sokrypton / sokrypton/ColabFold

Boltz1.ipynb: "RuntimeError: operator torchvision::nms does not exist"

Open
#717 6 comments 0 reactions 0 assignees View on GitHub

Nobody has claimed this yet.

Dominant language
Jupyter Notebook
Stars
2.9k
Forks
747
PR merge metrics
No merged PRs in 30d

Description

Using the Boltz1 script on Google Colab, I keep getting this error no matter what sample I run (default test or my own sequences) or which GPU type. It was working fine for me a few weeks ago.

Any ideas what's going on? Thanks in advance!

Traceback (most recent call last):
File "/usr/local/bin/boltz", line 5, in
from boltz.main import cli
File "/usr/local/lib/python3.11/dist-packages/boltz/main.py", line 16, in
from pytorch_lightning import Trainer, seed_everything
File "/usr/local/lib/python3.11/dist-packages/pytorch_lightning/init.py", line 27, in
from pytorch_lightning.callbacks import Callback # noqa: E402
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/pytorch_lightning/callbacks/init.py", line 14, in
from pytorch_lightning.callbacks.batch_size_finder import BatchSizeFinder
File "/usr/local/lib/python3.11/dist-packages/pytorch_lightning/callbacks/batch_size_finder.py", line 26, in
from pytorch_lightning.callbacks.callback import Callback
File "/usr/local/lib/python3.11/dist-packages/pytorch_lightning/callbacks/callback.py", line 22, in
from pytorch_lightning.utilities.types import STEP_OUTPUT
File "/usr/local/lib/python3.11/dist-packages/pytorch_lightning/utilities/types.py", line 36, in
from torchmetrics import Metric
File "/usr/local/lib/python3.11/dist-packages/torchmetrics/init.py", line 37, in
from torchmetrics import functional # noqa: E402
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/torchmetrics/functional/init.py", line 56, in
from torchmetrics.functional.image._deprecated import (
File "/usr/local/lib/python3.11/dist-packages/torchmetrics/functional/image/init.py", line 14, in
from torchmetrics.functional.image.arniqa import arniqa
File "/usr/local/lib/python3.11/dist-packages/torchmetrics/functional/image/arniqa.py", line 31, in
from torchvision import transforms
File "/usr/local/lib/python3.11/dist-packages/torchvision/init.py", line 10, in
from torchvision import _meta_registrations, datasets, io, models, ops, transforms, utils # usort:skip
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/torchvision/_meta_registrations.py", line 163, in
@torch.library.register_fake("torchvision::nms")
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/torch/library.py", line 1023, in register
use_lib._register_fake(op_name, func, _stacklevel=stacklevel + 1)
File "/usr/local/lib/python3.11/dist-packages/torch/library.py", line 214, in _register_fake
handle = entry.fake_impl.register(func_to_register, source)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/torch/_library/fake_impl.py", line 31, in register
if torch._C._dispatch_has_kernel_for_dispatch_key(self.qualname, "Meta"):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
RuntimeError: operator torchvision::nms does not exist

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start with Boltz1.ipynb and the traceback's import chain through pytorch_lightning, torchmetrics, and torchvision. Inspect the installed torch and torchvision versions in Google Colab and reproduce the import failure; done means the notebook starts without the torchvision::nms error.

Written by the indexing model from the issue text.

Assessment

Tech stack
jupyter-notebook, python, pytorch
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

Get new issues in your inbox

A short digest of beginner-friendly GitHub issues.