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.
workspace(id, auth, api_endpoint, management_endpoint,
config = getOption("AzureML.config"), ..., .validate = TRUE)
Optional workspace id from ML studio -> settings -> WORKSPACE ID. See the section "Finding your AzureML credentials" for more details.
Optional authorization token from ML studio -> settings -> AUTHORIZATION TOKENS. See the section "Finding your AzureML credentials" for more details.
Optional AzureML API web service URI. Defaults to https://studioapi.azureml.net
if not provided and not specified in config. See note.
Optional AzureML management web service URI. Defaults to https://management.azureml.net
if not provided and not specified in config. See note.
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
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.
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
You can find your Azure Machine Learning workspace id
and authorization token
in the Azure Machine Learning Studio interface.
Workspace ID
Authorization token
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" }}
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"] }}
datasets
, experiments
, refresh
,
services
, consume
, publishWebService
Other dataset functions: datasets
,
delete.datasets
,
download.intermediate.dataset
,
upload.dataset
Other experiment functions: download.intermediate.dataset
,
experiments
Other discovery functions: discoverSchema
,
endpointHelp
, endpoints
,
services
Other consumption functions: consume
Other publishing functions: deleteWebService
,
publishWebService