Parallel computing for Reinforcement Learning training on VM
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Hi,
I am writing to ask if there's a way to increase the number of vCPU assigned to a worker when using parallel training for Reinforcement Learning application?
I noticed that the number of vCPU used at 100% is the same as the number of workers (set using parpool(numworkers)). When testing my model and running simulations on my local computer, the computational load exceeds the processing power of 1 vCPU. It took approximately 3-4 cores (50% of a typical intel i7 CPU) to run the simulation and train the agent.
Therefore, I would like to increase the number of vCPU assigned to a worker. I've tried to set 'numthreads' to 4 per worker, but that doesn't seem to solve the problem.
I am using a Ubuntu 18.04 Virtual Machine to run Matlab 2021a.
Thank you!
2 Commenti
Emmanouil Tzorakoleftherakis
il 9 Giu 2021
Any reason why you do not increase the number of workers instead?
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