workspace: Create a reference to an AzureML Studio workspace.

Description

Create a reference to an AzureML Studio workspace, returning a Workspace object that is an R environment containing details and data associated with the AzureML work space. Data corresponding to services, experiments, and datasets in the workspace are cached in the result object environment. See refresh about updating cached data.

Usage

workspace(id, auth, api_endpoint, management_endpoint,
  config = getOption("AzureML.config"), ..., .validate = TRUE)

Arguments

Optional workspace id from ML studio -> settings -> WORKSPACE ID. See the section "Finding your AzureML credentials" for more details.

auth

Optional authorization token from ML studio -> settings -> AUTHORIZATION TOKENS. See the section "Finding your AzureML credentials" for more details.

api_endpoint

Optional AzureML API web service URI. Defaults to https://studioapi.azureml.net if not provided and not specified in config. See note.

management_endpoint

Optional AzureML management web service URI. Defaults to https://management.azureml.net if not provided and not specified in config. See note.

config

Optional settings file containing id and authorization info. Used if any of the other arguments are missing. The default config file is ~/.azureml/settings.json, but you can change this location by setting options(AzureML.config = "newlocation"). See the section "Using a settings.json file" for more details.

...

ignored

.validate

If TRUE, makes a request to the AzureML API to retrieve some data. This validates whether the workspace id and authorization token are valid. Specifically, the function calls datasets. This should normally be set to TRUE. Set this to FALSE for testing, or if you know that your credentials are correct and you don't want to retrieve the datasets.

Value

An R environment of class Workspace containing at least the following objects:

experiments: Collection of experiments in the workspace represented as an Experiments object. See experiments
datasets: Collection of datasets in the workspace represented as a Datasets object. See datasets
services: Collection of web services in the workspace represented as a Services object. See services

Finding your AzureML credentials

You can find your Azure Machine Learning workspace id and authorization token in the Azure Machine Learning Studio interface.

Workspace ID

Figure: workspace_id.png

Authorization token

Figure: authorization_token.png

Using a settings.json file

If any of the id, auth, api_endpoint or management_endpoint arguments are missing, the function attempts to read values from the config file with JSON format:

 {"workspace":{
   "id": "enter your AzureML workspace id here",
   "authorization_token": "enter your AzureML authorization token here",
   "api_endpoint": "https://studioapi.azureml.net",
 }}

To explicitly add the management endpoint in the JSON file, use:

 {"workspace":{
   "id": "enter your AzureML workspace id here",
   "authorization_token": "enter your AzureML authorization token here",
   "api_endpoint": "https://studioapi.azureml.net",
   "management_endpoint": "https://management.azureml.net"
 }}

Using a workspace in different Azure Machine Learning regions

By default, the Azure Machine Learning workspace is located in US South Central, but it is possible to create a workspace in different regions, including Europe West and Asia Southeast.

To use a workspace in Asia Southeast, you can modify the api endpoint line in the JSON file:

  {"workspace": {
    "api_endpoint": ["https://asiasoutheast.studio.azureml.net"]
  }}

Similarly, for a workspace in Europe West:

  {"workspace": {
    "api_endpoint": ["https://europewest.studio.azureml.net"]
  }}