Horovod is an open-source distributed deep learning framework created by Uber. It is designed to make distributed deep learning fast and easy to use. Horovod achieves this by leveraging Open MPI or MPICH for communication between nodes, allowing for efficient scaling of training across multiple GPUs and nodes.
When using Horovod, you might encounter an error message stating: 'function not implemented'. This error typically occurs during the execution of a distributed training job, causing the process to halt unexpectedly.
This error is often observed when attempting to use certain functions or features that are not supported in the current environment or configuration.
The 'function not implemented' error indicates that Horovod is trying to execute a function that is either not available or not supported in the current setup. This can happen due to several reasons, including:
Horovod relies heavily on the underlying MPI implementation to perform distributed operations. If the MPI version or configuration does not support a particular function, Horovod will raise this error. For example, certain collective operations might not be available if the MPI library is not compiled with the necessary flags.
To resolve the 'function not implemented' error, follow these steps:
Ensure that your environment is correctly set up with all necessary dependencies. Check that Horovod is installed with the correct version of MPI. You can verify the MPI version using the following command:
mpirun --version
If the MPI version is correct, try reinstalling Horovod to ensure all components are correctly configured. Use the following command to reinstall Horovod:
pip uninstall horovod
HOROVOD_WITH_MPI=1 pip install horovod
Review the features you are trying to use and ensure they are supported by your MPI version and hardware. Consult the Horovod documentation for compatibility information.
By following these steps, you should be able to diagnose and resolve the 'function not implemented' error in Horovod. Ensuring that your environment is correctly configured and that you are using supported features is crucial for successful distributed training. For further assistance, consider reaching out to the Horovod community on GitHub.
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