Someone else on the team cannot reproduce your training results. What could be different?

Usually one of five things: the data snapshot, the random seeds, library and CUDA versions, nondeterministic GPU kernels, or hardware differences in floating point behavior. Pin the data by dated snapshot rather than a live query, seed every random source including data loader workers, containerize the environment, and accept that exact bitwise determinism on GPU costs performance and is not always worth buying.

I go down a list, most likely first. Data, almost always. If the training query reads a live table, it changed between my run and theirs, and no amount of seeding fixes that, so training reads a dated snapshot with a recorded row count. Next, seeds, and not just the framework seed: Python's random, NumPy, the framework, and critically the data loader workers, each of which needs its own seed so shuffling and augmentation are reproducible too. Then environment, which is why training runs in a container with pinned library and CUDA versions rather than in whatever someone happens to have installed. After that you are into GPU nondeterminism. Some kernels use atomic accumulation, so the reduction order varies run to run and you get tiny floating point differences that compound over thousands of steps. You can force deterministic algorithms, but it disables the fast kernels and I have seen it cost twenty percent or more. So I ask what we actually need. For a debugging investigation or a regulated model I pay for bitwise determinism. Normally I only need results that match within noise, and I demonstrate that by running the same config twice and showing the spread.