This PR standardize the logging of every algorithm, in particular:
steps_per_second of both environment interaction and training step are computed and synced distributedly thanks to TorchMetrics
We have introduced the timer class, which can be used both as a context manager and as a decorator to trace time of functions and pieces of code
We have added a simple tool to print the config at the beginning of the training
Type of Change
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New feature (non-breaking change that adds functionality)
Checklist
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[x] I have tested my changes locally and they work as expected. (Please describe the tests you performed.)
[x] I have added unit tests for my changes, or updated existing tests if necessary.
[x] I have updated the documentation, if applicable.
[x] I have installed pre-commit and run locally for my code changes.
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Summary
This PR standardize the logging of every algorithm, in particular:
steps_per_secondof both environment interaction and training step are computed and synced distributedly thanks to TorchMetricstimerclass, which can be used both as a context manager and as a decorator to trace time of functions and pieces of codeType of Change
Please select the one relevant option below:
Checklist
Please confirm that the following tasks have been completed:
Thank you for your contribution! Once you have filled out this template, please ensure that you have assigned the appropriate reviewers and that all tests have passed.