Create an Experiment in Azure Machine Learning

1.5 hours
  • 3 Learning Objectives

About this Hands-on Lab

The process of experimenting on a model’s architecture is hyperparameter tuning. Azure Machine Learning Studio provides Experiments as a way to track the results of hyperparameter tuning. In this lab, we teach how to set up and run a hyperparameter tuning job using Experiments. Then we view and evaluate the results in the Azure Portal.

Learning Objectives

Successfully complete this lab by achieving the following learning objectives:

Azure Setup
  1. Open Machine Learning Studio. Use the Preview Environment to become familiar with the new look and feel. Note: Create the workspace in the same region as your lab provided resource group.
  2. Create a Compute instance.
    • The Compute must be uniquely named. We can use the name of the Machine Learning Workspace as part of the Compute name to guarantee uniqueness.
    • This will take a few minutes to spin up. Grab a coffee or tea while waiting.
  3. Clone the Jupyter Notebook from GitHub.
    • Go to the Notebooks section of Machine Learning Studio.
    • Create a new Python notebook and edit it in Jupyter.
    • In the first cell, run the following:
      !git clone https://github.com/linuxacademy/content-dp100
    • After the repo is cloned, close this notebook and open the content-dp100/notebooks/MNIST_AzureExperiment.ipynb notebook, choosing to edit it in Jupyter.
Create and Run the Experiment
  1. Follow the steps in the notebook.
    • Run each code cell in the MNIST_AzureExperiment notebook and view the results.
    • Read the explanations to get a better understanding of what each cell is doing.
Evaluate the Results Of the Experiment
  1. View the results in Machine Learning Studio.
    • The final cell of the notebook provides a link directly to the mnist experiment. Run the cell and click the link.
    • Change the graph showing batch_size to instead show accuracy.
    • Remove the Compute target, Run type, and Tags columns from the table.
    • Add the accuracy column to the table.
  2. Evaluate how the changing batch_size affects the loss, accuracy, and training time. What would explain this phenomenon?
  3. Do Further Research.

The larger batch sizes are prone to becoming stuck in local minima or missing minima entirely. The models train much faster because less backpropagation has to happen in the network, but the cost is accuracy. Batch sizes between 32 and 256 are good starting points for most models, but this requires tuning like all hyperparameters.

Additional Resources

We have data and a model that works for predictions on that data, but could we change something about the model to make it better? With Azure Machine Learning Studio Experiments, we can automatically tweak our model, maintaining a history of results to find the optimal values for the best model.

We will use the MNIST dataset provided by Azure Open Datasets to run our experiments.

Lab Goals

  1. Open Machine Learning Studio
  2. Create a Compute Instance
  3. Clone the Jupyter notebook from GitHub
  4. Follow the steps in the notebook
  5. View the results in Machine Learning Studio

Logging in to the lab environment

To avoid issues with the lab, use a new Incognito or Private browser window to log in to the lab. This ensures that your personal account credentials, which may be active in your main window, are not used for the lab.

Note: Create the workspace in the same region as your lab provided resource group.

What are Hands-on Labs

Hands-on Labs are real environments created by industry experts to help you learn. These environments help you gain knowledge and experience, practice without compromising your system, test without risk, destroy without fear, and let you learn from your mistakes. Hands-on Labs: practice your skills before delivering in the real world.

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