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Parallelize ensemble of Q functions into a single model #3

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Aladoro
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@Aladoro Aladoro commented Sep 7, 2021

Parallelizing yields a 5-10% speedup on my machine with standard batch size and network width and should not affect any of the functionality. This change should also enable to trivially and efficiently scale up to larger number Q-functions.

@facebook-github-bot facebook-github-bot added the CLA Signed This label is managed by the Facebook bot. Authors need to sign the CLA before a PR can be reviewed. label Sep 7, 2021
@denisyarats
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Thanks, will run tests to make sure that it doesn't affect performance.

@denisyarats
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I run speed comparison on the cluster with V100 and it doesn't seem improving speed consistently. So maybe for now let's hold off with this PR to preserve simplicity.

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Aladoro commented Sep 15, 2021

Ah, that's interesting! I observed the speedup on a Titan XP on my home machine, I guess different hardware configurations have different bottlenecks impacting training speed ^^

@Aladoro Aladoro deleted the branch facebookresearch:main January 15, 2022 10:20
@Aladoro Aladoro closed this Jan 15, 2022
@Aladoro Aladoro deleted the main branch January 15, 2022 10:20
@Aladoro Aladoro restored the main branch January 15, 2022 10:22
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3 participants