
Copy API key from Edge Impulse project
ei.API_KEY
value in the following cell:

Filter models on Hugging Face

View files in Hugging Face model
Profile your model
To start, we need to list the possible target devices we can use for profiling. We need to pick from this list.cortex-m4f-80mhz
, as this is a relatively low-power microcontroller family. From there, we can use the Edge Impulse Python SDK to generate a profile for your model to ensure it fits on your target hardware and meets your timing requirements.
Deploy your model
Once you are happy with the performance of the model, you can deploy it to a number of possible hardware targets. To see the available hardware targets, run the following:'zip'
from the above list. We also need to tell Edge Impulse how we are planning to use the model. In this case, we want to perform classification, so we set the output type to Classification.
Note that instead of writing the raw bytes to a file, you can also specify an output_directory
argument in the .deploy()
function. Your deployment file(s) will be downloaded to that directory.