PyTorch on Azure
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 at scale using a built-in PyTorch environment within Azure Machine Learning, and rest assured that your entire PyTorch stack is fully supported through Azure Container for PyTorch.
Strengthen the ecosystem
Achieve more with the rich PyTorch ecosystem of tools and capabilities, including PyTorch Profiler. Microsoft actively contributes to the PyTorch environment to make the experience better.
Trusted by companies of all sizes
Yuji Fukaya, Manager AI Consulting Group AI Transformation Centre, Information Services International-Dentsu
"Other deep learning frameworks and cloud services are out there, but we think Azure, Azure Machine Learning, and PyTorch are the best choices because they enhance accuracy, efficiency, scalability, and speed of development."
Jeremy Jancsary, Sr. Principal Research Scientist, Nuance
"The new enterprise-level offering by Microsoft closes an important gap. Serving PyTorch models in production can be a challenge. The direct involvement of Microsoft lets us deploy new versions of PyTorch to Azure with confidence."
Tailai Wen, Lead Data Scientist, Crayon
"Crayon has been using PyTorch on Azure and enjoying the smooth integration. With PyTorch Enterprise, we have more confidence to leverage the most cutting-edge features offered by newer PyTorch versions in our customers' projects."
Pablo Castellanos Garcia, VP of Engineering, Wayve
"Running PyTorch on Azure gives us the best platform to build our embodied intelligence. It's easy for our engineers to run the experiments they need, all at once, at petabyte scale."
Zoiner Tejada, CEO of Solliance and CTO of Baseline
"With Azure AI and PyTorch, we combined focused applications of AI with journalistic processes and financial intelligence, yielding a solution that is unique in the market and valuable for cryptocurrency investors."
Tom Chmielenski, Principal MLOps Engineer
"We use Azure Machine Learning and PyTorch in our new framework to develop and move AI models into production faster, in a repeatable process that allows data scientists to work both on-premises and in Azure."
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 and cheaper 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, Meta jointly announced with Linux Foundation 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. Read the blog post from Meta to learn more about PyTorch Foundation.
ONNX Runtime: A runtime for accelerated inferencing and training of PyTorch models, supporting Windows, Mac, Linux, Android, and iOS, and optimised 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 optimisations 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. 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 come installed with PyTorch, the necessary GPU drivers, and a comprehensive suite of other popular data science tools. Get a 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.
Learn more about PyTorch on Azure
Read blogs about PyTorch
PyTorch, the PyTorch logo and any related marks are trademarks of Facebook, Inc.