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.
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.
This issue often arises when the system resources are insufficient or when the resource limits have been reached, particularly in environments with constrained resources.
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:
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.
To resolve the resource temporarily unavailable
error, you can take the following steps:
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.
Consider optimizing your training job to use resources more efficiently. This can include:
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.
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.
(Perfect for DevOps & SREs)
(Perfect for DevOps & SREs)