Horovod is an open-source distributed deep learning framework that makes it easier to run large-scale training jobs across multiple GPUs and nodes. Developed by Uber, Horovod is designed to improve the speed and efficiency of training deep learning models by leveraging data parallelism. It integrates seamlessly with popular deep learning frameworks like TensorFlow, PyTorch, and Keras.
When using Horovod, you might encounter an error message stating 'resource busy'. This error typically occurs when Horovod attempts to access a resource that is currently being used by another process or application. This can lead to a failure in executing your distributed training job.
The 'resource busy' error is indicative of a conflict in resource allocation. In distributed computing, resources such as GPUs, CPUs, or network ports may be shared among multiple processes. If a resource is already allocated to another process, Horovod will be unable to access it, resulting in this error.
To resolve the 'resource busy' error, follow these steps:
Ensure that the GPUs required by your Horovod job are not being used by other processes. You can use the nvidia-smi command to check the current GPU usage:
nvidia-smi
If other processes are using the GPUs, consider terminating those processes or scheduling your job when the GPUs are free.
Horovod requires certain network ports to be available for communication between nodes. Use the netstat or lsof command to check if the required ports are free:
netstat -tuln | grep <port_number>
If the ports are occupied, you may need to configure Horovod to use different ports by setting the HOROVOD_GLOO_RENDEZVOUS_ADDR environment variable.
If your job accesses shared files or directories, ensure that no other process has locked these resources. Use the lsof command to identify locks:
lsof | grep <file_or_directory>
Release any unnecessary locks to allow Horovod to access the required files.
For more information on troubleshooting Horovod, consider visiting the following resources:
By following these steps and utilizing the resources provided, you should be able to resolve the 'resource busy' error and successfully run your distributed training job with Horovod.
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