Packaging a code-base is a great way to learn more about it (especially when things do not go as planned). Install tensorflow=2.4.0-rc2 with pip (requires Python 3.7, Raspberry Pi 4, 64-bit OS) $ pip install Method #3: Build from Source Install tensorflow=2.4.0-rc2 with pip (requires Python 3.7, Raspberry Pi 4) $ pip install Where possible, I recommend using the official wheels because they’re more thoroughly tested. I maintain TensorFlow binaries that I build from source at /bitsy-ai/tensorflow-arm-bin.

Install tensorflow=2.3.0 with pip (requires Python 3.5, Raspberry Pi 3) $ pip install Method #2: Community-built Wheels Official TensorFlow wheels for Raspberry Pi. Binaries for aarch64 are not officially supported yet. Unfortunately, the Raspberry Pi wheels drift a bit behind the latest releases (2.3.1 and 2.4.0-rc2 at the time of this writing).

The TensorFlow team builds and tests binaries for a variety of platform and Python interpreter combination, listed at t /install/pip#package-location. Yes! You’ll have to do a bit of extra work, but here are 3 different ways to use TensorFlow 2.x in your next Raspberry Pi project. If you try to specify a higher version, you’ll see an error like: $ pip install tensorflow=2.3.1 Looking in indexes:, Collecting tensorflow=2.3.1 Could not find a version that satisfies the requirement tensorflow=2.3.1 (from versions: 0.11.0, 1.12.0, 1.13.1, 1.14.0) No matching distribution found for tensorflow=2.3.1 😱 Can I use TensorFlow 2? Oh no: the version of TensorFlow installed is 1.14. The first thing you might try is… $ pip install -no-cache-dir tensorflow Looking in indexes:, Collecting tensorflow Downloading Image Credit: Benchmarking TensorFlow Lite on the New Raspberry Pi 4, Model B by Alasdair Allan Installation is Half the Battle 😠Ĭool! So you’ve decided to build a TensorFlow application for your Raspberry Pi.

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