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reproducible-ml

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Mis à jour6 juin 2026 à 02:16

Use when an ML result must be reproducible — fixing seeds, pinning environments, versioning data, and structuring projects so runs can be exactly recreated. Covers determinism gotchas across NumPy, PyTorch, and CUDA.

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param087/agent-ml-skills

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Développeurs de logicielsProfessions informatiques et mathématiques·SOC 15-1252

SKILL.md

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name	reproducible-ml
description	Use when an ML result must be reproducible — fixing seeds, pinning environments, versioning data, and structuring projects so runs can be exactly recreated. Covers determinism gotchas across NumPy, PyTorch, and CUDA.

Reproducible ML

Overview

Reproducibility means: same code + same data + same config → same result. It is a prerequisite for trusting comparisons, debugging regressions, and shipping. Three pillars: seed everything, pin everything, version the data.

When to use

Results vary run-to-run.
Setting up a new project or research codebase.
Preparing work others must reproduce (papers, audits, reviews).

Pillar 1 — Seed everything

import os, random, numpy as np, torch

def seed_everything(seed: int = 42):
    os.environ["PYTHONHASHSEED"] = str(seed)
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = False  # disables nondeterministic autotuner

For DataLoaders, also set worker_init_fn and a generator so workers are deterministic.

Pillar 2 — Pin the environment

Pin exact versions: requirements.txt with ==, or uv.lock / poetry.lock / conda env export.
Record Python + CUDA + cuDNN versions in the run metadata.
Containerize (Docker) for cross-machine reproducibility.

Pillar 3 — Version the data

Hash datasets (sha256) and log the hash with every run.
Use DVC or dataset snapshots; never overwrite data.csv in place.
Treat data as immutable inputs keyed by version (data_v="2026-06-01").

Project layout that supports reproducibility

project/
├── data/            # raw (immutable) + processed, both versioned
├── src/             # importable code, no notebooks doing real work
├── configs/         # YAML/Hydra configs, one per experiment
├── scripts/         # entrypoints: train.py, evaluate.py
├── requirements.txt # or uv.lock / poetry.lock (pinned)
└── README.md        # exact commands to reproduce

Determinism gotchas

GPU reductions can be nondeterministic even with seeds — use torch.use_deterministic_algorithms(True) and set CUBLAS_WORKSPACE_CONFIG=:4096:8.
Parallel groupby/apply ordering can vary — sort before reducing.
set/dict ordering across processes — set PYTHONHASHSEED.
Non-pinned dependencies silently change behavior between installs.

Pitfalls

Seeding only NumPy but using PyTorch/CUDA RNGs.
"Works on my machine" — unpinned env + unversioned data.
Doing real computation in notebooks with hidden execution order — move logic to src/.

Hand-off

A repo where pip install -r requirements.txt && python scripts/train.py --config configs/exp.yaml recreates the exact result that experiment-tracking logged.

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name	reproducible-ml
description	Use when an ML result must be reproducible — fixing seeds, pinning environments, versioning data, and structuring projects so runs can be exactly recreated. Covers determinism gotchas across NumPy, PyTorch, and CUDA.

Reproducible ML

Overview

When to use

Results vary run-to-run.
Setting up a new project or research codebase.
Preparing work others must reproduce (papers, audits, reviews).

Pillar 1 — Seed everything

import os, random, numpy as np, torch

def seed_everything(seed: int = 42):
    os.environ["PYTHONHASHSEED"] = str(seed)
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = False  # disables nondeterministic autotuner

For DataLoaders, also set worker_init_fn and a generator so workers are deterministic.

Pillar 2 — Pin the environment

Pin exact versions: requirements.txt with ==, or uv.lock / poetry.lock / conda env export.
Record Python + CUDA + cuDNN versions in the run metadata.
Containerize (Docker) for cross-machine reproducibility.

Pillar 3 — Version the data

Hash datasets (sha256) and log the hash with every run.
Use DVC or dataset snapshots; never overwrite data.csv in place.
Treat data as immutable inputs keyed by version (data_v="2026-06-01").

Project layout that supports reproducibility

project/
├── data/            # raw (immutable) + processed, both versioned
├── src/             # importable code, no notebooks doing real work
├── configs/         # YAML/Hydra configs, one per experiment
├── scripts/         # entrypoints: train.py, evaluate.py
├── requirements.txt # or uv.lock / poetry.lock (pinned)
└── README.md        # exact commands to reproduce

Determinism gotchas

GPU reductions can be nondeterministic even with seeds — use torch.use_deterministic_algorithms(True) and set CUBLAS_WORKSPACE_CONFIG=:4096:8.
Parallel groupby/apply ordering can vary — sort before reducing.
set/dict ordering across processes — set PYTHONHASHSEED.
Non-pinned dependencies silently change behavior between installs.

Pitfalls

Seeding only NumPy but using PyTorch/CUDA RNGs.
"Works on my machine" — unpinned env + unversioned data.
Doing real computation in notebooks with hidden execution order — move logic to src/.

Hand-off

A repo where pip install -r requirements.txt && python scripts/train.py --config configs/exp.yaml recreates the exact result that experiment-tracking logged.