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Merge pull request #6123 from s-polly/stp_ml-client-7-18
updated usage of MLClient.from_config
2 parents 3bc673b + 29c38ea commit b36db53

7 files changed

+29
-23
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‎articles/machine-learning/how-to-access-data-batch-endpoints-jobs.md

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@@ -59,7 +59,7 @@ Use the Azure Machine Learning SDK for Python to sign in:
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from azure.ai.ml import MLClient
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from azure.identity import DefaultAzureCredential
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ml_client = MLClient.from_config(DefaultAzureCredential())
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ml_client = MLClient.from_config(credential=DefaultAzureCredential())
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```
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If your configuration runs outside an Azure Machine Learning compute instance, you need to specify the workspace where the endpoint is deployed:

‎articles/machine-learning/how-to-datastore.md

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@@ -36,8 +36,9 @@ In this article, you learn how to connect to Azure data storage services with Az
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```python
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from azure.ai.ml.entities import AzureBlobDatastore
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from azure.ai.ml import MLClient
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from azure.identity import DefaultAzureCredential
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ml_client = MLClient.from_config()
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ml_client = MLClient.from_config(credential=DefaultAzureCredential())
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store = AzureBlobDatastore(
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name="",
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from azure.ai.ml.entities import AzureBlobDatastore
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from azure.ai.ml.entities import AccountKeyConfiguration
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from azure.ai.ml import MLClient
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from azure.identity import DefaultAzureCredential
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ml_client = MLClient.from_config()
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ml_client = MLClient.from_config(credential=DefaultAzureCredential())
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store = AzureBlobDatastore(
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name="blob_protocol_example",
@@ -78,8 +80,9 @@ ml_client.create_or_update(store)
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from azure.ai.ml.entities import AzureBlobDatastore
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from azure.ai.ml.entities import SasTokenConfiguration
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from azure.ai.ml import MLClient
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from azure.identity import DefaultAzureCredential
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ml_client = MLClient.from_config()
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ml_client = MLClient.from_config(credential=DefaultAzureCredential())
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store = AzureBlobDatastore(
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name="blob_sas_example",
@@ -163,8 +166,9 @@ az ml datastore create --file my_blob_datastore.yml
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```python
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from azure.ai.ml.entities import AzureDataLakeGen2Datastore
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from azure.ai.ml import MLClient
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from azure.identity import DefaultAzureCredential
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ml_client = MLClient.from_config()
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ml_client = MLClient.from_config(credential=DefaultAzureCredential())
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store = AzureDataLakeGen2Datastore(
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name="",
@@ -183,8 +187,9 @@ from azure.ai.ml.entities import AzureDataLakeGen2Datastore
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from azure.ai.ml.entities._datastore.credentials import ServicePrincipalCredentials
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from azure.ai.ml import MLClient
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from azure.identity import DefaultAzureCredential
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ml_client = MLClient.from_config()
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ml_client = MLClient.from_config(credential=DefaultAzureCredential())
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store = AzureDataLakeGen2Datastore(
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name="adls_gen2_example",
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from azure.ai.ml.entities import AzureFileDatastore
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from azure.ai.ml.entities import AccountKeyConfiguration
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from azure.ai.ml import MLClient
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from azure.identity import DefaultAzureCredential
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ml_client = MLClient.from_config()
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ml_client = MLClient.from_config(credential=DefaultAzureCredential())
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store = AzureFileDatastore(
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name="file_example",
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from azure.ai.ml.entities import AzureFileDatastore
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from azure.ai.ml.entities import SasTokenConfiguration
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from azure.ai.ml import MLClient
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from azure.identity import DefaultAzureCredential
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ml_client = MLClient.from_config()
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ml_client = MLClient.from_config(credential=DefaultAzureCredential())
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store = AzureFileDatastore(
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name="file_sas_example",
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```python
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from azure.ai.ml.entities import AzureDataLakeGen1Datastore
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from azure.ai.ml import MLClient
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from azure.identity import DefaultAzureCredential
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ml_client = MLClient.from_config()
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ml_client = MLClient.from_config(credential=DefaultAzureCredential())
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store = AzureDataLakeGen1Datastore(
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name="",
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from azure.ai.ml.entities import AzureDataLakeGen1Datastore
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from azure.ai.ml.entities._datastore.credentials import ServicePrincipalCredentials
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from azure.ai.ml import MLClient
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from azure.identity import DefaultAzureCredential
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ml_client = MLClient.from_config()
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ml_client = MLClient.from_config(credential=DefaultAzureCredential())
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store = AzureDataLakeGen1Datastore(
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name="adls_gen1_example",
@@ -445,8 +454,9 @@ You will then find "Endpoint", "Workspace GUID" and "Artifact GUID" in "URL" and
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```python
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from azure.ai.ml.entities import OneLakeDatastore, OneLakeArtifact
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from azure.ai.ml import MLClient
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from azure.identity import DefaultAzureCredential
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ml_client = MLClient.from_config()
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ml_client = MLClient.from_config(credential=DefaultAzureCredential())
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store = OneLakeDatastore(
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name="onelake_example_id",
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# [Python SDK: Service principal](#tab/sdk-onelake-sp)
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```python
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from azure.ai.ml.entities import AzureDataLakeGen1Datastore
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from azure.ai.ml.entities import OneLakeDatastore, OneLakeArtifact
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from azure.ai.ml.entities._datastore.credentials import ServicePrincipalCredentials
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from azure.ai.ml import MLClient
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from azure.identity import DefaultAzureCredential
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ml_client = MLClient.from_config()
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rom azure.ai.ml.entities import OneLakeDatastore, OneLakeArtifact
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from azure.ai.ml import MLClient
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ml_client = MLClient.from_config()
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ml_client = MLClient.from_config(credential=DefaultAzureCredential())
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store = OneLakeDatastore(
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name="onelake_example_sp",

‎articles/machine-learning/how-to-package-models-app-service.md

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@@ -87,7 +87,7 @@ Follow these steps to prepare your system.
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1. If you're running in an Azure Machine Learning compute instance, create an `MLClient` as follows:
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```python
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ml_client = MLClient.from_config(DefaultAzureCredential())
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ml_client = MLClient.from_config(credential=DefaultAzureCredential())
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```
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Otherwise, configure workspace details and get a handle to the workspace:

‎articles/machine-learning/how-to-package-models-moe.md

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@@ -82,7 +82,7 @@ The workspace is the top-level resource for Azure Machine Learning, providing a
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2. If you're running in a compute instance in Azure Machine Learning, create an `MLClient` as follows:
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```python
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ml_client = MLClient.from_config(DefaultAzureCredential())
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ml_client = MLClient.from_config(credential=DefaultAzureCredential())
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```
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Otherwise, configure your workspace details and get a handle to the workspace:

‎articles/machine-learning/how-to-package-models.md

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@@ -99,7 +99,7 @@ The workspace is the top-level resource for Azure Machine Learning, providing a
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2. If you're running in a compute instance in Azure Machine Learning, create an `MLClient` as follows:
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```python
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ml_client = MLClient.from_config(DefaultAzureCredential())
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ml_client = MLClient.from_config(credential=DefaultAzureCredential())
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```
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Otherwise, configure your workspace details and get a handle to the workspace:

‎articles/machine-learning/migrate-to-v2-command-job.md

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@@ -61,7 +61,7 @@ This article gives a comparison of scenario(s) in SDK v1 and SDK v2.
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from azure.identity import DefaultAzureCredential
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#connect to the workspace
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ml_client = MLClient.from_config(DefaultAzureCredential())
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ml_client = MLClient.from_config(credential=DefaultAzureCredential())
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# set up pytorch environment
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env = Environment(

‎articles/machine-learning/migrate-to-v2-local-runs.md

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@@ -57,7 +57,7 @@ This article gives a comparison of scenario(s) in SDK v1 and SDK v2.
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from azure.identity import DefaultAzureCredential
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#connect to the workspace
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ml_client = MLClient.from_config(DefaultAzureCredential())
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ml_client = MLClient.from_config(credential=DefaultAzureCredential())
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# set up pytorch environment
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env = Environment(

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