pyTorchAutoForge

WARNING: work in progress. Do not hesitate to open issues for improvements or problems!

A library based on PyTorch (https://pytorch.org/) and designed to automate ML models development, tracking and deployment, integrated with MLflow and Optuna (https://mlflow.org/, https://optuna.org/). It also supports spiking networks libraries (WIP). Model optimization and deployment can be performed using ONNx, pyTorch facilities or TensorRT (WIP). The library aims to be compatible with Jetson Orin Nano Jetpack rev6.1. Several other functionalities and utilities for sklearn and pySR (https://github.com/MilesCranmer/PySR) are included (see README and documentation).

Installation using pip

This is the suggested installation method, the others are mostly intended for development and may not be completely up-to-date with the newest release versions. Run in a conda or virtual environment:

pip install pyTorchAutoForge

Dependencies for the core modules should be installed automatically using pip.

Manual installation (venv)

1. Clone the repository
2. Create a virtual environment using python >= 3.10 (tested with 3.11), using python -m venv <your_venv_name>
3. Activate the virtual environment using source <your_venv_name>/bin/activate
4. Install the requirements using pip install -r requirements.txt
5. Install the package using pip install . in the root folder of the repository

Manual installation (conda)

Option A:

1. Clone the repository
2. Create a new conda environment (python >=3.10) using the provided enrivonment.yml file

Option B;

1. Clone the repository
2. Use the automatic installation script conda_install.sh. There are several options, use those you need. It will automatically create a new environment named autoforge.