PyTorch is an open source, deep learning framework that makes it easy to develop machine learning models and deploy them to production. PyTorch provides a hybrid front-end that allows developers to iterate quickly on their models in the prototyping stage without sacrificing performance in the production stage. Using this hybrid front-end, developers can seamlessly transition between eager mode, which performs computations immediately for easy development, and graph mode which creates computational graphs for efficient execution in production environment. PyTorch supports dynamic computation graphs, which provides a flexible structure that is intuitive to work with and easy to debug. PyTorch also offers distributed training, deep integration into Python, and a rich ecosystem of tools and libraries, making it popular with researchers and engineers.
You can get started on AWS with a fully-managed PyTorch experience with Amazon SageMaker, a platform to quickly and easily build, train, and deploy machine learning models at scale. You can also use the AWS Deep Learning AMIs to build custom environments and workflows with PyTorch and other popular frameworks including TensorFlow, Apache MXNet, Chainer, Caffe2, Gluon, Keras, and Microsoft Cognitive Toolkit.
Seamlessly transition between rapid prototyping and production scale to take models from idea to implementation quickly.
INTEGRATION WITH PYTHON
Deep integration into Python allows the easy use of popular libraries and packages, such as Pillow, scipy, NLTK, and others for building neural network layers.
A rich ecosystem of tools and libraries, including Crayon, tensorboardX, AllenNLP, and torchvision, extends PyTorch and supports development in computer vision, natural language processing, and more.
Learn how to get started with PyTorch on Amazon SageMaker
Amazon SageMaker is a fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker supports popular deep learning frameworks including PyTorch and TensorFlow so you can use the framework you are already familiar with.