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## Sagemaker Edge Agent Controller client API for Pelion Edge #### PyPi: [https://pypi.org/project/pelion\_sagemaker\_controller/](https://pypi.org/project/pelion\_sagemaker\_controller/) This python package simplifies the Data Scientist's job of accessing, via a Sagemaker Jupyter Notebook, the Sagemaker Edge Agent running on their Pelion Edge enabled gateway. ### Controller API Instance Creation To create an instance of this API: # Required import from pelion_sagemaker_controller import pelion_sagemaker_controller # # Invoke constructor with Pelion API Key, Pelion GW Device ID # You can also optionally specify the Pelion API endpoint you want to use # api = pelion_sagemaker_controller.ControllerAPI( api_key='<ak_xxxx>', device_id='<pelion_gw_device_id>', api_endpoint='api.us-east-1.mbedcloud.com' ) ### Supported Commands The following commands are supported by this API: #### Get Configuration api.pelion_get_config() This call returns a JSON with the current Edge Device representing the Sagemaker service's configuration #### Set Configuration api.pelion_set_config({'foo':'bar'}) This call updates or adds key/values to the current Edge Device's configuration #### List Models api.pelion_list_models() This call returns a JSON outlining all of the loaded models #### Load Model api.pelion_load_model('model-name','compiled-model-x.y.tar.gz') This call loads up the requested Sagemaker-compiled model whose compiled contents are located within the S3 bucket defined in the configuration and utilized by the Sagemaker service #### Unload Model api.pelion_unload_model('model-name') This call unloads the loaded model referenced by the name 'model-name' #### Reload Model api.pelion_reload_model('model-name','compiled-model-x.y.tar.gz') This call is a convenience method for simply performing an "unload" followed by a "load" of a given model using the methods above. #### Predict api.pelion_predict( 'model-name', 's3:///input.data', 's3:///prediction_result.data' ) This call invokes the model prediction using the specified input.data file that is configured to be pulled from the Sagemaker S3 bucket (per configuration). The output result from the prediction will be stored in a file that will be saved to the same directory in the S3 bucket. In addition to S3 bucket support, you can locally reference input/output requirements using the "file:///" protocol - in this case the Sagemaker Edge Agent working directory on the Pelion Edge Gateway will contain the specified files. #### Last Command Result api.pelion_last_cmd_result() This call returns the last invocation/call results. In cases where predictions take a long time to complete, this call may be used in a polling situation to determine when the prediction operation has completed.