Reproducibility and performance in PyTorch

Performance refers to the run time; CuDNN has several ways of implementations, when cudnn.deterministic is set to true, you're telling CuDNN that you only need the deterministic implementations (or what we believe they are). In a nutshell, when you are doing this, you should expect the same results on the CPU or the GPU on the same system when feeding the same inputs. Why would it affect the performance? CuDNN uses heuristics for the choice of the implementation. So, it actually depends on your model how CuDNN will behave; choosing it to be deterministic may affect the runtime because their could have been, let's say, faster way of choosing them at the same point of running.

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Concerning your snippet, I do the exact seeding, it has been working good (in terms of reproducibility) for 100+ DL experiments.

"performance" in this context refer to run-time