pyTorchAutoForge 0.1.2.post1.dev0

PyTorchAutoForge library is based on raw PyTorch and designed to automate DNN development, model tracking and deployment, tightly integrated with MLflow and Optuna. It supports Spiking networks libraries (WIP). Deployment can be performed using ONNx, pyTorch facilities or TensorRT (WIP). The library is designed to be compatible with Jetson Orin Nano Jetpack rev6.1, with bash script to automatically configure virtualenv.

pyTorchAutoForge

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 also aims to be compatible with Jetson Orin Nano Jetpack rev6.1. ASeveral other functionalities and utilities for sklearn and pySR (https://github.com/MilesCranmer/PySR) are included (see README and documentation).

Installation using pip

Run in a conda or virtual environment:

pip install pyTorchAutoForge

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

Manual installation (bash)

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 on Linux
4. Install the requirements using pip install -r requirements.txt
5. Install the package using pip install . in the root folder of the repository