PyTorch on Azure
Get an enterprise-ready PyTorch experience in the cloud.
PyTorch is an open-source deep-learning framework that accelerates the path from research to production. Data scientists at Microsoft use PyTorch as the primary framework to develop models that enable new experiences in Microsoft 365, Bing, Xbox, and more. Microsoft is a top contributor to the PyTorch ecosystem with recent contributions such as PyTorch Profiler.
PyTorch on Azure—better together
Train and deploy models reliably and at scale using a built-in PyTorch environment within Azure Machine Learning to ensure that the latest PyTorch version is fully supported through Azure Container for PyTorch.
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Microsoft is an active contributor to an ecosystem of PyTorch open-source projects
PyTorch Profiler is an open-source tool that helps you understand the hardware resource consumption, such as time and memory, of various PyTorch operations in your model and resolve performance bottlenecks. This makes your model execute faster with less overhead.
ONNX Runtime on PyTorch
As deep-learning models get bigger, reducing training time becomes both a financial and environmental issue. ONNX Runtime accelerates large-scale, distributed training of PyTorch transformer models with a one-line code change. Combine with DeepSpeed to further improve training speed on PyTorch.
PyTorch on Windows
Microsoft maintains PyTorch builds for Windows so your team can enjoy well-tested and stable builds, simple and reliable installation, quickstarts and tutorials, high performance, and support for more advanced features such as distributed GPU training.
With the increasing importance of PyTorch to both AI research and production, Mark Zuckerberg and Linux Foundation jointly announced that PyTorch will transition to Linux Foundation to support continued community growth and provide a home for it to thrive for years to come. To contribute to the future enhancement of PyTorch, Microsoft joined PyTorch Foundation as a member of the governing board to lead the democratization and the collaboration of AI/ML. Explore the latest PyTorch capabilities.
ONNX Runtime: A runtime for accelerated inferencing and training of PyTorch models, supporting Windows, Mac, Linux, Android, and iOS, and optimized for a variety of hardware accelerators.
DeepSpeed: A library of algorithms for training of next-generation large models, including state-of-the-art model-parallel training algorithms and other optimizations for distributed training.
Hummingbird: A library that compiles traditional models like scikit-learn or LightGBM into PyTorch tensor computation for faster inference.
Two ways to use Azure for PyTorch development
Build, train, and deploy PyTorch models with ease using Azure Container for PyTorch. It's deeply integrated with Azure Machine Learning for experiment management and full machine learning lifecycle support. Azure Machine Learning removes the heavy lifting of end-to-end machine learning workflows while also handling housekeeping tasks such as data preparation and experiment tracking, which cuts time to production from weeks to hours.
Data Science Virtual Machines for PyTorch come with pre-installed and validated with the latest PyTorch version to reduce setup costs and accelerate time to value. The packages contain various optimization functionalities such as ONNX Runtime, DeepSpeed, and PySpark to get frictionless development experience out of the box and the ability to work with all Azure hardware configurations including GPUs.
Learn PyTorch fundamentals
Learn the fundamentals of deep learning with PyTorch on Microsoft Learn. This beginner-friendly learning path introduces key concepts to building machine learning models in multiple domains, including speech, vision, and natural language processing.
Get started with PyTorch on the AI Show
Learn the basics of PyTorch, including how to build and deploy a model and how to connect to the strong community of users.
Learn the basics of PyTorch
Get to know PyTorch concepts and modules. Learn how to load data, build deep neural networks, and train and save your models in this quickstart guide.