learn_api module

LearnApi

class edgeimpulse_api.api.learn_api.LearnApi(
		api_client=None
)

Parameters

• api_client=None

Methods

anomaly_trained_features

edgeimpulse_api.api.learn_api.anomaly_trained_features(
		self,
		project_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})],
		learn_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Learn Block ID, use the impulse functions to retrieve the ID', extra={})],
		feature_ax1: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Feature axis 1', extra={})],
		feature_ax2: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Feature axis 2', extra={})],
		**kwargs
) ‑> edgeimpulse_api.models.anomaly_trained_features_response.AnomalyTrainedFeaturesResponse

Trained features

Get a sample of trained features, this extracts a number of samples and their features.

Parameters

• self

• project_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})]

• learn_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Learn Block ID, use the impulse functions to retrieve the ID', extra={})]

• feature_ax1: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Feature axis 1', extra={})]

• feature_ax2: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Feature axis 2', extra={})]

• **kwargs

Return

edgeimpulse_api.models.anomaly_trained_features_response.AnomalyTrainedFeaturesResponse

anomaly_trained_features_per_sample

edgeimpulse_api.api.learn_api.anomaly_trained_features_per_sample(
		self,
		project_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})],
		learn_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Learn Block ID, use the impulse functions to retrieve the ID', extra={})],
		sample_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Sample ID', extra={})],
		**kwargs
) ‑> edgeimpulse_api.models.anomaly_trained_features_response.AnomalyTrainedFeaturesResponse

Trained features for sample

Get trained features for a single sample. This runs both the DSP prerequisites and the anomaly classifier.

Parameters

• self

• project_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})]

• learn_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Learn Block ID, use the impulse functions to retrieve the ID', extra={})]

• sample_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Sample ID', extra={})]

• **kwargs

Return

edgeimpulse_api.models.anomaly_trained_features_response.AnomalyTrainedFeaturesResponse

download_keras_data

edgeimpulse_api.api.learn_api.download_keras_data(
		self,
		project_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})],
		learn_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Learn Block ID, use the impulse functions to retrieve the ID', extra={})],
		**kwargs
) ‑> str

Download Keras data export

Download the data of an exported Keras block - needs to be exported via 'exportKerasBlockData' first

Parameters

• self

• project_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})]

• learn_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Learn Block ID, use the impulse functions to retrieve the ID', extra={})]

• **kwargs

Return

str

download_keras_export

edgeimpulse_api.api.learn_api.download_keras_export(
		self,
		project_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})],
		learn_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Learn Block ID, use the impulse functions to retrieve the ID', extra={})],
		**kwargs
) ‑> str

Download Keras export

Download an exported Keras block - needs to be exported via 'exportKerasBlock' first

Parameters

• self

• project_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})]

• learn_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Learn Block ID, use the impulse functions to retrieve the ID', extra={})]

• **kwargs

Return

str

download_learn_model

edgeimpulse_api.api.learn_api.download_learn_model(
		self,
		project_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})],
		learn_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Learn Block ID, use the impulse functions to retrieve the ID', extra={})],
		model_download_id: Annotated[pydantic.types.StrictStr, FieldInfo(default=Ellipsis, description='Model download ID, which can be obtained from the project information', extra={})],
		**kwargs
) ‑> str

Download trained model

Download a trained model for a learning block. Depending on the block this can be a TensorFlow model, or the cluster centroids.

Parameters

• self

• project_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})]

• learn_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Learn Block ID, use the impulse functions to retrieve the ID', extra={})]

• model_download_id: Annotated[pydantic.types.StrictStr, FieldInfo(default=Ellipsis, description='Model download ID, which can be obtained from the project information', extra={})]

• **kwargs

Return

str

download_pretrained_model

edgeimpulse_api.api.learn_api.download_pretrained_model(
		self,
		project_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})],
		pretrained_model_download_type: pydantic.types.StrictStr,
		impulse_id: Annotated[pydantic.types.StrictInt | None, FieldInfo(default=PydanticUndefined, description='Impulse ID. If this is unset then the default impulse is used.', extra={})] = None,
		**kwargs
) ‑> str

Download pretrained model

Download a pretrained model file

Parameters

• self

• project_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})]

• pretrained_model_download_type: pydantic.types.StrictStr

• impulse_id: Annotated[pydantic.types.StrictInt | None, FieldInfo(default=PydanticUndefined, description='Impulse ID. If this is unset then the default impulse is used.', extra={})] = None

• **kwargs

Return

str

get_anomaly

edgeimpulse_api.api.learn_api.get_anomaly(
		self,
		project_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})],
		learn_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Learn Block ID, use the impulse functions to retrieve the ID', extra={})],
		**kwargs
) ‑> edgeimpulse_api.models.anomaly_config_response.AnomalyConfigResponse

Anomaly information

Get information about an anomaly block, such as its dependencies. Use the impulse blocks to find the learnId.

Parameters

• self

• project_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})]

• learn_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Learn Block ID, use the impulse functions to retrieve the ID', extra={})]

• **kwargs

Return

edgeimpulse_api.models.anomaly_config_response.AnomalyConfigResponse

get_anomaly_metadata

edgeimpulse_api.api.learn_api.get_anomaly_metadata(
		self,
		project_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})],
		learn_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Learn Block ID, use the impulse functions to retrieve the ID', extra={})],
		**kwargs
) ‑> edgeimpulse_api.models.anomaly_model_metadata_response.AnomalyModelMetadataResponse

Anomaly metadata

Get metadata about a trained anomaly block. Use the impulse blocks to find the learnId.

Parameters

• self

• project_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})]

• learn_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Learn Block ID, use the impulse functions to retrieve the ID', extra={})]

• **kwargs

Return

edgeimpulse_api.models.anomaly_model_metadata_response.AnomalyModelMetadataResponse

get_gmm_metadata

edgeimpulse_api.api.learn_api.get_gmm_metadata(
		self,
		project_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})],
		learn_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Learn Block ID, use the impulse functions to retrieve the ID', extra={})],
		**kwargs
) ‑> edgeimpulse_api.models.anomaly_gmm_metadata_response.AnomalyGmmMetadataResponse

Anomaly GMM metadata

Get raw model metadata of the Gaussian mixture model (GMM) for a trained anomaly block. Use the impulse blocks to find the learnId.

Parameters

• self

• project_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})]

• learn_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Learn Block ID, use the impulse functions to retrieve the ID', extra={})]

• **kwargs

Return

edgeimpulse_api.models.anomaly_gmm_metadata_response.AnomalyGmmMetadataResponse

get_keras

edgeimpulse_api.api.learn_api.get_keras(
		self,
		project_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})],
		learn_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Learn Block ID, use the impulse functions to retrieve the ID', extra={})],
		**kwargs
) ‑> edgeimpulse_api.models.keras_response.KerasResponse

Keras information

Get information about a Keras block, such as its dependencies. Use the impulse blocks to find the learnId.

Parameters

• self

• project_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})]

• learn_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Learn Block ID, use the impulse functions to retrieve the ID', extra={})]

• **kwargs

Return

edgeimpulse_api.models.keras_response.KerasResponse

get_keras_data_explorer_features

edgeimpulse_api.api.learn_api.get_keras_data_explorer_features(
		self,
		project_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})],
		learn_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Learn Block ID, use the impulse functions to retrieve the ID', extra={})],
		**kwargs
) ‑> edgeimpulse_api.models.get_data_explorer_features_response.GetDataExplorerFeaturesResponse

Get data explorer features

t-SNE2 output of the raw dataset using embeddings from this Keras block

Parameters

• self

• project_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})]

• learn_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Learn Block ID, use the impulse functions to retrieve the ID', extra={})]

• **kwargs

Return

edgeimpulse_api.models.get_data_explorer_features_response.GetDataExplorerFeaturesResponse

get_keras_metadata

edgeimpulse_api.api.learn_api.get_keras_metadata(
		self,
		project_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})],
		learn_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Learn Block ID, use the impulse functions to retrieve the ID', extra={})],
		exclude_labels: Annotated[pydantic.types.StrictBool | None, FieldInfo(default=PydanticUndefined, description='If set to "true", the "labels" field is left empty (which can be big on e.g. regression projects).', extra={})] = None,
		**kwargs
) ‑> edgeimpulse_api.models.keras_model_metadata_response.KerasModelMetadataResponse

Keras metadata

Get metadata about a trained Keras block. Use the impulse blocks to find the learnId.

Parameters

• self

• project_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})]

• learn_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Learn Block ID, use the impulse functions to retrieve the ID', extra={})]

• exclude_labels: Annotated[pydantic.types.StrictBool | None, FieldInfo(default=PydanticUndefined, description='If set to "true", the "labels" field is left empty (which can be big on e.g. regression projects).', extra={})] = None

• **kwargs

Return

edgeimpulse_api.models.keras_model_metadata_response.KerasModelMetadataResponse

get_learn_x_data

edgeimpulse_api.api.learn_api.get_learn_x_data(
		self,
		project_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})],
		learn_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Learn Block ID, use the impulse functions to retrieve the ID', extra={})],
		**kwargs
) ‑> str

Download data

Download the processed data for this learning block. This is data already processed by the signal processing blocks.

Parameters

• self

• project_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})]

• learn_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Learn Block ID, use the impulse functions to retrieve the ID', extra={})]

• **kwargs

Return

str

get_learn_y_data

edgeimpulse_api.api.learn_api.get_learn_y_data(
		self,
		project_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})],
		learn_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Learn Block ID, use the impulse functions to retrieve the ID', extra={})],
		**kwargs
) ‑> str

Download labels

Download the labels for this learning block. This is data already processed by the signal processing blocks. Not all blocks support this function. If so, a GenericApiResponse is returned with an error message.

Parameters

• self

• project_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})]

• learn_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Learn Block ID, use the impulse functions to retrieve the ID', extra={})]

• **kwargs

Return

str

get_pretrained_model_info

edgeimpulse_api.api.learn_api.get_pretrained_model_info(
		self,
		project_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})],
		impulse_id: Annotated[pydantic.types.StrictInt | None, FieldInfo(default=PydanticUndefined, description='Impulse ID. If this is unset then the default impulse is used.', extra={})] = None,
		**kwargs
) ‑> edgeimpulse_api.models.get_pretrained_model_response.GetPretrainedModelResponse

Get pretrained model

Receive info back about the earlier uploaded pretrained model (via uploadPretrainedModel) input/output tensors. If you want to deploy a pretrained model from the API, see startDeployPretrainedModelJob.

Parameters

• self

• project_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})]

• impulse_id: Annotated[pydantic.types.StrictInt | None, FieldInfo(default=PydanticUndefined, description='Impulse ID. If this is unset then the default impulse is used.', extra={})] = None

• **kwargs

Return

edgeimpulse_api.models.get_pretrained_model_response.GetPretrainedModelResponse

profile_pretrained_model

edgeimpulse_api.api.learn_api.profile_pretrained_model(
		self,
		project_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})],
		impulse_id: Annotated[pydantic.types.StrictInt | None, FieldInfo(default=PydanticUndefined, description='Impulse ID. If this is unset then the default impulse is used.', extra={})] = None,
		**kwargs
) ‑> edgeimpulse_api.models.start_job_response.StartJobResponse

Profile pretrained model

Returns the latency, RAM and ROM used for the pretrained model - upload first via uploadPretrainedModel. This is using the project's selected latency device. Updates are streamed over the websocket API (or can be retrieved through the /stdout endpoint). Use getProfileTfliteJobResult to get the results when the job is completed.

Parameters

• self

• project_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})]

• impulse_id: Annotated[pydantic.types.StrictInt | None, FieldInfo(default=PydanticUndefined, description='Impulse ID. If this is unset then the default impulse is used.', extra={})] = None

• **kwargs

Return

edgeimpulse_api.models.start_job_response.StartJobResponse

save_pretrained_model_parameters

edgeimpulse_api.api.learn_api.save_pretrained_model_parameters(
		self,
		project_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})],
		save_pretrained_model_request: edgeimpulse_api.models.save_pretrained_model_request.SavePretrainedModelRequest,
		impulse_id: Annotated[pydantic.types.StrictInt | None, FieldInfo(default=PydanticUndefined, description='Impulse ID. If this is unset then the default impulse is used.', extra={})] = None,
		**kwargs
) ‑> edgeimpulse_api.models.generic_api_response.GenericApiResponse

Save parameters for pretrained model

Save input / model configuration for a pretrained model. This overrides the current impulse. If you want to deploy a pretrained model from the API, see startDeployPretrainedModelJob.

Parameters

• self

• project_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})]

• save_pretrained_model_request: edgeimpulse_api.models.save_pretrained_model_request.SavePretrainedModelRequest

• impulse_id: Annotated[pydantic.types.StrictInt | None, FieldInfo(default=PydanticUndefined, description='Impulse ID. If this is unset then the default impulse is used.', extra={})] = None

• **kwargs

Return

edgeimpulse_api.models.generic_api_response.GenericApiResponse

set_anomaly

edgeimpulse_api.api.learn_api.set_anomaly(
		self,
		project_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})],
		learn_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Learn Block ID, use the impulse functions to retrieve the ID', extra={})],
		set_anomaly_parameter_request: edgeimpulse_api.models.set_anomaly_parameter_request.SetAnomalyParameterRequest,
		**kwargs
) ‑> edgeimpulse_api.models.generic_api_response.GenericApiResponse

Anomaly settings

Configure the anomaly block, such as its minimum confidence score. Use the impulse blocks to find the learnId.

Parameters

• self

• project_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})]

• learn_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Learn Block ID, use the impulse functions to retrieve the ID', extra={})]

• set_anomaly_parameter_request: edgeimpulse_api.models.set_anomaly_parameter_request.SetAnomalyParameterRequest

• **kwargs

Return

edgeimpulse_api.models.generic_api_response.GenericApiResponse

set_keras

edgeimpulse_api.api.learn_api.set_keras(
		self,
		project_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})],
		learn_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Learn Block ID, use the impulse functions to retrieve the ID', extra={})],
		set_keras_parameter_request: edgeimpulse_api.models.set_keras_parameter_request.SetKerasParameterRequest,
		**kwargs
) ‑> edgeimpulse_api.models.generic_api_response.GenericApiResponse

Keras settings

Configure the Keras block, such as its minimum confidence score. Use the impulse blocks to find the learnId.

Parameters

• self

• project_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})]

• learn_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Learn Block ID, use the impulse functions to retrieve the ID', extra={})]

• set_keras_parameter_request: edgeimpulse_api.models.set_keras_parameter_request.SetKerasParameterRequest

• **kwargs

Return

edgeimpulse_api.models.generic_api_response.GenericApiResponse

test_pretrained_model

edgeimpulse_api.api.learn_api.test_pretrained_model(
		self,
		project_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})],
		test_pretrained_model_request: edgeimpulse_api.models.test_pretrained_model_request.TestPretrainedModelRequest,
		impulse_id: Annotated[pydantic.types.StrictInt | None, FieldInfo(default=PydanticUndefined, description='Impulse ID. If this is unset then the default impulse is used.', extra={})] = None,
		**kwargs
) ‑> edgeimpulse_api.models.test_pretrained_model_response.TestPretrainedModelResponse

Test pretrained model

Test out a pretrained model (using raw features) - upload first via uploadPretrainedModel. If you want to deploy a pretrained model from the API, see startDeployPretrainedModelJob.

Parameters

• self

• project_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})]

• test_pretrained_model_request: edgeimpulse_api.models.test_pretrained_model_request.TestPretrainedModelRequest

• impulse_id: Annotated[pydantic.types.StrictInt | None, FieldInfo(default=PydanticUndefined, description='Impulse ID. If this is unset then the default impulse is used.', extra={})] = None

• **kwargs

Return

edgeimpulse_api.models.test_pretrained_model_response.TestPretrainedModelResponse

upload_pretrained_model

edgeimpulse_api.api.learn_api.upload_pretrained_model(
		self,
		project_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})],
		model_file: pydantic.types.StrictStr,
		model_file_name: pydantic.types.StrictStr,
		model_file_type: pydantic.types.StrictStr,
		impulse_id: Annotated[pydantic.types.StrictInt | None, FieldInfo(default=PydanticUndefined, description='Impulse ID. If this is unset then the default impulse is used.', extra={})] = None,
		representative_features: pydantic.types.StrictStr | None = None,
		device: Annotated[pydantic.types.StrictStr | None, FieldInfo(default=PydanticUndefined, description='MCU used for calculating latency, query `latencyDevices` in `listProject` for a list of supported devices (and use the \\"mcu\\" property here). If this is kept empty then we\'ll show an overview of multiple devices.', extra={})] = None,
		**kwargs
) ‑> edgeimpulse_api.models.start_job_response.StartJobResponse

Upload a pretrained model

Upload a pretrained model and receive info back about the input/output tensors. If you want to deploy a pretrained model from the API, see startDeployPretrainedModelJob.

Parameters

• self

• project_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})]

• model_file: pydantic.types.StrictStr

• model_file_name: pydantic.types.StrictStr

• model_file_type: pydantic.types.StrictStr

• impulse_id: Annotated[pydantic.types.StrictInt | None, FieldInfo(default=PydanticUndefined, description='Impulse ID. If this is unset then the default impulse is used.', extra={})] = None

• representative_features: pydantic.types.StrictStr | None = None

• device: Annotated[pydantic.types.StrictStr | None, FieldInfo(default=PydanticUndefined, description='MCU used for calculating latency, query latencyDevices in listProject for a list of supported devices (and use the \"mcu\" property here). If this is kept empty then we'll show an overview of multiple devices.', extra={})] = None

• **kwargs

Return

edgeimpulse_api.models.start_job_response.StartJobResponse