mojolearn

Bitwise-identical machine learning across Apple, NVIDIA and AMD GPUs.

The same machine-learning workload can produce different bits on different GPUs, changing predictions, learned models and subsequent training updates. mojolearn is a GPU machine-learning library whose default identical mode produces bitwise-identical results across verified Apple, NVIDIA and AMD GPUs and x86-64 and Arm CPUs. Results agree bit for bit, not merely within a numerical tolerance.

• 57 machine-learning algorithms in 12 families, from decision trees to neural networks, with training and inference for every one of them.
• Serve models trained elsewhere. Neural models trained in other frameworks can be served with identical outputs across vendors.
• Move training between vendors. A training run can be handed off from one GPU vendor to another mid-run, or shared by Apple, NVIDIA and AMD GPUs working on one model at once, and the training state after every step is bitwise identical to a single GPU's.
• Full FP32. Neural training and inference run in full FP32 floating-point arithmetic, without reducing the computation to integers.
• Every algorithm runs on the Mac's own GPU, where XGBoost, LightGBM, CatBoost and cuML have no supported GPU path.

To the author's knowledge, mojolearn is the first system to enable these capabilities.

One Mojo codebase implements these algorithms across Metal, CUDA and HIP under a shared numerical contract. Mojo compiles code for different GPUs, but maintaining that contract and implementing the algorithms is original work.

Why it matters

A model may serve predictions on NVIDIA GPUs, run locally on an Apple laptop and later be evaluated on an AMD server. With identical weights and inputs, different GPUs normally return different output bits, so hardware becomes another variable in serving, regression testing and auditing. In a tree learner one rounding difference can change the winning split and every node beneath it. In a training loop it perturbs a gradient, then the optimizer state, then every step after that.

mojolearn removes hardware from that list. A team can test a deployed model on different hardware, replay a recorded decision during an audit, replace serving hardware without numerical change, and resume a checkpoint on another vendor's GPU on the same trajectory as uninterrupted training.

How it works

Floating-point addition is not associative, and GPU vendors differ in reduction order, fused multiply-add contraction, denormal handling, square root, tie-breaking and elementary functions. Recompiling does not fix any of that. mojolearn's numerical contract fixes the operations that determine model bits, including reduction order, rounding and elementary functions, and leaves tiling, thread layout, memory placement and kernel scheduling free, so each backend can be tuned for its own hardware without changing a bit. The kernels are mojolearn's own, written in Mojo, and replace vendor math routines wherever their results would depend on the backend.

Algorithms

Also provided are evaluation metrics, cross-validation, CPU training and inference, Hugging Face checkpoint loading and decoding, BF16 and INT8 weight storage, tokenized corpora, multi-GPU execution plans and a built-in verifier.

Numeric modes

identical is the default and, outside the tree learners, the only mode. Gradient boosting, random forests and Extra Trees also offer two opt-in modes.

Select a mode per estimator with numeric_mode=, per process with mojolearn.set_numeric_mode(...), or before import with MOJOLEARN_NUMERIC_MODE. A configuration that cannot meet its mode's contract raises a named error rather than silently returning something weaker.

Install

pip install mojolearn

Version 0.8.17 is on PyPI as a macOS arm64 wheel (Apple Metal) and a Linux x86-64 wheel carrying NVIDIA CUDA and AMD HIP together. Both wheels train and predict on the CPU as well.

mojolearn doctor

reports what the installed wheel supports on this machine.

Quick start

import numpy as np
import mojolearn

rng = np.random.default_rng(0)
X = rng.random((100_000, 20), dtype=np.float32)
y = (X[:, 0] + X[:, 1] > 1.0).astype(np.float32)

model = mojolearn.GradientBoosting(loss="Logloss", n_estimators=200, max_depth=6)
model.fit(X, y)
print(model.predict_proba(X[:5]))
print(model.numeric_mode_used(), mojolearn.vendor())

Run the same script on an Apple, NVIDIA or AMD GPU and the model and its predictions are the same bits.

Verify it yourself

The wheel ships the reference results recorded on Apple, NVIDIA, AMD and CPU, and a verifier that checks your machine against them.

python -m mojolearn verify --quick   # one lane per family
python -m mojolearn verify --all     # every lane

Each part reads IDENTICAL or DIVERGENT against the recorded references. docs/VERIFY.md describes the verifier and docs/VERIFY_EXTERNALLY.md shows how to check the claims from outside the project.

Scope

The guarantee holds for the same code revision, numerical profile, input bytes, hyperparameters and seed, in identical mode, on configurations the verification record covers. It is an FP32 guarantee. Weights trained in another framework reproduce under mojolearn's arithmetic, not that framework's. SUPPORT_MATRIX.md lists the verified devices and configurations.

Documentation

• Support matrix
• Verification
• Changelog
• Roadmap
• Getting started as a contributor
• Contributing and governance

Trademarks and affiliation

mojolearn is an independent project by Andrew Hendel, licensed under Apache-2.0. It is not affiliated with, sponsored by, or endorsed by Modular, Inc. MAX® and Mojo® are trademarks of Modular, Inc. Binary wheels include unmodified Modular runtime components redistributed under Modular's own license; see NOTICE.

Citation

To cite mojolearn, use CITATION.cff. The concept DOI is 10.5281/zenodo.22068632.