Environment Capture and Determinism Guarantees

Environment capture means recording everything about the execution context that could affect model behavior: container image digest, library lockfile checksum, hardware profile including CPU model and GPU model with driver versions, and all random number generator (RNG) seeds for framework, data loader, and operating system. Treat the environment as a first class artifact in your lineage graph. Without this, you cannot distinguish whether a performance change came from your code or from a CUDA driver update that changed numerical behavior. The determinism trade-off is stark. Enabling deterministic algorithms and fixed seeds can reduce GPU throughput by 10 to 30 percent because you disable fast non-deterministic kernels and constrain parallelism. A training job that takes 8 hours with standard settings might take 10 hours with full determinism. Use strict bitwise determinism for audits and regulatory contexts where you must reproduce exact outputs. Use algorithmic determinism for most production work where numerical equivalence within floating point precision suffices. Use statistical reproducibility for iterative research and large scale hyperparameter optimization, running multiple trials and reporting confidence intervals. Hidden non-determinism sources are common failure modes. Parallel data loaders reading files in non-deterministic order cause different batches. GPU kernels with non-associative floating point reductions give different sums depending on execution order. Asynchronous distributed training changes gradient accumulation order across workers. The fix is to pin all seeds, set deterministic execution flags in your framework, fix data ordering with sorted file lists, and record the number of workers and threads. Document your expected reproducibility level explicitly so users know whether to expect bitwise identical results or statistical equivalence. Google TFX records hardware profiles and environment digests as part of pipeline metadata. For critical models, they pin base container images immutably so reruns use identical CUDA and cuDNN versions. Meta FBLearner Flow captures code commit hashes and execution environments, allowing engineers to replay experiments with the same dependencies. Netflix Metaflow automatically snapshots code and logs the Python environment, making it trivial to restart a failed workflow step with the exact same setup even weeks later.

PyTorch determinism setup: torch.manual_seed(42), torch.backends.cudnn.deterministic=True, torch.backends.cudnn.benchmark=False, workers sorted file list, but expect 20% slower training on V100 GPUs

Environment capture: {"image_digest": "sha256:a7f3...", "cuda_version": "11.8", "cudnn_version": "8.6.0", "gpu_model": "A100-SXM4-40GB", "driver_version": "520.61.05", "framework_seed": 42, "dataloader_seed": 123}

Netflix Metaflow: Automatically snapshots code into /metaflow/<flow>/<run_id>/code.tar.gz and logs pip freeze output, enabling restart of failed workflow steps weeks later with identical environment