First project#

In this section we will set up a Union project. A Union project consists of:

  • A local directory (typically the checkout a Git repository, or a subdirectory of a Git repository) holding your workflow code.

  • A project registered on Union to which you deploy this workflow code.

You deploy your code from your local project to the corresponding Union project by “registering” your workflows and tasks using the union CLI.

Union provides a default project (called flytesnacks) where all your workflows will be registered unless you specify otherwise. We will use this default project for the rest of this guide. To create additional projects, see Setting up a project. In actual production usage, you would typically name your local project directory after the Union project it corresponds to, but here we will just use the default project.

Initialize a project from scratch

In this example we will start with an existing Git repository. Alternatively, you can initialize a fresh project from a template with the union init command, which you then check into Git. See Setting up a project for more details.

Clone the example project#

We will use the Getting started: Machine learning example, found in the https://github.com/unionai/unionai-examples/ GitHub repository under user-guide/getting-started.

Clone the repository to your local machine and cd to the project directory:

$ git clone https://github.com/unionai/unionai-examples/
$ cd unionai-examples/user-guide/getting-started

Ensure that your Python virtual environment is properly set up with the required dependencies.

Using uv, you can install the dependencies with the command:

$ uv sync

You can then activate the virtual environment with:

source .venv/bin/activate

activate vs uv run

When running the union CLI within your local project you must run it in the virtual environment associated with that project. This differs from our earlier usage of the tool when we installed union globally in order to set up its configuration.

To run union within your project’s virtual environment using uv, you can prefix it use the uv run command. For example:

uv run union ...

Alternatively, you can activate the virtual environment with source .venv/bin/activate and then run the union command directly.

In our examples we assume that you are doing the latter.

Run the code locally#

Because tasks and workflows are defined as regular Python functions, they can be executed in your local Python environment.

You can run the workflow locally with the command union run <FILE> <WORKFLOW>:

$ union run src/ml_workflow.py main

If the code runs successfully, you should see output like this:

Running Execution on local.
0.9767441860465116