Horovod Horovod fails with 'too many open files'
Horovod Horovod fails with 'too many open files'
Understanding Horovod
Horovod is an open-source distributed deep learning framework that makes it easy to train models across multiple GPUs and nodes. Developed by Uber, Horovod is designed to improve the speed and efficiency of training large-scale machine learning models by leveraging data parallelism. It integrates seamlessly with popular deep learning libraries like TensorFlow, Keras, and PyTorch, allowing developers to scale their training workloads with minimal code changes.
Identifying the Symptom
When using Horovod, you might encounter an error message stating 'too many open files'. This error typically occurs during the initialization or execution of a distributed training job, causing the process to fail unexpectedly. The error message indicates that the system has reached its limit for open file descriptors, which are resources used by the operating system to manage open files and network connections.
Exploring the Issue
The 'too many open files' error is a common issue in environments where multiple processes or threads are running concurrently, each requiring access to files or network sockets. In the context of Horovod, this can happen when multiple workers are launched across different nodes, each establishing connections and opening files needed for training. The default limit for open file descriptors may be insufficient for large-scale distributed training jobs, leading to this error.
Understanding File Descriptors
File descriptors are integral to how operating systems manage resources. Each open file, socket, or network connection is assigned a unique file descriptor. The operating system imposes a limit on the number of file descriptors that can be open simultaneously to prevent resource exhaustion. This limit can be configured at both the user and system level.
Steps to Resolve the Issue
To resolve the 'too many open files' error in Horovod, you can increase the limit for open file descriptors. Here are the steps to do so:
Check Current Limits
First, check the current limits for open file descriptors using the following command:
ulimit -n
This command will display the current limit for the number of open files.
Increase the Limit Temporarily
To increase the limit temporarily for the current session, use the following command:
ulimit -n 65536
This command sets the limit to 65536, which should be sufficient for most distributed training jobs. Note that this change will only persist for the duration of the session.
Increase the Limit Permanently
To make the change permanent, you need to edit the /etc/security/limits.conf file. Add the following lines to the file:
* soft nofile 65536
* hard nofile 65536
After making these changes, log out and log back in for the changes to take effect.
Verify the Changes
After increasing the limit, verify the changes by running ulimit -n again to ensure the new limit is applied.
Additional Resources
For more information on configuring system limits, refer to the following resources:
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