Comments (3)
Kaixhin
commented on June 2, 2024
You're getting a 3x speedup on GPU, so it's reasonable? Bottleneck is that there's a lot of data transfer with RL, but speedup mainly comes with training on minibatches I believe. I'm running other stuff on my one PC atm so can't give you full speeds, but getting 53it/s on GPU (GTX 1080 Ti) vs. 13it/s on CPU (i7-3820), so similar speedup ratio on my side.
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stringie
commented on June 2, 2024
Thank you for your answer. If you ever decide to run Rainbow again could you please share those speeds here for me ... if you remember to of course. I'm asking all this because I find it worrisome that for the process to complete I have to wait (as it says next to the it/s) 66h ... which seems crazy to me. Do you also wait around that much when training your models? Thanks again.
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Kaixhin
commented on June 2, 2024
Not sure I'll have a chance any time soon, but training definitely takes on the order of a week. It's very sequential, takes pixel inputs, and has a lot of environment interactions, so it is naturally slow.
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Related Issues (20)
- Infinite loop in ReplayMemory._get_sample_from_segment HOT 1
- Noob Question : Need help running the code. It seems to be running for forever. HOT 1
- rainbow for multiagent setting HOT 1
- Policy and reward function HOT 4
- ploting the result HOT 1
- To run a demo HOT 2
- disable env.reset() after every episode HOT 5
- --learn-start HOT 2
- Delayed reward HOT 1
- load a model HOT 1
- Explanation of Q statistics in plots for "val_mem"? HOT 1
- Running Rainbow on a Cluster HOT 1
- IndexError: index 262141 is out of bounds for axis 0 with size 231071 HOT 2
- Evaluate the pretrained model HOT 1
- The problem about training with GPU HOT 1
- Stuck in memory._retrieve when batch size > 32 HOT 1
- A problem about one game in ALE cannot be trained HOT 1
- Montezuma's revenge - has this been tried using this codebase? HOT 2
- Data Effiecient Rainbow with Skiing does not work HOT 4
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