Pytorch on Windows
PyTorch is a popular open-source machine learning library developed by Facebook's AI Research (FAIR) team. Since its initial release in 2016, PyTorch has been used by leading AI companies such as OpenAI. The library supports various operating systems, including Windows, and is commonly utilized in computer vision and natural language processing applications.
Industry: Open Source, AI/ML Big Data
Technologies: C & C++, C# & .NET, Windows
Solutions: Open Source, Porting, Software Maintainer, IoT & Embedded Systems
Addressing these challenges was pivotal, not just for democratizing PyTorch's access but also to future-proof one of the most influential machine learning libraries extensively adopted today. Microsoft embarked on a long-term partnership with Janea Systems to leverage our expertise in maintaining and updating open source software.
Upgrading C++
Navigating the complexity of integrating advanced C++ features into PyTorch for Windows.
Windows CI Test Failures
Tackling persistent CI challenges to elevate PyTorch's Windows experience.
Device Compatibility
Ensuring compatibility for devices running on the ARM-based processors under the Linux environment.
.NET Integration Initiative
Microsoft had a mission to expand PyTorch's reach to the .NET developer community.
Linux
Python
.NET
C#
Continuous Integration (CI) Tools
C++17 Performance Optimization
Transitioned PyTorch to leverage the latest advancements of C++17, supercharging performance and code efficiency.
Enhanced Stability for Windows
Addressed multiple CI test failures, ensuring a more stable PyTorch experience for Windows developers.
ARM64 Architectural Harmony
Enabled PyTorch's operation on ARM 64 architecture devices like Raspberry Pi and select mobile phones, minimizing cloud reliance.
Integration with .NET via TorchSharp
Facilitated PyTorch's reach to the .NET developer community, expanding the user base and fostering wider adoption.
Our ongoing work enables developers and data scientists to utilize PyTorch to its full potential, across the Windows ecosystem. This project was of the upmost importance as it future proofs of one of the most prolific machine learning libraries used today.
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