Requirements Warning + Issue With OpenMP

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Introduction

When working with complex software projects, it's not uncommon to encounter issues related to dependencies and compatibility. In this article, we'll explore a specific scenario where a project is experiencing issues with OpenMP, NumPy, and PyTorch versions. We'll delve into the error messages, potential causes, and possible solutions to get the project up and running smoothly.

OpenMP Issue

The first issue we encounter is related to OpenMP. When running the program, we get the following error message:

OMP: Error #15: Initializing libiomp5.dylib, but found libiomp5.dylib already initialized.
OMP: Hint This means that multiple copies of the OpenMP runtime have been linked into the program. That is dangerous, since it can degrade performance or cause incorrect results. The best thing to do is to ensure that only a single OpenMP runtime is linked into the process, e.g. by avoiding static linking of the OpenMP runtime in any library. As an unsafe, unsupported, undocumented workaround you can set the environment variable KMP_DUPLICATE_LIB_OK=TRUE to allow the program to continue to execute, but that may cause crashes or silently produce incorrect results. For more information, please see http://www.intel.com/software/products/support/.

This error occurs when multiple copies of the OpenMP runtime are linked into the program, which can lead to performance degradation or incorrect results. To resolve this issue, we can set the environment variable KMP_DUPLICATE_LIB_OK=TRUE to allow the program to continue executing. However, this is not a recommended solution, as it may cause crashes or produce incorrect results.

NumPy and PyTorch Version Incompatibility

The next issue we encounter is related to NumPy and PyTorch version incompatibility. When running the program, we get the following error message:

A module that was compiled using NumPy 1.x cannot be run in
NumPy 2.1.3 as it may crash. To support both 1.x and 2.x
versions of NumPy, modules must be compiled with NumPy 2.0.
Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.

If you are a user of the module, the easiest solution will be to
downgrade to 'numpy<2' or try to upgrade the affected module.
We expect that some modules will need time to support NumPy 2.

This error occurs because the NumPy version used to compile the module is incompatible with the current NumPy version. To resolve this issue, we can either downgrade to a previous version of NumPy or try to upgrade the affected module.

Program Stuck After Audio Extraction

After setting the environment variable KMP_DUPLICATE_LIB_OK=TRUE, the program appears to be stuck after extracting the audio. We don't see any further log messages, and the program doesn't terminate. This behavior is unusual, and we need to investigate further to determine the cause.

Intel MKL Warning

Finally, we encounter an Intel MKL warning:

Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.

This warning indicates that the Intel MKL library is deprecated and will require Intel AVX instructions in the future. However, this warning doesn't seem to be related to the current issues we're experiencing.

Conclusion

In conclusion, we've encountered several issues with OpenMP, NumPy, and PyTorch versions. We've set the environment variable KMP_DUPLICATE_LIB_OK=TRUE to resolve the OpenMP issue, but this is not a recommended solution. We've also encountered a NumPy and PyTorch version incompatibility issue, which requires us to either downgrade to a previous version of NumPy or try to upgrade the affected module. Finally, we've observed unusual behavior where the program appears to be stuck after extracting the audio. To resolve these issues, we need to investigate further and potentially rebuild the affected modules.

Recommendations

Based on our analysis, we recommend the following:

  1. Rebuild affected modules: We need to rebuild the affected modules to ensure they are compatible with the current NumPy version.
  2. Downgrade to previous NumPy version: If rebuilding the affected modules is not feasible, we can downgrade to a previous version of NumPy to resolve the incompatibility issue.
  3. Upgrade affected module: We can try to upgrade the affected module to ensure it is compatible with the current NumPy version.
  4. Investigate program stuck issue: We need to investigate further to determine the cause of the program being stuck after extracting the audio.

Q: What is the cause of the OpenMP issue?

A: The OpenMP issue is caused by multiple copies of the OpenMP runtime being linked into the program. This can lead to performance degradation or incorrect results.

Q: How can I resolve the OpenMP issue?

A: To resolve the OpenMP issue, you can set the environment variable KMP_DUPLICATE_LIB_OK=TRUE to allow the program to continue executing. However, this is not a recommended solution, as it may cause crashes or produce incorrect results.

Q: What is the cause of the NumPy and PyTorch version incompatibility issue?

A: The NumPy and PyTorch version incompatibility issue is caused by the NumPy version used to compile the module being incompatible with the current NumPy version.

Q: How can I resolve the NumPy and PyTorch version incompatibility issue?

A: To resolve the NumPy and PyTorch version incompatibility issue, you can either downgrade to a previous version of NumPy or try to upgrade the affected module.

Q: Why is the program stuck after extracting the audio?

A: The program is stuck after extracting the audio because of an unknown issue. We need to investigate further to determine the cause.

Q: What is the Intel MKL warning, and is it related to the current issues?

A: The Intel MKL warning is a deprecated warning that indicates the Intel MKL library will require Intel AVX instructions in the future. However, this warning is not related to the current issues we're experiencing.

Q: How can I rebuild the affected modules?

A: To rebuild the affected modules, you need to recompile the modules using the current NumPy version. This may require modifying the build process or using a different build tool.

Q: How can I downgrade to a previous version of NumPy?

A: To downgrade to a previous version of NumPy, you can use a package manager like pip or conda to install a previous version of NumPy.

Q: How can I upgrade the affected module?

A: To upgrade the affected module, you need to modify the module to use the current NumPy version. This may require modifying the module's code or using a different library.

Q: What are the potential causes of the program being stuck after extracting the audio?

A: The potential causes of the program being stuck after extracting the audio are:

  • Unknown issue with the audio extraction process
  • Issue with the program's logic or flow
  • Problem with the program's dependencies or libraries

Q: How can I investigate the program being stuck after extracting the audio?

A: To investigate the program being stuck after extracting the audio, you can:

  • Check the program's logs for any error messages or warnings
  • Use a debugger to step through the program's code and identify the issue
  • Modify the program's code to add additional logging or debugging statements

By following these steps, you can resolve the issues we've encountered and get the project up and running smoothly.