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 Office 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 PyTorch Enterprise.
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.
Microsoft is a founding member of the PyTorch Enterprise Support Group, creating a reliable production experience with enterprise-grade support that benefits both Azure customers and the PyTorch community users. With PyTorch Enterprise, you can be confident that Azure is the best place to run PyTorch.
Microsoft will provide commercial support for the public PyTorch codebase. Microsoft provides long-term support (LTS) to selected versions of PyTorch for up to two years, enabling a stable production experience without frequent major upgrade investment.
Microsoft Premier and Unified support customers are automatically eligible for PyTorch Enterprise at no additional cost. The dedicated PyTorch team in Azure will prioritize, develop, and deliver hotfixes to customers as needed.
The latest release of PyTorch will be integrated with Azure Machine Learning, along with other PyTorch add-ons including ONNX Runtime for faster inferencing. Microsoft will continue to invest to improve PyTorch inference and training speed.
PyTorch Enterprise benefits not only Azure customers but also the PyTorch community users. Selected code that aligns with PyTorch will be fed back to the public PyTorch distribution so everyone in the community can benefit.
"Scientists have to comb through massive amounts of data to deliver life-changing medicines. Microsoft and PyTorch are helping global biopharmaceutical company AstraZeneca to accelerate its drug discovery research."Gavin Edwards, Machine Learning Engineer, AstraZeneca
"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. It's very easy to combine PyTorch and Azure Machine Learning. It's easy to build a GPU-based learning environment—no complicated setup is necessary, and we can run the same code on a GPU or CPU using PyTorch."Yuji Fukaya, Manager AI Consulting Group AI Transformation Center, Faisal Hadiputra, Data Scientist
"This new enterprise-level offering by Microsoft closes an important gap. PyTorch gives our researchers unprecedented flexibility in designing their models and running their experiments. Serving these models in production, however, can be a challenge. The direct involvement of Microsoft lets us deploy new versions of PyTorch to Azure with confidence."Jeremy Jancsary, Senior Principal Research Scientist, Nuance
"Scaling up machine learning models to enterprise-level AI applications is a challenge Crayon addresses for our customers every day. We have 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."Tailai Wen, Lead Data Scientist, Crayon
Microsoft is an active contributor to an ecosystem of PyTorch open-source projects
Introducing PyTorch Profiler, the new and improved performance debugging tool. Developed as part of a collaboration between Microsoft and Facebook, PyTorch Profiler is an open-source tool that enables accurate and efficient performance analysis and troubleshooting for large-scale deep learning models.
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.
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. 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 as well as 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.
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.
DeepSpeed—PyTorch Developer Day
In this talk, Yuxiong He, a partner research manager at Microsoft, presented DeepSpeed, an open-source deep-learning training optimization library compatible with PyTorch.
PyTorch on Windows
Maxim Lukiyanov, a product manager for the Azure AI platform, described improvements within the Windows platform support made in PyTorch version 1.7.
Deep learning fundamentals with PyTorch
See how to use PyTorch to solve a simple image classification problem.
Learn the basics with 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
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