util module
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://studio.edgeimpulse.com/v1'
Return
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
Return
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
Return
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
Return
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
Return
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
Return
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
Return
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._local.Path | str | bytes | Any,
tempdir: str
) ‑> Tuple[str, str]
Load tflite model.
Parameters
model: pathlib._local.Path | str | bytes | Any
tempdir: str
Return
Tuple[str, str]
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
data: pathlib._local.Path | str | bytes | Any
Return
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.
Return
bool
onnx_installed
edgeimpulse.util.onnx_installed(
) ‑> bool
Return True if ONNX is installed, otherwise False.
Return
bool
pandas_installed
edgeimpulse.util.pandas_installed(
) ‑> bool
Return True if pandas is installed, otherwise False.
Return
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
Return
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
Return
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
Return
None
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
model: pathlib._local.Path | str | bytes
directory: str
Return
str
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
data: pathlib._local.Path | str | bytes
directory: str
Return
str
tensorflow_installed
edgeimpulse.util.tensorflow_installed(
) ‑> bool
Return True if TensorFlow is installed, otherwise False.
Return
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._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
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
Return
edgeimpulse_api.models.get_job_response.GetJobResponse
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