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‎NBSETUP.md

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# Setting up environment
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# Notebook setup
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---
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To run the notebooks in this repository use one of the two options.
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To run the notebooks in this repository use one of these methods:
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## Option 1: Use Azure Notebooks
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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.
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## Use Azure Notebooks - Jupyter based notebooks in the Azure cloud
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1. [![Azure Notebooks](https://notebooks.azure.com/launch.png)](https://aka.ms/aml-clone-azure-notebooks)
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[Import sample notebooks ](https://aka.ms/aml-clone-azure-notebooks) into Azure Notebooks
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![set kernel to Python 3.6](images/python36.png)
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## **Option 2: Use your own notebook server**
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## **Use your own notebook server**
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### Quick installation
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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.
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```sh
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# install just the base SDK
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pip install azureml-sdk
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# clone the sample repoistory
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git clone https://github.com/Azure/MachineLearningNotebooks.git
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# below steps are optional
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# install the base SDK and a Jupyter notebook server
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pip install azureml-sdk[notebooks]
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# install the data prep component
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pip install azureml-dataprep
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# install model explainability component
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pip install azureml-sdk[explain]
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# install automated ml components
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pip install azureml-sdk[automl]
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# install experimental features (not ready for production use)
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pip install azureml-sdk[contrib]
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```
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### Full instructions
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[Install the Azure Machine Learning SDK](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-create-workspace-with-python)
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Please make sure you start with the [Configuration](configuration.ipynb) notebook to create and connect to a workspace.
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Video walkthrough:
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[![Get Started video](images/yt_cover.png)](https://youtu.be/VIsXeTuW3FU)
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### Video walkthrough:
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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)
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1. Clone [this repository](https://aka.ms/aml-notebooks)
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1. You may need to install other packages for specific notebook
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- For example, to run the Azure Machine Learning Data Prep notebooks, install the extra dataprep SDK:
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```bash
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pip install azureml-dataprep
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```
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[![Get Started video](images/yt_cover.png)](https://youtu.be/VIsXeTuW3FU)
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1. Start your notebook server
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1. Follow the instructions in the [Configuration](configuration.ipynb) notebook to create and connect to a workspace
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1. Open one of the sample notebooks

‎README.md

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@@ -55,5 +55,4 @@ The [How to use Azure ML](./how-to-use-azureml) folder contains specific example
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Visit following repos to see projects contributed by Azure ML users:
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- [Fine tune natural language processing models using Azure Machine Learning service](https://github.com/Microsoft/AzureML-BERT)
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- [Fashion MNIST with Azure ML SDK](https://github.com/amynic/azureml-sdk-fashion)
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- [Fashion MNIST with Azure ML SDK](https://github.com/amynic/azureml-sdk-fashion)

‎configuration.ipynb

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"source": [
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"import azureml.core\n",
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"\n",
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"print(\"This notebook was created using version 1.0.6 of the Azure ML SDK\")\n",
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"print(\"This notebook was created using version 1.0.10 of the Azure ML SDK\")\n",
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"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
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]
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},
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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}

‎how-to-use-azureml/automated-machine-learning/automl_setup.cmd

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@echo off
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set conda_env_name=%1
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set automl_env_file=%2
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set options=%3
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set PIP_NO_WARN_SCRIPT_LOCATION=0
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IF "%conda_env_name%"=="" SET conda_env_name="azure_automl"
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echo ***************************************
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echo * AutoML setup completed successfully *
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echo ***************************************
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echo.
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echo Starting jupyter notebook - please run the configuration notebook
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echo.
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jupyter notebook --log-level=50 --notebook-dir='..\..'
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IF NOT "%options%"=="nolaunch" (
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echo.
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echo Starting jupyter notebook - please run the configuration notebook
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echo.
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jupyter notebook --log-level=50 --notebook-dir='..\..'
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)
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goto End
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‎how-to-use-azureml/automated-machine-learning/automl_setup_linux.sh

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CONDA_ENV_NAME=$1
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AUTOML_ENV_FILE=$2
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OPTIONS=$3
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PIP_NO_WARN_SCRIPT_LOCATION=0
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if [ "$CONDA_ENV_NAME" == "" ]
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echo "***************************************" &&
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echo "* AutoML setup completed successfully *" &&
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echo "***************************************" &&
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echo "" &&
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echo "Starting jupyter notebook - please run the configuration notebook" &&
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echo "" &&
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jupyter notebook --log-level=50 --notebook-dir '../..'
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if [ "$OPTIONS" != "nolaunch" ]
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then
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echo "" &&
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echo "Starting jupyter notebook - please run the configuration notebook" &&
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echo "" &&
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jupyter notebook --log-level=50 --notebook-dir '../..'
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fi
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fi
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if [ $? -gt 0 ]

‎how-to-use-azureml/automated-machine-learning/automl_setup_mac.sh

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CONDA_ENV_NAME=$1
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AUTOML_ENV_FILE=$2
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OPTIONS=$3
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PIP_NO_WARN_SCRIPT_LOCATION=0
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if [ "$CONDA_ENV_NAME" == "" ]
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echo "***************************************" &&
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echo "* AutoML setup completed successfully *" &&
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echo "***************************************" &&
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echo "" &&
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echo "Starting jupyter notebook - please run the configuration notebook" &&
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echo "" &&
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jupyter notebook --log-level=50 --notebook-dir '../..'
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if [ "$OPTIONS" != "nolaunch" ]
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then
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echo "" &&
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echo "Starting jupyter notebook - please run the configuration notebook" &&
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echo "" &&
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jupyter notebook --log-level=50 --notebook-dir '../..'
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fi
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fi
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if [ $? -gt 0 ]

‎how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Setup\n",
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"\n",
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"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."
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"## Setup\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"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. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"try:\n",
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" import ftk\n",
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" print('Using FTK version ' + ftk.__version__)\n",
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"except ImportError:\n",
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" print(\"Unable to import forecasting package. This notebook does not work without this package.\\n\"\n",
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" + \"Please open a command prompt and run `pip install azuremlftk` to install the package. \\n\"\n",
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" + \"Make sure you install the package into AutoML's Python environment.\\n\\n\"\n",
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" + \"For instance, if AutoML is installed in a conda environment called `python36`, run:\\n\"\n",
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" + \"> activate python36\\n> pip install azuremlftk\")"
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]
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},
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{
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"from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"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."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.6"
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"version": "3.6.8"
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}
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},
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"nbformat": 4,

‎how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Setup\n",
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"\n",
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"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. "
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"## Setup"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"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."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"try:\n",
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" import ftk\n",
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" print('Using FTK version ' + ftk.__version__)\n",
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"except ImportError:\n",
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" print(\"Unable to import forecasting package. This notebook does not work without this package.\\n\"\n",
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" + \"Please open a command prompt and run `pip install azuremlftk` to install the package. \\n\"\n",
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" + \"Make sure you install the package into AutoML's Python environment.\\n\\n\"\n",
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" + \"For instance, if AutoML is installed in a conda environment called `python36`, run:\\n\"\n",
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" + \"> activate python36\\n> pip install azuremlftk\")"
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]
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},
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{
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"from sklearn.metrics import mean_absolute_error, mean_squared_error"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"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. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.6"
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"version": "3.6.8"
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}
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},
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"nbformat": 4,

‎how-to-use-azureml/automated-machine-learning/remote-execution/auto-ml-remote-execution.ipynb

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" dsvm_compute = DsvmCompute.create(ws, name = dsvm_name, provisioning_configuration = dsvm_config)\n",
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" dsvm_compute.wait_for_completion(show_output = True)\n",
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" print(\"Waiting one minute for ssh to be accessible\")\n",
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" time.sleep(60) # Wait for ssh to be accessible"
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" time.sleep(90) # Wait for ssh to be accessible"
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]
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},
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{

‎how-to-use-azureml/azure-databricks/README.md

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- Every run (including the best run) is available as a pipeline, which you can tune further if needed.
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- 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.
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**Create Azure Databricks Cluster:**
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Select New Cluster and fill in following detail:
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- Cluster name: _yourclustername_
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- Databricks Runtime: Any **non ML** runtime (non ML 4.x, 5.x)
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- Python version: **3**
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- Workers: 2 or higher.
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These settings are only for using Automated Machine Learning on Databricks.
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- Max. number of **concurrent iterations** in Automated ML settings is **<=** to the number of **worker nodes** in your Databricks cluster.
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- Worker node VM types: **Memory optimized VM** preferred.
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- Uncheck _Enable Autoscaling_
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It will take few minutes to create the cluster. Please ensure that the cluster state is running before proceeding further.
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**Install Azure ML SDK without Automated ML capability on your Azure Databricks cluster**
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- Select Import library
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- Source: Upload Python Egg or PyPI
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- PyPi Name: **azureml-sdk[databricks]**
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**Install Azure ML with Automated ML SDK on your Azure Databricks cluster**
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- Select Import library
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- Source: Upload Python Egg or PyPI
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- PyPi Name: **azureml-sdk[automl_databricks]**
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**For installation with or without Automated ML**
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- Click Install Library
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- Do not select _Attach automatically to all clusters_. In case you selected this earlier, please go to your Home folder and deselect it.
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- Select the check box _Attach_ next to your cluster name
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(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) )
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- Ensure that there are no errors until Status changes to _Attached_. It may take a couple of minutes.
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**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.
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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.
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**Single file** -
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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.

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