Azure · rastala · Jan 28, 2019 · Jan 28, 2019 · Jan 28, 2019 · Jan 28, 2019
diff --git a/NBSETUP.md b/NBSETUP.md
@@ -1,11 +1,10 @@
-# Setting up environment
+# Notebook setup
 
 ---
 
-To run the notebooks in this repository use one of the two options.
+To run the notebooks in this repository use one of these methods:
 
-## Option 1: Use Azure Notebooks
-Azure Notebooks is a hosted Jupyter-based notebook service in the Azure cloud. Azure Machine Learning Python SDK is already pre-installed in the Azure Notebooks `Python 3.6` kernel.
+## Use Azure Notebooks - Jupyter based notebooks in the Azure cloud
 
 1. [![Azure Notebooks](https://notebooks.azure.com/launch.png)](https://aka.ms/aml-clone-azure-notebooks)
 [Import sample notebooks ](https://aka.ms/aml-clone-azure-notebooks) into Azure Notebooks
@@ -16,40 +15,20 @@ Azure Notebooks is a hosted Jupyter-based notebook service in the Azure cloud. A
 
     ![set kernel to Python 3.6](images/python36.png)
 
-## **Option 2: Use your own notebook server**
+## **Use your own notebook server**
 
-### Quick installation
-We recommend you create a Python virtual environment ([Miniconda](https://conda.io/miniconda.html) preferred but [virtualenv](https://virtualenv.pypa.io/en/latest/) works too) and install the SDK in it.
-```sh
-# install just the base SDK
-pip install azureml-sdk
-
-# clone the sample repoistory
-git clone https://github.com/Azure/MachineLearningNotebooks.git
-
-# below steps are optional
-# install the base SDK and a Jupyter notebook server
-pip install azureml-sdk[notebooks]
-
-# install the data prep component
-pip install azureml-dataprep
-
-# install model explainability component
-pip install azureml-sdk[explain]
-
-# install automated ml components
-pip install azureml-sdk[automl]
-
-# install experimental features (not ready for production use)
-pip install azureml-sdk[contrib]
-```
-
-### Full instructions
-[Install the Azure Machine Learning SDK](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-create-workspace-with-python)
-
-Please make sure you start with the [Configuration](configuration.ipynb) notebook to create and connect to a workspace.
+Video walkthrough:
 
+[![Get Started video](images/yt_cover.png)](https://youtu.be/VIsXeTuW3FU)
 
-### Video walkthrough:
+1. Setup a Jupyter Notebook server and [install the Azure Machine Learning SDK](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-create-workspace-with-python)
+1. Clone [this repository](https://aka.ms/aml-notebooks)
+1. You may need to install other packages for specific notebook
+    - For example, to run the Azure Machine Learning Data Prep notebooks, install the extra dataprep SDK:
+    ```bash
+     pip install azureml-dataprep
+    ```
 
-[![Get Started video](images/yt_cover.png)](https://youtu.be/VIsXeTuW3FU)
+1. Start your notebook server
+1. Follow the instructions in the [Configuration](configuration.ipynb) notebook to create and connect to a workspace
+1. Open one of the sample notebooks
diff --git a/README.md b/README.md
@@ -55,5 +55,4 @@ The [How to use Azure ML](./how-to-use-azureml) folder contains specific example
 Visit following repos to see projects contributed by Azure ML users:
 
  - [Fine tune natural language processing models using Azure Machine Learning service](https://github.com/Microsoft/AzureML-BERT)
- - [Fashion MNIST with Azure ML SDK](https://github.com/amynic/azureml-sdk-fashion)
-
+ - [Fashion MNIST with Azure ML SDK](https://github.com/amynic/azureml-sdk-fashion)
diff --git a/configuration.ipynb b/configuration.ipynb
@@ -96,7 +96,7 @@
       "source": [
         "import azureml.core\n",
         "\n",
-        "print(\"This notebook was created using version 1.0.6 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.0.10 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -373,4 +373,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 2
-}
+}
diff --git a/how-to-use-azureml/automated-machine-learning/automl_setup.cmd b/how-to-use-azureml/automated-machine-learning/automl_setup.cmd
@@ -1,6 +1,7 @@
 @echo off
 set conda_env_name=%1
 set automl_env_file=%2
+set options=%3
 set PIP_NO_WARN_SCRIPT_LOCATION=0
 
 IF "%conda_env_name%"=="" SET conda_env_name="azure_automl"
@@ -32,10 +33,12 @@ echo.
 echo ***************************************
 echo * AutoML setup completed successfully *
 echo ***************************************
-echo.
-echo Starting jupyter notebook - please run the configuration notebook 
-echo.
-jupyter notebook --log-level=50 --notebook-dir='..\..'
+IF NOT "%options%"=="nolaunch" (
+  echo.
+  echo Starting jupyter notebook - please run the configuration notebook 
+  echo.
+  jupyter notebook --log-level=50 --notebook-dir='..\..'
+)
 
 goto End
 

diff --git a/how-to-use-azureml/automated-machine-learning/automl_setup_linux.sh b/how-to-use-azureml/automated-machine-learning/automl_setup_linux.sh
@@ -2,6 +2,7 @@
 
 CONDA_ENV_NAME=$1
 AUTOML_ENV_FILE=$2
+OPTIONS=$3
 PIP_NO_WARN_SCRIPT_LOCATION=0
 
 if [ "$CONDA_ENV_NAME" == "" ]
@@ -34,10 +35,13 @@ else
    echo "***************************************" &&
    echo "* AutoML setup completed successfully *" &&
    echo "***************************************" &&
-   echo "" &&
-   echo "Starting jupyter notebook - please run the configuration notebook" &&
-   echo "" &&
-   jupyter notebook --log-level=50 --notebook-dir '../..'
+   if [ "$OPTIONS" != "nolaunch" ]
+   then
+      echo "" &&
+      echo "Starting jupyter notebook - please run the configuration notebook" &&
+      echo "" &&
+      jupyter notebook --log-level=50 --notebook-dir '../..'
+   fi
 fi
 
 if [ $? -gt 0 ]

diff --git a/how-to-use-azureml/automated-machine-learning/automl_setup_mac.sh b/how-to-use-azureml/automated-machine-learning/automl_setup_mac.sh
@@ -2,6 +2,7 @@
 
 CONDA_ENV_NAME=$1
 AUTOML_ENV_FILE=$2
+OPTIONS=$3
 PIP_NO_WARN_SCRIPT_LOCATION=0
 
 if [ "$CONDA_ENV_NAME" == "" ]
@@ -36,10 +37,13 @@ else
    echo "***************************************" &&
    echo "* AutoML setup completed successfully *" &&
    echo "***************************************" &&
-   echo "" &&
-   echo "Starting jupyter notebook - please run the configuration notebook" &&
-   echo "" &&
-   jupyter notebook --log-level=50 --notebook-dir '../..'
+   if [ "$OPTIONS" != "nolaunch" ]
+   then
+      echo "" &&
+      echo "Starting jupyter notebook - please run the configuration notebook" &&
+      echo "" &&
+      jupyter notebook --log-level=50 --notebook-dir '../..'
+   fi
 fi
 
 if [ $? -gt 0 ]

diff --git a/...omated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb b/...omated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb
@@ -44,9 +44,31 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Setup\n",
-        "\n",
-        "As part of the setup you have already created a <b>Workspace</b>. For AutoML you would need to create an <b>Experiment</b>. An <b>Experiment</b> is a named object in a <b>Workspace</b>, which is used to run experiments."
+        "## Setup\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "To use the *forecasting* task in AutoML, you need to have the **azuremlftk** package installed in your environment. The following cell tests whether this package is installed locally and, if not, gives you instructions for installing it.  "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "try:\n",
+        "    import ftk\n",
+        "    print('Using FTK version ' + ftk.__version__)\n",
+        "except ImportError:\n",
+        "    print(\"Unable to import forecasting package. This notebook does not work without this package.\\n\"\n",
+        "          + \"Please open a command prompt and run `pip install azuremlftk` to install the package. \\n\"\n",
+        "          + \"Make sure you install the package into AutoML's Python environment.\\n\\n\"\n",
+        "          + \"For instance, if AutoML is installed in a conda environment called `python36`, run:\\n\"\n",
+        "          + \"> activate python36\\n> pip install azuremlftk\")"
       ]
     },
     {
@@ -71,6 +93,13 @@
         "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "As part of the setup you have already created a <b>Workspace</b>. For AutoML you would need to create an <b>Experiment</b>. An <b>Experiment</b> is a named object in a <b>Workspace</b>, which is used to run experiments."
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -368,7 +397,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.6"
+      "version": "3.6.8"
     }
   },
   "nbformat": 4,

diff --git a/...hine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb b/...hine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb
@@ -45,9 +45,31 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Setup\n",
-        "\n",
-        "As part of the setup you have already created a <b>Workspace</b>. To run AutoML, you also need to create an <b>Experiment</b>. An Experiment is a named object in a Workspace which represents a predictive task, the output of which is a trained model and a set of evaluation metrics for the model. "
+        "## Setup"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "To use the *forecasting* task in AutoML, you need to have the **azuremlftk** package installed in your environment. The following cell tests whether this package is installed locally and, if not, gives you instructions for installing it."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "try:\n",
+        "    import ftk\n",
+        "    print('Using FTK version ' + ftk.__version__)\n",
+        "except ImportError:\n",
+        "    print(\"Unable to import forecasting package. This notebook does not work without this package.\\n\"\n",
+        "          + \"Please open a command prompt and run `pip install azuremlftk` to install the package. \\n\"\n",
+        "          + \"Make sure you install the package into AutoML's Python environment.\\n\\n\"\n",
+        "          + \"For instance, if AutoML is installed in a conda environment called `python36`, run:\\n\"\n",
+        "          + \"> activate python36\\n> pip install azuremlftk\")"
       ]
     },
     {
@@ -71,6 +93,13 @@
         "from sklearn.metrics import mean_absolute_error, mean_squared_error"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "As part of the setup you have already created a <b>Workspace</b>. To run AutoML, you also need to create an <b>Experiment</b>. An Experiment is a named object in a Workspace which represents a predictive task, the output of which is a trained model and a set of evaluation metrics for the model. "
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -404,7 +433,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.6"
+      "version": "3.6.8"
     }
   },
   "nbformat": 4,

diff --git a/...to-use-azureml/automated-machine-learning/remote-execution/auto-ml-remote-execution.ipynb b/...to-use-azureml/automated-machine-learning/remote-execution/auto-ml-remote-execution.ipynb
@@ -150,7 +150,7 @@
         "    dsvm_compute = DsvmCompute.create(ws, name = dsvm_name, provisioning_configuration = dsvm_config)\n",
         "    dsvm_compute.wait_for_completion(show_output = True)\n",
         "    print(\"Waiting one minute for ssh to be accessible\")\n",
-        "    time.sleep(60) # Wait for ssh to be accessible"
+        "    time.sleep(90) # Wait for ssh to be accessible"
       ]
     },
     {

diff --git a/how-to-use-azureml/azure-databricks/README.md b/how-to-use-azureml/azure-databricks/README.md
@@ -10,52 +10,7 @@ In this section, you will find sample notebooks on how to use Azure Machine Lear
 - Every run (including the best run) is available as a pipeline, which you can tune further if needed. 
 - The model trained using Azure Databricks can be registered in Azure ML SDK workspace and then deployed to Azure managed compute (ACI or AKS) using the Azure Machine learning SDK.
 
-
-**Create Azure Databricks Cluster:**
-
-Select New Cluster and fill in following detail:
- - Cluster name: _yourclustername_
- - Databricks Runtime: Any **non ML** runtime (non ML 4.x, 5.x)
- - Python version: **3**
- - Workers: 2 or higher.  
-
-These settings are only for using Automated Machine Learning on Databricks.
- - Max. number of **concurrent iterations** in Automated ML settings is **<=** to the number of **worker nodes** in your Databricks cluster.
- - Worker node VM types: **Memory optimized VM** preferred. 
- - Uncheck _Enable Autoscaling_
-
-
-It will take few minutes to create the cluster. Please ensure that the cluster state is running before proceeding further.
-
-**Install Azure ML SDK without Automated ML capability on your Azure Databricks cluster**
-
-- Select Import library
-
-- Source: Upload Python Egg or PyPI
-
-- PyPi Name: **azureml-sdk[databricks]**
-
-**Install Azure ML with Automated ML SDK on your Azure Databricks cluster**
-
-- Select Import library
-
-- Source: Upload Python Egg or PyPI
-
-- PyPi Name: **azureml-sdk[automl_databricks]**
-
-**For installation with or without Automated ML**
-
-- Click Install Library
-
-- Do not select _Attach automatically to all clusters_. In case you selected this earlier, please go to your Home folder and deselect it.
-
-- Select the check box _Attach_ next to your cluster name
-
-(More details on how to attach and detach libs are here - [https://docs.databricks.com/user-guide/libraries.html#attach-a-library-to-a-cluster](https://docs.databricks.com/user-guide/libraries.html#attach-a-library-to-a-cluster) )
-
-- Ensure that there are no errors until Status changes to _Attached_. It may take a couple of minutes.
-
-**Note** - If you have an old SDK version, please deselect it from cluster’s installed libs > move to trash. Install the new SDK verdion and restart the cluster. If there is an issue after this, please detach and reattach your cluster.
+Please follow our [Azure doc](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-environment#azure-databricks) to install the sdk in your Azure Databricks cluster before trying any of the sample notebooks.
 
 **Single file** - 
 The following archive contains all the sample notebooks. You can the run notebooks after importing [DBC](Databricks_AMLSDK_1-4_6.dbc) in your Databricks workspace instead of downloading individually.