Workspace Class
Defines an Azure Machine Learning resource for managing training and deployment artifacts.
A Workspace is a fundamental resource for machine learning in Azure Machine Learning. You use a workspace to experiment, train, and deploy machine learning models. Each workspace is tied to an Azure subscription and resource group, and has an associated SKU.
For more information about workspaces, see:
Class Workspace constructor to load an existing Azure Machine Learning Workspace.
- Inheritance
-
builtins.objectWorkspace
Constructor
Workspace(subscription_id, resource_group, workspace_name, auth=None, _location=None, _disable_service_check=False, _workspace_id=None, sku='basic', tags=None, _cloud='AzureCloud')Parameters
The authentication object. For more details, see https://aka.ms/aml-notebook-auth. If None, the default Azure CLI credentials will be used or the API will prompt for credentials.
- workspace_name
- str
The workspace name. The name must be between 2 and 32 characters long. The first character of the name must be alphanumeric (letter or number), but the rest of the name may contain alphanumerics, hyphens, and underscores. Whitespace is not allowed.
The authentication object. For more details, see https://aka.ms/aml-notebook-auth. If None, the default Azure CLI credentials will be used or the API will prompt for credentials.
Remarks
The following sample shows how to create a workspace.
from azureml.core import Workspace
ws = Workspace.create(name='myworkspace',
subscription_id='<azure-subscription-id>',
resource_group='myresourcegroup',
create_resource_group=True,
location='eastus2'
)
Set create_resource_group to False if you have an existing Azure resource group that
you want to use for the workspace.
To use the same workspace in multiple environments, create a JSON configuration file. The configuration file saves your subscription, resource, and workspace name so that it can be easily loaded. To save the configuration use the write_config method.
ws.write_config(path="./file-path", file_name="ws_config.json")
See Create a workspace configuration file for an example of the configuration file.
To load the workspace from the configuration file, use the from_config method.
ws = Workspace.from_config()
ws.get_details()
Alternatively, use the get method to load an existing workspace without using configuration files.
ws = Workspace.get(name="myworkspace",
subscription_id='<azure-subscription-id>',
resource_group='myresourcegroup')
The samples above may prompt you for Azure authentication credentials using an interactive login dialog. For other use cases, including using the Azure CLI to authenticate and authentication in automated workflows, see Authentication in Azure Machine Learning.
Methods
| add_private_endpoint |
Add a private endpoint to the workspace. |
| create |
Create a new Azure Machine Learning Workspace. Throws an exception if the workspace already exists or any of the workspace requirements are not satisfied. |
| delete |
Delete the Azure Machine Learning Workspace associated resources. |
| delete_connection |
Delete a connection of the workspace. |
| delete_private_endpoint_connection |
Delete the private endpoint connection to the workspace. |
| diagnose_workspace |
Diagnose workspace setup issues. |
| from_config |
Return a workspace object from an existing Azure Machine Learning Workspace. Reads workspace configuration from a file. Throws an exception if the config file can't be found. The method provides a simple way to reuse the same workspace across multiple Python notebooks or projects. Users can save the workspace Azure Resource Manager (ARM) properties using the write_config method, and use this method to load the same workspace in different Python notebooks or projects without retyping the workspace ARM properties. |
| get |
Return a workspace object for an existing Azure Machine Learning Workspace. Throws an exception if the workspace does not exist or the required fields do not uniquely identify a workspace. |
| get_connection |
Get a connection of the workspace. |
| get_default_compute_target |
Get the default compute target for the workspace. |
| get_default_datastore |
Get the default datastore for the workspace. |
| get_default_keyvault |
Get the default key vault object for the workspace. |
| get_details |
Return the details of the workspace. |
| get_mlflow_tracking_uri |
Get the MLflow tracking URI for the workspace. MLflow (https://mlflow.org/) is an open-source platform for tracking machine learning experiments and managing models. You can use MLflow logging APIs with Azure Machine Learning so that metrics, models and artifacts are logged to your Azure Machine Learning workspace. |
| get_run |
Return the run with the specified run_id in the workspace. |
| list |
List all workspaces that the user has access to within the subscription. The list of workspaces can be filtered based on the resource group. |
| list_connections |
List connections under this workspace. |
| list_keys |
List keys for the current workspace. |
| set_connection |
Add or update a connection under the workspace. |
| set_default_datastore |
Set the default datastore for the workspace. |
| setup |
Create a new workspace or retrieve an existing workspace. |
| sync_keys |
Triggers the workspace to immediately synchronize keys. If keys for any resource in the workspace are changed, it can take around an hour for them to automatically be updated. This function enables keys to be updated upon request. An example scenario is needing immediate access to storage after regenerating storage keys. |
| update |
Update friendly name, description, tags, image build compute and other settings associated with a workspace. |
| update_dependencies |
Update existing the associated resources for workspace in the following cases. a) When a user accidently deletes an existing associated resource and would like to update it with a new one without having to recreate the whole workspace. b) When a user has an existing associated resource and wants to replace the current one that is associated with the workspace. c) When an associated resource hasn't been created yet and they want to use an existing one that they already have (only applies to container registry). |
| write_config |
Write the workspace Azure Resource Manager (ARM) properties to a config file. Workspace ARM properties can be loaded later using the from_config method. The The method provides a simple way of reusing the same workspace across multiple Python notebooks or projects. Users can save the workspace ARM properties using this function, and use from_config to load the same workspace in different Python notebooks or projects without retyping the workspace ARM properties. |
add_private_endpoint
Add a private endpoint to the workspace.
add_private_endpoint(private_endpoint_config, private_endpoint_auto_approval=True, location=None, show_output=True, tags=None)Parameters
- private_endpoint_config
- PrivateEndPointConfig
The private endpoint configuration to create a private endpoint to workspace.
- private_endpoint_auto_approval
- bool
A boolean flag that denotes if the private endpoint creation should be auto-approved or manually-approved from Azure Private Link Center. In case of manual approval, users can view the pending request in Private Link portal to approve/reject the request.
- location
- string
Location of the private endpoint, default is the workspace location
Returns
The PrivateEndPoint object created.
Return type
create
Create a new Azure Machine Learning Workspace.
Throws an exception if the workspace already exists or any of the workspace requirements are not satisfied.
static create(name, auth=None, subscription_id=None, resource_group=None, location=None, create_resource_group=True, sku='basic', tags=None, friendly_name=None, storage_account=None, key_vault=None, app_insights=None, container_registry=None, adb_workspace=None, primary_user_assigned_identity=None, cmk_keyvault=None, resource_cmk_uri=None, hbi_workspace=False, default_cpu_compute_target=None, default_gpu_compute_target=None, private_endpoint_config=None, private_endpoint_auto_approval=True, exist_ok=False, show_output=True, user_assigned_identity_for_cmk_encryption=None, system_datastores_auth_mode='accessKey', v1_legacy_mode=None)Parameters
- name
- str
The new workspace name. The name must be between 2 and 32 characters long. The first character of the name must be alphanumeric (letter or number), but the rest of the name may contain alphanumerics, hyphens, and underscores. Whitespace is not allowed.
The authentication object. For more details, see https://aka.ms/aml-notebook-auth. If None, the default Azure CLI credentials will be used or the API will prompt for credentials.
- subscription_id
- str
The subscription ID of the containing subscription for the new workspace. The parameter is required if the user has access to more than one subscription.
- resource_group
- str
The Azure resource group that contains the workspace. The parameter defaults to a mutation of the workspace name.
- location
- str
The location of the workspace. The parameter defaults to the resource group location. The location has to be a supported region for Azure Machine Learning.
- create_resource_group
- bool
Indicates whether to create the resource group if it doesn't exist.
- friendly_name
- str
An optional friendly name for the workspace that can be displayed in the UI.
- storage_account
- str
An existing storage account in the Azure resource ID format. The storage will be used by the workspace to save run outputs, code, logs etc. If None, a new storage account will be created.
- key_vault
- str
An existing key vault in the Azure resource ID format. See example code below for details of the Azure resource ID format. The key vault will be used by the workspace to store credentials added to the workspace by the users. If None, a new key vault will be created.
- app_insights
- str
An existing Application Insights in the Azure resource ID format. See example code below for details of the Azure resource ID format. The Application Insights will be used by the workspace to log webservices events. If None, a new Application Insights will be created.
- container_registry
- str
An existing container registry in the Azure resource ID format (see example code below for details of the Azure resource ID format). The container registry will be used by the workspace to pull and push both experimentation and webservices images. If None, a new container registry will be created only when needed and not along with workspace creation.
- adb_workspace
- str
An existing Adb Workspace in the Azure resource ID format (see example code below for details of the Azure resource ID format). The Adb Workspace will be used to link with the workspace. If None, the workspace link won't happen.
- primary_user_assigned_identity
- str
The resource id of the user assigned identity that used to represent the workspace
- cmk_keyvault
- str
The key vault containing the customer managed key in the Azure resource ID
format:
/subscriptions/<azure-subscription-id>/resourcegroups/<azure-resource-group>/providers/microsoft.keyvault/vaults/<azure-keyvault-name>
For example:
'/subscriptions/d139f240-94e6-4175-87a7-954b9d27db16/resourcegroups/myresourcegroup/providers/microsoft.keyvault/vaults/mykeyvault'
See the example code in the Remarks below for more details on the Azure resource ID format.
- resource_cmk_uri
- str
The key URI of the customer managed key to encrypt the data at rest.
The URI format is: https://<keyvault-dns-name>/keys/<key-name>/<key-version>.
For example, 'https://mykeyvault.vault.azure.net/keys/mykey/bc5dce6d01df49w2na7ffb11a2ee008b'.
Refer to https://docs.microsoft.com/azure-stack/user/azure-stack-key-vault-manage-portal for steps on how
to create a key and get its URI.
- hbi_workspace
- bool
Specifies whether the workspace contains data of High Business Impact (HBI), i.e., contains sensitive business information. This flag can be set only during workspace creation. Its value cannot be changed after the workspace is created. The default value is False.
When set to True, further encryption steps are performed, and depending on the SDK component, results in redacted information in internally-collected telemetry. For more information, see Data encryption.
When this flag is set to True, one possible impact is increased difficulty troubleshooting issues. This could happen because some telemetry isn't sent to Microsoft and there is less visibility into success rates or problem types, and therefore may not be able to react as proactively when this flag is True. The recommendation is use the default of False for this flag unless strictly required to be True.
- default_cpu_compute_target
- AmlComputeProvisioningConfiguration
(DEPRECATED) A configuration that will be used to create a CPU compute. The parameter defaults to {min_nodes=0, max_nodes=2, vm_size="STANDARD_DS2_V2", vm_priority="dedicated"} If None, no compute will be created.
- default_gpu_compute_target
- AmlComputeProvisioningConfiguration
(DEPRECATED) A configuration that will be used to create a GPU compute. The parameter defaults to {min_nodes=0, max_nodes=2, vm_size="STANDARD_NC6", vm_priority="dedicated"} If None, no compute will be created.
- private_endpoint_config
- PrivateEndPointConfig
The private endpoint configuration to create a private endpoint to Azure ML workspace.
- private_endpoint_auto_approval
- bool
A boolean flag that denotes if the private endpoint creation should be auto-approved or manually-approved from Azure Private Link Center. In case of manual approval, users can view the pending request in Private Link portal to approve/reject the request.
- exist_ok
- bool
Indicates whether this method succeeds if the workspace already exists. If False, this method fails if the workspace exists. If True, this method returns the existing workspace if it exists.
- show_output
- bool
Indicates whether this method will print out incremental progress.
- user_assigned_identity_for_cmk_encryption
- str
The resource id of the user assigned identity that needs to be used to access the customer manage key
- system_datastores_auth_mode
- str
Determines whether or not to use credentials for the system datastores of the workspace 'workspaceblobstore' and 'workspacefilestore'. The default value is 'accessKey', in which case, the workspace will create the system datastores with credentials. If set to 'identity', the workspace will create the system datastores with no credentials.
- v1_legacy_mode
- bool
Prevent using v2 API service on public Azure Resource Manager
Returns
The workspace object.
Return type
Exceptions
Raised for problems creating the workspace.
Remarks
This first example requires only minimal specification, and all dependent resources as well as the resource group will be created automatically.
from azureml.core import Workspace
ws = Workspace.create(name='myworkspace',
subscription_id='<azure-subscription-id>',
resource_group='myresourcegroup',
create_resource_group=True,
location='eastus2')
The following example shows how to reuse existing Azure resources utilizing the Azure resource ID format. The specific Azure resource IDs can be retrieved through the Azure Portal or SDK. This assumes that the resource group, storage account, key vault, App Insights and container registry already exist.
import os
from azureml.core import Workspace
from azureml.core.authentication import ServicePrincipalAuthentication
service_principal_password = os.environ.get("AZUREML_PASSWORD")
service_principal_auth = ServicePrincipalAuthentication(
tenant_id="<tenant-id>",
username="<application-id>",
password=service_principal_password)
ws = Workspace.create(name='myworkspace',
auth=service_principal_auth,
subscription_id='<azure-subscription-id>',
resource_group='myresourcegroup',
create_resource_group=False,
location='eastus2',
friendly_name='My workspace',
storage_account='subscriptions/<azure-subscription-id>/resourcegroups/myresourcegroup/providers/microsoft.storage/storageaccounts/mystorageaccount',
key_vault='subscriptions/<azure-subscription-id>/resourcegroups/myresourcegroup/providers/microsoft.keyvault/vaults/mykeyvault',
app_insights='subscriptions/<azure-subscription-id>/resourcegroups/myresourcegroup/providers/microsoft.insights/components/myappinsights',
container_registry='subscriptions/<azure-subscription-id>/resourcegroups/myresourcegroup/providers/microsoft.containerregistry/registries/mycontainerregistry',
exist_ok=False)
delete
Delete the Azure Machine Learning Workspace associated resources.
delete(delete_dependent_resources=False, no_wait=False)Parameters
- delete_dependent_resources
- bool
Whether to delete resources associated with the workspace, i.e., container registry, storage account, key vault, and application insights. The default is False. Set to True to delete these resources.
Returns
None if successful; otherwise, throws an error.
Return type
delete_connection
Delete a connection of the workspace.
delete_connection(name)Parameters
delete_private_endpoint_connection
Delete the private endpoint connection to the workspace.
delete_private_endpoint_connection(private_endpoint_connection_name)Parameters
- private_endpoint_connection_name
- str
The unique name of private endpoint connection under the workspace
diagnose_workspace
Diagnose workspace setup issues.
diagnose_workspace(diagnose_parameters)Parameters
- diagnose_parameters
- <xref:_restclient.models.DiagnoseWorkspaceParameters>
The parameter of diagnosing workspace health
Returns
An instance of AzureOperationPoller that returns DiagnoseResponseResult
Return type
from_config
Return a workspace object from an existing Azure Machine Learning Workspace.
Reads workspace configuration from a file. Throws an exception if the config file can't be found.
The method provides a simple way to reuse the same workspace across multiple Python notebooks or projects. Users can save the workspace Azure Resource Manager (ARM) properties using the write_config method, and use this method to load the same workspace in different Python notebooks or projects without retyping the workspace ARM properties.
static from_config(path=None, auth=None, _logger=None, _file_name=None)Parameters
- path
- str
The path to the config file or starting directory to search. The parameter defaults to starting the search in the current directory.
The authentication object. For more details, see https://aka.ms/aml-notebook-auth. If None, the default Azure CLI credentials will be used or the API will prompt for credentials.
- _file_name
- str
Allows overriding the config file name to search for when path is a directory path.
Returns
The workspace object for an existing Azure ML Workspace.
Return type
get
Return a workspace object for an existing Azure Machine Learning Workspace.
Throws an exception if the workspace does not exist or the required fields do not uniquely identify a workspace.
static get(name, auth=None, subscription_id=None, resource_group=None, location=None, cloud='AzureCloud', id=None)Parameters
The authentication object. For more details refer to https://aka.ms/aml-notebook-auth. If None, the default Azure CLI credentials will be used or the API will prompt for credentials.
- subscription_id
- str
The subscription ID to use. The parameter is required if the user has access to more than one subscription.
- resource_group
- str
The resource group to use. If None, the method will search all resource groups in the subscription.
- cloud
- str
The name of the target cloud. Can be one of "AzureCloud", "AzureChinaCloud", or "AzureUSGovernment". If no cloud is specified "AzureCloud" is used.
Returns
The workspace object.
Return type
get_connection
Get a connection of the workspace.
get_connection(name)Parameters
get_default_compute_target
Get the default compute target for the workspace.
get_default_compute_target(type)Parameters
Returns
The default compute target for given compute type.
Return type
get_default_datastore
Get the default datastore for the workspace.
get_default_datastore()Returns
The default datastore.
Return type
get_default_keyvault
Get the default key vault object for the workspace.
get_default_keyvault()Returns
The KeyVault object associated with the workspace.
Return type
get_details
Return the details of the workspace.
get_details()Returns
Workspace details in dictionary format.
Return type
Remarks
The returned dictionary contains the following key-value pairs.
id: URI pointing to this workspace resource, containing subscription ID, resource group, and workspace name.
name: The name of this workspace.
location: The workspace region.
type: A URI of the format "{providerName}/workspaces".
tags: Not currently used.
workspaceid: The ID of this workspace.
description: Not currently used.
friendlyName: A friendly name for the workspace displayed in the UI.
creationTime: Time this workspace was created, in ISO8601 format.
containerRegistry: The workspace container registry used to pull and push both experimentation and webservices images.
keyVault: The workspace key vault used to store credentials added to the workspace by the users.
applicationInsights: The Application Insights will be used by the workspace to log webservices events.
identityPrincipalId:
identityTenantId
identityType
storageAccount: The storage will be used by the workspace to save run outputs, code, logs, etc.
sku: The workspace SKU (also referred as edition). The parameter is present for backwards compatibility and is ignored.
resourceCmkUri: The key URI of the customer managed key to encrypt the data at rest. Refer to https://docs.microsoft.com/en-us/azure-stack/user/azure-stack-key-vault-manage-portal?view=azs-1910 for steps on how to create a key and get its URI.
hbiWorkspace: Specifies if the customer data is of high business impact.
imageBuildCompute: The compute target for image build.
systemDatastoresAuthMode: Determines whether or not to use credentials for the system datastores of the workspace 'workspaceblobstore' and 'workspacefilestore'. The default value is 'accessKey', in which case, the workspace will create the system datastores with credentials. If set to 'identity', the workspace will create the system datastores with no credentials.
For more information on these key-value pairs, see create.
get_mlflow_tracking_uri
Get the MLflow tracking URI for the workspace.
MLflow (https://mlflow.org/) is an open-source platform for tracking machine learning experiments and managing models. You can use MLflow logging APIs with Azure Machine Learning so that metrics, models and artifacts are logged to your Azure Machine Learning workspace.
get_mlflow_tracking_uri(_with_auth=False)Parameters
Returns
The MLflow-compatible tracking URI.
Return type
Remarks
Use the following sample to configure MLflow tracking to send data to the Azure ML Workspace:
import mlflow
from azureml.core import Workspace
workspace = Workspace.from_config()
mlflow.set_tracking_uri(workspace.get_mlflow_tracking_uri())
get_run
Return the run with the specified run_id in the workspace.
get_run(run_id)Parameters
Returns
The submitted run.
Return type
list
List all workspaces that the user has access to within the subscription.
The list of workspaces can be filtered based on the resource group.
static list(subscription_id, auth=None, resource_group=None)Parameters
The authentication object. For more details refer to https://aka.ms/aml-notebook-auth. If None, the default Azure CLI credentials will be used or the API will prompt for credentials.
- resource_group
- str
A resource group to filter the returned workspaces. If None, the method will list all the workspaces within the specified subscription.
Returns
A dictionary where the key is workspace name and the value is a list of Workspace objects.
Return type
list_connections
List connections under this workspace.
list_connections(category=None, target=None)Parameters
- category
list_keys
set_connection
Add or update a connection under the workspace.
set_connection(name, category, target, authType, value)Parameters
set_default_datastore
Set the default datastore for the workspace.
set_default_datastore(name)Parameters
setup
Create a new workspace or retrieve an existing workspace.
static setup()Returns
A Workspace object.
Return type
sync_keys
Triggers the workspace to immediately synchronize keys.
If keys for any resource in the workspace are changed, it can take around an hour for them to automatically be updated. This function enables keys to be updated upon request. An example scenario is needing immediate access to storage after regenerating storage keys.
sync_keys(no_wait=False)Parameters
Returns
None if successful; otherwise, throws an error.
Return type
update
Update friendly name, description, tags, image build compute and other settings associated with a workspace.
update(friendly_name=None, description=None, tags=None, image_build_compute=None, service_managed_resources_settings=None, primary_user_assigned_identity=None, allow_public_access_when_behind_vnet=None, v1_legacy_mode=None)Parameters
- friendly_name
- str
A friendly name for the workspace that can be displayed in the UI.
- service_managed_resources_settings
- <xref:azureml._base_sdk_common.workspace.models.ServiceManagedResourcesSettings>
The service managed resources settings.
- primary_user_assigned_identity
- str
The user assigned identity resource id that represents the workspace identity.
- allow_public_access_when_behind_vnet
- bool
Allow public access to private link workspace.
- v1_legacy_mode
- bool
Prevent using v2 API service on public Azure Resource Manager
Returns
A dictionary of updated information.
Return type
update_dependencies
Update existing the associated resources for workspace in the following cases.
a) When a user accidently deletes an existing associated resource and would like to update it with a new one without having to recreate the whole workspace. b) When a user has an existing associated resource and wants to replace the current one that is associated with the workspace. c) When an associated resource hasn't been created yet and they want to use an existing one that they already have (only applies to container registry).
update_dependencies(container_registry=None, force=False)Parameters
- force
- bool
If force updating dependent resources without prompted confirmation.
Return type
write_config
Write the workspace Azure Resource Manager (ARM) properties to a config file.
Workspace ARM properties can be loaded later using the from_config method. The path defaults
to '.azureml/' in the current working directory and file_name defaults to 'config.json'.
The method provides a simple way of reusing the same workspace across multiple Python notebooks or projects. Users can save the workspace ARM properties using this function, and use from_config to load the same workspace in different Python notebooks or projects without retyping the workspace ARM properties.
write_config(path=None, file_name=None)Parameters
- path
- str
User provided location to write the config.json file. The parameter defaults to '.azureml/' in the current working directory.
- file_name
- str
Name to use for the config file. The parameter defaults to config.json.
Attributes
compute_targets
List all compute targets in the workspace.
Returns
A dictionary with key as compute target name and value as ComputeTarget object.
Return type
datasets
List all datasets in the workspace.
Returns
A dictionary with key as dataset name and value as Dataset object.
Return type
datastores
List all datastores in the workspace. This operation does not return credentials of the datastores.
Returns
A dictionary with key as datastore name and value as Datastore object.
Return type
discovery_url
Return the discovery URL of this workspace.
Returns
The discovery URL of this workspace.
Return type
environments
List all environments in the workspace.
Returns
A dictionary with key as environment name and value as Environment object.
Return type
experiments
List all experiments in the workspace.
Returns
A dictionary with key as experiment name and value as Experiment object.
Return type
images
Return the list of images in the workspace.
Raises a WebserviceException if there was a problem interacting with model management service.
Returns
A dictionary with key as image name and value as Image object.
Return type
Exceptions
There was a problem interacting with the model management service.
linked_services
List all linked services in the workspace.
Returns
A dictionary where key is a linked service name and value is a LinkedService object.
Return type
location
models
Return a list of model in the workspace.
Raises a WebserviceException if there was a problem interacting with model management service.
Returns
A dictionary of model with key as model name and value as Model object.
Return type
Exceptions
There was a problem interacting with the model management service.
name
private_endpoints
List all private endpoint of the workspace.
Returns
A dict of PrivateEndPoint objects associated with the workspace. The key is private endpoint name.
Return type
resource_group
service_context
Return the service context for this workspace.
Returns
Returns the ServiceContext object.
Return type
sku
subscription_id
tags
webservices
Return a list of webservices in the workspace.
Raises a WebserviceException if there was a problem returning the list.
Returns
A list of webservices in the workspace.
Return type
Exceptions
There was a problem returning the list.
DEFAULT_CPU_CLUSTER_CONFIGURATION
DEFAULT_CPU_CLUSTER_CONFIGURATION = <azureml.core.compute.amlcompute.AmlComputeProvisioningConfiguration object>DEFAULT_CPU_CLUSTER_NAME
DEFAULT_CPU_CLUSTER_NAME = 'cpu-cluster'DEFAULT_GPU_CLUSTER_CONFIGURATION
DEFAULT_GPU_CLUSTER_CONFIGURATION = <azureml.core.compute.amlcompute.AmlComputeProvisioningConfiguration object>DEFAULT_GPU_CLUSTER_NAME
DEFAULT_GPU_CLUSTER_NAME = 'gpu-cluster'Feedback
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