Skip to content
This repository has been archived by the owner on Dec 11, 2022. It is now read-only.

Checkpointing Features #177

Open
bbalaji-ucsd opened this issue Jan 2, 2019 · 4 comments
Open

Checkpointing Features #177

bbalaji-ucsd opened this issue Jan 2, 2019 · 4 comments
Labels
priority/p3 enhancements not currently in focus or low impact bugs
Projects

Comments

@bbalaji-ucsd
Copy link

Right now Coach saves checkpoints every X seconds. It would be great if I can save checkpoints every X iterations or save checkpoints if it reaches an evaluation reward threshold.

It will also help if I can save checkpoints as a protobuf file, so I can use the saved model independent of Coach.

@safrooze
Copy link
Contributor

safrooze commented Jan 3, 2019

So you want to save a checkpoint at a certain reward threshold but continue training? Is this in case you notice overfit later on and can go back to an earlier set of parameters?

@bbalaji-ucsd
Copy link
Author

Yes, to both questions. Although flexibility to stop training after a reward threshold also helps. In the latter case, it would help to save a checkpoint at the end of training.

@safrooze
Copy link
Contributor

safrooze commented Jan 4, 2019

There already is ability to stop training when a specific reward is achieved: "-asc" or "--apply_stop_condition" will stop early if a specific reward is hit and saves a checkpoint at the end.

@bbalaji-ucsd
Copy link
Author

Ok, that solves one of the problems then :)

@scttl scttl added this to To do in Coach Dev Jan 10, 2019
@galnov galnov moved this from To do to P2 in Coach Dev Jan 13, 2019
@galnov galnov moved this from P2 to P3 in Coach Dev Jan 13, 2019
@balajismaniam balajismaniam added the priority/p3 enhancements not currently in focus or low impact bugs label Jan 16, 2019
@balajismaniam balajismaniam moved this from P3 to Groomed but Not Started in Coach Dev Jan 16, 2019
Sign up for free to subscribe to this conversation on GitHub. Already have an account? Sign in.
Labels
priority/p3 enhancements not currently in focus or low impact bugs
3 participants