Review Random Seed

A seed is a starting value used to make random processes reproducible, such as sampling, shuffling, or initialization.

A random seed is the starting value supplied to a pseudorandom process. Reusing the same seed under the same conditions can reproduce the same sequence of random choices.

Seeds help debug and compare experiments by reducing one source of variation. They are useful evidence for reproducibility, but they do not control every nondeterministic operation or environment difference.

A training run uses seed 42 for data shuffling and parameter initialization so a second run can reproduce those random choices.

Setting a seed does not guarantee complete reproducibility across hardware, software versions, parallel execution, or nondeterministic algorithms.

The seed starts the random sequence; the environment still matters.

* [[stochastic-process|Stochastic Process]] * [[training|Training]] * [[verification|Verification]] * [[dataset|Dataset]]

* [PyTorch, “Reproducibility”](https://docs.pytorch.org/docs/stable/notes/randomness.html) — explains random-number seeding and the remaining limits on reproducibility.

* Compare [[stochastic-process|Stochastic Process]], [[training|Training]], [[verification|Verification]], and [[dataset|Dataset]] to place Random Seed in the surrounding system. * Follow the cited source material above for the method, standard, or research context behind this draft.