ | 23/31 [03:20<01:08, 8.56s/it]
Examining conflict for pytorch torchvision pip python setuptools wheel: 77%|███████████████████████████████████████████████████████████▌ | 24/31 [03:20<01:01, 8.76s/it]
Examining conflict for pytorch torchvision torchaudio pip python tzdata setuptools wheel: 77%|█████████████████████████████████████████████▋ | 24/31 [03:25<01:01, 8.76s/it]
Examining conflict for pytorch torchvision torchaudio pip python tzdata setuptools wheel: 81%|███████████████████████████████████████████████▌ | 25/31 [03:25<00:44, 7.49s/it]
Examining conflict for pip python wheel: 81%|███████████████████████████████████████████████████████████████████████████████████████ | 25/31 [03:34<00:44, 7.49s/it]
Examining conflict for pip python wheel: 84%|██████████████████████████████████████████████████████████████████████████████████████████▌ | 26/31 [03:34<00:39, 8.00s/it]
Examining conflict for libgomp _openmp_mutex pytorch libgcc-ng: 84%|███████████████████████████████████████████████████████████████████████▎ | 26/31 [03:34<00:39, 8.00s/it]
UnsatisfiableError: The following specifications were found to be incompatible with each other:
Output in format: Requested package -> Available versions
Package xz conflicts for:
setuptools -> python[version='>=3.7'] -> xz[version='>=5.2.10,<6.0a0|>=5.2.6,<6.0a0|>=5.4.2,<6.0a0|>=5.2.8,<6.0a0|>=5.2.5,<6.0a0|>=5.2.4,<6.0a0|>=5.2.3,<6.0a0']
python=3.10 -> xz[version='>=5.2.10,<6.0a0|>=5.2.6,<6.0a0|>=5.4.2,<6.0a0|>=5.2.8,<6.0a0|>=5.2.5,<6.0a0']
pip -> python[version='>=3.7'] -> xz[version='>=5.2.10,<6.0a0|>=5.2.6,<6.0a0|>=5.4.2,<6.0a0|>=5.2.8,<6.0a0|>=5.2.5,<6.0a0|>=5.2.4,<6.0a0|>=5.2.3,<6.0a0']
torchvision -> python[version='>=3.8,<3.9.0a0'] -> xz[version='>=5.2.10,<6.0a0|>=5.2.6,<6.0a0|>=5.2.5,<6.0a0|>=5.2.4,<6.0a0|>=5.4.2,<6.0a0|>=5.2.8,<6.0a0|>=5.2.3,<6.0a0']
xz
pytorch -> python[version='>=3.9,<3.10.0a0'] -> xz[version='>=5.2.10,<6.0a0|>=5.4.2,<6.0a0|>=5.2.8,<6.0a0|>=5.2.6,<6.0a0|>=5.2.5,<6.0a0|>=5.2.4,<6.0a0|>=5.2.3,<6.0a0']
wheel -> python[version='>=3.7'] -> xz[version='>=5.2.10,<6.0a0|>=5.2.6,<6.0a0|>=5.4.2,<6.0a0|>=5.2.8,<6.0a0|>=5.2.5,<6.0a0|>=5.2.4,<6.0a0|>=5.2.3,<6.0a0']
torchaudio -> python[version='>=3.9,<3.10.0a0'] -> xz[version='>=5.2.10,<6.0a0|>=5.4.2,<6.0a0|>=5.2.8,<6.0a0|>=5.2.6,<6.0a0|>=5.2.5,<6.0a0|>=5.2.4,<6.0a0|>=5.2.3,<6.0a0']
Package _openmp_mutex conflicts for:
libffi -> libgcc-ng[version='>=9.4.0'] -> _openmp_mutex[version='>=4.5']
libzlib -> libgcc-ng[version='>=12'] -> _openmp_mutex[version='>=4.5']
libgcc-ng -> _openmp_mutex[version='>=4.5']
openssl -> libgcc-ng[version='>=12'] -> _openmp_mutex[version='>=4.5']
bzip2 -> libgcc-ng[version='>=9.3.0'] -> _openmp_mutex[version='>=4.5']
libnsl -> libgcc-ng[version='>=9.4.0'] -> _openmp_mutex[version='>=4.5']
_openmp_mutex
xz -> libgcc-ng[version='>=12'] -> _openmp_mutex[version='>=4.5']
ncurses -> libgcc-ng[version='>=12'] -> _openmp_mutex[version='>=4.5']
python=3.10 -> libgcc-ng[version='>=12'] -> _openmp_mutex[version='>=4.5']
pytorch -> _openmp_mutex
tk -> libgcc-ng[version='>=9.4.0'] -> _openmp_mutex[version='>=4.5']
torchaudio -> pytorch==1.13.1 -> _openmp_mutex
torchvision -> pytorch==1.13.1 -> _openmp_mutex[version='>=4.5']
pytorch -> libgcc-ng[version='>=9.3.0'] -> _openmp_mutex[version='>=4.5']
libuuid -> libgcc-ng[version='>=12'] -> _openmp_mutex[version='>=4.5']
libsqlite -> libgcc-ng[version='>=12'] -> _openmp_mutex[version='>=4.5']
Package pytorch-cuda conflicts for:
torchvision -> pytorch-cuda[version='11.6.*|11.7.*|11.8.*']
torchvision -> pytorch==2.0.1 -> pytorch-cuda[version='>=11.6,<11.7|>=11.7,<11.8|>=11.8,<11.9']
torchaudio -> pytorch==2.0.1 -> pytorch-cuda[version='>=11.6,<11.7|>=11.7,<11.8|>=11.8,<11.9']
pytorch -> pytorch-cuda[version='>=11.6,<11.7|>=11.7,<11.8|>=11.8,<11.9']
pytorch-cuda=11.7
torchaudio -> pytorch-cuda[version='11.6.*|11.7.*|11.8.*']
Package libgcc-ng conflicts for:
libsqlite -> libgcc-ng[version='>=12']
libuuid -> libgcc-ng[version='>=11.2.0|>=12|>=7.5.0|>=7.2.0']
libnsl -> libgcc-ng[version='>=11.2.0|>=9.4.0']
openssl -> libgcc-ng[version='>=11.2.0|>=12|>=7.5.0|>=7.3.0|>=7.2.0']
tk -> libzlib[version='>=1.2.11,<1.3.0a0'] -> libgcc-ng[version='>=11.2.0|>=12']
libgcc-ng
tk -> libgcc-ng[version='>=7.2.0|>=7.3.0|>=7.5.0|>=9.4.0']
torchvision -> libgcc-ng[version='>=11.2.0|>=7.3.0|>=5.4.0']
wheel -> python[version='>=3.7'] -> libgcc-ng[version='>=11.2.0|>=12|>=7.5.0|>=7.3.0|>=7.2.0']
setuptools -> python[version='>=3.7'] -> libgcc-ng[version='>=11.2.0|>=12|>=7.5.0|>=7.3.0|>=7.2.0']
bzip2 -> libgcc-ng[version='>=7.2.0|>=7.3.0|>=9.3.0']
torchaudio -> numpy[version='>=1.11'] -> libgcc-ng[version='>=11.2.0|>=7.5.0|>=7.3.0|>=7.2.0|>=12|>=9.3.0|>=9.4.0']
xz -> libgcc-ng[version='>=11.2.0|>=12|>=7.5.0|>=7.3.0|>=7.2.0']
pytorch -> python[version='>=3.10,<3.11.0a0'] -> libgcc-ng[version='>=12|>=7.2.0|>=9.4.0|>=8.2.0']
libffi -> libgcc-ng[version='>=11.2.0|>=9.4.0|>=7.5.0|>=7.3.0|>=7.2.0']
libzlib -> libgcc-ng[version='>=12']
torchvision -> jpeg -> libgcc-ng[version='>=12|>=7.2.0|>=7.5.0|>=8.4.0|>=9.3.0|>=9.4.0']
ncurses -> libgcc-ng[version='>=11.2.0|>=12|>=7.5.0|>=7.3.0|>=7.2.0']
pytorch -> libgcc-ng[version='>=11.2.0|>=9.3.0|>=7.5.0|>=7.3.0|>=5.4.0']
pip -> python[version='>=3.7'] -> libgcc-ng[version='>=11.2.0|>=12|>=7.5.0|>=7.3.0|>=7.2.0']
python=3.10 -> bzip2[version='>=1.0.8,<2.0a0'] -> libgcc-ng[version='>=7.2.0|>=7.3.0|>=9.3.0|>=9.4.0']
python=3.10 -> libgcc-ng[version='>=11.2.0|>=12|>=7.5.0']
Package certifi conflicts for:
setuptools -> certifi[version='>=2016.09|>=2016.9.26']
wheel -> setuptools -> certifi[version='>=2016.09|>=2016.9.26']
torchvision -> requests -> certifi[version='>=2016.09|>=2016.9.26|>=2017.4.17']
pip -> setuptools -> certifi[version='>=2016.09|>=2016.9.26']
Package ld_impl_linux-64 conflicts for:
setuptools -> python[version='>=3.7'] -> ld_impl_linux-64[version='>=2.35.1|>=2.36.1']
torchvision -> python[version='>=3.8,<3.9.0a0'] -> ld_impl_linux-64[version='>=2.35.1|>=2.36.1']
torchaudio -> python[version='>=3.9,<3.10.0a0'] -> ld_impl_linux-64[version='>=2.35.1|>=2.36.1']
pytorch -> python[version='>=3.9,<3.10.0a0'] -> ld_impl_linux-64[version='>=2.35.1|>=2.36.1']
pip -> python[version='>=3.7'] -> ld_impl_linux-64[version='>=2.35.1|>=2.36.1']
python=3.10 -> ld_impl_linux-64[version='>=2.35.1|>=2.36.1']
ld_impl_linux-64
wheel -> python[version='>=3.7'] -> ld_impl_linux-64[version='>=2.35.1|>=2.36.1']
Package libsqlite conflicts for:
libsqlite
wheel -> python[version='>=3.7'] -> libsqlite[version='>=3.41.2,<4.0a0']
torchaudio -> python[version='>=3.10,<3.11.0a0'] -> libsqlite[version='>=3.41.2,<4.0a0']
setuptools -> python[version='>=3.7'] -> libsqlite[version='>=3.41.2,<4.0a0']
pytorch -> python[version='>=3.10,<3.11.0a0'] -> libsqlite[version='>=3.41.2,<4.0a0']
python=3.10 -> libsqlite[version='>=3.41.2,<4.0a0']
pip -> python[version='>=3.7'] -> libsqlite[version='>=3.41.2,<4.0a0']
torchvision -> python[version='>=3.10,<3.11.0a0'] -> libsqlite[version='>=3.41.2,<4.0a0']
Package openssl conflicts for:
pip -> python[version='>=3.7'] -> openssl[version='1.0.*|>=1.0.2m,<1.0.3a|>=1.0.2n,<1.0.3a|>=1.0.2o,<1.0.3a|>=1.0.2p,<1.0.3a|>=1.1.1a,<1.1.2a|>=1.1.1b,<1.1.2a|>=1.1.1c,<1.1.2a|>=1.1.1d,<1.1.2a|>=1.1.1e,<1.1.2a|>=1.1.1g,<1.1.2a|>=1.1.1h,<1.1.2a|>=1.1.1i,<1.1.2a|>=1.1.1j,<1.1.2a|>=1.1.1k,<1.1.2a|>=1.1.1l,<1.1.2a|>=1.1.1m,<1.1.2a|>=1.1.1n,<1.1.2a|>=1.1.1q,<1.1.2a|>=1.1.1s,<1.1.2a|>=1.1.1t,<1.1.2a|>=3.0.8,<4.0a0|>=3.1.0,<4.0a0|>=1.1.1o,<1.1.2a|>=1.0.2l,<1.0.3a']
torchaudio -> python[version='>=3.9,<3.10.0a0'] -> openssl[version='1.0.*|>=1.0.2m,<1.0.3a|>=1.0.2n,<1.0.3a|>=1.0.2o,<1.0.3a|>=1.0.2p,<1.0.3a|>=1.1.1a,<1.1.2a|>=1.1.1b,<1.1.2a|>=1.1.1c,<1.1.2a|>=1.1.1d,<1.1.2a|>=1.1.1e,<1.1.2a|>=1.1.1g,<1.1.2a|>=1.1.1h,<1.1.2a|>=1.1.1i,<1.1.2a|>=1.1.1j,<1.1.2a|>=1.1.1k,<1.1.2a|>=1.1.1l,<1.1.2a|>=1.1.1n,<1.1.2a|>=1.1.1o,<1.1.2a|>=1.1.1q,<1.1.2a|>=1.1.1s,<1.1.2a|>=1.1.1t,<1.1.2a|>=3.0.8,<4.0a0|>=3.1.0,<4.0a0|>=1.1.1m,<1.1.2a|>=1.0.2l,<1.0.3a']
torchvision -> ffmpeg[version='>=4.2'] -> openssl[version='1.0.*|>=1.0.2m,<1.0.3a|>=1.0.2n,<1.0.3a|>=1.0.2o,<1.0.3a|>=1.0.2p,<1.0.3a|>=1.1.1a,<1.1.2a|>=1.1.1b,<1.1.2a|>=1.1.1c,<1.1.2a|>=1.1.1d,<1.1.2a|>=1.1.1t,<1.1.2a|>=1.1.1s,<1.1.2a|>=1.1.1q,<1.1.2a|>=1.1.1n,<1.1.2a|>=1.1.1l,<1.1.2a|>=1.1.1k,<1.1.2a|>=1.1.1j,<1.1.2a|>=1.1.1g,<1.1.2a|>=1.1.1e,<1.1.2a|>=3.0.8,<4.0a0|>=1.1.1o,<1.1.2a|>=1.1.1i,<1.1.2a|>=1.1.1h,<1.1.2a|>=3.1.0,<4.0a0|>=1.1.1m,<1.1.2a|>=1.0.2l,<1.0.3a']
python=3.10 -> openssl[version='>=1.1.1l,<1.1.2a|>=1.1.1m,<1.1.2a|>=1.1.1n,<1.1.2a|>=1.1.1q,<1.1.2a|>=1.1.1s,<1.1.2a|>=1.1.1t,<1.1.2a|>=3.0.8,<4.0a0|>=3.1.0,<4.0a0']
pytorch -> python[version='>=3.9,<3.10.0a0'] -> openssl[version='1.0.*|>=1.0.2m,<1.0.3a|>=1.0.2n,<1.0.3a|>=1.0.2o,<1.0.3a|>=1.0.2p,<1.0.3a|>=1.1.1a,<1.1.2a|>=1.1.1b,<1.1.2a|>=1.1.1c,<1.1.2a|>=1.1.1d,<1.1.2a|>=1.1.1e,<1.1.2a|>=1.1.1g,<1.1.2a|>=1.1.1h,<1.1.2a|>=1.1.1i,<1.1.2a|>=1.1.1j,<1.1.2a|>=1.1.1k,<1.1.2a|>=1.1.1l,<1.1.2a|>=1.1.1n,<1.1.2a|>=1.1.1o,<1.1.2a|>=1.1.1q,<1.1.2a|>=1.1.1s,<1.1.2a|>=1.1.1t,<1.1.2a|>=3.0.8,<4.0a0|>=3.1.0,<4.0a0|>=1.1.1m,<1.1.2a|>=1.0.2l,<1.0.3a']
wheel -> python[version='>=3.7'] -> openssl[version='1.0.*|>=1.0.2m,<1.0.3a|>=1.0.2n,<1.0.3a|>=1.0.2o,<1.0.3a|>=1.0.2p,<1.0.3a|>=1.1.1a,<1.1.2a|>=1.1.1b,<1.1.2a|>=1.1.1c,<1.1.2a|>=1.1.1d,<1.1.2a|>=1.1.1e,<1.1.2a|>=1.1.1g,<1.1.2a|>=1.1.1h,<1.1.2a|>=1.1.1i,<1.1.2a|>=1.1.1j,<1.1.2a|>=1.1.1k,<1.1.2a|>=1.1.1l,<1.1.2a|>=1.1.1m,<1.1.2a|>=1.1.1n,<1.1.2a|>=1.1.1q,<1.1.2a|>=1.1.1s,<1.1.2a|>=1.1.1t,<1.1.2a|>=3.0.8,<4.0a0|>=3.1.0,<4.0a0|>=1.1.1o,<1.1.2a|>=1.0.2l,<1.0.3a']
setuptools -> python[version='>=3.7'] -> openssl[version='1.0.*|>=1.0.2m,<1.0.3a|>=1.0.2n,<1.0.3a|>=1.0.2o,<1.0.3a|>=1.0.2p,<1.0.3a|>=1.1.1a,<1.1.2a|>=1.1.1b,<1.1.2a|>=1.1.1c,<1.1.2a|>=1.1.1d,<1.1.2a|>=1.1.1e,<1.1.2a|>=1.1.1g,<1.1.2a|>=1.1.1h,<1.1.2a|>=1.1.1i,<1.1.2a|>=1.1.1j,<1.1.2a|>=1.1.1k,<1.1.2a|>=1.1.1l,<1.1.2a|>=1.1.1m,<1.1.2a|>=1.1.1n,<1.1.2a|>=1.1.1q,<1.1.2a|>=1.1.1s,<1.1.2a|>=1.1.1t,<1.1.2a|>=3.0.8,<4.0a0|>=3.1.0,<4.0a0|>=1.1.1o,<1.1.2a|>=1.0.2l,<1.0.3a']
openssl
Package _libgcc_mutex conflicts for:
libuuid -> libgcc-ng[version='>=12'] -> _libgcc_mutex[version='*|0.1',build='main|conda_forge|main']
_libgcc_mutex
_openmp_mutex -> _libgcc_mutex==0.1[build='main|conda_forge']
torchvision -> libgcc-ng[version='>=11.2.0'] -> _libgcc_mutex[version='*|0.1',build='main|conda_forge|main']
libffi -> libgcc-ng[version='>=9.4.0'] -> _libgcc_mutex[version='*|0.1',build='main|conda_forge|main']
openssl -> libgcc-ng[version='>=12'] -> _libgcc_mutex[version='*|0.1',build='main|conda_forge|main']
pytorch -> _openmp_mutex -> _libgcc_mutex[version='*|0.1',build='main|conda_forge|main']
libzlib -> libgcc-ng[version='>=12'] -> _libgcc_mutex==0.1=conda_forge
xz -> libgcc-ng[version='>=12'] -> _libgcc_mutex[version='*|0.1',build='main|conda_forge|main']
libgomp -> _libgcc_mutex==0.1[build='main|conda_forge']
bzip2 -> libgcc-ng[version='>=9.3.0'] -> _libgcc_mutex[version='*|0.1',build='main|conda_forge|main']
python=3.10 -> libgcc-ng[version='>=12'] -> _libgcc_mutex[version='*|0.1',build='main|conda_forge|main']
ncurses -> libgcc-ng[version='>=12'] -> _libgcc_mutex[version='*|0.1',build='main|conda_forge|main']
libgcc-ng -> _libgcc_mutex[version='*|0.1',build='main|conda_forge|main']
libsqlite -> libgcc-ng[version='>=12'] -> _libgcc_mutex==0.1=conda_forge
libnsl -> libgcc-ng[version='>=9.4.0'] -> _libgcc_mutex[version='*|0.1',build='main|conda_forge|main']
tk -> libgcc-ng[version='>=9.4.0'] -> _libgcc_mutex[version='*|0.1',build='main|conda_forge|main']
Package libstdcxx-ng conflicts for:
pytorch -> libstdcxx-ng[version='>=11.2.0|>=9.3.0|>=7.5.0|>=7.3.0|>=5.4.0']
torchvision -> libstdcxx-ng[version='>=11.2.0|>=7.3.0|>=5.4.0']
pip -> python[version='>=3.7'] -> libstdcxx-ng[version='>=11.2.0|>=7.5.0|>=7.3.0|>=7.2.0']
readline -> ncurses[version='>=6.2,<7.0a0'] -> libstdcxx-ng[version='>=7.2.0|>=7.3.0']
libffi -> libstdcxx-ng[version='>=11.2.0|>=7.5.0|>=7.3.0|>=7.2.0']
torchvision -> numpy[version='>=1.11'] -> libstdcxx-ng[version='>=7.2.0|>=7.5.0|>=8.4.0|>=9.3.0|>=9.4.0']
torchaudio -> numpy[version='>=1.11'] -> libstdcxx-ng[version='>=11.2.0|>=7.5.0|>=7.3.0|>=7.2.0|>=9.3.0|>=9.4.0']
ncurses -> libstdcxx-ng[version='>=7.2.0|>=7.3.0']
wheel -> python[version='>=3.7'] -> libstdcxx-ng[version='>=11.2.0|>=7.5.0|>=7.3.0|>=7.2.0']
setuptools -> python[version='>=3.7'] -> libstdcxx-ng[version='>=11.2.0|>=7.5.0|>=7.3.0|>=7.2.0']
pytorch -> python[version='>=3.7,<3.8.0a0'] -> libstdcxx-ng[version='>=7.2.0|>=9.4.0']
python=3.10 -> libffi[version='>=3.4,<4.0a0'] -> libstdcxx-ng[version='>=11.2.0|>=7.5.0|>=7.3.0']
Package tzdata conflicts for:
setuptools -> python[version='>=3.7'] -> tzdata
wheel -> python[version='>=3.7'] -> tzdata
tzdata
python=3.10 -> tzdata
pip -> python[version='>=3.7'] -> tzdata
torchaudio -> python[version='>=3.9,<3.10.0a0'] -> tzdata
torchvision -> python[version='>=3.9,<3.10.0a0'] -> tzdata
pytorch -> python[version='>=3.9,<3.10.0a0'] -> tzdata
Package ncurses conflicts for:
pytorch -> python[version='>=3.9,<3.10.0a0'] -> ncurses[version='6.0.*|>=6.0,<7.0a0|>=6.1,<7.0a0|>=6.2,<7.0a0|>=6.3,<7.0a0|>=6.4,<7.0a0']
wheel -> python[version='>=3.7'] -> ncurses[version='6.0.*|>=6.0,<7.0a0|>=6.1,<7.0a0|>=6.2,<7.0a0|>=6.3,<7.0a0|>=6.4,<7.0a0']
setuptools -> python[version='>=3.7'] -> ncurses[version='6.0.*|>=6.0,<7.0a0|>=6.1,<7.0a0|>=6.2,<7.0a0|>=6.3,<7.0a0|>=6.4,<7.0a0']
ncurses
pip -> python[version='>=3.7'] -> ncurses[version='6.0.*|>=6.0,<7.0a0|>=6.1,<7.0a0|>=6.2,<7.0a0|>=6.3,<7.0a0|>=6.4,<7.0a0']
torchvision -> python[version='>=3.8,<3.9.0a0'] -> ncurses[version='6.0.*|>=6.0,<7.0a0|>=6.1,<7.0a0|>=6.2,<7.0a0|>=6.3,<7.0a0|>=6.4,<7.0a0']
torchaudio -> python[version='>=3.9,<3.10.0a0'] -> ncurses[version='6.0.*|>=6.0,<7.0a0|>=6.1,<7.0a0|>=6.2,<7.0a0|>=6.3,<7.0a0|>=6.4,<7.0a0']
python=3.10 -> ncurses[version='>=6.2,<7.0a0|>=6.3,<7.0a0|>=6.4,<7.0a0']
python=3.10 -> readline[version='>=8.0,<9.0a0'] -> ncurses[version='>=6.1,<7.0a0']
Package setuptools conflicts for:
wheel -> setuptools
setuptools
torchvision -> setuptools
python=3.10 -> pip -> setuptools
pytorch -> jinja2 -> setuptools
pip -> setuptools
Package tk conflicts for:
wheel -> python[version='>=3.7'] -> tk[version='8.6.*|>=8.6.10,<8.7.0a0|>=8.6.11,<8.7.0a0|>=8.6.12,<8.7.0a0|>=8.6.8,<8.7.0a0|>=8.6.7,<8.7.0a0']
tk
setuptools -> python[version='>=3.7'] -> tk[version='8.6.*|>=8.6.10,<8.7.0a0|>=8.6.11,<8.7.0a0|>=8.6.12,<8.7.0a0|>=8.6.8,<8.7.0a0|>=8.6.7,<8.7.0a0']
pytorch -> python[version='>=3.9,<3.10.0a0'] -> tk[version='8.6.*|>=8.6.10,<8.7.0a0|>=8.6.11,<8.7.0a0|>=8.6.12,<8.7.0a0|>=8.6.8,<8.7.0a0|>=8.6.7,<8.7.0a0']
torchaudio -> python[version='>=3.9,<3.10.0a0'] -> tk[version='8.6.*|>=8.6.10,<8.7.0a0|>=8.6.11,<8.7.0a0|>=8.6.12,<8.7.0a0|>=8.6.8,<8.7.0a0|>=8.6.7,<8.7.0a0']
torchvision -> pillow[version='>=5.3.0,!=8.3.*'] -> tk[version='8.6.*|>=8.6.10,<8.7.0a0|>=8.6.12,<8.7.0a0|>=8.6.8,<8.7.0a0|>=8.6.11,<8.7.0a0|>=8.6.7,<8.7.0a0']
pip -> python[version='>=3.7'] -> tk[version='8.6.*|>=8.6.10,<8.7.0a0|>=8.6.11,<8.7.0a0|>=8.6.12,<8.7.0a0|>=8.6.8,<8.7.0a0|>=8.6.7,<8.7.0a0']
python=3.10 -> tk[version='>=8.6.11,<8.7.0a0|>=8.6.12,<8.7.0a0']
Package libffi conflicts for:
wheel -> python[version='>=3.7'] -> libffi[version='3.2.*|>=3.2.1,<3.3a0|>=3.3,<3.4.0a0|>=3.4,<3.5|>=3.4,<4.0a0']
torchaudio -> python[version='>=3.9,<3.10.0a0'] -> libffi[version='3.2.*|>=3.2.1,<3.3a0|>=3.3,<3.4.0a0|>=3.4,<3.5|>=3.4,<4.0a0']
pip -> python[version='>=3.7'] -> libffi[version='3.2.*|>=3.2.1,<3.3a0|>=3.3,<3.4.0a0|>=3.4,<3.5|>=3.4,<4.0a0']
python=3.10 -> libffi[version='>=3.3,<3.4.0a0|>=3.4,<3.5|>=3.4,<4.0a0']
libffi
setuptools -> python[version='>=3.7'] -> libffi[version='3.2.*|>=3.2.1,<3.3a0|>=3.3,<3.4.0a0|>=3.4,<3.5|>=3.4,<4.0a0']
torchvision -> python[version='>=3.8,<3.9.0a0'] -> libffi[version='3.2.*|>=3.2.1,<3.3a0|>=3.3,<3.4.0a0|>=3.4,<3.5|>=3.4,<4.0a0']
pytorch -> python[version='>=3.9,<3.10.0a0'] -> libffi[version='3.2.*|>=3.2.1,<3.3a0|>=3.3,<3.4.0a0|>=3.4,<3.5|>=3.4,<4.0a0|>=3.3']
Package libzlib conflicts for:
setuptools -> python[version='>=3.7'] -> libzlib[version='>=1.2.13,<1.3.0a0']
wheel -> python[version='>=3.7'] -> libzlib[version='>=1.2.13,<1.3.0a0']
python=3.10 -> libzlib[version='>=1.2.13,<1.3.0a0']
pip -> python[version='>=3.7'] -> libzlib[version='>=1.2.13,<1.3.0a0']
libzlib
python=3.10 -> tk[version='>=8.6.12,<8.7.0a0'] -> libzlib[version='>=1.2.11,<1.3.0a0']
libsqlite -> libzlib[version='>=1.2.13,<1.3.0a0']
pytorch -> python[version='>=3.10,<3.11.0a0'] -> libzlib[version='>=1.2.13,<1.3.0a0']
tk -> libzlib[version='>=1.2.11,<1.3.0a0']
torchaudio -> python[version='>=3.10,<3.11.0a0'] -> libzlib[version='>=1.2.13,<1.3.0a0']
torchvision -> python[version='>=3.10,<3.11.0a0'] -> libzlib[version='>=1.2.13,<1.3.0a0']
Package ca-certificates conflicts for:
pytorch -> python[version='>=2.7,<2.8.0a0'] -> ca-certificates
ca-certificates
python=3.10 -> openssl[version='>=3.1.0,<4.0a0'] -> ca-certificates
pip -> python[version='>=2.7,<2.8.0a0'] -> ca-certificates
wheel -> python -> ca-certificates
torchvision -> python[version='>=2.7,<2.8.0a0'] -> ca-certificates
openssl -> ca-certificates
setuptools -> python[version='>=2.7,<2.8.0a0'] -> ca-certificates
Package pip conflicts for:
torchaudio -> python[version='>=3.9,<3.10.0a0'] -> pip
python=3.10 -> pip
pip
wheel -> python[version='>=3.7'] -> pip
setuptools -> python[version='>=3.7'] -> pip
pytorch -> python[version='>=3.9,<3.10.0a0'] -> pip
torchvision -> python[version='>=3.8,<3.9.0a0'] -> pip
Package bzip2 conflicts for:
wheel -> python[version='>=3.7'] -> bzip2[version='>=1.0.8,<2.0a0']
torchaudio -> python[version='>=3.10,<3.11.0a0'] -> bzip2[version='>=1.0.8,<2.0a0']
setuptools -> python[version='>=3.7'] -> bzip2[version='>=1.0.8,<2.0a0']
pytorch -> python[version='>=3.10,<3.11.0a0'] -> bzip2[version='>=1.0.8,<2.0a0']
bzip2
torchvision -> ffmpeg[version='>=4.2'] -> bzip2[version='>=1.0.8,<2.0a0']
pip -> python[version='>=3.7'] -> bzip2[version='>=1.0.8,<2.0a0']
python=3.10 -> bzip2[version='>=1.0.8,<2.0a0']
Package six conflicts for:
torchvision -> six
pytorch -> mkl-service[version='>=2.3.0,<3.0a0'] -> six
Package readline conflicts for:
torchaudio -> python[version='>=3.9,<3.10.0a0'] -> readline[version='7.*|>=7.0,<8.0a0|>=8.0,<9.0a0|>=8.2,<9.0a0']
pytorch -> python[version='>=3.9,<3.10.0a0'] -> readline[version='7.*|>=7.0,<8.0a0|>=8.0,<9.0a0|>=8.2,<9.0a0']
wheel -> python[version='>=3.7'] -> readline[version='7.*|>=7.0,<8.0a0|>=8.0,<9.0a0|>=8.2,<9.0a0']
readline
setuptools -> python[version='>=3.7'] -> readline[version='7.*|>=7.0,<8.0a0|>=8.0,<9.0a0|>=8.2,<9.0a0']
pip -> python[version='>=3.7'] -> readline[version='7.*|>=7.0,<8.0a0|>=8.0,<9.0a0|>=8.2,<9.0a0']
python=3.10 -> readline[version='>=8.0,<9.0a0|>=8.2,<9.0a0']
torchvision -> python[version='>=3.8,<3.9.0a0'] -> readline[version='7.*|>=7.0,<8.0a0|>=8.0,<9.0a0|>=8.2,<9.0a0']
Package libgomp conflicts for:
pytorch -> _openmp_mutex -> libgomp[version='>=7.5.0']
libgcc-ng -> _openmp_mutex[version='>=4.5'] -> libgomp[version='>=7.5.0']
_openmp_mutex -> libgomp[version='>=7.5.0']
libgomp
Package libnsl conflicts for:
wheel -> python[version='>=3.7'] -> libnsl[version='>=2.0.0,<2.1.0a0']
setuptools -> python[version='>=3.7'] -> libnsl[version='>=2.0.0,<2.1.0a0']
python=3.10 -> libnsl[version='>=2.0.0,<2.1.0a0']
pip -> python[version='>=3.7'] -> libnsl[version='>=2.0.0,<2.1.0a0']
libnsl
torchaudio -> python[version='>=3.10,<3.11.0a0'] -> libnsl[version='>=2.0.0,<2.1.0a0']
pytorch -> python[version='>=3.10,<3.11.0a0'] -> libnsl[version='>=2.0.0,<2.1.0a0']
torchvision -> python[version='>=3.10,<3.11.0a0'] -> libnsl[version='>=2.0.0,<2.1.0a0']
Package wheel conflicts for:
wheel
pip -> wheel
python=3.10 -> pip -> wheel
Package libuuid conflicts for:
libuuid
torchaudio -> python[version='>=3.10,<3.11.0a0'] -> libuuid[version='>=1.0.3,<2.0a0|>=1.41.5,<2.0a0|>=2.38.1,<3.0a0']
torchvision -> python[version='>=3.11,<3.12.0a0'] -> libuuid[version='>=1.0.3,<2.0a0|>=1.41.5,<2.0a0|>=2.38.1,<3.0a0']
pytorch -> python[version='>=3.10,<3.11.0a0'] -> libuuid[version='>=1.0.3,<2.0a0|>=1.41.5,<2.0a0|>=2.38.1,<3.0a0']
wheel -> python[version='>=3.7'] -> libuuid[version='>=1.0.3,<2.0a0|>=1.41.5,<2.0a0|>=2.38.1,<3.0a0']
python=3.10 -> libuuid[version='>=1.0.3,<2.0a0|>=1.41.5,<2.0a0|>=2.38.1,<3.0a0']
setuptools -> python[version='>=3.7'] -> libuuid[version='>=1.0.3,<2.0a0|>=1.41.5,<2.0a0|>=2.38.1,<3.0a0']
pip -> python[version='>=3.7'] -> libuuid[version='>=1.0.3,<2.0a0|>=1.41.5,<2.0a0|>=2.38.1,<3.0a0']
Package typing conflicts for:
pytorch -> typing
pytorch -> typing_extensions -> typing[version='>=3.7.4']
Package pytorch conflicts for:
torchvision -> pytorch[version='1.1.*|1.10.0|1.10.1|1.10.2|1.11.0|1.12.0|1.12.1|1.13.0|1.13.1|2.0.0|2.0.1|1.9.1|1.9.0|1.8.1|1.8.0|1.7.1|1.7.0|1.6.0|1.5.1|1.7.1.*|1.3.1.*|1.2.0.*|>=0.4|>=0.3']
pytorch
torchaudio -> pytorch[version='1.10.0|1.10.1|1.10.2|1.11.0|1.12.0|1.12.1|1.13.0|1.13.1|2.0.0|2.0.1|1.9.1|1.9.0|1.8.1|1.8.0|1.7.1|1.7.0|1.6.0|1.5.1']The following specifications were found to be incompatible with your system:
- feature:/linux-64::__glibc==2.35=0
- feature:|@/linux-64::__glibc==2.35=0
- bzip2 -> libgcc-ng[version='>=9.3.0'] -> __glibc[version='>=2.17']
- libffi -> libgcc-ng[version='>=9.4.0'] -> __glibc[version='>=2.17']
- libgcc-ng -> __glibc[version='>=2.17']
- libnsl -> libgcc-ng[version='>=9.4.0'] -> __glibc[version='>=2.17']
- libuuid -> libgcc-ng[version='>=11.2.0'] -> __glibc[version='>=2.17']
- ncurses -> libgcc-ng[version='>=11.2.0'] -> __glibc[version='>=2.17']
- openssl -> libgcc-ng[version='>=7.5.0'] -> __glibc[version='>=2.17']
- python=3.10 -> libgcc-ng[version='>=11.2.0'] -> __glibc[version='>=2.17']
- pytorch -> cudatoolkit[version='>=11.3,<11.4'] -> __glibc[version='>=2.17|>=2.17,<3.0.a0']
- tk -> libgcc-ng[version='>=9.4.0'] -> __glibc[version='>=2.17']
- torchaudio -> cudatoolkit[version='>=11.6,<11.7'] -> __glibc[version='>=2.17,<3.0.a0']
- torchvision -> __glibc[version='>=2.17,<3.0.a0']
- torchvision -> libgcc-ng[version='>=11.2.0'] -> __glibc[version='>=2.17']
- xz -> libgcc-ng[version='>=11.2.0'] -> __glibc[version='>=2.17']
Your installed version is: 2.35
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
bokeh 2.4.3 requires Jinja2>=2.9, which is not installed.
bokeh 2.4.3 requires PyYAML>=3.10, which is not installed.
bokeh 2.4.3 requires tornado>=5.1, which is not installed.
bokeh 2.4.3 requires typing-extensions>=3.10.0, which is not installed.
harmonypy 0.0.9 requires scikit-learn, which is not installed.
lightning-utilities 0.8.0 requires typing-extensions, which is not installed.
ml-collections 0.1.1 requires PyYAML, which is not installed.
orbax-checkpoint 0.2.3 requires importlib_resources, which is not installed.
orbax-checkpoint 0.2.3 requires msgpack, which is not installed.
orbax-checkpoint 0.2.3 requires nest_asyncio, which is not installed.
orbax-checkpoint 0.2.3 requires pyyaml, which is not installed.
orbax-checkpoint 0.2.3 requires typing_extensions, which is not installed.
panel 0.14.4 requires bleach, which is not installed.
panel 0.14.4 requires markdown, which is not installed.
panel 0.14.4 requires requests, which is not installed.
panel 0.14.4 requires typing-extensions, which is not installed.
pynndescent 0.5.10 requires joblib>=0.11, which is not installed.
pynndescent 0.5.10 requires llvmlite>=0.30, which is not installed.
pynndescent 0.5.10 requires scikit-learn>=0.18, which is not installed.
pytorch-lightning 1.9.5 requires fsspec[http]>2021.06.0, which is not installed.
pytorch-lightning 1.9.5 requires PyYAML>=5.4, which is not installed.
pytorch-lightning 1.9.5 requires typing-extensions>=4.0.0, which is not installed.
sam-algorithm 1.0.2 requires scikit-learn>=0.23.1, which is not installed.
scanpy 1.9.3 requires joblib, which is not installed.
scanpy 1.9.3 requires networkx>=2.3, which is not installed.
scanpy 1.9.3 requires scikit-learn>=0.22, which is not installed.
scvi-tools 0.20.3 requires openpyxl>=3.0, which is not installed.
scvi-tools 0.20.3 requires scikit-learn>=0.21.2, which is not installed.
torchvision 0.15.2 requires requests, which is not installed.
umap-learn 0.5.3 requires scikit-learn>=0.22, which is not installed.
/home/tcroll/anaconda3/envs/model_angelo/lib/python3.10/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated.
!!
********************************************************************************
Please avoid running ``setup.py`` directly.
Instead, use pypa/build, pypa/installer, pypa/build or
other standards-based tools.
See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.
********************************************************************************
!!
self.initialize_options()
/home/tcroll/anaconda3/envs/model_angelo/lib/python3.10/site-packages/setuptools/_distutils/cmd.py:66: EasyInstallDeprecationWarning: easy_install command is deprecated.
!!
********************************************************************************
Please avoid running ``setup.py`` and ``easy_install``.
Instead, use pypa/build, pypa/installer, pypa/build or
other standards-based tools.
See https://github.com/pypa/setuptools/issues/917 for details.
********************************************************************************
!!
self.initialize_options()
zip_safe flag not set; analyzing archive contents...
model_angelo.data.__pycache__.dataset_preprocess.cpython-310: module references __file__
model_angelo.utils.__pycache__.residue_constants.cpython-310: module references __file__
error in running model-angelo.
Hi, I encountered an error when running it in a Linux box with miniconda3 installed in my home directory.
The error is in the next. Please help fix it. Thanks.
2023-01-31 at 23:42:26 | ERROR | Error in ModelAngelo
Traceback (most recent call last):
File "/home/qxjiang/miniconda3/envs/model_angelo/bin/model_angelo", line 33, in
sys.exit(load_entry_point('model-angelo==0.2.2', 'console_scripts', 'model_angelo')())
â â â <function importlib_load_entry_point at 0x2b5180891160>
â â
â <module 'sys' (built-in)>
File "/home/qxjiang/miniconda3/envs/model_angelo/lib/python3.9/site-packages/model_angelo-0.2.2-py3.9.egg/model_angelo/main.py", line 51, in main
args.func(args)
â â â Namespace(volume_path='bCHGBmap.mrc', fasta_path='bCHGBseq.fasta', output_dir='output', mask_path=None, device='cuda:0', conf...
â â <function main at 0x2b5226dff1f0>
â Namespace(volume_path='bCHGBmap.mrc', fasta_path='bCHGBseq.fasta', output_dir='output', mask_path=None, device='cuda:0', conf...
File "/home/qxjiang/miniconda3/envs/model_angelo/lib/python3.9/site-packages/model_angelo-0.2.2-py3.9.egg/model_angelo/apps/build.py", line 225, in main
gnn_output = gnn_infer(gnn_infer_args)
â â {'num_rounds': 3, 'crop_length': 200, 'repeat_per_residue': 3, 'esm_model': 'esm1b_t33_650M_UR50S', 'aggressive_pruning': Tru...
â <function infer at 0x2b5226dff160>
File "/home/qxjiang/miniconda3/envs/model_angelo/lib/python3.9/site-packages/model_angelo-0.2.2-py3.9.egg/model_angelo/gnn/inference.py", line 243, in infer
protein = get_lm_embeddings_for_protein(lang_model, batch_converter, protein)
â â â â Protein(atom_positions=None, atom14_positions=None, aatype=None, atom_mask=None, atom14_mask=None, residue_index=None, chain_...
â â â <esm.data.BatchConverter object at 0x2b5229630ac0>
â â ProteinBertModel(
â (embed_tokens): Embedding(33, 1280, padding_idx=1)
â (layers): ModuleList(
â (0): TransformerLayer(
â ...
â <function get_lm_embeddings_for_protein at 0x2b5225fe7e50>
File "/home/qxjiang/miniconda3/envs/model_angelo/lib/python3.9/site-packages/model_angelo-0.2.2-py3.9.egg/model_angelo/data/generate_complete_prot_files.py", line 32, in get_lm_embeddings_for_protein
[result[s]["representations"][33].cpu().numpy() for s in seq_names],
â â ['0']
â {}
File "/home/qxjiang/miniconda3/envs/model_angelo/lib/python3.9/site-packages/model_angelo-0.2.2-py3.9.egg/model_angelo/data/generate_complete_prot_files.py", line 32, in
[result[s]["representations"][33].cpu().numpy() for s in seq_names],
â â â '0'
â â '0'
â {}
KeyError: '0'
installation issue
Dear colleagues,
I have installation issue on both centos and mac systems.
After installation, on the step 3 (conda activate model_angelo).
When I run model_angelo build -h -- nothing happens
-on centos
[user@dataanalysisserver1 model_angelo]$ conda activate model_angelo
(model_angelo) model_angelo build -h
bash: model_angelo: command not found...
- on mac
import torch
ModuleNotFoundError: No module named 'torch'
Any ideas?
Thank you.
Sincerely,
Dmitry
how to skip original.zip Downloading
Hello, I have downloaded the original.zip on my Windows system. Can I skip the download step when running it for the first time? Downloading on Ubuntu is too slow. I have tried some attempts, but I still cannot skip the download step.
Thanks.
UnboundLocalError: local variable 'grid_np' referenced before assignment
Hi,
I've recently installed version 1.0.1 from Github and my runs end shortly after a few seconds without any meaningul output to stdout or stderr:
diogori@worker08:model-angelo$ time model_angelo build -v urease_emd_10835.map -f urease_rcsb_pdb_6YL3.fasta -o urease_seq
---------------------------- ModelAngelo -----------------------------
By Kiarash Jamali, Scheres Group, MRC Laboratory of Molecular Biology
--------------------- Initial C-alpha prediction ---------------------
real 0m4.335s
user 0m3.720s
sys 0m6.969s
However, upon inspecting urease_seq/model_angelo.log I find the following error:
2023-06-14 at 10:37:39 | INFO | ModelAngelo with args: {'volume_path': 'urease_emd_10835.map', 'protein_fasta': 'urease_rcsb_pdb_6YL3.fasta', 'rna_fasta': None, 'dna_fasta': None, 'output_dir': 'urease_seq', 'mask_path': None, 'device': None, 'config_path': None, 'model_bundle_name': 'nucleotides', 'model_bundle_path': None, 'keep_intermediate_results': False, 'pipeline_control': False, 'func': <function main at 0x7fc646066560>}
2023-06-14 at 10:37:39 | INFO | Initial C-alpha prediction with args: {'model_checkpoint': 'chkpt.torch', 'bfactor': 0, 'batch_size': 4, 'box_size': 64, 'stride': 16, 'dont_mask_input': True, 'threshold': 0.05, 'save_real_coordinates': False, 'save_cryo_em_grid': False, 'do_nucleotides': True, 'save_backbone_trace': False, 'save_output_grid': False, 'crop': 6, 'log_dir': '/scicore/home/engel0006/GROUP/pool-engel/soft/model-angelo/weights/hub/checkpoints/model_angelo_v1.0/nucleotides/c_alpha', 'map_path': 'urease_emd_10835.map', 'output_path': 'urease_seq/see_alpha_output', 'mask_path': None, 'device': None, 'auto_mask': False}
2023-06-14 at 10:37:39 | INFO | Using model file /scicore/home/engel0006/GROUP/pool-engel/soft/model-angelo/weights/hub/checkpoints/model_angelo_v1.0/nucleotides/c_alpha/model.py
2023-06-14 at 10:37:39 | INFO | Using checkpoint file /scicore/home/engel0006/GROUP/pool-engel/soft/model-angelo/weights/hub/checkpoints/model_angelo_v1.0/nucleotides/c_alpha/chkpt.torch
2023-06-14 at 10:37:39 | ERROR | Error in ModelAngelo
Traceback (most recent call last):
File "/scicore/home/engel0006/GROUP/pool-engel/soft/miniconda/miniconda3/envs/model_angelo_v1.0.1/bin/model_angelo", line 33, in <module>
sys.exit(load_entry_point('model-angelo==1.0.1', 'console_scripts', 'model_angelo')())
\u2502 \u2502 \u2514 <function importlib_load_entry_point at 0x7fc7888ed360>
\u2502 \u2514 <built-in function exit>
\u2514 <module 'sys' (built-in)>
File "/scicore/home/engel0006/GROUP/pool-engel/soft/miniconda/miniconda3/envs/model_angelo_v1.0.1/lib/python3.10/site-packages/model_angelo-1.0.1-py3.10.egg/model_angelo/__main__.py", line 52, in main
args.func(args)
\u2502 \u2502 \u2514 Namespace(volume_path='urease_emd_10835.map', protein_fasta='urease_rcsb_pdb_6YL3.fasta', rna_fasta=None, dna_fasta=None, out...
\u2502 \u2514 <function main at 0x7fc646066560>
\u2514 Namespace(volume_path='urease_emd_10835.map', protein_fasta='urease_rcsb_pdb_6YL3.fasta', rna_fasta=None, dna_fasta=None, out...
> File "/scicore/home/engel0006/GROUP/pool-engel/soft/miniconda/miniconda3/envs/model_angelo_v1.0.1/lib/python3.10/site-packages/model_angelo-1.0.1-py3.10.egg/model_angelo/apps/build.py", line 207, in main
ca_cif_path = c_alpha_infer(ca_infer_args)
\u2502 \u2514 {'model_checkpoint': 'chkpt.torch', 'bfactor': 0, 'batch_size': 4, 'box_size': 64, 'stride': 16, 'dont_mask_input': True, 'th...
\u2514 <function infer at 0x7fc645fad480>
File "/scicore/home/engel0006/GROUP/pool-engel/soft/miniconda/miniconda3/envs/model_angelo_v1.0.1/lib/python3.10/site-packages/model_angelo-1.0.1-py3.10.egg/model_angelo/c_alpha/inference.py", line 176, in infer
mask_grid = np.ones_like(grid_np)
\u2502 \u2514 <function ones_like at 0x7fc6f20dab00>
\u2514 <module 'numpy' from '/scicore/home/engel0006/GROUP/pool-engel/soft/miniconda/miniconda3/envs/model_angelo_v1.0.1/lib/python3...
UnboundLocalError: local variable 'grid_np' referenced before assignment
Is this a bug or is there something wrong with my installation?
Thank you!
V1.0 stops after first refinement iteration
local variable 'grid_np' referenced before assignment - model_angelo/c_alpha/inference.py
Hi,
Just upgraded our model_angelo to the lastest and now get an error:
2023-05-19 at 12:30:30 | INFO | Using model file /s/ems/s/model_angelo_weights/hub/checkpoints/model_angelo_v1.0/nucleotides/c_alpha/model.py
2023-05-19 at 12:30:30 | INFO | Using checkpoint file /s/ems/s/model_angelo_weights/hub/checkpoints/model_angelo_v1.0/nucleotides/c_alpha/chkpt.torch
2023-05-19 at 12:30:30 | ERROR | Error in ModelAngelo
Traceback (most recent call last):
File "/s/ems/s/anaconda/v4.8.4/envs/model_angelo/bin/model_angelo", line 33, in
sys.exit(load_entry_point('model-angelo==1.0.1', 'console_scripts', 'model_angelo')())
│ │ └ <function importlib_load_entry_point at 0x7f59615d10d0>
│ └
└ <module 'sys' (built-in)>
File "/s/ems/s/anaconda/v4.8.4/envs/model_angelo/lib/python3.9/site-packages/model_angelo-1.0.1-py3.9.egg/model_angelo/main.py", line 52, in main
args.func(args)
│ │ └ Namespace(volume_path='sharpened_map.ccp4', protein_fasta='TB_23nov22_data.fasta', rna_fasta=None, dna_fasta=None, output_dir...
│ └ <function main at 0x7f587d376d30>
└ Namespace(volume_path='sharpened_map.ccp4', protein_fasta='TB_23nov22_data.fasta', rna_fasta=None, dna_fasta=None, output_dir...
File "/s/ems/s/anaconda/v4.8.4/envs/model_angelo/lib/python3.9/site-packages/model_angelo-1.0.1-py3.9.egg/model_angelo/apps/build.py", line 207, in main
ca_cif_path = c_alpha_infer(ca_infer_args)
│ └ {'model_checkpoint': 'chkpt.torch', 'bfactor': 0, 'batch_size': 4, 'box_size': 64, 'stride': 16, 'dont_mask_input': True, 'th...
└ <function infer at 0x7f587de0f9d0>
File "/s/ems/s/anaconda/v4.8.4/envs/model_angelo/lib/python3.9/site-packages/model_angelo-1.0.1-py3.9.egg/model_angelo/c_alpha/inference.py", line 176, in infer
mask_grid = np.ones_like(grid_np)
│ └ <function ones_like at 0x7f58d7d97430>
└ <module 'numpy' from '/s/ems/s/anaconda/v4.8.4/envs/model_angelo/lib/python3.9/site-packages/numpy/init.py'>
UnboundLocalError: local variable 'grid_np' referenced before assignment
I'm running model_angelo without a mask - just a volume and a fasta seq, which worked fine in the previous version.
Any help ? Many thanks, Cheers, Dave H.
ModuleNotFoundError: No module named 'model_angelo.c_alpha'
Hi there,
I tried following the instructions, but unfortunately I get the following error.
Traceback (most recent call last):
File "/fs/pool/pool-schulman-soft/hpcl8/ModelAngelo/miniconda/envs/model_angelo/bin/model_angelo", line 33, in
sys.exit(load_entry_point('model-angelo==0.0.1', 'console_scripts', 'model_angelo')())
File "/fs/pool/pool-schulman-soft/hpcl8/ModelAngelo/miniconda/envs/model_angelo/lib/python3.9/site-packages/model_angelo-0.0.1-py3.9.egg/model_angelo/main.py", line 23, in main
File "", line 259, in load_module
File "/fs/pool/pool-schulman-soft/hpcl8/ModelAngelo/miniconda/envs/model_angelo/lib/python3.9/site-packages/model_angelo-0.0.1-py3.9.egg/model_angelo/apps/build.py", line 18, in
ModuleNotFoundError: No module named 'model_angelo.c_alpha'
It is trying to look for folders after the egg file thats confusing..
Model angelo not using GPUs
Dear developers,
hi, I'm trying to use model angelo to build model on a 8x Rtx5000 machine with CentOS 7, cuda 10.1, and when I ran the 'model_angelo build -v Refs/Sharp.mrc -f astas/sequence.fa -o OutPut' command, I got the folloing output as:
/home/linhua/Programs/anaconda3/envs/model_angelo/lib/python3.9/site-packages/torch/cuda/init.py:83: UserWarning: CUDA initialization: CUDA driver initialization failed, you might not have a CUDA gpu. (Triggered internally at /opt/conda/conda-bld/pytorch_1659484809662/work/c10/cuda/CUDAFunctions.cpp:109.)
return torch._C._cuda_getDeviceCount() > 0
It seems model angelo's not using GPUs while I do have them?
During compiling the package, I got the folloing message at the end:
.
Installed /home/linhua/Programs/anaconda3/envs/model_angelo/lib/python3.9/site-packages/model_angelo-0.0.1-py3.9.egg
Processing dependencies for model-angelo==0.0.1
Finished processing dependencies for model-angelo==0.0.1
Did not download weights because the flag -w or --download-weights was not specified
[linhua@localhost model-angelo]$
Does not able to download the weights have something to do with not able to use GPUs?
Thank you very much in advance!
Sincerely,
Linhua Tai
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.