changed performance mode to deterministic mode -- which is off by def… #444
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…ault, so we can make more effective use of compute by default and only run deterministically when explicitly requested by the user
younik
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Nov 30, 2025
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| else: | ||
| # For GPU training, we can use multiple threads for CPU operations | ||
| os.environ["OMP_NUM_THREADS"] = str(num_cpus) | ||
| os.environ["MKL_NUM_THREADS"] = str(num_cpus) |
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isn't this the default behavior? If yes, I think we can remove this else branch
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I'm not entirely sure what is default or not, so I'd like to keep this in for now. It was really hard to nail down the cause of the irreproducibility errors we were seeing before in distributed training.
src/gfn/utils/common.py
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| # Each worker gets a distinct seed in the same pattern used for ranks. | ||
| set_seed(base_seed + worker_id, performance_mode=False) | ||
| # TODO: Can this be false? | ||
| set_seed(base_seed + worker_id, deterministic_mode=True) |
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I think it should be false by default; as it is the default torch behavior
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fixed (it is now passed to the function, and default is False)
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Description
changed performance mode to deterministic mode -- which is off by default, so we can make more effective use of compute by default and only run deterministically when explicitly requested by the user