Supported Software

Deep Learning & AI


PyTorch is a production ready, open source machine learning framework for accelerating AI research prototyping and production deployment. PyTorch is used by countless users in the AI and Deep Learning industry including big commercial names like Amazon and well known universities like Stanford.

Capabilities & Features

Production Ready

Transition seamlessly between eager and graph modes with TorchScript, and accelerate the path to production with TorchServe.

Distributed Training

Scalable distributed training and performance optimization in research and production is enabled by the torch.distributed backend.

Robust Ecosystem

A rich ecosystem of tools and libraries extends PyTorch and supports development in computer vision, NLP and more.

Back Prop

Why Use PyTorch for Deep Learning?

PyTorch is extremely powerful for creating computational graphs. Compared to Tensorflow's static graph, PyTorch believes in a dynamic graph. Instead of first having to define the entire computation graph of the model before running your model (as in Tensorflow), in PyTorch, you can define and manipulate your graph on-the-fly. This feature is what makes PyTorch an extremely powerful tool for researchers, particularly when developing Recurrent Neural Networks (RNNs).

The PyTorch Ecosystem

The PyTorch Ecosystem offers a rich set of tools and libraries to support the development of AI applications. Featured projects include:


Model Interpretability for PyTorch          

PyTorch Geometric

PyTorch Geometric (PyG) is a geometric deep learning extension library for PyTorch.


A scikit-learn compatible neural network library that wraps PyTorch.

PyTorch Library Modules


A Tensor library, similar to NumPy, but with powerful GPU support.


A tape-based automatic differentiation library that supports differentiable Tensor operations in torch.


The heart of PyTorch deep learning, torch.nn is a neural networks library deeply integrated with autograd designed for maximum flexibility.


Python multiprocessing, but with magical memory sharing of torch Tensors across processes.


DataLoader, Trainer and other utility functions for convenience.


Legacy code ported over from torch for backward compatibility.