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[RLlib] Torch algos use now-framework-agnostic MultiGPUTrainOneStep execution op (~33% speedup for PPO-torch + GPU). #17371
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sven1977
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[RLlib] Torch algos use now-framework-agnostic MultiGPUTrainOneStep execution op (~33% speedup for PPO-torch + GPU). #17371
sven1977
merged 45 commits into
ray-project:master
from
sven1977:torch_uses_multi_gpu_train_one_step
Aug 3, 2021
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…ctor_tf_dynamic_policy_multi_gpu_optim
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…h_uses_multi_gpu_train_one_step # Conflicts: # rllib/agents/trainer.py # rllib/utils/tf_ops.py
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[RLlib] Torch algos use now-framework-agnostic MultiGPUTrainOneStep execution op.
[RLlib] Torch algos use now-framework-agnostic MultiGPUTrainOneStep execution op (~33% speedup for PPO-torch).
Jul 28, 2021
sven1977
changed the title
[RLlib] Torch algos use now-framework-agnostic MultiGPUTrainOneStep execution op (~33% speedup for PPO-torch).
[RLlib] Torch algos use now-framework-agnostic MultiGPUTrainOneStep execution op (~33% speedup for PPO-torch + GPU).
Jul 28, 2021
…h_uses_multi_gpu_train_one_step
…h_uses_multi_gpu_train_one_step
michaelzhiluo
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This PR makes all torch algos use the formerly
TrainTFMultiGPU
(now made framework agnostic and renamed intoMultiGPUTrainOneStep
).This allows for pre-loading of batches into GPU memory and thus not having to re-load them every time we do an SGD iter.
First experiments suggest a ~33% speedup on a single GPU for PPO torch on Atari and a ~60+% speedup for 2GPUs)!
Benchmarks for measuring PPO torch speedups (1 GPU + 11 workers on Breakout-v4 on a p3dn.24xlarge machine; batch size 5k; lr=0.00005):
For 2 GPUs, the situation changed as follows (doubling batch size to 10k and lr to 0.0001):
Why are these changes needed?
Related issue number
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scripts/format.sh
to lint the changes in this PR.