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Parallel Hyperparameter Search #84
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Any idea why black is complaining as I've run the linter on my end? |
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PR Description
This PR makes hyperparameter search more scalable by running the training loop for each agent in parallel. This is especially useful when running the search on a cluster (e.g. Slurm).
num_workers
parameter to define the size of the pool of processes. Still not sure about this one though as we still need to wait for all of the workers from the same iteration so that we can get the hypervolume. So maybe we can just spawn the same number of processes asnum_seeds
.device
as anid
making it essentialy always default to the same device.TODO:
Example Configs on a Slurm Cluster
Using 4 GPUs + 4 workers
Using 4 CPUs + 4 workers
Each worker will use
auto
and then each algo instance will default tocpu
as CUDA is not available.Example Runs on a Slurm Cluster
Example Runs:
11%
12%
10%
5%
5%
5%
Workers
corresponds tonum_workers
,CPUs
corresponds to--cpus-per-task
,GPUs
corresponds to-G
srun -s --jobid <job-id> --pty nvidia-smi
command while running the job.seff
command after finishing the job.