Now TRex, if installed within the same environment, has the full power of your Mac at its disposal.
#OWNERS MANUAL FOR TREX BIKE COMPUTER INSTALL#
To install tensorflow inside your activated environment, just run:Ĭonda install - c apple tensorflow - deps & python - m pip install tensorflow - macos tensorflow - metal An Apple Silicone MacBook (2020) only needs ~50ms/step and (with the same data and code) is not much slower than my fast i7 PC with an NVIDIA Geforce 1070 – running at roughly ~21ms/step. But – yay – Apple provides their own version for macOS including a native macOS ( ) backend. There is no official tensorflow package yet, which is why TRex will not allow you to use machine learning right away. Once you’re done, you can run the same command as above (only that now everything will be all fast and native arm64 code). This way, hardware accelerated machine learning on your M1 Macbook is possible! Simply follow the instructions here for installing miniforge: /apple/tensorflow_macos. However, I would strongly encourage installing TRex via miniforge, which is like Anaconda but supports native arm64 packages. If you own a new Mac with an Apple Silicone CPU, the Intel version (above) works fine in Rosetta. If you need to use TGrabs with machine vision cameras, or need as much speed as possible/the newest version, please consider compiling the software yourself. For example, the conda version does not offer support for Basler cameras. The down-side is that pre-built binaries are compiled with fewer optimzations and features than a manually compiled one (due to compatibility and licensing issues) and thus are slightly slower =(. # macOS, Windows, Linux conda create - n tracking - c trexing trex
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