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A lightweight Python package that supplies batteries-included abstractions for PyTorch workflows

Project description

torch-batteries

PyPI version License: MIT Docs

torch-batteries
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A lightweight Python package that supplies batteries-included abstractions for:

  • Data loading pipelines
  • Model training loops
  • Evaluation workflows
  • Metrics computation
  • Experiment tracking (Weights & Biases)

Designed to reduce boilerplate and standardize experiment code.

Installation

pip install torch-batteries

Optional Dependencies

For experiment tracking with Weights & Biases:

pip install torch-batteries[wandb]

Examples

Explore practical examples demonstrating torch-batteries features:

Example Description Notebook Colab
Function Fitting with MLP Train a neural network to approximate a polynomial function using the event-driven training approach function_fitting.ipynb Open In Colab
Image Classification with CNN Train a Convolutional Neural Network (CNN) for image classification image_classification.ipynb Open In Colab
Learning Rate Sweep with Early Stopping Conduct a learning rate sweep on MNIST classification with aggressive early stopping and log results to Weights & Biases lr_sweep_early_stopping.ipynb Open In Colab

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