PyTorch Overview

The NVIDIA Deep Learning SDK accelerates widely-used deep learning frameworks such as PyTorch.

PyTorch is a GPU accelerated tensor computational framework with a Python front end. Functionality can be easily extended with common Python libraries such as NumPy, SciPy and Cython. Automatic differentiation is done with a tape-based system at both a functional and neural network layer level. This functionality brings a high level of flexibility and speed as a deep learning framework and provides accelerated NumPy-like functionality.

PyTorch also includes standard defined neural network layers, deep learning optimizers, data loading utilities, and multi-gpu and multi-node support. Functions are executed immediately instead of enqueued in a static graph, improving ease of use and a sophisticated debugging experience.

See /workspace/ inside the container for information on customizing your PyTorch image. For more information about PyTorch, including tutorials, documentation, and examples, see:

This document describes the key features, software enhancements and improvements, any known issues, and how to run this container.