anatools.anaclient.ml module¶
Machine Learning Functions
- create_ml_inference(self, datasetId, modelId, mapId, workspaceId=None)¶
Create a new machine learning inference job.
- Parameters
datasetId (str) – Dataset ID
modelId (str) – Model ID
mapId (str) – Map ID
workspaceId (str) – Workspace ID
- Returns
Inference ID
- Return type
str
- create_ml_model(self, datasetId, architectureId, name, description, parameters, workspaceId=None)¶
Creates a new machine learning model.
- Parameters
architectureId (str) – Architecture ID
datasetId (str) – Dataset ID
name (str) – Model name
description (str) – Model description
paramters (str) – JSON string of model parameters
workspaceId (str) – Workspace ID
- Returns
Machine learning model ID
- Return type
str
- delete_ml_inference(self, inferenceId, workspaceId=None)¶
Deletes or cancels a machine learning inference job.
- Parameters
inferenceId (str) – Inference ID
workspaceId (str) – Workspace ID
- Returns
Success / failure
- Return type
bool
- delete_ml_model(self, modelId, workspaceId=None)¶
Deletes or cancels a machine learning model.
- Parameters
modelId (str) – Model ID
workspaceId (str) – Workspace ID
- Returns
Success / failure
- Return type
bool
- download_ml_inference(self, inferenceId, localDir=None, workspaceId=None)¶
Download the inference detections.
- Parameters
inferencId (str) – Inference ID
localDir (str) – Local directory to save the model
workspaceId (str) – Workspace ID
- Returns
Success / failure
- Return type
bool
- download_ml_model(self, modelId, localDir=None, workspaceId=None)¶
Download the machine learning model.
- Parameters
modelId (str) – Model ID
localDir (str) – Local directory to save the model
workspaceId (str) – Workspace ID
- Returns
Success / failure
- Return type
bool
- edit_ml_model(self, modelId, name, description, workspaceId=None)¶
Edit the name or description of a machine learning model.
- Parameters
modelId (str) – Model ID
name (str) – Model name
description (str) – Model description
workspaceId (str) – Workspace ID
- Returns
Success / failure
- Return type
bool
- get_ml_architectures(self)¶
Retrieves the machine learning model architectures available on the platform.
- Parameters
None –
- Returns
Machine learning model architectures
- Return type
dict
- get_ml_inference_metrics(self, inferenceId, workspaceId=None)¶
Get the metrics from an inference job.
- Parameters
inferenceId (str) – Inference ID
workspaceId (str) – Workspace ID
- Returns
Metric data
- Return type
dict
- get_ml_inferences(self, inferenceId=None, datasetId=None, modelId=None, workspaceId=None)¶
Get the inferences of a machine learning model.
- Parameters
inferenceId (str) – Inference ID
datasetId (str) – Dataset ID
modelId (str) – Model ID
workspaceId (str) – Workspace ID
- Returns
Inference data
- Return type
dict
- get_ml_models(self, workspaceId=None, datasetId=None, modelId=None)¶
Retrieves the machine learning model architectures available on the platform.
- Parameters
workspaceId (str) – Workspace ID
datasetId (str) – Dataset ID
modelId (str) – Model ID
- Returns
Machine learning model architectures
- Return type
dict
- upload_ml_model(self, name, description, modelfile, parameters, workspaceId=None)¶
Upload a machine learning model.
- Parameters
name (str) – Model name
description (str) – Model description
modelfile (str) – The filepath of the model file
parameters (str) – Model training parameters
workspaceId (str) – Workspace ID
- Returns
Success / failure
- Return type
bool
