Environment Setup

Prerequisites

• Make sure you have docker and git installed.

• Install flytectl, the commandline interface for Flyte.

Repo setup

As we intend to execute the code locally, duplicate this code block into hello_world.py.

from flytekit import task, workflow

@task
def say_hello() -> str:
    return "Hello, World!"

@workflow
def hello_world_wf() -> str:
    res = say_hello()
    return res

if __name__ == "__main__":
    print(f"Running my_wf() {my_wf()}")

To install flytekit, run the following command:

pip install flytekit

Tip

Recommended: Create a new python virtual environment to make sure it doesn’t interfere with your development environment. You can do this by running the following commands in your terminal:

python -m venv ~/venvs/flyte-examples
source ~/venvs/flyte-examples/bin/activate

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

python hello_world.py

Expected output:

Running my_wf() hello world

Create a local demo Flyte cluster

Important

Make sure the Docker daemon is running before starting the demo cluster.

Use flytectl to start a demo Flyte cluster:

flytectl demo start

Running workflows

Now you can run the example workflow locally using the default Docker image bundled with flytekit:

pyflyte run hello_world.py hello_world_wf

Note

The initial arguments of pyflyte run take the form of path/to/script.py <task_or_workflow_name>, where <task_or_workflow_name> refers to the function decorated with @task or @workflow that you wish to run.

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

pyflyte run --remote hello_world.py hello_world_wf

You can also run the code directly from a remote source:

pyflyte run --remote \
    https://raw.githubusercontent.com/flyteorg/flytesnacks/master/examples/basics/basics/hello_world.py \
    hello_world_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:

Finally, run a workflow that takes some inputs, for example the workflow.py example:

pyflyte run --remote \
    https://raw.githubusercontent.com/flyteorg/flytesnacks/master/examples/basics/basics/workflow.py \
    simple_wf --x '[-3,0,3]' --y '[7,4,-2]'

Note

We’re passing in the workflow inputs as additional options to pyflyte run. In the above example, the inputs are --x '[-3,0,3]' and --y '[7,4,-2]'. 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 \
    --version <version> \
    -o doturl \
    basics.workflow.simple_wf

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

flytectl get workflows \
    --project flytesnacks \
    --domain development \
    --version <version> \
    -o dot \
    basics.workflow.simple_wf

Replace <version> with the version obtained from the console UI, which might resemble something like BLrGKJaYsW2ME1PaoirK1g==.

Tip

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 such as 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 the Basics section.