Community Detection (TastyBites)

Detect customer communities with graph algorithms, Louvain clustering, and 3D visualizations—right inside Snowflake using RelationalAI.

Download the Sample Notebook

Instructions

Snowflake

Follow these steps to run the sample notebook using Snowflake Notebooks.

1. Log in to Snowflake at app.snowflake.com.

2. Import the notebook into Snowflake.

   

   1. Click on the + Create button in the top left corner of the Snowflake interface.
   2. Select Notebook from the dropdown menu.
   3. In the popover menu, click on the Import .ipynb File option.
   4. (Not pictured) Select the downloaded notebook file in the file picker dialog, then click Open.

3. Configure the notebook resources.

   

   1. Choose a name for your notebook.

   2. Select the database and schema where you want to save the notebook.

   3. Select the Run on container Python environment.

      Note

      RelationalAI is supported in the Run on warehouse Python environment, but requires an additional installation step. We recommend using the Run on container environment for a smoother experience.

      For more information, see Getting Started with Snowflake Notebooks.

   4. Choose the Snowflake ML Runtime CPU 1.0 runtime.

   5. Choose the compute pool you want to use for the notebook’s Python kernel.

      Tip

      If your Admin used our installation notebook, the NOTEBOOK_CPU_XS compute pool should be available.

      No compute pool? See how to set one up.

   6. Choose the warehouse you want to use to execute the notebook’s SQL queries.

      Tip

      If your Admin used our installation notebook, the NOTEBOOKS_WH warehouse should be available.

      No warehouse? See how to set one up.

   7. Click the Create button to create and open the notebook.

   8. (Not pictured) Follow the instructions in the notebook to run the code cells and explore the sample data.

Jupyter

Follow these steps to run the sample notebook using Python and Jupyter installed in a your local machine.

1. Ensure you have a compatible version of Python.

   RelationalAI supports Python 3.9, 3.10, and 3.11. To check your current Python version, run the following command in your terminal:

   Windows

   python --version

   macOS/Linux

   python3 --version

   If you don’t have a compatible version of Python, you’ll need to install one.

   How to install a compatible version of Python.

   Select your operating system and follow the steps to install Python on your machine:

   Windows

   1. Navigate to the Python 3.11 download page, scroll down to the Files section, and download the Windows installer (64-bit).

   2. Open the installer and follow the prompts to install Python 3.11. Check the box to add Python to your PATH.

   3. Open a terminal and verify that Python 3.11 is installed by running:

      python --version

      The output should be similar to Python 3.11.9.

   macOS

   1. Navigate to the Python 3.11 download page, scroll down to the Files section, and download the macOS 64-bit universal2 installer.

   2. Open the installer and follow the prompts to install Python 3.11. Use the default installation options. When the installation is complete, double click the Install Certificates command in the Finder window.

   3. Open a terminal and verify that Python 3.11 is installed by running:

      python3.11 --version

      The output should be similar to Python 3.11.9.

   Linux

   1. Use your distribution’s package manager to install Python 3.11. Alternatively, navigate to the Python 3.11 download page, scroll down to the Files section, download the Gzipped source tarball and compile it yourself. See the Python documentation for details.

   2. Open a terminal and verify that Python 3.11 is installed by running:

      python3.11 --version

      The output should be similar to Python 3.11.9.

2. Create a project folder.

   mkdir ~/rai-sample-notebooks
   cd ~/rai-sample-notebooks

3. Move the downloaded notebook into your project folder.

   Use the button at the top of this page to download the notebook file, then move it into your project folder.

4. Create a virtual environment and install Jupyter.

   Windows

   python -m venv venv
   venv\Scripts\activate
   python -m pip install --upgrade pip
   python -m pip install jupyter

   macOS/Linux

   python3 -m venv venv
   source venv/bin/activate
   python3 -m pip install --upgrade pip
   python3 -m pip install jupyter

5. Open the notebook in Jupyter Lab.

   Run the following commands in your terminal to open Jupyter Lab:

   jupyter trust *.ipynb
   jupyter lab

   Note

   Trusting the file enables the graph visualizations to display when the notebook is opened.

   Open the notebook file in Jupyter Lab and run the cells. If the data for the example has not already been loaded in your Snowflake account, you’ll need to go to the Appendix and run the data-loading operations before running the main cells.

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