Metadata-Version: 2.4
Name: mojolearn
Version: 0.7.0
Summary: GPU machine learning in Mojo for Metal, CUDA, and HIP, with fast, deterministic, and cross-vendor identical modes for certified configurations
Author: Andrew Hendel
Maintainer: Andrew Hendel
License-Expression: Apache-2.0
Project-URL: Homepage, https://github.com/mojolearn/mojolearn
Project-URL: Repository, https://github.com/mojolearn/mojolearn
Project-URL: Issues, https://github.com/mojolearn/mojolearn/issues
Project-URL: DOI, https://doi.org/10.5281/zenodo.22068632
Project-URL: Citation, https://doi.org/10.5281/zenodo.22068632
Keywords: machine learning,gpu,metal,apple silicon,mojo,gradient boosting,random forest,extra trees,clustering,k-means,nearest neighbors,dbscan,pca,svm,kernel density,hierarchical clustering,spectral clustering,isolation forest,time series,metrics,bitwise reproducibility,determinism,reproducibility,cuda,rocm,hip
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Environment :: GPU :: NVIDIA CUDA
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
License-File: NOTICE
Requires-Dist: numpy>=1.24
Dynamic: license-file

# mojolearn

[![PyPI](https://img.shields.io/pypi/v/mojolearn.svg)](https://pypi.org/project/mojolearn/)
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.22068632.svg)](https://doi.org/10.5281/zenodo.22068632)

**GPU machine learning in Mojo for Apple, NVIDIA and AMD GPUs, with an
explicit reproducibility contract.**

CatBoost, XGBoost, LightGBM and cuML have no Metal backend. mojolearn ports
the GPU kernels of CatBoost, cuML, cuVS, RAFT and FAISS into one Mojo source
that builds for Apple Metal, NVIDIA CUDA and AMD HIP, under the repository's
copy-do-not-improve rule ([PORTING_RULES.md](PORTING_RULES.md)), so GPU tree
training and GPU classical learning run on Apple silicon, hardware the
originals cannot reach. On top of that port sits a numerical contract. The
same machine-learning workload can produce different bits on different GPUs,
changing predictions, learned models and subsequent training updates. In
mojolearn's opt-in `identical` mode, supported inference and training return
the same bits on certified Apple, NVIDIA and AMD GPUs. The claim is proven by
stage-level identity cards and separating sabotage tests, never inferred from
a final-output hash, and it applies only to configurations recorded in
[the support matrix](SUPPORT_MATRIX.md).

What the contract covers today, with the evidence each claim rests on:

- **Neural inference and training.** Mamba and transformer forward
  computations agree bit for bit across the three vendors on their recorded
  fixtures, as do gradients, optimizer updates and checkpoint bytes in
  fixed-shape transformer training. A two-block, 34,944-parameter byte-level
  language model trained on real text ran 128 steps with byte-identical
  parameters, gradients, optimizer state and loss on Apple Metal, NVIDIA CUDA
  and AMD HIP; held-out loss fell from 5.5413 to 2.8436 on all three
  ([three-vendor record](bench/results/resume/2026-09-07-root-byte-lm-three-vendor/README.md)).
  Checkpoint continuation between NVIDIA and AMD, in both directions,
  preserves the uninterrupted training trajectory
  ([cross-vendor record](bench/results/resume/2026-09-06-root-byte-lm-cross-vendor/README.md)).
  Metal checkpoint resume remains open.
- **Trees and classical learning.** Gradient boosting, random forests, Extra
  Trees, k-means, DBSCAN, k-NN, PCA, truncated SVD, OLS, ridge, logistic
  regression, FP32 matrix multiplication, isolation forest and ARIMA filtering
  carry three-vendor cards for recorded configurations. Model state and
  recorded training stages match, not only predictions.
- **UMAP.** Neighbor selection and iterative updates match across the three
  vendors on named fixtures.

Two other modes sit beside `identical`, selectable at runtime on every
supported estimator:

| mode | contract |
|---|---|
| `fast` | Optimize for throughput; repeated fits need not return identical bits. |
| `deterministic` | The same build, input, and device return the same bits on repeated runs. It makes no cross-vendor promise. |
| `identical` | Certified configurations return the same bits across Metal, CUDA, and HIP. |

Bitwise identity carries implementation and execution costs. Measured against
cuML, cuBLAS and PyTorch, `identical` mode is competitive on some measured
tree workloads and substantially slower on many classical, matrix and neural
workloads. Those measurements reflect both the numerical constraints and
optimization gaps in the current kernels. The numbers are in the accompanying
paper; the raw records behind them live under `bench/results/`.

The 0.6.1 source in this checkout defaults to **`identical`** mode. Set
`MOJOLEARN_NUMERIC_MODE=fast` or `deterministic` explicitly to opt into
another supported mode. Certification remains configuration-specific; this
default does not certify every feature. The published 0.6.0 files are
unchanged.

## Who this is for

- People on Apple silicon who want GPU gradient boosting, random forests,
  Extra Trees, clustering, nearest neighbors, decompositions and linear
  models without leaving the machine.
- People who need a reproducibility contract on trees or classical models,
  same bits on repeated runs or across vendors, and will pay for it in time.
  The cost is small on some measured tree workloads and large elsewhere; see
  the paper before deciding.
- Not yet people training real neural networks. The certified trainers are
  fixed small shapes, an MLP and the two-block byte LM above. Larger models,
  other shapes and other optimizers are outside the evidence, and the byte-LM
  native trainer is not in any published wheel.

## Install

```sh
python3 -m venv .venv
source .venv/bin/activate
pip install mojolearn==0.6.0
```

Version **0.6.0 is published on PyPI** as an alpha API release for macOS
arm64 and AMD Linux x86-64 (`gfx942`). It exposes public `linalg`, `umap`,
`training`, Mamba and Transformer APIs, including UMAP transform/CSR support.
**NVIDIA Linux remains source-build-only.** The Linux wheel contains HIP
binaries, not CUDA. New byte-LM native training needs a separate source build.
The wheels preserve identified base native/runtime bytes; newer Python API
exposure does not inherit every numerical certificate. See the
[release evidence](bench/results/releases/2026-09-06-alpha-api/README.md) and
[support matrix](SUPPORT_MATRIX.md) for exact artifacts and limits.
There is no CPU fallback.
Run the diagnostic command before depending on a new machine:

```sh
mojolearn doctor
```

The exact wheel, architecture, Python, and evidence boundaries live in
[SUPPORT_MATRIX.md](SUPPORT_MATRIX.md). Source builds may support hardware
outside the architectures packaged in a released wheel; that is not the same
as released-wheel support.

## Project status

### Stability and release cadence

mojolearn went from 0.1.0 on 2026-08-23 to 0.6.0 on 2026-09-06, six PyPI
releases in two weeks (0.1.0, 0.2.0, 0.3.0, 0.3.1, 0.5.0, 0.6.0; 0.3.2 and
0.4.0 are recorded in [CHANGELOG.md](CHANGELOG.md) but were not published to
PyPI). One release was yanked. 0.3.0, published 2026-08-30 as the first
release with a Linux wheel, had been compiled for the build machine's CPU and
carried unconditional AVX-512 instructions in its host code, so every numeric
mode died with SIGILL on any x86-64 host without AVX-512. It is yanked on PyPI
with the reason "SIGILL on x86-64 without AVX-512; use 0.3.1". 0.3.1 pinned
the Linux baseline to x86-64-v3 and added a gate on the shipped binary; the
defect and both gates are documented in
`packaging/linux/isa_baseline_linux.py` and `packaging/wheel_ci.py`.

The Python API is beta and will change between minor versions. The stable
surface is the set of numerical profiles (`fast`, `deterministic`,
`identical`) and the certified configurations recorded in
[SUPPORT_MATRIX.md](SUPPORT_MATRIX.md): a profile version changes only
through an explicit decision, and a numerical change must either prove itself
bit-inert or introduce a new profile version. For production or archival work
pin both the package version and the numeric profile, in code or through
`MOJOLEARN_NUMERIC_MODE`. A certificate names a commit, a configuration (the
fixture, the numeric profile, the parameters) and the devices it ran on, and
never more. A newer version, a different shape or an unrun vendor column is
not covered by it.

### Maintenance and bus factor

The project has one maintainer today. Three things limit what that means for
a reader.

Every claim in this repository is backed by a recorded artifact under
`bench/results/` that names its commit, device, toolchain, mode and
limitations, and each is reproducible from the commands in the docs
([verification](docs/VERIFY.md), [conformance bundles](docs/CONFORMANCE.md),
[release runbook](docs/PYPI_RELEASE.md)). Historical cards and investigations
under `bench/results/` and `archive/` are evidence, not current guidance;
[SUPPORT_MATRIX.md](SUPPORT_MATRIX.md) is updated only from recorded evidence.

Contributions are governed by [CONTRIBUTING.md](CONTRIBUTING.md) and
[GOVERNANCE.md](GOVERNANCE.md). A contributor needs one GPU of any vendor and
marks the vendor columns they did not run `cross-vendor-pending`; closing a
cross-vendor claim is a maintainer job. Any change that can move `identical`
bits must show that it is bit-inert, supply a separating fixture and a
profile-version decision, or add a named refusal. External pull requests get
an admission report and a hosted CPU report; there is no GPU automation and
no automatic merge. Governance uses lazy consensus with a seven-day objection
window, maintainership is explicitly transferable, a sole maintainer records
nominations in a public issue, and the succession steps for a sole maintainer
(nominate two successors, transfer access, document release and certification
steps, rotate credentials, publish open blockers) are written down. The code
is Apache-2.0.

You can verify a certificate without trusting the maintainer. On any
supported GPU, `MOJOLEARN_NUMERIC_MODE=identical python -m mojolearn verify`
runs a pinned fixture, captures its stage-level identity card and compares it
with the reference card shipped in the installation; `python -m mojolearn
check-fixture` checks the fixture's input hashes without a GPU. Recorded
cards carry stage tags, dtypes, element counts and raw-bit hashes and are
compared with `tools/identity_trace_diff.py`, the one comparator the
repository uses. `python -m mojolearn conformance` exports and validates
bundles so another implementation can compare itself without running Mojo,
and `tools/verify_umap_qualification.py` rechecks retained release evidence
against a wheel without GPU work. One local run establishes one build on one
device; a cross-vendor claim needs every named leg, and the cards for each
leg are in the tree.

## Quick start

```python
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,
    numeric_mode="deterministic",
)
model.fit(X, y)
print(model.predict_proba(X[:5]))
print(model.numeric_mode_used(), mojolearn.vendor())
```

Choose a process default with `mojolearn.set_numeric_mode("identical")`, or
set the starting default before import:

```sh
MOJOLEARN_NUMERIC_MODE=identical python train.py
```

More than one tier may be loaded in one process through per-estimator
`numeric_mode=` arguments.

## Public API

Classical estimators include:

- Gradient boosting, random forests, and Extra Trees
- K-means, nearest-neighbor estimators, DBSCAN, hierarchical and spectral clustering
- PCA, truncated SVD, linear and logistic regression, ridge, lasso, and elastic net
- SVC, SVR, kernel density, isolation forest, and Gaussian-process regression
- Exponential smoothing and batched ARIMA
- UMAP embeddings with dense Euclidean input and 2D/3D spectral initialization;
  version 0.6.0 adds unseen-sample transformation and CSR graph storage

Additional modules provide scoring metrics, FP32 matrix multiplication,
optimizer/training primitives, and reference-pinned Mamba and transformer
blocks. These surfaces do not all have the same validation depth; consult the
support matrix before treating an experimental surface as release-qualified.

UMAP in the 0.5.0 API supports fitting and embedding the supplied samples:

```python
X = np.array([0, 1, 2.2, 4, 6.5, 10, 14.5, 20], dtype=np.float32)[:, None]
embedding = mojolearn.UMAP(
    n_neighbors=3, n_components=2, n_epochs=4, random_state=19,
    numeric_mode="identical",
).fit_transform(X)
```

The 0.5.0 implementation stores a dense graph and does not support
`transform`. In **0.6.0**, public fitting stores
the graph in CSR form, using O(n_samples × n_neighbors) graph space, and
`transform(X_new)` embeds unseen samples against a frozen fitted model. Input
remains a dense Euclidean array; CSR describes internal graph storage.
Exact neighbor search still performs quadratic pair comparisons.

Source checks for the integrated fit/transform API passed all three numeric
modes on Apple, NVIDIA and AMD. The named IDENTICAL held-out embeddings
match across all three vendors. The macOS 0.6.0 candidate also passed clean
installed fit/transform and quality checks. See the [version-specific evidence](SUPPORT_MATRIX.md#umap-060-release-candidate).

Transformation retains private training data and embedding copies. Changing
parameters or numeric mode requires refitting, and changing query batching
can change results. Supervised targets, alternate metrics and alternate
initialization remain unsupported.

The APIs intentionally resemble scikit-learn, but mojolearn is not a drop-in
replacement. Defaults follow the upstream GPU implementation mirrored by an
algorithm where applicable. Unsupported parameters raise explicitly rather
than being silently ignored.

## What the identity claim means

Fix a source commit, a supported configuration, a seed and byte-identical
input. On any two certified machines, every recorded training stage has the
same bits, and either model produces exactly the same predictions. This is a
claim about the trained model, not byte-for-byte equality of archive
metadata. If a configuration cannot meet the contract, the library raises a
named error instead of silently returning a possibly different model; a
refusal is reported as a refusal, never counted as a pass.

Cross-vendor identity is a profile, not a statement that every GPU operation
is universally identical. A profile fixes relevant reduction order,
partitioning, FMA policy, flush-to-zero seams, transcendental spellings, and
tie rules. A numerical change must either prove bit-inertness against the
profile or introduce a new profile version. Additional devices must pass the
same identity checks; the guarantee covers only devices and configurations
that have.

The project distinguishes four artifact classes:

```text
source check -> Python binding -> built native artifact -> installed wheel
```

Evidence for one class does not automatically validate the next. Current
certificates, configurations, and outstanding vendor legs are listed in
[SUPPORT_MATRIX.md](SUPPORT_MATRIX.md). Historical cards and investigations
under `bench/results/` and `archive/` are evidence, not current guidance.

## Limitations

What will get in your way first:

- GPU hardware is required. There is no CPU fallback, and the library refuses
  rather than silently running elsewhere.
- mojolearn is not a drop-in replacement for scikit-learn, CatBoost or cuML.
  Parameter coverage is intentionally smaller than any of them, and
  unsupported parameters raise.
- Source builds need the Mojo toolchain through [pixi](https://pixi.sh), and
  one build targets one GPU architecture. NVIDIA Linux is source-build-only
  today.
- The support matrix is honest about gaps. Several public surfaces still have
  vendor legs or independent-reference checks pending, and an unrun column is
  pending, never inferred.

And the standing limits of the contract itself:

- Released-wheel support is narrower than source-build support.
- `fast` deliberately makes no repeatability promise.
- `deterministic` does not promise agreement between different devices.
- `identical` covers certified profiles and fixtures, not arbitrary untested shapes or future toolchains.
- Some recent Python and neural-operator surfaces still have vendor legs or independent-reference checks pending.
- Parameter coverage is intentionally smaller than scikit-learn, CatBoost, or cuML.
- The experimental k-NN selector remains behind an explicit build flag; normal
  wheel builds retain the existing dispatch.

mojolearn is beta software. Pin the package version and numerical profile for
production or archival work.

## Development

Start with [docs/START_HERE.md](docs/START_HERE.md). The shortest full local
check is `pixi run probe`.

A numerical test counts as evidence only after a separating arm demonstrates
that it fails when the relevant rule is broken. Contributors need one
supported GPU; maintainers close cross-vendor certification columns.

Current priorities are in [ROADMAP.md](ROADMAP.md). See also
[verification](docs/VERIFY.md), [release](docs/PYPI_RELEASE.md),
[porting rules](PORTING_RULES.md), [contributing](CONTRIBUTING.md),
[governance](GOVERNANCE.md), and [attribution](NOTICE).

## Provenance and citation

The shipped implementation is Mojo. Algorithmic designs derive in part from
CatBoost, cuML, cuVS, RAFT, and FAISS; exact provenance and licenses are in
`NOTICE`, `DERIVATION_MAP.tsv`, source headers, and the archived derivation
ledger.

To cite mojolearn, use [CITATION.cff](CITATION.cff). The concept DOI is
[10.5281/zenodo.22068632](https://doi.org/10.5281/zenodo.22068632).
