Creating a Flyte project#

About Flyte projects#

A Flyte project is a directory containing task and workflow code, internal Python source code, configuration files, and other artifacts needed to package up your code so that it can be registered to a Flyte cluster.

Prerequisites#

Steps#

1. Activate your Python virtual environment#

If you are using conda or another Python virtual environment manager, first, activate the virtual environment you will use to manage dependencies for your Flyte project:

conda activate flyte-example

2. Initialize your Flyte project#

Next, initialize your Flyte project. The flytekit-python-template GitHub repository contains Flyte project templates with sample code that you can run as is or modify to suit your needs.

In this example, we will initialize the basic-template-imagespec project template.

pyflyte init my_project

Note

If you need to use a Dockerfile for your project, you can initialize the Dockerfile template:

pyflyte init --template basic-template-dockerfile my_project

3. Install additional requirements#

After initializing your Flyte project, you will need to install requirements listed in requirements.txt:

cd my_project
pip install -r requirements.txt

4. (Optional) Version your Flyte project with git#

We highly recommend putting your Flyte project code under version control. To do so, initialize a git repository in the Flyte project directory:

git init

Note

If you are using a Dockerfile instead of ImageSpec, you will need to initialize a git repository and create at least one commit, since the commit hash is used to tag the image when it is built.

5. Run your workflow in a local Python environment#

To check that your Flyte project was set up correctly, run the workflow in a local Python environment:

cd workflows
pyflyte run example.py wf

Next steps#

To learn about the parts of a Flyte project, including tasks and workflows, see “Flyte project components”.

To run the workflow in your Flyte project in a local Flyte cluster, see the “Running a workflow in a local cluster” section of “Running a workflow locally”.