Environment Setup#


  • Make sure you have docker and git installed.

  • Install flytectl, the commandline interface for flyte.

Repo Setup#

Clone the flytesnacks repo and install its dependencies, which includes flytekit :


[Recommended] Create a new python virtual environment to make sure it doesn’t interfere with your development environment, e.g. with python -m venv <path/to/env>.

git clone https://github.com/flyteorg/flytesnacks
cd flytesnacks/cookbook
pip install -r core/requirements.txt

To make sure everything is working in your virtual environment, run hello_world.py locally:

python core/flyte_basics/hello_world.py

Expected output:

Running my_wf() hello world

Create a Local Demo Flyte Cluster#

Use flytectl to start a demo Flyte cluster:

flytectl demo start

Running Workflows#

Now you can run all of the example workflows locally using the default Docker image bundled with flytekit:

pyflyte run core/flyte_basics/hello_world.py my_wf


The first couple arguments of pyflyte run is in the form of path/to/script.py <workflow_name>, where <workflow_name> is the function decorated with @workflow that you want to run.

To run the workflow on the demo Flyte cluster, all you need to do is supply the --remote flag:

pyflyte run --remote core/flyte_basics/hello_world.py my_wf

You should see an output that looks like:

Go to https://<flyte_admin_url>/console/projects/flytesnacks/domains/development/executions/<execution_name> to see execution in the console.

You can visit this url to inspect the execution as it runs:

A quick visual tour for launching your first Workflow.

Finally, let’s run a workflow that takes some inputs, for example the basic_workflow.py example:

pyflyte run --remote core/flyte_basics/basic_workflow.py my_wf --a 5 --b hello


We’re passing in the workflow inputs as additional options to pyflyte run, in the above example the inputs are --a 5 and --b hello. For snake-case argument names like arg_name, you can provide the option as --arg-name.

Visualizing Workflows#

Workflows can be visualized as DAGs on the UI. However, you can visualize workflows on the browser and in the terminal by just using your terminal.

To view workflow on the browser:

flytectl get workflows --project flytesnacks --domain development flyte.workflows.example.my_wf --version <version> -o doturl

To view workflow as a strict digraph on the command line:

flytectl get workflows --project flytesnacks --domain development flyte.workflows.example.my_wf --version <version> -o dot

Replace <version> with version from console UI, it may look something like BLrGKJaYsW2ME1PaoirK1g==


Running most of the examples in the User Guide only requires the default Docker image that ships with Flyte. Many examples in the Tutorials and Integrations section depend on additional libraries, sklearn, pytorch, or tensorflow, which will not work with the default docker image used by pyflyte run.

These examples will explicitly show you which images to use for running these examples by passing in the docker image you want to use with the --image option in pyflyte run.

🎉 Congrats! Now you can run all the examples in the User Guide 🎉

What’s Next?#

Try out the examples in Flyte Basics section.