cv_classification

The 'cv_classification' project was generated by using the default-python template.

• src/: Python source code for this project.
  • src/cv_classification/: Shared Python code that can be used by jobs and pipelines.
• resources/: Resource configurations (jobs, pipelines, etc.)
• tests/: Unit tests for the shared Python code.
• fixtures/: Fixtures for data sets (primarily used for testing).

Getting started

Choose how you want to work on this project:

(a) Directly in your Databricks workspace, see https://docs.databricks.com/dev-tools/bundles/workspace.

(b) Locally with an IDE like Cursor or VS Code, see https://docs.databricks.com/dev-tools/vscode-ext.html.

(c) With command line tools, see https://docs.databricks.com/dev-tools/cli/databricks-cli.html

If you're developing with an IDE, dependencies for this project should be installed using uv:

• Make sure you have the UV package manager installed. It's an alternative to tools like pip: https://docs.astral.sh/uv/getting-started/installation/.
• Run uv sync --dev to install the project's dependencies.

Using this project using the CLI

The Databricks workspace and IDE extensions provide a graphical interface for working with this project. It's also possible to interact with it directly using the CLI:

1. Authenticate to your Databricks workspace, if you have not done so already:

   $ databricks configure
   

2. To deploy a development copy of this project, type:

   $ databricks bundle deploy --target dev
   

   (Note that "dev" is the default target, so the --target parameter is optional here.)

   This deploys everything that's defined for this project.

3. Similarly, to deploy a production copy, type:

   $ databricks bundle deploy --target prod
   

4. To run a job or pipeline, use the "run" command:

   $ databricks bundle run
   

5. Finally, to run tests locally, use pytest:

   $ uv run pytest