Regarding issue running inference

[ananyananda-dasari]

I tried running the following command on a list of pairs of protein and ligand. For some reason, it looks like it just stops and there are no results saved in the results folder. Could you please help me?

!python -m inference --protein_ligand_csv /tmp/input_protein_ligand.csv --out_dir results/user_predictions_small --inference_steps 20 --samples_per_complex 40 --batch_size 6

I'm getting the following error:

/content/DiffDock
Reading molecules and generating local structures with RDKit
100% 5430/5430 [22:24<00:00, 4.04it/s]
Reading language model embeddings.
/content/DiffDock/datasets/pdbbind.py:221: FutureWarning: You are using torch.load with weights_only=False (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for weights_only will be flipped to True. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via torch.serialization.add_safe_globals. We recommend you start setting weights_only=True for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
lm_embeddings_chains.append(torch.load(embeddings_path)['representations'][33])
^C
time: 23min 9s (started: 2025-03-10 01:08:03 +00:00)

[MinsikBae]

samples_per_complex 40 --batch_size 6

You can probably match the sample complex and batch size. If you ste the sample complex for 40, the batch size has to be set 5 or 10. For 40 samples, ensure your batch size is a multiple of 5 for complete processing.