Horovod Horovod fails with 'resource temporarily unavailable'
Horovod Horovod fails with 'resource temporarily unavailable'
Understanding Horovod
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) and NCCL (NVIDIA Collective Communications Library) to provide efficient communication between nodes in a cluster.
Identifying the Symptom
When running a distributed training job using Horovod, you might encounter an error message stating: resource temporarily unavailable. This error typically halts the execution of your training job, preventing it from progressing further.
Common Occurrence
This issue often arises when the system resources are insufficient or when the resource limits have been reached, particularly in environments with constrained resources.
Exploring the Issue
The error message resource temporarily unavailable indicates that the system is unable to allocate the necessary resources required by Horovod to continue its operation. This can be due to various factors such as:
- Exceeding the maximum number of open files allowed by the system.
- Reaching the limit of available CPU or GPU resources.
- Network bandwidth constraints.
System Resource Limits
Operating systems impose limits on the number of resources a process can use. These limits are often set to prevent any single process from consuming all available resources, which can lead to system instability.
Steps to Resolve the Issue
To resolve the resource temporarily unavailable error, you can take the following steps:
1. Increase System Resource Limits
Check and increase the system resource limits to ensure that Horovod has enough resources to operate. You can do this by modifying the /etc/security/limits.conf file on Linux systems:
* soft nofile 65536
* hard nofile 65536
After making changes, log out and log back in for the changes to take effect.
2. Optimize Resource Usage
Consider optimizing your training job to use resources more efficiently. This can include:
- Reducing the batch size to lower memory usage.
- Using mixed precision training to reduce GPU memory consumption.
- Ensuring that your network setup is optimized for high throughput and low latency.
3. Monitor System Resources
Use monitoring tools like Grafana or Prometheus to keep track of system resource usage. This can help you identify bottlenecks and adjust resource allocations accordingly.
Conclusion
By understanding the resource requirements of Horovod and ensuring that your system is configured to meet these needs, you can effectively resolve the resource temporarily unavailable error. Proper resource management and monitoring are key to successful distributed training with Horovod.
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