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Create your first project

A project is a means to explore and analyze data from a Jupyter notebook.

note

Each project has a Git repository. Learn why.

For your first project, using data we provide, you'll complete this simple workflow:

  • Create the project
  • Deploy the engine
  • Connect to the data and run a workload
  • Suspend the engine

Before you begin​

  • Your AI Unlimited admin is the person at your organization who set up AI Unlimited. From your admin, get these items:

    • The IP address or hostname of the AI Unlimited manager.

    • These environment variables:

      AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, and AWS_SESSION_TOKEN

  • From your profile, get your API key.

  • To run the sample workload, you'll need the salescenter.csv file. Download the file from the AI Unlimited GitHub repository, and upload it to your Amazon Simple Storage Service (S3) or Azure Blob Storage location. Or if you're using your own file, modify the example accordingly.

Connect, and run your first workload​

tip

Run %help for details on all the magic commands available in your notebook. Run %help <command> for details on one of them.

Or learn about the magic commands specific to AI Unlimited.

  1. Connect to JupyterLab, open a notebook, and select the AI Unlimited kernel.

  2. Connect to the manager.

  3. Create the project.

  4. Create an object store authorization to store the cloud service provider credentials.

    Replace ACCESS_KEY_ID, SECRET_ACCESS_KEY, and REGION with your values.

  5. Deploy the engine.

    The size can be small, medium, large, or extralarge. The default is small. See AWS or Azure pricing information.

    The deployment process takes a few minutes. It generates a password.

  6. Connect to the project.

    When the connection is made, provide the generated password.

  7. Run the sample workload.

    note

    Make sure you do not have tables named SalesCenter or SalesDemo in the selected database. Replace Authorization_Name and the salescenter.csv_file_location values in the following example with your own values.

    a. Create a table to store the sales center data.

    b. Verify that the data was inserted.

    c. Create a table with the sales demo data.

    d. Verify that the sales demo data was inserted successfully.

    Open the Navigator for your connection and verify that the tables were created. Run a row count on the tables to verify that the data was loaded.

    e. Chart the data to visualize the result.

    Provide X and Y axes for your chart.

    f. Drop the tables.

  8. Back up your project metadata and object definitions (the schema) in your Git repository.

    This backs up your project, but it does not suspend the engine.

  9. Suspend the engine to avoid paying for unneeded engine resources.

    This backs up your project and suspends the engine. Typically you will not use %project_backup immediately followed by %project_engine_suspend.

Congratulations! You've run your first workload.