mlx

MLX: An array framework for Apple silicon

Science Score: 54.0%

This score indicates how likely this project is to be science-related based on various indicators:

✓
CITATION.cff file
Found CITATION.cff file
✓
codemeta.json file
Found codemeta.json file
✓
.zenodo.json file
Found .zenodo.json file
○
DOI references
○
Academic publication links
✓
Committers with academic emails
4 of 155 committers (2.6%) from academic institutions
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Institutional organization owner
○
JOSS paper metadata
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Scientific vocabulary similarity
Low similarity (13.1%) to scientific vocabulary

Keywords

mlx

Keywords from Contributors

distributed deep-neural-networks transformers jax cryptography cryptocurrencies tensor agents automatic-differentiation web-crawler

Last synced: 6 months ago · JSON representation ·

Repository

MLX: An array framework for Apple silicon

Basic Info

Host: GitHub
Owner: ml-explore
License: mit
Language: C++
Default Branch: main
Homepage: https://ml-explore.github.io/mlx/
Size: 60.3 MB

Statistics

Stars: 22,114
Watchers: 179
Forks: 1,323
Open Issues: 156
Releases: 60

Topics

mlx

Created over 2 years ago · Last pushed 6 months ago

Metadata Files

Readme Contributing License Code of conduct Citation

MLX

Quickstart | Installation | Documentation | Examples

MLX is an array framework for machine learning on Apple silicon, brought to you by Apple machine learning research.

Some key features of MLX include:

Familiar APIs: MLX has a Python API that closely follows NumPy. MLX also has fully featured C++, C, and Swift APIs, which closely mirror the Python API. MLX has higher-level packages like mlx.nn and mlx.optimizers with APIs that closely follow PyTorch to simplify building more complex models.
Composable function transformations: MLX supports composable function transformations for automatic differentiation, automatic vectorization, and computation graph optimization.
Lazy computation: Computations in MLX are lazy. Arrays are only materialized when needed.
Dynamic graph construction: Computation graphs in MLX are constructed dynamically. Changing the shapes of function arguments does not trigger slow compilations, and debugging is simple and intuitive.
Multi-device: Operations can run on any of the supported devices (currently the CPU and the GPU).
Unified memory: A notable difference from MLX and other frameworks is the unified memory model. Arrays in MLX live in shared memory. Operations on MLX arrays can be performed on any of the supported device types without transferring data.

MLX is designed by machine learning researchers for machine learning researchers. The framework is intended to be user-friendly, but still efficient to train and deploy models. The design of the framework itself is also conceptually simple. We intend to make it easy for researchers to extend and improve MLX with the goal of quickly exploring new ideas.

The design of MLX is inspired by frameworks like NumPy, PyTorch, Jax, and ArrayFire.

Examples

The MLX examples repo has a variety of examples, including:

Transformer language model training.
Large-scale text generation with LLaMA and finetuning with LoRA.
Generating images with Stable Diffusion.
Speech recognition with OpenAI's Whisper.

Quickstart

See the quick start guide in the documentation.

Installation

MLX is available on PyPI. To install MLX on macOS, run:

bash pip install mlx

To install the CUDA backend on Linux, run:

bash pip install mlx[cuda]

To install a CPU-only Linux package, run:

bash pip install mlx[cpu]

Checkout the documentation for more information on building the C++ and Python APIs from source.

Contributing

Check out the contribution guidelines for more information on contributing to MLX. See the docs for more information on building from source, and running tests.

We are grateful for all of our contributors. If you contribute to MLX and wish to be acknowledged, please add your name to the list in your pull request.

Citing MLX

The MLX software suite was initially developed with equal contribution by Awni Hannun, Jagrit Digani, Angelos Katharopoulos, and Ronan Collobert. If you find MLX useful in your research and wish to cite it, please use the following BibTex entry:

@software{mlx2023, author = {Awni Hannun and Jagrit Digani and Angelos Katharopoulos and Ronan Collobert}, title = {{MLX}: Efficient and flexible machine learning on Apple silicon}, url = {https://github.com/ml-explore}, version = {0.0}, year = {2023}, }

Owner

Name: ml-explore
Login: ml-explore
Kind: organization

Repositories: 1
Profile: https://github.com/ml-explore

Machine learning research on your laptop or in a data center

Citation (CITATION.cff)

cff-version: 1.2.0
title: mlx
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Awni
    family-names: Hannun
    affiliation: Apple
  - given-names: Jagrit
    family-names: Digani
    affiliation: Apple
  - given-names: Angelos
    family-names: Katharopoulos
    affiliation: Apple
  - given-names: Ronan
    family-names: Collobert
    affiliation: Apple
repository-code: 'https://github.com/ml-explore'
abstract: >-
  MLX: efficient and flexible machine learning on Apple
  silicon
license: MIT

Committers

Last synced: 9 months ago

All Time

Total Commits: 1,160
Total Committers: 155
Avg Commits per committer: 7.484
Development Distribution Score (DDS): 0.57

Past Year

Commits: 605
Committers: 71
Avg Commits per committer: 8.521
Development Distribution Score (DDS): 0.498

Top Committers

Name	Email	Commits
Awni Hannun	a**i@a**m	499
Angelos Katharopoulos	a**s@a**m	158
Cheng	g**t@z**m	73
Jagrit Digani	d**i@a**m	49
Alex Barron	b****x	45
Diogo	d**3@g**m	18
Chunyang Wen	c**n@g**m	18
Nripesh Niketan	8****N	17
Jesper Stemann Andersen	j**r@s**k	13
Daniel Strobusch	1****u	9
David Koski	4****i	8
nicolov	n****v	7
Abe Leininger	9****n	7
AtomicVar	g**1@f**m	6
__mo_san__	5****n	6
Luca Arnaboldi	l**a@a**u	5
Hazem Essam	h****m	4
Gabrijel Boduljak	g****k	4
toji	m**d@g**m	4
Jacket	4****0	4
Yi Wang	y**5@g**m	4
Vijay Krish	v**h@a**m	4
Juarez Bochi	j**i@g**m	4
Ikko Eltociear Ashimine	e**r@g**m	4
Avikant Srivastava	a**t@g**m	4
Rifur13	g**y@g**m	4
Piotr Rybiec	p**8@g**m	3
Ronan Collobert	c**t@a**m	3
Mike Drob	m**b@a**m	3
AmirHossein_Razlighi	7****i	3
and 125 more...

Committer Domains (Top 20 + Academic)

apple.com: 9 zcbenz.com: 2 pm.me: 2 qq.com: 2 me.com: 2 verrot.fr: 1 jeethurao.com: 1 sait.dk: 1 foxmail.com: 1 arnaboldi.lu: 1 brandeis.edu: 1 phoenix.dj: 1 carlo.cab: 1 nyu.edu: 1 meatfreezer.com: 1 tiplink.io: 1 uwucocoa.moe: 1 northeastern.edu: 1 gymni.ch: 1 srimukh.com: 1 fsu.edu: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 721
Total pull requests: 2,064
Average time to close issues: 17 days
Average time to close pull requests: 4 days
Total issue authors: 332
Total pull request authors: 199
Average comments per issue: 2.42
Average comments per pull request: 1.16
Merged pull requests: 1,626
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 317
Pull requests: 1,341
Average time to close issues: 6 days
Average time to close pull requests: 3 days
Issue authors: 154
Pull request authors: 94
Average comments per issue: 1.56
Average comments per pull request: 0.85
Merged pull requests: 1,069
Bot issues: 0
Bot pull requests: 0

View more stats

Top Authors

Issue Authors

awni (53)
Redmept1on (39)
zcbenz (24)
sck-at-ucy (18)
thegodone (16)
davidkoski (14)
abdussamettrkr (13)
acsweet (11)
Redempt1onzzZZ (11)
sachinraja13 (11)
mzbac (8)
a1eaiactaest (8)
kyrollosyanny (8)
ifsheldon (6)
dc-dc-dc (6)

Pull Request Authors

awni (865)
zcbenz (276)
angeloskath (247)
barronalex (73)
jagrit06 (62)
NripeshN (29)
stemann (27)
chunyang-wen (25)
dc-dc-dc (18)
nicolov (11)
ParamThakkar123 (11)
mlaves (8)
wangkuiyi (8)
AtomicVar (8)
abeleinin (8)

Top Labels

Issue Labels

enhancement (80) bug (76) good first issue (23) performance (8) wontfix (7) documentation (7) question (5) help wanted (2) bug-unconfirmed (1) low priority (1)

Pull Request Labels

bug (2) enhancement (1)

Packages

Total packages: 13
Total downloads:
- pypi 606,820 last-month
- homebrew 405 last-month

Total dependent packages: 27
(may contain duplicates)
Total dependent repositories: 2
(may contain duplicates)
Total versions: 276
Total maintainers: 3

pypi.org: mlx

A framework for machine learning on Apple silicon.

Homepage: https://github.com/ml-explore/mlx
Documentation: https://mlx.readthedocs.io/
License: MIT
Latest release: 0.28.0
published 7 months ago

Versions: 107
Dependent Packages: 27
Dependent Repositories: 2
Downloads: 341,194 Last month

Rankings

Downloads: 3.0%

Dependent packages count: 4.8%

Average: 6.4%

Dependent repos count: 11.5%

Maintainers (3)

katharas awni mlx-dev

Last synced: 6 months ago

proxy.golang.org: github.com/ml-explore/mlx

Documentation: https://pkg.go.dev/github.com/ml-explore/mlx#section-documentation
License: mit
Latest release: v0.28.0
published 7 months ago

Versions: 69
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Dependent packages count: 6.5%

Average: 6.7%

Dependent repos count: 7.0%

Last synced: 6 months ago

pypi.org: mlx-awni-cpu-2

A framework for machine learning on Apple silicon.

Homepage: https://github.com/ml-explore/mlx
Documentation: https://mlx-awni-cpu-2.readthedocs.io/
License: MIT
Latest release: 0.0.2
published 8 months ago

Versions: 1
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 7 Last month

Rankings

Stargazers count: 0.4%

Forks count: 1.6%

Dependent packages count: 8.8%

Average: 15.2%

Dependent repos count: 49.9%

Maintainers (1)

awni

Last synced: 6 months ago

pypi.org: mlx-metal-awni-test

A framework for machine learning on Apple silicon.

Homepage: https://github.com/ml-explore/mlx
Documentation: https://mlx-metal-awni-test.readthedocs.io/
License: MIT
Latest release: 0.26.5
published 7 months ago

Versions: 2
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 19 Last month

Rankings

Dependent packages count: 8.8%

Average: 29.3%

Dependent repos count: 49.8%

Maintainers (1)

awni

Last synced: 6 months ago

pypi.org: mlx-metal

A framework for machine learning on Apple silicon.

Homepage: https://github.com/ml-explore/mlx
Documentation: https://mlx-metal.readthedocs.io/
License: MIT
Latest release: 0.28.0
published 7 months ago

Versions: 14
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 261,457 Last month

Rankings

Dependent packages count: 8.8%

Average: 29.3%

Dependent repos count: 49.8%

Maintainers (2)

awni mlx-dev

Last synced: 6 months ago

pypi.org: mlx-cuda-awni-test

A framework for machine learning on Apple silicon.

Homepage: https://github.com/ml-explore/mlx
Documentation: https://mlx-cuda-awni-test.readthedocs.io/
License: MIT
Latest release: 0.26.4
published 8 months ago

Versions: 2
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 17 Last month

Rankings

Dependent packages count: 8.8%

Average: 29.3%

Dependent repos count: 49.8%

Maintainers (1)

awni

Last synced: 6 months ago

pypi.org: mlx-cpu

A framework for machine learning on Apple silicon.

Homepage: https://github.com/ml-explore/mlx
Documentation: https://mlx-cpu.readthedocs.io/
License: MIT
Latest release: 0.28.0
published 7 months ago

Versions: 14
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 2,858 Last month

Rankings

Dependent packages count: 8.8%

Average: 29.3%

Dependent repos count: 49.8%

Maintainers (2)

awni mlx-dev

Last synced: 6 months ago

pypi.org: mlx-awni-cpu

A framework for machine learning on Apple silicon.

Homepage: https://github.com/ml-explore/mlx
Documentation: https://mlx-awni-cpu.readthedocs.io/
License: MIT
Latest release: 0.0.2
published 8 months ago

Versions: 2
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 15 Last month

Rankings

Dependent packages count: 8.8%

Average: 29.4%

Dependent repos count: 49.9%

Maintainers (1)

awni

Last synced: 6 months ago

pypi.org: mlx-cpu-awni-test

A framework for machine learning on Apple silicon.

Homepage: https://github.com/ml-explore/mlx
Documentation: https://mlx-cpu-awni-test.readthedocs.io/
License: MIT
Latest release: 0.26.4
published 8 months ago

Versions: 2
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 14 Last month

Rankings

Dependent packages count: 8.8%

Average: 29.4%

Dependent repos count: 49.9%

Maintainers (1)

awni

Last synced: 6 months ago

pypi.org: mlx-awni-test-2

A framework for machine learning on Apple silicon.

Homepage: https://github.com/ml-explore/mlx
Documentation: https://mlx-awni-test-2.readthedocs.io/
License: MIT
Latest release: 0.0.2
published 8 months ago

Versions: 1
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Dependent packages count: 8.8%

Average: 29.4%

Dependent repos count: 49.9%

Maintainers (1)

awni

Last synced: 8 months ago

pypi.org: mlx-cuda

A framework for machine learning on Apple silicon.

Homepage: https://github.com/ml-explore/mlx
Documentation: https://mlx-cuda.readthedocs.io/
License: MIT
Latest release: 0.28.0
published 7 months ago

Versions: 14
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 1,194 Last month

Rankings

Dependent packages count: 9.0%

Average: 29.8%

Dependent repos count: 50.6%

Maintainers (2)

awni mlx-dev

Last synced: 6 months ago

pypi.org: mlx-awni-test

A framework for machine learning on Apple silicon.

Homepage: https://github.com/ml-explore/mlx
Documentation: https://mlx-awni-test.readthedocs.io/
License: MIT
Latest release: 0.26.5
published 7 months ago

Versions: 8
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 45 Last month

Rankings

Dependent packages count: 9.7%

Average: 37.0%

Dependent repos count: 64.3%

Maintainers (1)

awni

Last synced: 6 months ago

formulae.brew.sh: mlx

Array framework for Apple silicon

Homepage: https://github.com/ml-explore/mlx
License: MIT AND Apache-2.0
Latest release: 0.28.0
published 7 months ago

Versions: 40
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 405 Last month

Rankings

Dependent packages count: 19.2%

Average: 51.3%

Dependent repos count: 56.7%

Downloads: 78.0%

Last synced: 6 months ago

Dependencies

examples/extensions/setup.py pypi

pyproject.toml pypi

setup.py pypi

.github/workflows/pull_request.yml actions

actions/checkout v4 composite
actions/setup-python v4 composite

examples/extensions/pyproject.toml pypi

mlx

Science Score: 54.0%

Keywords

Keywords from Contributors

Repository

Basic Info

Statistics

Topics

Metadata Files

README.md

MLX

Examples

Quickstart

Installation

Contributing

Citing MLX

Owner

Citation (CITATION.cff)

Committers

All Time

Past Year

Top Committers

Committer Domains (Top 20 + Academic)

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

Pull Request Authors

Top Labels

Issue Labels

Pull Request Labels

Packages

pypi.org: mlx

Rankings

Maintainers (3)

proxy.golang.org: github.com/ml-explore/mlx

Rankings

pypi.org: mlx-awni-cpu-2

Rankings

Maintainers (1)

pypi.org: mlx-metal-awni-test

Rankings

Maintainers (1)

pypi.org: mlx-metal

Rankings

Maintainers (2)

pypi.org: mlx-cuda-awni-test

Rankings

Maintainers (1)

pypi.org: mlx-cpu

Rankings

Maintainers (2)

pypi.org: mlx-awni-cpu

Rankings

Maintainers (1)

pypi.org: mlx-cpu-awni-test

Rankings

Maintainers (1)

pypi.org: mlx-awni-test-2

Rankings

Maintainers (1)

pypi.org: mlx-cuda

Rankings

Maintainers (2)

pypi.org: mlx-awni-test

Rankings

Maintainers (1)

formulae.brew.sh: mlx

Rankings

Dependencies