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name="TinyML" |
Preparing a custom version of MicroPython with TensorFlow
Introduction |
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Here are the steps to build this custom microPython version: |
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- The steps to build the firmware can be found in tflite-micro-micropython/.github/workflows/build_esp32.yml
In my case the Espressif development environment espidf has already been downloaded and set up earlier. I use espidf version 4.3.1, the latest stable version at time of writing.
Activate the virtual Python environment needed for espidf
Make sure that the modules Pillow and Wave have been installed on this virtual environment. If not, install them with pip:
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- The steps to build the firmware can be found in tflite-micro-micropython/.github/workflows/build_esp32.yml
In my case the Espressif development environment espidf has already been downloaded and set up earlier. I use espidf version 4.3.1, the latest stable version at time of writing.
Activate the virtual Python environment needed for espidf (if venvwrapper is installed: workon espidf)
Make sure that the modules Pillow and Wave have been installed on this virtual environment. If not, install them with pip:
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- pip3 install Pillow
- pip3 install Wave
- Setup the sub-modules needed for tflm:
- cd tflite-micro-micropython
- git submodule init
- git submodule update --recursive
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- Regenerate the microlite/tfm directory
- cd tensorflow
- ../micropython-modules/microlite/prepare-tflm-esp.sh
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- Setup the sub-modules for the ESP32 port of MicroPython
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- git submodule update --init lib/axtls
- git update --init lib/berkeley-db-1.xx
- Get the esp32-camera driver from Espressif
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