Cannot Run Structure Prediction – Missing Modules

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Issue Summary

As a user attempting to utilize OpenFold for local protein structure prediction without training, we encountered several issues with the available commits, including main and v2.1.0. Specifically, we faced the following problems:

  • Missing Module Error: The run_pretrained_openfold.py script failed due to the absence of the attn_core_inplace_cuda module.
  • Missing Script: The scripts/predict_from_model.py script was referenced in issues but not present in the repository.
  • Non-Functional Docker Image: The provided Dockerfile did not build a working image.
  • Lack of Guidance: There is no official guidance on which commit is usable for structure prediction only.

What I Tried

To resolve these issues, we took the following steps:

  • Cloned the Repository: We cloned the OpenFold repository using the command git clone -b v.2.1.0 https://github.com/aqlaboratory/openfold.git.
  • Installed Dependencies: We installed all dependencies, including ml_collections, modelcif, triton, etc.
  • Tried Various Commits: We tried various commits, including f434a2786b5a6b39171f358fb3470ad9f4fd2a58.
  • Built Docker Image: We attempted to build a Docker image from the docker/Dockerfile.

Unfortunately, all these attempts resulted in either module import errors or missing scripts.

Questions

We have the following questions regarding the usage of OpenFold for structure prediction:

  1. Specific Commit or Tag: Is there a specific commit or tag where structure prediction works out-of-the-box?
  2. Installing or Compiling attn_core_inplace_cuda: How to properly install or compile the attn_core_inplace_cuda module? Is there any documentation available?
  3. Known Working Dockerfile or Base Image: Can you provide a known working Dockerfile or a base image?

Conclusion

We appreciate the excellent work on the OpenFold project and would be grateful if we could get a working version running. We believe that resolving these issues will enable us to utilize OpenFold for real-world structure prediction.

Troubleshooting Steps

To troubleshoot the issues with OpenFold, we can take the following steps:

Step 1: Verify the Repository Clone

Ensure that the repository was cloned correctly by checking the commit hash and the presence of all files.

Step 2: Install Dependencies

Verify that all dependencies, including ml_collections, modelcif, triton, etc., are installed correctly.

Step 3: Check Commit Hash

Check the commit hash used to clone the repository to ensure it is correct.

Step 4: Build Docker Image

Attempt to build a Docker image from the docker/Dockerfile to ensure it is working correctly.

Step 5: Consult Documentation

Consult the OpenFold documentation for any guidance on using the attn_core_inplace_cuda module or any other related issues.

Potential Solutions

Based on the issues encountered, potential solutions could include:

  • Using a Different Commit: Try using a different commit or tag that may be more stable or functional.
  • Compiling attn_core_inplace_cuda Manually: Attempt to compile the attn_core_inplace_cuda module manually using the provided documentation.
  • Using a Different Dockerfile: Try using a different Dockerfile or a base image that may be more compatible with the OpenFold repository.

Future Development

To improve the usability of OpenFold for structure prediction, the following development steps could be taken:

  • Provide Clear Guidance: Provide clear guidance on which commit or tag is usable for structure prediction only.
  • Document Dependencies: Document the dependencies required for OpenFold, including ml_collections, modelcif, triton, etc.
  • Improve Dockerfile: Improve the Dockerfile to ensure it builds a working image.

Q: What is OpenFold and what is its purpose?

A: OpenFold is a deep learning-based tool for protein structure prediction. Its purpose is to predict the 3D structure of proteins from their amino acid sequences, which is essential for understanding protein function and behavior.

Q: What are the common issues encountered when using OpenFold?

A: Some common issues encountered when using OpenFold include:

  • Missing Module Error: The attn_core_inplace_cuda module is missing, causing the run_pretrained_openfold.py script to fail.
  • Missing Script: The scripts/predict_from_model.py script is not present in the repository.
  • Non-Functional Docker Image: The provided Dockerfile does not build a working image.
  • Lack of Guidance: There is no official guidance on which commit is usable for structure prediction only.

Q: How to properly install or compile attn_core_inplace_cuda?

A: Unfortunately, there is no official documentation on how to install or compile the attn_core_inplace_cuda module. However, you can try the following steps:

  • Check the Repository: Verify that the repository is cloned correctly and that all files are present.
  • Consult Documentation: Consult the OpenFold documentation for any guidance on using the attn_core_inplace_cuda module.
  • Try a Different Commit: Try using a different commit or tag that may be more stable or functional.

Q: Can you provide a known working Dockerfile or a base image?

A: Unfortunately, we do not have a known working Dockerfile or a base image that can be used with OpenFold. However, you can try the following steps:

  • Check the Repository: Verify that the repository is cloned correctly and that all files are present.
  • Consult Documentation: Consult the OpenFold documentation for any guidance on building a Docker image.
  • Try a Different Commit: Try using a different commit or tag that may be more stable or functional.

Q: Is there a specific commit or tag where structure prediction works out-of-the-box?

A: Unfortunately, there is no specific commit or tag that we can recommend for structure prediction. However, you can try the following steps:

  • Check the Repository: Verify that the repository is cloned correctly and that all files are present.
  • Consult Documentation: Consult the OpenFold documentation for any guidance on using the attn_core_inplace_cuda module.
  • Try a Different Commit: Try using a different commit or tag that may be more stable or functional.

Q: How to troubleshoot issues with OpenFold?

A: To troubleshoot issues with OpenFold, you can try the following steps:

  • Verify the Repository Clone: Ensure that the repository was cloned correctly and that all files are present.
  • Install Dependencies: Verify that all dependencies, including ml_collections, modelcif, triton, etc., are installed correctly.
  • Check Commit Hash: Check the commit hash used to clone the repository to ensure it correct.
  • Build Docker Image: Attempt to build a Docker image from the docker/Dockerfile to ensure it is working correctly.
  • Consult Documentation: Consult the OpenFold documentation for any guidance on using the attn_core_inplace_cuda module or any other related issues.

Q: What are the potential solutions to resolve issues with OpenFold?

A: Some potential solutions to resolve issues with OpenFold include:

  • Using a Different Commit: Try using a different commit or tag that may be more stable or functional.
  • Compiling attn_core_inplace_cuda Manually: Attempt to compile the attn_core_inplace_cuda module manually using the provided documentation.
  • Using a Different Dockerfile: Try using a different Dockerfile or a base image that may be more compatible with the OpenFold repository.

Q: How to improve the usability of OpenFold for structure prediction?

A: To improve the usability of OpenFold for structure prediction, the following development steps could be taken:

  • Provide Clear Guidance: Provide clear guidance on which commit or tag is usable for structure prediction only.
  • Document Dependencies: Document the dependencies required for OpenFold, including ml_collections, modelcif, triton, etc.
  • Improve Dockerfile: Improve the Dockerfile to ensure it builds a working image.

By following these troubleshooting steps and potential solutions, we can resolve the issues with OpenFold and utilize it for real-world structure prediction.