Merge branch 'master' of https://github.com/MicrosoftDocs/azure-docs-pr into dirFix

Author: roygara

articles/machine-learning/service/media/tutorial-1st-experiment-sdk-setup/clone-tutorials.png

@@ -9,7 +9,7 @@ ms.topic: tutorial
 author: trevorbye
 ms.author: trbye
 ms.reviewer: trbye
-ms.date: 08/28/2019
+ms.date: 09/25/2019
 ---
 
 # Tutorial: Get started creating your first ML experiment with the Python SDK
@@ -20,44 +20,65 @@ In this tutorial, you:
 
 > [!div class="checklist"]
 > * Create an [Azure Machine Learning Workspace](concept-workspace.md) to use in the next tutorial.
+> * Clone the tutorials notebook to your folder in the workspace.
 > * Create a cloud-based Jupyter notebook VM with Azure Machine Learning Python SDK installed and pre-configured.
 
 If you don’t have an Azure subscription, create a free account before you begin. Try the [free or paid version of Azure Machine Learning](https://aka.ms/AMLFree) today.
 
 ## Create a workspace
 
-An Azure Machine Learning workspace is a foundational resource in the cloud that you use to experiment, train, and deploy machine learning models. It ties your Azure subscription and resource group to an easily consumed object in the SDK. If you already have an Azure Machine Learning workspace, skip to the [next section](#azure). Otherwise, create one now.
+An Azure Machine Learning workspace is a foundational resource in the cloud that you use to experiment, train, and deploy machine learning models. It ties your Azure subscription and resource group to an easily consumed object in the service. 
+
+You create a workspace via the Azure portal, a web-based console for managing your Azure resources. 
 
 [!INCLUDE [aml-create-portal](../../../includes/aml-create-in-portal.md)]
 
-## <a name="azure"></a>Create a cloud notebook server
+>[!IMPORTANT] 
+> Take note of your **workspace** and **subscription**. You'll need these to ensure you create your experiment in the right place. 
+
+
+## <a name="azure"></a>Clone a notebook folder
 
 This example uses the cloud notebook server in your workspace for an install-free and pre-configured experience. Use [your own environment](how-to-configure-environment.md#local) if you prefer to have control over your environment, packages and dependencies.
 
-From your workspace, you create a cloud resource to get started using Jupyter notebooks. This resource is a cloud-based Linux virtual machine pre-configured with everything you need to run Azure Machine Learning.
+You complete the following experiment set-up and run steps in the workspace landing page (preview), a consolidated interface that includes machine learning tools to perform data science scenarios for data science practitioners of all skill levels.
+
+1. Sign in to the [workspace landing page](https://ml.azure.com/).
+
+1. Select your subscription and the workspace you created.
+
+1. Select **Notebooks and Files** on the left.
+
+1. Open the **samples** folder.
+
+1. Select the **"..."** at the right of the **tutorials** folder and then select **Clone**.
+
+    ![Clone folder](media/tutorial-1st-experiment-sdk-setup/clone-tutorials.png)
+
+1. There is a folder displayed for each user who accesses the workspace.  Select your folder to clone the **tutorial**  folder there.
 
-1. Open your workspace in the [Azure portal](https://portal.azure.com/).  If you're not sure how to locate your workspace in the portal, see how to [find your workspace](how-to-manage-workspace.md#view).
+## <a name="open">Select a VM to run the notebook
 
-1. On your workspace page in the Azure portal, select **Notebook VMs** on the left.
+1. Under **User Files** open your folder and then open the cloned **tutorials** folder.
 
-1. Select **+New** to create a notebook VM.
+    ![Open tutorials folder](media/tutorial-1st-experiment-sdk-setup/expand-user-folder.png)
 
-     ![Select New VM](./media/tutorial-1st-experiment-sdk-setup/add-workstation.png)
+    > [!IMPORTANT]
+    > You can view notebooks in the **samples** folder but you cannot run a notebook from there.  In order to run a notebook, make sure you open the cloned version of the notebook in the **User Files** section.
+    
+1. Select the **tutorial-1st-experiment-sdk-train.ipynb** file in your **tutorials** folder.
 
-1. Provide a name for your VM. 
-   + Your Notebook VM name must be between 2 to 16 characters. Valid characters are letters, digits, and the - character.  
-   + The name must also be unique across your Azure subscription.
+1. On the top bar, select a Notebook VM to use to run the notebook. These VMs are pre-configured with everything you need to run Azure Machine Learning. You can select a VM created by any user of your workspace. 
 
-1. Then select **Create**. It can take a moment to set up your VM.
+1. If no VMs are found, select **+ New VM** to create the VM.
 
-1. Wait until the status changes to **Running**.
-   After your VM is running, use the **Notebook VMs** section to launch the Jupyter web interface.
+    ![Create a VM](media/tutorial-1st-experiment-sdk-setup/no-vm.png)
 
-1. Select **Jupyter** in the **URI** column for your VM.
+    1. When you create a VM, provide a name.  The name must be between 2 to 16 characters. Valid characters are letters, digits, and the - character, and must also be unique across your Azure subscription.
 
-    ![Start the Jupyter notebook server](./media/tutorial-1st-experiment-sdk-setup/start-server.png)
+    1. Then select **Create**. It can take approximately 5 minutes to set up your VM.
 
-   The link starts your notebook server and opens the Jupyter notebook webpage in a new browser tab.  This link will only work for the person who creates the VM. Each user of the workspace must create their own VM.
+1. Once the VM is available it will be displayed in the top toolbar.  You can now run the notebook either by using **Run all** in the toolbar, or by using **Shift+Enter** in the code cells of the notebook.
 
 
 ## Next steps

articles/machine-learning/service/media/tutorial-1st-experiment-sdk-setup/expand-user-folder.png

@@ -30,21 +30,12 @@ The only prerequisite is to run part one of this tutorial, [Setup environment an
 
 In this part of the tutorial, you run the code in the sample Jupyter notebook `tutorials/tutorial-1st-experiment-sdk-train.ipynb` opened at the end of part one. This article walks through the same code that is in the notebook.
 
-## Launch Jupyter web interface
+## Open the notebook
 
-1. On your workspace page in the Azure portal, select **Notebook VMs** on the left.
+1. Sign in to the [workspace landing page](https://ml.azure.com/).
 
-1. Select **Jupyter** in the **URI** column for the VM you created in part one of this tutorial.
+1. Open the **tutorial-1st-experiment-sdk-train.ipynb** in your folder as shown in [part one](tutorial-1st-experiment-sdk-setup.md#open).
 
-    ![Start the Jupyter notebook server](./media/tutorial-1st-experiment-sdk-setup/start-server.png)
-
-   The link starts your notebook server and opens the Jupyter notebook webpage in a new browser tab.  This link will only work for the person who creates the VM. Each user of the workspace must create their own VM.
-
-1. On the Jupyter notebook webpage, select the top foldername, which has your username.  
-
-   This folder exists in the workspace [storage account](concept-workspace.md#resources) rather than on the notebook VM itself.  If you delete the notebook VM, you'll still keep all your work.  When you create a new notebook VM later, it will load this same folder. If you share your workspace with others, they will see your folder and you will see theirs.
-
-1. Open the `samples-*` subdirectory, then open the Jupyter notebook `tutorials/tutorial-1st-experiment-sdk-train.ipynb`, **not** the `.yml` file of the same name. 
 
 > [!Warning]
 > Do **not** create a *new* notebook in the Jupyter interface! The notebook `tutorials/tutorial-1st-experiment-sdk-train.ipynb` is inclusive of **all code and data needed** 
@@ -56,7 +47,7 @@ In this part of the tutorial, you run the code in the sample Jupyter notebook `t
 > The rest of this article contains the same content as you see in the notebook.  
 >
 > Switch to the Jupyter notebook now if you want to read along as you run the code. 
-> To run a single code cell in a notebook, click the code cell and hit **Shift+Enter**. Or, run the entire notebook by choosing **Cell > Run All** from the top menu.
+> To run a single code cell in a notebook, click the code cell and hit **Shift+Enter**. Or, run the entire notebook by choosing **Run all** from the top toolbar.
 
 Import the `Workspace` class, and load your subscription information from the file `config.json` using the function `from_config().` This looks for the JSON file in the current directory by default, but you can also specify a path parameter to point to the file using `from_config(path="your/file/path")`. In a cloud notebook server, the file is automatically in the root directory.
 
@@ -134,11 +125,11 @@ After the training has completed, call the `experiment` variable to fetch a link
 experiment
 ```
 
-<table style="width:100%"><tr><th>Name</th><th>Workspace</th><th>Report Page</th><th>Docs Page</th></tr><tr><td>diabetes-experiment</td><td>your-workspace-name</td><td>Link to Azure Portal</td><td>Link to Documentation</td></tr></table>
+<table style="width:100%"><tr><th>Name</th><th>Workspace</th><th>Report Page</th><th>Docs Page</th></tr><tr><td>diabetes-experiment</td><td>your-workspace-name</td><td>Link to Azure portal</td><td>Link to Documentation</td></tr></table>
 
 ## View training results in portal
 
-Following the **Link to Azure Portal** takes you to the main experiment page. Here you see all the individual runs in the experiment. Any custom-logged values (`alpha_value` and `rmse`, in this case) become fields for each run, and also become available for the charts and tiles at the top of the experiment page. To add a logged metric to a chart or tile, hover over it, click the edit button, and find your custom-logged metric.
+Following the **Link to Azure portal** takes you to the main experiment page. Here you see all the individual runs in the experiment. Any custom-logged values (`alpha_value` and `rmse`, in this case) become fields for each run, and also become available for the charts and tiles at the top of the experiment page. To add a logged metric to a chart or tile, hover over it, click the edit button, and find your custom-logged metric.
 
 When training models at scale over hundreds and thousands of separate runs, this page makes it easy to see every model you trained, specifically how they were trained, and how your unique metrics have changed over time.
 
@@ -178,7 +169,7 @@ print("Best run_id rmse: " + str(minimum_rmse))
     Best run_id: 864f5ce7-6729-405d-b457-83250da99c80
     Best run_id rmse: 57.234760283951765
 
-Use the best run id to fetch the individual run using the `Run` constructor along with the experiment object. Then call `get_file_names()` to see all the files available for download from this run. In this case, you only uploaded one file for each run during training.
+Use the best run ID to fetch the individual run using the `Run` constructor along with the experiment object. Then call `get_file_names()` to see all the files available for download from this run. In this case, you only uploaded one file for each run during training.
 
 ```python
 from azureml.core import Run

articles/machine-learning/service/media/tutorial-1st-experiment-sdk-setup/no-vm.png

@@ -35,7 +35,7 @@ ms.date: 08/14/2019
 1. After you are finished configuring the workspace, select **Create**. 
 
    > [!Warning] 
-   > It can take a several minutes to create your workspace in the cloud.
+   > It can take several minutes to create your workspace in the cloud.
 
    When the process is finished, a deployment success message appears.