MY PLANNER STUDIO INSTALL
MY PLANNER STUDIO CODE

MY PLANNER STUDIO CODE

Y_test = test_df.pop('default payment next month')Īdd code to start autologging with MLflow, so that you can track the metrics and results. Y_train = train_df.pop('default payment next month')

Get the data ready for training: # Extracting the label column In this tutorial, you read the data from a file on the internet. Next, load and process the data for this experiment. import osįrom sklearn.ensemble import GradientBoostingClassifierįrom trics import classification_reportįrom sklearn.model_selection import train_test_split Start with code that imports the packages and libraries you'll use in the training script. This code uses sklearn for training and MLflow for logging the metrics. In this section, you develop a Python training script that predicts credit card default payments, using the prepared test and training datasets from the UCI dataset. Switch to use the Tutorial Workstation Env kernel. You'll see the notebook is connected to the default kernel in the top right. Name your new notebook develop-tutorial.ipynb (or enter your preferred name). Select Add files, and choose Create new file. Next you'll open a notebook and use this kernel.

MY PLANNER STUDIO INSTALL

python -m ipykernel install -user -name workstation_env -display-name 'Tutorial Workstation Env' conda env listĬreate a new Jupyter kernel based on your active environment. Validate the correct environment is active, again looking for the environment marked with a *. conda env create -f workstation_env.ymlĪctivate the new environment. It takes a few minutes to build this environment. If you created a subfolder for this tutorial, cd to that folder now.Ĭreate the environment based on the conda file provided.

The active environment is marked with a *. Once the compute is running, you see a welcome message in the terminal, and you can start typing commands. If the compute instance is stopped, select Start compute and wait until it's running. You can also open the terminal from the left command bar: Select Terminal to open a terminal window. Now use the Azure Machine Learning terminal to create a new Jupyter kernel, based on the workstation_env.yml file. Select this file to preview it, and see what dependencies it specifies. You'll see the workstation_env.yml file under your username folder in the Files tab. Select workstation_env.yml file you downloaded. Select Add files, then select Upload files to upload it to your workspace. This section helps you create an environment tailored to your code. To create the new Jupyter kernel your notebook connects to, you'll use a YAML file that defines the dependencies.įiles you upload are stored in an Azure file share, and these files are mounted to each compute instance and shared within the workspace.ĭownload this conda environment file, workstation_env.yml to your computer. In order for your script to run, you need to be working in an environment configured with the dependencies and libraries the code expects. Set up a new environment for prototyping (optional) You'll use Terminal later in this tutorial.) (If you already have a compute instance, you'll instead see Terminal in that spot. Select Create compute and fill out the form. If you don't have a compute instance, you'll see Create compute in the middle of the screen. On the left navigation, select Notebooks. Select your workspace if it isn't already open. Sign in to Azure Machine Learning studio. Here you can connect to compute resources, work with a terminal, and edit and run Jupyter Notebooks and scripts. The Notebooks section in your workspace is a good place to start learning about Azure Machine Learning and its capabilities. If you don't have one, complete Create resources you need to get started to create a workspace and learn more about using it. To use Azure Machine Learning, you'll first need a workspace. Use MLflow to track your model metrics, all from within a notebook.Use cloud-based development environments.Your cloud workstation is powered by an Azure Machine Learning compute instance, which is pre-configured with environments to support your various model development needs. Set up and configuring the cloud workstation.

My planner studio