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MMPose - Implementation Report

2 min read

Setting up MMPose on cloud platforms like Google Colab or Kaggle is not straightforward. There are frequent dependency conflicts, Python version issues, and GPU compatibility problems. During my attempts, I faced several challenges that required careful handling to make the environment stable.

Main Challenges

1. Package Version Conflicts

  • Installing mim, mmcv-full, and MMPose often clashes with pre-installed packages on Colab/Kaggle.

  • Example error:

ERROR: pip's dependency resolver does not currently take into account all the packages installed.  
ipython 7.34.0 requires jedi>=0.16, which is not installed.  
google-colab 1.0.0 requires requests==2.32.4, but you have requests 2.28.2.
  • Fixing this needs uninstalling/reinstalling packages or forcing compatible versions.

2. Python Version Issues

  • Colab’s default Python may not work with MMPose.

  • Switching Python versions requires installing Miniconda, creating a new environment, and restarting the runtime.

3. Installation Order Matters

  • Installing mmcv-full before MMPose is mandatory. Wrong order causes errors.

  • Recommended sequence:

!pip install openmim
!mim install mmcv-full
!pip install -e .

4. Runtime Restart is Necessary

  • Even after installing all packages, Colab/Kaggle may not detect them until runtime is restarted.

  • Commands like !which python or !pip list may show wrong paths before restart.

5. GPU / CUDA Compatibility

  • MMPose needs GPU acceleration.

  • Mismatched CUDA versions or limited GPU support on Colab/Kaggle can trigger runtime errors.

6. Conda Environment Activation

  • Running MMPose commands in the same cell as environment setup often fails.
!source activate /usr/local/miniconda/envs/openmmlab
!which python
  • Activating the environment first and then running commands separately works reliably.

Core Problem: Version Mismatch

The main issue on both cloud and local machines is library and package version mismatch:

  • MMPose requires specific versions of mmcv-full, PyTorch, CUDA, and other libraries.

  • Default packages on Colab/Kaggle or existing local installations often differ.

  • Using isolated conda environments or Docker containers helps maintain consistency.

Important Tips:

  1. Always check library versions before installing MMPose.

  2. Use environment isolation to prevent conflicts.

  3. Restart runtime after major installations to update Python paths.

Conclusion

Setting up MMPose is challenging because of dependency conflicts, Python version issues, and GPU/CUDA compatibility. The biggest problem is version mismatches, which occur on both cloud and local setups.

By following strict installation order, using isolated environments, and checking versions carefully, one can avoid most errors and run MMPose reliably for research or applications.

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