Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Checkpointing support with ray Tune #5

Open
AdamGleave opened this issue Mar 16, 2019 · 1 comment
Open

Checkpointing support with ray Tune #5

AdamGleave opened this issue Mar 16, 2019 · 1 comment
Assignees
Labels
enhancement New feature or request

Comments

@AdamGleave
Copy link
Member

It would be nice to make modelfree.hyperparams.train_rl a tune.Trainable rather than a function, adding checkpointing support. This would let us use the HyperBand and Population Based Training schedulers. Conceptually this is easy enough: we already supporting saving models via save_callbacks, and can restore using load_path. However, the interfaces don't quite line up: Ray expects _train to perform one small training step, with _save called in between. There's no good way to make Stable Baselines return part-way. We could call it repeatedly with small total_timesteps, but this would make the progress be wrong, breaking annealers.

@AdamGleave AdamGleave added the enhancement New feature or request label Mar 16, 2019
@AdamGleave
Copy link
Member Author

Add support for this once ray-project/ray#6162 is closed / ray-project/ray#6192 is merged

@AdamGleave AdamGleave self-assigned this Nov 24, 2019
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
enhancement New feature or request
Projects
None yet
Development

No branches or pull requests

1 participant