edgeimpulse.util

Functions

check_response_errors

edgeimpulse.util.check_response_errors(
	request
)

Check for standard errors and raise an exception with the details if found.

Parameters
`request`

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
`key`	`str`
`key_type`	`str \| None = 'api'`
`host`	`str \| None = 'https`

Returns
`edgeimpulse_api.api_client.ApiClient`

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`
`host`	`str = None`

Returns
`socketio.client.Client`

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
`client`	`edgeimpulse_api.api_client.ApiClient`

Returns
`int`

encode_file_as_base64

edgeimpulse.util.encode_file_as_base64(
	filename: str
)

Encode a file as base64.

Parameters
`filename`	`str`

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_id`	`int`
`host`	`str = None`

Returns
`socketio.client.Client`

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
`client`	`edgeimpulse_api.api_client.ApiClient`
`project_id`	`int \| None = None`

Returns
`List[str]`

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
`client`	`edgeimpulse_api.api_client.ApiClient`
`project_id`	`int \| None = None`

Returns
`List[str]`

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_id`	`int`
`host`	`str = None`

Returns
`socketio.client.Client`

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`

inspect_model

edgeimpulse.util.inspect_model(
	model: pathlib.Path | str | bytes | Any,
	tempdir: str
) ‑> Tuple[str, str]

Load tflite model.

Parameters
`model`	`pathlib.Path \| str \| bytes \| Any`
`tempdir`	`str`

Returns
`Tuple[str, str]`

inspect_representative_data

edgeimpulse.util.inspect_representative_data(
	data: pathlib.Path | str | bytes | Any
) ‑> str | None

Ensure representative data is saved to disk for upload.

Parameters
`data`	`pathlib.Path \| str \| bytes \| Any`

Returns
`str \| None`

is_keras_model

edgeimpulse.util.is_keras_model(
	model
)

Check if model is a keras model.

Parameters
`model`

is_numpy_array

edgeimpulse.util.is_numpy_array(
	array
)

Check if array is a numpy array.

Parameters
`array`

is_onnx_model

edgeimpulse.util.is_onnx_model(
	model
)

Check if given model is an onnx model.

Parameters
`model`

is_path_to_numpy_file

edgeimpulse.util.is_path_to_numpy_file(
	path
)

Check if given path is a numpy file.

Parameters
`path`

is_path_to_onnx_model

edgeimpulse.util.is_path_to_onnx_model(
	path
)

Check if given path is a onnx file.

Parameters
`path`

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`

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`

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`

make_zip_archive

edgeimpulse.util.make_zip_archive(
	saved_model_path
)

Create zip archive from a model path.

Parameters
`saved_model_path`

numpy_installed

edgeimpulse.util.numpy_installed(
	
) ‑> bool

Return True if NumPy is installed, otherwise False.

Returns
`bool`

onnx_installed

edgeimpulse.util.onnx_installed(
	
) ‑> bool

Return True if ONNX is installed, otherwise False.

Returns
`bool`

pandas_installed

edgeimpulse.util.pandas_installed(
	
) ‑> bool

Return True if pandas is installed, otherwise False.

Returns
`bool`

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_client`	`edgeimpulse_api.api.jobs_api.JobsApi`
`project_id`	`int`
`job_id`	`int`
`timeout_sec`	`float \| None = None`

Returns
`edgeimpulse_api.models.get_job_response.GetJobResponse`

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_id`	`int`
`data_cb=None`
`timeout_sec`	`int \| None = None`

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_id`	`int`
`job_id`	`int`
`data_cb=None`
`client=None`
`timeout_sec`	`int \| None = None`

Returns
`None`

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_id`	`int`
`data_cb=None`
`client=None`
`project_id`	`int \| None = None`
`timeout_sec`	`int \| None = None`

Returns
`None`

save_model

edgeimpulse.util.save_model(
	model: pathlib.Path | str | bytes,
	directory: str
) ‑> str

Save a machine learning model to the specified directory.

Parameters
`model`	`pathlib.Path \| str \| bytes`
`directory`	`str`

Returns
`str`

save_representative_data

edgeimpulse.util.save_representative_data(
	data: pathlib.Path | str | bytes,
	directory: str
) ‑> str

Save the representive data to a directory.

Parameters
`data`	`pathlib.Path \| str \| bytes`
`directory`	`str`

Returns
`str`

tensorflow_installed

edgeimpulse.util.tensorflow_installed(
	
) ‑> bool

Return True if TensorFlow is installed, otherwise False.

Returns
`bool`

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.Path | str | bytes | Any,
	representative_data: pathlib.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
`tempdir`	`str`
`client`	`edgeimpulse_api.api_client.ApiClient`
`project_id`	`int`
`model`	`pathlib.Path \| str \| bytes \| Any`
`representative_data`	`pathlib.Path \| str \| bytes \| Any \| None = None`
`device`	`str \| None = None`
`timeout_sec`	`float \| None = None`

Returns
`edgeimpulse_api.models.get_job_response.GetJobResponse`

OVERVIEW

CLIs

LIBRARIES

SPECIFICATIONS

PROTOCOLS

Functions

check_response_errors

configure_generic_client

connect_websocket

default_project_id_for

encode_file_as_base64

get_organization_websocket

get_profile_devices

get_project_deploy_targets

get_project_websocket

get_user_agent

inspect_model

inspect_representative_data

is_keras_model

is_numpy_array

is_onnx_model

is_path_to_numpy_file

is_path_to_onnx_model

is_path_to_tf_saved_model_directory

is_path_to_tf_saved_model_zipped

is_type_accepted_by_open

make_zip_archive

numpy_installed

onnx_installed

pandas_installed

poll

run_job_until_completion

run_organization_job_until_completion

run_project_job_until_completion

save_model

save_representative_data

tensorflow_installed

upload_pretrained_model_and_data

​Functions

​check_response_errors

​configure_generic_client

​connect_websocket

​default_project_id_for

​encode_file_as_base64

​get_organization_websocket

​get_profile_devices

​get_project_deploy_targets

​get_project_websocket

​get_user_agent

​inspect_model

​inspect_representative_data

​is_keras_model

​is_numpy_array

​is_onnx_model

​is_path_to_numpy_file

​is_path_to_onnx_model

​is_path_to_tf_saved_model_directory

​is_path_to_tf_saved_model_zipped

​is_type_accepted_by_open

​make_zip_archive

​numpy_installed

​onnx_installed

​pandas_installed

​poll

​run_job_until_completion

​run_organization_job_until_completion

​run_project_job_until_completion

​save_model

​save_representative_data

​tensorflow_installed

​upload_pretrained_model_and_data

Functions

check_response_errors

configure_generic_client

connect_websocket

default_project_id_for

encode_file_as_base64

get_organization_websocket

get_profile_devices

get_project_deploy_targets

get_project_websocket

get_user_agent

inspect_model

inspect_representative_data

is_keras_model

is_numpy_array

is_onnx_model

is_path_to_numpy_file

is_path_to_onnx_model

is_path_to_tf_saved_model_directory

is_path_to_tf_saved_model_zipped

is_type_accepted_by_open

make_zip_archive

numpy_installed

onnx_installed

pandas_installed

poll

run_job_until_completion

run_organization_job_until_completion

run_project_job_until_completion

save_model

save_representative_data

tensorflow_installed

upload_pretrained_model_and_data