sokrypton / sokrypton/ColabFold
Traceback (most recent call last)
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- Jupyter Notebook
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Description
WARNING: found GPU Tesla K80: limited to total length < 1000
Downloading alphafold2 weights to .: 100%|██████████| 3.47G/3.47G [00:39<00:00, 94.6MB/s]
2022-02-25 09:41:06,761 Running colabfold 1.2.0 (b66951a9c01b94c535e084682b413ce7fc3c2169)
2022-02-25 09:41:06,779 Found 8 citations for tools or databases
RuntimeError Traceback (most recent call last)
in ()
50 pair_mode=pair_mode,
51 stop_at_score=float(100),
---> 52 prediction_callback=prediction_callback,
53 )
10 frames
/usr/local/lib/python3.7/dist-packages/jax/interpreters/xla.py in _execute_compiled(name, compiled, output_buffer_counts, handlers, kept_var_idx, *args)
1098 for i, x in enumerate(args)
1099 if x is not token and i in kept_var_idx))
-> 1100 out_bufs = compiled.execute(input_bufs)
1101 check_special(name, out_bufs)
1102 if output_buffer_counts is None:
RuntimeError: RESOURCE_EXHAUSTED: Out of memory while trying to allocate 50331648 bytes.

Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Reproduce the failing notebook cell shown in the traceback with the reported Tesla K80 warning, then inspect the JAX execution that raises RESOURCE_EXHAUSTED. Determine whether the failure is specific to the reported input or GPU limits; done should be a verified fix or a clearly documented limitation.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- jupyter-notebook, python
- Domain
- bioinformatics, machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100