edgeimpulse.model

Modules

• edgeimpulse.model.input_type
• edgeimpulse.model.model_info
• edgeimpulse.model.output_type

Functions

deploy

edgeimpulse.model.deploy(
	model: pathlib.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.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.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.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

Returns
_io.BytesIO

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

Returns
List[str]

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

Returns
List[str]

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

Returns
List[str]

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

Returns
List[str]

profile

edgeimpulse.model.profile(
	model: pathlib.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.Path | str | bytes | Any
device	str | None = None
api_key	str | None = None
timeout_sec	float | None = None

Returns
edgeimpulse.model._functions.profile.ProfileResponse