Notatnik do tego ćwiczenia

Lab 3A: Running Experiments

Jobs are at the core of a data scientist's work. In Azure Machine Learning SDK v2, you submit and track jobs that run a script, and use MLflow tracking - which is built into Azure Machine Learning - to record metrics and outputs.

Before You Start

Before you start this lab, ensure that you have completed Lab 1A and Lab 1B, which include tasks to create the Azure Machine Learning workspace and other resources used in this lab.

Task 1: Run Experiments in a Compute Instance

An Azure Machine Learning Compute Instance provides a useful environment for running experiment code, right in your workspace.

1. In Azure Machine Learning studio, view the Compute page for your workspace; and on the Compute Instances tab, ensure your compute instance (mymachine) is running. If not, start it.
2. When the compute instance is running, click the JupyterLab link to open JupyterLab in a new browser tab.
3. In the JupyterLab file browser, open the MachineLearningCourse/materialy/azure-ml/en folder you cloned in Lab 1B (~/cloudfiles/code/Users/<your-user-name>/MachineLearningCourse/materialy/azure-ml/en), then open the 03A - Running Experiments.ipynb notebook. Confirm that it uses the Python 3.10 - SDK v2 kernel, then read the notes in the notebook, running each code cell in turn.

Note: If you intend to continue straight to the next exercise, leave your compute instance running. If you're taking a break, you might want to close all JupyterLab tabs and Stop your compute instance to avoid incurring unnecessary costs.