https://github.com/rcghpge/pymo
A framework in Mojo for AI/ML/DL applications and other domains.
https://github.com/rcghpge/pymo
deep-learning deep-neural-networks machine-learning machine-learning-models magic-cli max-gpu modular mojo mojo-language numpy pandas pymo python python-language scikit-learn
Last synced: 3 months ago
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A framework in Mojo for AI/ML/DL applications and other domains.
- Host: GitHub
- URL: https://github.com/rcghpge/pymo
- Owner: rcghpge
- License: mit
- Created: 2025-01-19T14:30:50.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-02-14T09:39:11.000Z (4 months ago)
- Last Synced: 2025-02-14T10:32:46.491Z (4 months ago)
- Topics: deep-learning, deep-neural-networks, machine-learning, machine-learning-models, magic-cli, max-gpu, modular, mojo, mojo-language, numpy, pandas, pymo, python, python-language, scikit-learn
- Language: Mojo
- Homepage:
- Size: 5.43 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
- Codeowners: .github/CODEOWNERS
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README
# PyMo
A framework in Mojo for AI/ML/DL applications and other domains.
## An extended framework for Mojo:
- The [Mojo🔥](https://www.modular.com/mojo)programming language
- Mojo is built by Modular Inc. and the Mojo community.
## Overview
PyMo is a framework designed to leverage the capabilities of the Mojo programming language. The goal is to
provide a seamless transition for Python developers into the realm of AI, Machine Learning, Deep Learning
(AI/ML/DL), and other domains - with the performance enhancements offered by Mojo. Since Mojo is designed as a
systems programming language, its capabilities go beyond AI/ML/DL. This extends to PyMo as well - it can
be leveraged in other programming languages and domains. See `docs` for more information.
## Project Structure
```
├── LICENSE
├── README.md
├── __inti__.🔥
├── data
├── docs
├── examples
├── hello.mojo
├── magic.lock
├── mojoproject.toml
├── pymo
├── sandbox
├── .github
├── test
└── test_pymo.mojo
```
## Installation & Setup
To install and set up PyMo (on Linux), follow these steps:
**Install Magic Build System & Package Manager**
- If your machine does not have Modular's Magic software, you may need to install it on your local machine.
- See Magic documentation for installation instructions: [Magic docs](https://docs.modular.com/magic/).
**Clone the repository**
```bash
git clone [email protected]:rcghpge/pymo.git
cd pymo
```
**Initialize PyMo environment with Magic**
- Add dependencies (optional)
```bash
# Initialize PyMo
magic shell
# Add additional dependencies
magic add xgboost
```
**Run Test(s)**
```bash
# Workflow/Development environment
magic run test
mojo test
# Test individual Mojo source code at the file level
mojo -I . hello.mojo
# Initialize debugging session
mojo test --debug
# Initialize REPL environment
mojo repl
```
**Initialize a Jupyter Notebook Environment (Optional)**
```bash
magic run jupyter notebook
```
**Format Project's Code (Optional)**
```bash
magic run format
```
**Build & Ship Package(s) and Module(s) in Mojo (Optional)**
```bash
magic run build
```
## Features
### Interoperability with Python:
PyMo showcases Mojo's potential to become a superset of Python, focusing on enhancing Python's AI/ML/DL capabilities
while providing a glimpse into Mojo's language and domain-agnostic potential.
### Integration with Popular Libraries:
Here's how PyMo interacts with some well-known Python libraries and frameworks:
- **scikit-learn:** For machine learning models.
- **pandas:** For data manipulation and analysis.
- **numpy:** For numerical computing.
- **seaborn:** For statistical data visualization.
- **matplotlib:** For plotting visualizations.
- **pytest** Testing framework for Python.
- and more.
### Getting Started
Check out `examples/` and `test/` directories for sample code demonstrating how to use PyMo for building out packages and modules in Mojo and AI/ML/DL methods. The `test/` directory is for port testing ecosystems and the `sandbox/` directory is for ecosystem development eg libraries, Mojo, packages, modules etc.
**MAX**
- check out [MAX](https://www.modular.com/max) - an AI inference platform built by Modular
- PyMo can be leveraged in this framework.
**Python**
- If you are new to programming, check out [PyPi](https://pypi.org) - The Python Package Index. A repository of software for the Python programming language and also see [Python](https://docs.python.org/3/) documentation. Python is an awesome programming language to get started with in programming.
**Machine Learning University**
- [MLU-Explain](https://mlu-explain.github.io) - Visual explanations of core machine learning concepts
## Contributing
Contributions are welcome! Here's how you can contribute:
- If you are interested to build out this project. Feel free to contact me. See contact below.
- Join the discussion. See repository's Discussions board.
- Report bugs or request features by opening an issue.
- Fix bugs or implement features by opening a pull request.
- Please ensure your code adheres to the project's and Mojo's coding standards before submission.
## License
The PyMo project is primarily licensed under the [MIT License](./LICENSE).
Additionally, PyMo also incorporates components, each governed by its respective license:
- **LLVM**: Components utilized from LLVM are licensed under the Apache License v2.0 with LLVM Exceptions. See the LLVM [License](https://llvm.org/LICENSE.txt).
- **MAX and Mojo**: Usage and distribution are licensed under the [MAX & Mojo Community License](https://www.modular.com/legal/max-mojo-license).
## Acknowledgements
- The Mojo and Python community.
- The open source community.
## Contact
For any inquiries or further information, you can find my email in contacts [here](https://robertcocker.com).
---