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LearnApi

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

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Methods

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

​
start_anomaly_profile_job

edgeimpulse_api.api.learn_api.start_anomaly_profile_job(
		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.generic_api_response.GenericApiResponse
Start a profile job for an anomaly learn block Starts an asynchronous profiling job, if there’s no profiling information for the currently selected latency device. Afterwards, re-fetch model metadata to get the profiling job IDs. 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.generic_api_response.GenericApiResponse

​
start_keras_profile_job

edgeimpulse_api.api.learn_api.start_keras_profile_job(
		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.generic_api_response.GenericApiResponse
Start a profile job for a Keras learn block Starts an asynchronous profiling job, if there’s no profiling information for the currently selected latency device. Afterwards, re-fetch model metadata to get the profiling job IDs. 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.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

​
test_pretrained_model_images

edgeimpulse_api.api.learn_api.test_pretrained_model_images(
		self,
		project_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})],
		test_pretrained_model_images_request: edgeimpulse_api.models.test_pretrained_model_images_request.TestPretrainedModelImagesRequest,
		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 using image data Test out a pretrained model (using image data) - upload first via uploadPretrainedModel. If you want to deploy a pretrained model from the API, see startDeployPretrainedModelJob. This will transform raw image data (e.g. RGB to grayscale, resize) before classifying. To classify raw features, see testPretrainedModel. Parameters
  • self
  • project_id: Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description=‘Project ID’, extra=)]
  • test_pretrained_model_images_request: edgeimpulse_api.models.test_pretrained_model_images_request.TestPretrainedModelImagesRequest
  • 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
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