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Empowering Windows-Based AI Progress

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Pytorch on Windows

The Software

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.

The Challenge

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.

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    Upgrading C++

    Navigating the complexity of integrating advanced C++ features into PyTorch for Windows.

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    Windows CI Test Failures

    Tackling persistent CI challenges to elevate PyTorch's Windows experience.

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    Device Compatibility

    Ensuring compatibility for devices running on the ARM-based processors under the Linux environment.

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    .NET Integration Initiative

    Microsoft had a mission to expand PyTorch's reach to the .NET developer community.

Tech stack

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Linux

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Python

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.NET

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C#

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Continuous Integration (CI) Tools

Client impact

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    C++17 Performance Optimization

    Transitioned PyTorch to leverage the latest advancements of C++17, supercharging performance and code efficiency.

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    Enhanced Stability for Windows

    Addressed multiple CI test failures, ensuring a more stable PyTorch experience for Windows developers.

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    ARM64 Architectural Harmony

    Enabled PyTorch's operation on ARM 64 architecture devices like Raspberry Pi and select mobile phones, minimizing cloud reliance.

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    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.

The Team

Other Client Successes

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