Documentation Index
Fetch the complete documentation index at: https://docs.edgeimpulse.com/llms.txt
Use this file to discover all available pages before exploring further.
Modules
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
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 |
list_deployment_targets
edgeimpulse.model.list_deployment_targets(
api_key: str | None = None
) ‑> List[str]
| Parameters | |
|---|
api_key | str | None = None |
list_engines
edgeimpulse.model.list_engines(
) ‑> List[str]
deploy()’s engine parameter.
Returns:
List[str]: List of engines
list_model_types
edgeimpulse.model.list_model_types(
) ‑> List[str]
deploy()’s deploy_model_type parameter.
Returns:
List[str]: List of model types
list_profile_devices
edgeimpulse.model.list_profile_devices(
api_key: str | None = None
) ‑> List[str]
device field when calling edgeimpulse.model.profile().
| Parameters | |
|---|
api_key | str | None = None |
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
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 |
