Merge pull request #57774 from sdgilley/sdg-m2

reference code snippets

Author: ktoliver

.openpublishing.publish.config.json

@@ -260,6 +260,12 @@
       "branch": "master",
       "branch_mapping": {}
     },
+    {
+      "path_to_root": "aml-sdk-samples",
+      "url": "https://github.com/Azure/MachineLearningNotebooks",
+      "branch": "sdk-codetest",
+      "branch_mapping": {}
+    },
     {
       "path_to_root": "cognitive-services-vision-csharp-sdk-quickstarts",
       "url": "https://github.com/Azure-Samples/cognitive-services-vision-csharp-sdk-quickstarts",

articles/machine-learning/service/quickstart-create-workspace-with-python.md

@@ -34,6 +34,9 @@ The following Azure resources are added automatically to your workspace when the
 - [Azure Application Insights](https://azure.microsoft.com/services/application-insights/) 
 - [Azure Key Vault](https://azure.microsoft.com/services/key-vault/)
 
+>[!NOTE]
+> Code in this article was tested with Azure Machine Learning SDK version 0.1.74 
+
 If you don’t have an Azure subscription, create a [free account](https://azure.microsoft.com/free/?WT.mc_id=A261C142F) before you begin.
 
 
@@ -52,64 +55,39 @@ Before you install the SDK, we recommend that you create an isolated Python envi
 
 Open a command-line window. Then create a new conda environment named `myenv` with Python 3.6.
 
-```sh
+```shell
 conda create -n myenv -y Python=3.6
 ```
 
 Activate the environment.
 
-  ```sh
+  ```shell
   conda activate myenv
   ```
 
 ### Install the SDK
 
 In the activated conda environment, install the SDK. The below command installs the core components of the Machine Learning SDK. It also installs a Jupyter Notebook server in the `myenv` conda environment. The installation takes a few minutes to finish, depending on the configuration of your machine.
 
-```sh
+```shell
 # install the base SDK and Jupyter Notebook
 pip install azureml-sdk[notebooks]
-
-# install run histry widget
-jupyter nbextension install --py --user azureml.train.widgets
-
-# enable run histry widget
-jupyter nbextension enable --py --user azureml.train.widgets
 ```
 
-You can also use different "extra" keywords to install additional components of the SDK.
-
-```sh
-# install the base SDK and auto ml components
-pip install azureml-sdk[automl]
-
-# install the base SDK and model explainability component
-pip install azureml-sdk[explain]
-
-# install the base SDK and experimental components
-pip install azureml-sdk[contrib]
-
-# install the base SDK and automl components in Azure Databricks environment
-# read more at: https://github.com/Azure/MachineLearningNotebooks/tree/master/databricks
-pip install azureml-sdk[databricks]
-```
 
 
 ## Create a workspace
 
 To launch the Jupyter Notebook, enter this command.
-```sh
+```shell
 jupyter notebook
 ```
 
 In the browser window, create a new notebook by using the default `Python 3` kernel. 
 
 To display the SDK version, enter the following Python code in a notebook cell and execute it.
 
-```python
-import azureml.core
-print(azureml.core.VERSION)
-```
+[!code-python[](~/aml-sdk-samples/ignore/doc-qa/quickstart-create-workspace-with-python/quickstart.py?name=import)]
 
 Create a new Azure resource group and a new workspace.
 
@@ -118,35 +96,28 @@ Find a value for `<azure-subscription-id>` in the [subscriptions list in the Azu
 ```python
 from azureml.core import Workspace
 ws = Workspace.create(name='myworkspace',
-                      subscription_id='<azure-subscription-id>',
+                      subscription_id='<azure-subscription-id>',	
                       resource_group='myresourcegroup',
                       create_resource_group=True,
-                      location='eastus2' # or other supported Azure region
+                      location='eastus2' # or other supported Azure region	
                      )
 ```
 
 Executing the preceding code might trigger a new browser window for you to sign into your Azure account. After you sign in, the authentication token is cached locally.
 
 To see the workspace details, such as associated storage, container registry, and key vault, enter the following code.
 
-```python
-ws.get_details()
-```
+[!code-python[](~/aml-sdk-samples/ignore/doc-qa/quickstart-create-workspace-with-python/quickstart.py?name=getDetails)]
+
 
 ## Write a configuration file
 
 Save the details of your workspace in a configuration file into the current directory. This file is called 'aml_config\config.json'.  
 
 This workspace configuration file makes it easy to load this same workspace later. You can load it with other notebooks and scripts in the same directory or a subdirectory. 
 
-```python
-# Create the configuration file.
-ws.write_config()
+[!code-python[](~/aml-sdk-samples/ignore/doc-qa/quickstart-create-workspace-with-python/quickstart.py?name=writeConfig)]
 
-# Use this code to load the workspace from 
-# other scripts and notebooks in this directory.
-# ws = Workspace.from_config()
-```
 
 The `write_config()` API call creates the configuration file in the current directory. The `config.json` file contains the following script.
 
@@ -162,24 +133,8 @@ The `write_config()` API call creates the configuration file in the current dire
 
 Write some code that uses the basic APIs of the SDK to track experiment runs.
 
-```python
-from azureml.core import Experiment
-
-# create a new experiment
-exp = Experiment(workspace=ws, name='myexp')
+[!code-python[](~/aml-sdk-samples/ignore/doc-qa/quickstart-create-workspace-with-python/quickstart.py?name=useWs)]
 
-# start a run
-run = exp.start_logging()
-
-# log a number
-run.log('my magic number', 42)
-
-# log a list (Fibonacci numbers)
-run.log_list('my list', [1, 1, 2, 3, 5, 8, 13, 21, 34, 55]) 
-
-# finish the run
-run.complete()
-```
 
 ## View logged results
 When the run finishes, you can view the experiment run in the Azure portal. Use the following code to print a URL to the results for the last run.
@@ -198,9 +153,8 @@ Use the link to view the logged values in the Azure portal in your browser.
 
 If you don't plan to use the resources you created here, delete them so you don't incur any charges.
 
-```python
-ws.delete(delete_dependent_resources=True)
-```
+[!code-python[](~/aml-sdk-samples/ignore/doc-qa/quickstart-create-workspace-with-python/quickstart.py?name=delete)]
+
 
 ## Next steps
 
@@ -212,11 +166,35 @@ You need a few more packages in your environment to use it with Machine Learning
 1. In the command-line window, use `Ctrl`+`C` to stop the notebook server.
 1. Install additional packages.
 
-    ```sh
+    ```shell
     conda install -y cython matplotlib scikit-learn pandas numpy
     pip install azureml-sdk[automl]
+
+    # install run history widget
+    jupyter nbextension install --py --user azureml.train.widgets
+
+    # enable run history widget
+    jupyter nbextension enable --py --user azureml.train.widgets
     ```
 
+    You can also use different "extra" keywords to install additional components of the SDK.
+
+    ```shell
+    # install the base SDK and auto ml components
+    pip install azureml-sdk[automl]
+
+    # install the base SDK and model explainability component
+    pip install azureml-sdk[explain]
+
+    # install the base SDK and experimental components
+    pip install azureml-sdk[contrib]
+
+    # install the base SDK and automl components in Azure Databricks environment
+    # read more at: https://github.com/Azure/MachineLearningNotebooks/tree/master/databricks
+    pip install azureml-sdk[databricks]
+    ```
+
+
 After you install these packages, follow the tutorials to train and deploy a model. 
 
 > [!div class="nextstepaction"]