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).
A production ready, open source machine learning framework for accelerating AI research prototyping and production deployment.
Capabilities & Features
Transition seamlessly between eager and graph modes with TorchScript, and accelerate the path to production with TorchServe.
Scalable distributed training and performance optimization in research and production is enabled by the torch.distributed backend.
A rich ecosystem of tools and libraries extends PyTorch and supports development in computer vision, NLP and more.
Why Use PyTorch for Deep Learning?
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 (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.