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 MPI (Message Passing Interface) or NCCL (NVIDIA Collective Communications Library) 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: 'transport endpoint is not connected'. This error typically occurs during the initialization or execution of a distributed training job, causing the process to fail.
The error 'transport endpoint is not connected' indicates a problem with the network connection between the nodes involved in the distributed training. This can happen if the network configuration is incorrect or if there is a disruption in the network connectivity.
To resolve the 'transport endpoint is not connected' error, follow these steps:
Ensure that all nodes are correctly configured to communicate with each other. Check the network settings and ensure that the IP addresses and ports are correctly set up. You can use the ping command to test connectivity between nodes:
ping
Inspect the network hardware such as cables, switches, and routers to ensure they are functioning correctly. Replace any faulty hardware if necessary.
Ensure that the firewall and security settings on each node allow for the necessary communication. You may need to open specific ports used by Horovod and MPI/NCCL. For example, you can use the iptables command to list current rules:
sudo iptables -L
To further diagnose the issue, try running a simple MPI program to verify that the MPI setup is working correctly. This can help isolate whether the problem is with Horovod or the underlying MPI configuration.
For more information on configuring Horovod and troubleshooting network issues, consider visiting the following resources:
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