Distributed Training with tune

Ray Libraries (Data, Train, Tune, Serve) Ray Tune
1

Currently, I have a deep reinforcement learning framework setup where a pytorch model is trained with DistributedDataParallel and the data comes from interacting with a simulator. These simulators are run inside child processes spawned by the distributed ranks.

I would like to apply tune to tune the hyperparameter, but I’m having difficulty using tune.report. Specifically, I want the child process to be able to connect to the training instance, created in a parent process.

Is there a way to report metrics from a child process to an ancester trainer?

2

Can you try using the Distributed Trainable Creator?

Here is an example: https://docs.ray.io/en/master/tune/examples/ddp_mnist_torch.html

3

No, this would create the workers responsible for the gradient updating, but each of these need to create child processes that interact with the environment. These children compute the metrics that i want to send to tune.

4

Ok I see. Can you instead use the parent trainer to report the metrics, while use a Multiprocessing Queue to produce/consume metrics from the child process?