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Functions

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check_response_errors

edgeimpulse.util.check_response_errors(
	request
)
Check for standard errors and raise an exception with the details if found.
Parameters
request

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configure_generic_client

edgeimpulse.util.configure_generic_client(
	key: str,
	key_type: str | None = 'api',
	host: str | None = 'https://studio.edgeimpulse.com/v1'
) ‑> edgeimpulse_api.api_client.ApiClient
Configure generic api client which the right key.
Parameters
keystr
key_typestr | None = 'api'
hoststr | None = 'https
Returns
edgeimpulse_api.api_client.ApiClient

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connect_websocket

edgeimpulse.util.connect_websocket(
	token,
	host: str = None
) ‑> socketio.client.Client
Connects to the websocket server. Parameters: token (str): The authentication token. host (str, optional): The hostname. If None, API_ENDPOINT will be used. Returns: object: Websocket object.
Parameters
token
hoststr = None
Returns
socketio.client.Client

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default_project_id_for

edgeimpulse.util.default_project_id_for(
	client: edgeimpulse_api.api_client.ApiClient
) ‑> int
Derive project id from api_key used to configure generic client.
Parameters
clientedgeimpulse_api.api_client.ApiClient
Returns
int

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encode_file_as_base64

edgeimpulse.util.encode_file_as_base64(
	filename: str
)
Encode a file as base64.
Parameters
filenamestr

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get_organization_websocket

edgeimpulse.util.get_organization_websocket(
	client,
	organization_id: int,
	host: str = None
) ‑> socketio.client.Client
Gets a websocket to listen to organization events.
Parameters
client
organization_idint
hoststr = None
Returns
socketio.client.Client

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get_profile_devices

edgeimpulse.util.get_profile_devices(
	client: edgeimpulse_api.api_client.ApiClient,
	project_id: int | None = None
) ‑> List[str]
Pull a list of profile devices.
Parameters
clientedgeimpulse_api.api_client.ApiClient
project_idint | None = None
Returns
List[str]

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get_project_deploy_targets

edgeimpulse.util.get_project_deploy_targets(
	client: edgeimpulse_api.api_client.ApiClient,
	project_id: int | None = None
) ‑> List[str]
Pull a list of deploy targets.
Parameters
clientedgeimpulse_api.api_client.ApiClient
project_idint | None = None
Returns
List[str]

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get_project_websocket

edgeimpulse.util.get_project_websocket(
	client,
	project_id: int,
	host: str = None
) ‑> socketio.client.Client
Gets a websocket to listen to project events.
Parameters
client
project_idint
hoststr = None
Returns
socketio.client.Client

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get_user_agent

edgeimpulse.util.get_user_agent(
	add_platform_info=False
)
Get user agent string for API calls so we can track usage.
Parameters
add_platform_info=False

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inspect_model

edgeimpulse.util.inspect_model(
	model: pathlib._local.Path | str | bytes | Any,
	tempdir: str
) ‑> Tuple[str, str]
Load tflite model.
Parameters
modelpathlib._local.Path | str | bytes | Any
tempdirstr
Returns
Tuple[str, str]

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inspect_representative_data

edgeimpulse.util.inspect_representative_data(
	data: pathlib._local.Path | str | bytes | Any
) ‑> str | None
Ensure representative data is saved to disk for upload.
Parameters
datapathlib._local.Path | str | bytes | Any
Returns
str | None

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is_keras_model

edgeimpulse.util.is_keras_model(
	model
)
Check if model is a keras model.
Parameters
model

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is_numpy_array

edgeimpulse.util.is_numpy_array(
	array
)
Check if array is a numpy array.
Parameters
array

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is_onnx_model

edgeimpulse.util.is_onnx_model(
	model
)
Check if given model is an onnx model.
Parameters
model

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is_path_to_numpy_file

edgeimpulse.util.is_path_to_numpy_file(
	path
)
Check if given path is a numpy file.
Parameters
path

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is_path_to_onnx_model

edgeimpulse.util.is_path_to_onnx_model(
	path
)
Check if given path is a onnx file.
Parameters
path

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is_path_to_tf_saved_model_directory

edgeimpulse.util.is_path_to_tf_saved_model_directory(
	model_dir
)
Check if directory contains a saved model.
Parameters
model_dir

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is_path_to_tf_saved_model_zipped

edgeimpulse.util.is_path_to_tf_saved_model_zipped(
	model
)
Check if path is poiting to a zipped model.
Parameters
model

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is_type_accepted_by_open

edgeimpulse.util.is_type_accepted_by_open(
	path
)
Check if given path is a. string or a Path.
Parameters
path

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make_zip_archive

edgeimpulse.util.make_zip_archive(
	saved_model_path
)
Create zip archive from a model path.
Parameters
saved_model_path

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numpy_installed

edgeimpulse.util.numpy_installed(
	
) ‑> bool
Return True if NumPy is installed, otherwise False.
Returns
bool

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onnx_installed

edgeimpulse.util.onnx_installed(
	
) ‑> bool
Return True if ONNX is installed, otherwise False.
Returns
bool

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pandas_installed

edgeimpulse.util.pandas_installed(
	
) ‑> bool
Return True if pandas is installed, otherwise False.
Returns
bool

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poll

edgeimpulse.util.poll(
	jobs_client: edgeimpulse_api.api.jobs_api.JobsApi,
	project_id: int,
	job_id: int,
	timeout_sec: float | None = None
) ‑> edgeimpulse_api.models.get_job_response.GetJobResponse
Poll a specific job within a project until done or timmeout is reached.
Parameters
jobs_clientedgeimpulse_api.api.jobs_api.JobsApi
project_idint
job_idint
timeout_secfloat | None = None
Returns
edgeimpulse_api.models.get_job_response.GetJobResponse

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run_job_until_completion

edgeimpulse.util.run_job_until_completion(
	ws,
	job_id: int,
	data_cb=None,
	timeout_sec: int | None = None
)
Runs a project or organization job until completion.
Parameters
ws
job_idint
data_cb=None
timeout_secint | None = None

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run_organization_job_until_completion

edgeimpulse.util.run_organization_job_until_completion(
	organization_id: int,
	job_id: int,
	data_cb=None,
	client=None,
	timeout_sec: int | None = None
) ‑> None
Runs an organization job until completion.
Parameters
organization_idint
job_idint
data_cb=None
client=None
timeout_secint | None = None
Returns
None

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run_project_job_until_completion

edgeimpulse.util.run_project_job_until_completion(
	job_id: int,
	data_cb=None,
	client=None,
	project_id: int | None = None,
	timeout_sec: int | None = None
) ‑> None
Runs a project job until completion.
Parameters
job_idint
data_cb=None
client=None
project_idint | None = None
timeout_secint | None = None
Returns
None

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save_model

edgeimpulse.util.save_model(
	model: pathlib._local.Path | str | bytes,
	directory: str
) ‑> str
Save a machine learning model to the specified directory.
Parameters
modelpathlib._local.Path | str | bytes
directorystr
Returns
str

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save_representative_data

edgeimpulse.util.save_representative_data(
	data: pathlib._local.Path | str | bytes,
	directory: str
) ‑> str
Save the representive data to a directory.
Parameters
datapathlib._local.Path | str | bytes
directorystr
Returns
str

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tensorflow_installed

edgeimpulse.util.tensorflow_installed(
	
) ‑> bool
Return True if TensorFlow is installed, otherwise False.
Returns
bool

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upload_pretrained_model_and_data

edgeimpulse.util.upload_pretrained_model_and_data(
	tempdir: str,
	client: edgeimpulse_api.api_client.ApiClient,
	project_id: int,
	model: pathlib._local.Path | str | bytes | Any,
	representative_data: pathlib._local.Path | str | bytes | Any | None = None,
	device: str | None = None,
	timeout_sec: float | None = None
) ‑> edgeimpulse_api.models.get_job_response.GetJobResponse
Upload a model and data to Edge Impulse servers.
Parameters
tempdirstr
clientedgeimpulse_api.api_client.ApiClient
project_idint
modelpathlib._local.Path | str | bytes | Any
representative_datapathlib._local.Path | str | bytes | Any | None = None
devicestr | None = None
timeout_secfloat | None = None
Returns
edgeimpulse_api.models.get_job_response.GetJobResponse