Reproducibility Checklist & Determinism

The Reproducibility Checklist, Environment Freezing & Deterministic Computation Protocol provides the engineering rigor ensuring deep learning experiments reproduce identical numbers bit-for-bit across different GPU clusters; the 6 operational pillars encompass: 1) Deterministic Compute Flags (`torch.use_deterministic_algorithms(True)`, `torch.backends.cudnn.deterministic = True`, banning non-deterministic CUDA atomicAdd kernels); 2) Global Seed Synchronization across all libraries; 3) Immutable Data Versioning (DVC/Git LFS pinning dataset SHA256 hashes); 4) Containerized Environment Freezing (Docker + Poetry lockfiles pinning PyTorch, CUDA, and cuDNN driver versions); 5) Configuration Immutability (Hydra/OmegaConf capturing full CLI flags alongside exact Git commit hashes); 6) Telemetry & Checkpoint Archival (logging per-step losses, grad norms, and learning rates to W&B).

Academic paper reproducibility artifact releases, enterprise AI asset archiving, and multi-node training replication.

Eliminates the crisis where models cannot reproduce their own benchmark results due to silent CUDA non-determinism or floating-point dependency drift.

Write the complete PyTorch deterministic setup configuring CuDNN flags and `CUBLAS_WORKSPACE_CONFIG` to ban non-deterministic CUDA kernels?

Explain why omitting `worker_init_fn` in multi-process PyTorch DataLoaders causes worker threads to sample identical mini-batch sequences?

Explain why floating-point non-associativity in parallel reduction operations causes tiny numerical divergence across GPU architectures?

Detail the 4-step canonical workflow for reproducing published papers from baseline alignment to ablation verification?

Explain the key audit criteria required by NeurIPS Reproducibility Checklists for empirical machine learning submissions?