Policy_Server num_rollout_workers>0

How severe does this issue affect your experience of using Ray?

  • Low: It annoys or frustrates me for a moment.

Hi all,

I have set up a working policy_server + policy_client training workflow with 1 server serving ~6 clients with inference_mode=remote. However, I am noticing that the server at times struggles to serve all the incoming requests. The cpu + gpu is around 25%-35% with less than half of the gpu vram utilised.

The server uses num_rollout_workers=0 because a long time ago (years) I had issues with setting the value >0.
So my question is what happens when I set it to 2? Will it create 2 copies of my model on the server’s gpu (assuming it can handle it) and load-distribute incoming GET_ACTION requests amongst the two workers?

From Policy_Server example:

 if ioctx.worker_index > 0 or ioctx.worker.num_workers == 0:
            return PolicyServerInput(
                args.port + ioctx.worker_index - (1 if ioctx.worker_index > 0 else 0),

Or will I have to manually specify the ip+port for each client for which worker to use? In this case, the main policy_server doesn’t load balance, but instead communincates with each work to get the data batches from clients?