Best Practice: Save Deep-Learning Checkpoints¶
Run:ai can pause unattended executions, giving your GPU resources to another workload. When the time comes, Run:ai will give you back the resources and restore your workload. Thus, it is a good practice to save the state of your run at various checkpoints and start a workload from the latest checkpoint (typically between epochs).
How to Save Checkpoints¶
TensorFlow, PyTorch, and others have mechanisms to help save checkpoints (e.g. https://www.tensorflow.org/guide/checkpoint for TensorFlow and https://pytorch.org/tutorials/recipes/recipes/saving_and_loading_a_general_checkpoint.html for PyTorch).
This document uses Keras as an example. The code itself can be found here
Where to Save Checkpoints¶
It is important to save the checkpoints to network storage and not the machine itself. When your workload resumes, it can, in all probability, be allocated to a different node (machine) than the original node. Example:
The command saves the checkpoints in an NFS checkpoints folder
When to Save Checkpoints¶
It is a best practice to save checkpoints at intervals. For example, every epoch as the Keras code below shows:
Save on Exit Signal¶
If periodic checkpoints are not enough, you can use a signal-hook provided by Run:ai (via Kubernetes). The hook is python code that is called before your Job is suspended and allows you to save your checkpoints as well as other state data you may wish to store.
By default, you will have 30 seconds to save your checkpoints.
For the signal to be captured, it must be propagated from the startup script to the python child process. See code here
Resuming using Saved Checkpoints¶
A Run:ai unattended workload that is resumed, will run the same startup script as on the first run. It is the responsibility of the script developer to add code that:
- Checks if saved checkpoints exist (see above)
- If saved checkpoints exist, load them and start the run using these checkpoints