Support checkpointing new-style `jax.random.key` about orbax HOT 6 CLOSED

You are not the first person to request this, so I'll just say that the issue is on our radar, but has not yet risen to a high priority. In the meantime, you'll probably have to convert the key to a jax.Array to save it.

Thanks for reporting though, this will affect our prioritization going forward.

from orbax.

Thanks for your sharing. We have a new release of orbax-checkpoint 0.5.1 including two new JaxRandomKeyCheckpointHanlder and NumpyRandomKeyCheckpointHandler. We recommend to use these outside of train state PyTree because the random keys are more metadata.

Documentation is here. Usage examples can be found here

from orbax.

Great, thanks for your response!

from orbax.

@hylkedonker I am looking into adding support to store jax.random.key in Orbax. I have a couple questions on how these states are stored?

1. How often should the random keys be saved? Is it necessary to store them in every training step?
2. Do all machines share the same keys, or does each machine need to store its own ones?

from orbax.

Thanks for getting in touch.
I use the pseudo random number generator keys to train variational inference (VI) models. Concretely, each training step I consume a PRNG key to make a Monte Carlo estimate of the ELBO (evidence lower bound). In practice, I make the key part of Flax's TrainState which I checkpoint every now and then.
So to get back to your questions:

1. The key needs to be tracked every training step, but I don't save the TrainState every training step.
2. I currently don't have a lot of experience sharding the computation across different machines. But I would imagine that one might pmap the Monte Carlo estimate over different machines (so that each machine gets its own key).

I hope this helps. If not, let me know how I can further clarify.

from orbax.

Great work, thanks!

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