Regarding Issue Running Inference

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Introduction

Running inference on a list of protein-ligand pairs can be a crucial step in various applications, including drug discovery and molecular modeling. However, encountering issues during this process can be frustrating and time-consuming. In this article, we will explore a common issue related to running inference and provide a step-by-step guide to troubleshoot and resolve the problem.

Understanding the Issue

The issue at hand involves running a command using the inference module, which appears to stop abruptly without producing any results in the specified output directory. The command in question is as follows:

!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

This command is designed to perform inference on a list of protein-ligand pairs stored in a CSV file, with the results saved in the results/user_predictions_small directory. However, the process seems to terminate prematurely, leaving no output in the specified directory.

Error Message Analysis

Upon closer inspection of the error message, we notice a warning related to the use of torch.load with weights_only=False. This warning suggests that the default behavior of torch.load may be changed in the future to prevent potential security vulnerabilities. The warning is as follows:

/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)

Troubleshooting Steps

To troubleshoot and resolve the issue, we can follow these steps:

Step 1: Check the Input File

First, let's verify that the input CSV file is correctly formatted and contains the required information. We can do this by inspecting the file manually or using a tool like head to preview the contents.

head /tmp/input_protein_ligand.csv

Step 2: Verify the Output Directory

Next, let's ensure that the output directory exists and is writable. We can do this by checking the directory's existence and permissions using the ls and chmod commands.

ls -ld results/user_predictions_small
chmod 755 results/user_predictions_small

Step 3: Check the Inference Steps

The inference_steps parameter specifies the number of steps to perform during the inference process. Let's verify that this value is correctly set and not causing the process to terminate prematurely.

echo $inference_steps

Step 4: Investigate the Language Model Embeddings

The warning message suggests that the language model embeddings may be causing issues. Let's investigate this further by checking the embeddings file and its contents.

cat /content/DiffDock/datasets/pdbbind.py

Step 5: Update the torch.load Behavior

As mentioned in the warning message, we can update the torch.load behavior to use weights_only=True to prevent potential security vulnerabilities.

torch.serialization.add_safe_globals()

Conclusion

In conclusion, the issue of running inference on a list of protein-ligand pairs can be caused by various factors, including incorrect input files, output directory issues, and language model embeddings problems. By following the troubleshooting steps outlined in this article, we can identify and resolve the issue, ensuring that the inference process completes successfully and produces the desired results.

Additional Tips and Recommendations

  • Always verify the input file and output directory before running the inference process.
  • Use the head command to preview the contents of the input file.
  • Check the permissions and existence of the output directory using the ls and chmod commands.
  • Verify the inference_steps value and adjust it as needed.
  • Investigate the language model embeddings file and its contents.
  • Update the torch.load behavior to use weights_only=True to prevent potential security vulnerabilities.

Q: What is the main issue with running inference on a list of protein-ligand pairs?

A: The main issue is that the process appears to stop abruptly without producing any results in the specified output directory.

Q: What is the cause of the issue?

A: The cause of the issue is likely due to a combination of factors, including incorrect input files, output directory issues, and language model embeddings problems.

Q: How can I troubleshoot the issue?

A: To troubleshoot the issue, you can follow the steps outlined in the article, including checking the input file, verifying the output directory, checking the inference steps, investigating the language model embeddings, and updating the torch.load behavior.

Q: What is the warning message about torch.load with weights_only=False?

A: The warning message is related to the use of torch.load with weights_only=False, which may be changed in the future to prevent potential security vulnerabilities. It is recommended to update the behavior to use weights_only=True.

Q: How can I update the torch.load behavior?

A: To update the torch.load behavior, you can use the torch.serialization.add_safe_globals() function to add the necessary safe globals.

Q: What are some additional tips and recommendations for running inference?

A: Some additional tips and recommendations include verifying the input file and output directory, using the head command to preview the contents of the input file, checking the permissions and existence of the output directory, verifying the inference_steps value, investigating the language model embeddings file and its contents, and updating the torch.load behavior.

Q: Can you provide more information about the language model embeddings?

A: The language model embeddings are a crucial component of the inference process. They are used to represent the molecular structures and interactions between the protein and ligand. Investigating the language model embeddings file and its contents can help identify potential issues with the inference process.

Q: How can I ensure a smooth and successful inference process?

A: To ensure a smooth and successful inference process, you can follow the troubleshooting steps outlined in the article, verify the input file and output directory, use the head command to preview the contents of the input file, check the permissions and existence of the output directory, verify the inference_steps value, investigate the language model embeddings file and its contents, and update the torch.load behavior.

Q: What are some common issues that can cause the inference process to fail?

A: Some common issues that can cause the inference process to fail include incorrect input files, output directory issues, language model embeddings problems, and torch.load behavior issues.

Q: How can I prevent potential security vulnerabilities in the inference process?

A: To prevent potential security vulnerabilities in the inference process, you can update the torch.load behavior to use weights_only=True and add the necessary safe globals using the torch.serialization.add_safe_globals() function.

Q: Can you provide more information about the torch.serialization.add_safe_globals() function?

A: The torch.serialization.add_safe_globals() function is used to add the necessary safe globals to the torch.load behavior. This can help prevent potential security vulnerabilities in the inference process.