dockerizeasml 0.1.1

A tool to dockerize machine learning projects

ML-Dockerizer

ML-Dockerizer is a Python library that allows users to effortlessly dockerize an entire machine learning (ML) project with a single command-line instruction. It automatically recognizes the path of ML model files in the project directory and generates an appropriate Dockerfile.

Installation

You can install ML-Dockerizer using pip: pip install ml-dockerizer

Usage

To dockerize your ML project, navigate to your project's root directory and run: ml-dockerizer /path/to/your/ml/project

By default, ML-Dockerizer will use main.py as the entry point for your Docker container. If you want to specify a different entry point, use the --entry-point option:

Features

• Automatic detection of common ML model file formats (pkl, h5, joblib, pt, pth, onnx)
• Generation of a Dockerfile tailored to your ML project
• Customizable entry point for your dockerized application
• Easy-to-use command-line interface

Requirements

• Python 3.6+
• Docker (for building and running the generated Docker image)

How it works

1. ML-Dockerizer scans your project directory for ML model files.

2. It generates a Dockerfile that:

   • Uses a Python 3.8 slim base image
   • Copies and installs the requirements from requirements.txt
   • Copies the identified model files
   • Copies the rest of the project files
   • Sets the default command to run your specified entry point

3. You can then use standard Docker commands to build and run your containerized ML project.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request (git repository coming soon !).

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Thanks to all contributors who have helped shape ML-Dockerizer.
• Inspired by the need for simplified ML project containerization in the data science community.

Contact

If you have any questions, feel free to reach out to [aviroop-pal] at [[e-mail]].