There's work in progress to deliver Google's deep learning framework to Windows, but it's still a ways from being a drop-in-and-go project When will it be possible to run Google’s TensorFlow deep learning system on Windows with full GPU support? The short answer: soon. The real holdup, though, hasn’t even been TensorFlow. It’s been the lack of a working Windows version of Bazel, Google’s in-house tool that delivers TensorFlow builds. TensorFlow on Windows seems like a no-brainer. Support for GPU-accelerated applications on Windows is highly robust, and Windows is as popular a platform as you could ask for. To that end, a GitHub issue has been open with TensorFlow for providing Windows support since November of last year. But the lack of a Windows version of Bazel has kept TensorFlow off Windows — until now. A working edition of Bazel has finally shipped for Windows, and it’s even available to developers through the Chocolatey package management system. The other delay is adding GPU support to TensorFlow on Windows. While TensorFlow can fall back to CPUs across multiple nodes as a compatibility measure, it’s best run with full GPU support. After some work, support for Windows is now on the verge of being merged into the project’s mainline. An earlier fork of the project, produced two months ago, provided a Windows build for TensorFlow via CMake and Visual Studio 2015 rather than Bazel. But it lacked support for GPU acceleration, and the cost of not using Bazel for the build process might have been unsupportable over time. Getting TensorFlow on Windows, then, is a double milestone. Aside from putting a powerful and useful deep learning tool into the hands of a much broader audience of users, the process of bringing it to that audience means future Google projects built with Bazel will have native Windows versions sooner, too. Artificial IntelligenceAnalyticsData ManagementOpen SourceSoftware DevelopmentTechnology IndustrySmall and Medium Business