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deploy

edgeimpulse.model.deploy(
		model: pathlib._local.Path | str | bytes | Any,
		model_output_type: edgeimpulse.model.output_type.Classification | edgeimpulse.model.output_type.Regression | edgeimpulse.model.output_type.ObjectDetection,
		model_input_type: edgeimpulse.model.input_type.ImageInput | edgeimpulse.model.input_type.AudioInput | edgeimpulse.model.input_type.TimeSeriesInput | edgeimpulse.model.input_type.OtherInput | None = None,
		representative_data_for_quantization: pathlib._local.Path | str | bytes | Any | None = None,
		deploy_model_type: str | None = None,
		engine: str = 'tflite',
		deploy_target: str = 'zip',
		output_directory: str | None = None,
		api_key: str | None = None,
		timeout_sec: float | None = None
) ‑> _io.BytesIO
Transform a machine learning model into a library for an edge device. Transforms a trained model into a library, package, or firmware ready to deploy on an embedded device. Can optionally apply post-training quantization if a representative data sample is uploaded. Supported model formats:
  • Keras Model instance <https://www.tensorflow.org/api_docs/python/tf/keras/Model>_
  • TensorFlow SavedModel <https://www.tensorflow.org/guide/saved_model>_ (as path to directory or .zip file)
  • ONNX model file <https://learn.microsoft.com/en-us/windows/ai/windows-ml/get-onnx-model>_ (as path to .onnx file)
  • TensorFlow Lite file <https://www.tensorflow.org/lite/guide>_ (as bytes, or path to any file that is not .zip or .onnx)
Representative data for quantization:
  • Must be a numpy array or .npy file.
  • Each element must have the same shape as your model’s input.
  • Must be representative of the range (maximum and minimum) of values in your training data.
Note: the available deployment options will change depending on the values given for model, model_output_type, and model_input_type. For example, the openmv deployment option is only available if model_input_type is set to ImageInput. If you attempt to deploy to an unavailable target, you will receive the error Could not deploy: deploy_target: .... Parameters
  • model: pathlib._local.Path | str | bytes | Any
  • model_output_type: edgeimpulse.model.output_type.Classification | edgeimpulse.model.output_type.Regression | edgeimpulse.model.output_type.ObjectDetection
  • model_input_type: edgeimpulse.model.input_type.ImageInput | edgeimpulse.model.input_type.AudioInput | edgeimpulse.model.input_type.TimeSeriesInput | edgeimpulse.model.input_type.OtherInput | None = None
  • representative_data_for_quantization: pathlib._local.Path | str | bytes | Any | None = None
  • deploy_model_type: str | None = None
  • engine: str = ‘tflite’
  • deploy_target: str = ‘zip’
  • output_directory: str | None = None
  • api_key: str | None = None
  • timeout_sec: float | None = None
Return _io.BytesIO

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list_deployment_targets

edgeimpulse.model.list_deployment_targets(
		api_key: str | None = None
) ‑> List[str]
List suitable deployment targets for the project associated with configured or provided api key. Parameters
  • api_key: str | None = None
Return List[str]

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list_engines

edgeimpulse.model.list_engines(

) ‑> List[str]
List all the engines that can be passed to deploy()’s engine parameter. Returns: List[str]: List of engines Return List[str]

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list_model_types

edgeimpulse.model.list_model_types(

) ‑> List[str]
List all the model types that can passed to deploy()’s deploy_model_type parameter. Returns: List[str]: List of model types Return List[str]

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list_profile_devices

edgeimpulse.model.list_profile_devices(
		api_key: str | None = None
) ‑> List[str]
List possible values for the device field when calling edgeimpulse.model.profile(). Parameters
  • api_key: str | None = None
Return List[str]

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profile

edgeimpulse.model.profile(
		model: pathlib._local.Path | str | bytes | Any,
		device: str | None = None,
		api_key: str | None = None,
		timeout_sec: float | None = None
) ‑> edgeimpulse.model._functions.profile.ProfileResponse
Profile the performance of a trained model on a range of embedded targets, or a specific device. The response includes estimates of memory usage and latency for the model across a range of targets, including low-end MCU, high-end MCU, high-end MCU with accelerator, microprocessor unit (MPU), and a GPU or neural network accelerator. It will also include details of any conditions that preclude operation on a given type of device. If you request a specific device, the results will also include estimates for that specific device. A list of devices can be obtained from edgeimpulse.model.list_profile_devices(). You can call .summary() on the response to obtain a more readable version of the most relevant information. Parameters
  • model: pathlib._local.Path | str | bytes | Any
  • device: str | None = None
  • api_key: str | None = None
  • timeout_sec: float | None = None
Return edgeimpulse.model._functions.profile.ProfileResponse
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