Environment setup


  • 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

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

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

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

To install flytekit, run the following command:

pip install flytekit


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 hello_world_wf() Hello, World!

Create a local demo Flyte cluster


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

Use flytectl to start a demo Flyte cluster:

flytectl demo start

After this completes, be sure to export the Flyte config as it will be essential later. Run the command in the output that looks like this:

export FLYTECTL_CONFIG= ~/<pathTo>/.flyte/config-sandbox.yaml

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


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/example_code/basics/basics/hello_world.py \

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

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


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 \

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

flytectl get workflows \
    --project flytesnacks \
    --domain development \
    --version <version> \
    -o dot \

Replace <version> with the version obtained from the console UI, which might resemble 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 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 🎉!

Configuring the demo cluster to use additional resources

Depending on how resource intensive your workflows are, you may encounter errors such as OOM (Out of Memory) errors or find pods with the status OOMKilled. It is crucial to understand that the demo cluster is not set up to immediately accommodate all workflow requirements, and some resource requests may be ignored based on the cluster’s limits.


Keep in mind that, for production deployments, you should give careful consideration to these configurations rather than simply setting large numbers.

Here’s how you can go about modifying the configurations:

  1. Add cluster resource attributes to cra.yaml:

    projectQuotaCpu: "1000"
    projectQuotaMemory: 5Ti
project: flytesnacks
domain: development
  1. Add task resource attributes to tra.yaml:

    cpu: "2"
    memory: 1Gi
    cpu: "1000"
    memory: 5Ti
project: flytesnacks
domain: development
  1. Apply the two configuration files using the following commands:

$ flytectl update task-resource-attribute --attrFile tra.yaml
$ flytectl update cluster-resource-attribute --attrFile cra.yaml
  1. Confirm that the configuration is applied using the following commands:

$ flytectl get task-resource-attribute -p flytesnacks -d development

$ flytectl get cluster-resource-attribute -p flytesnacks -d development

And that’s it! You have successfully modified your Flyte demo cluster to accommodate resource intensive workloads.

For more information, refer to the Configuring Custom K8s Resources guide.

Local registry

If you find yourself using tasks dependent on ImageSpec containers built with envd on the demo cluster, before you submit your workflow, you will need to inform envd how to push the images it builds to the cluster. This can be done via:

pip install flytekitplugins-envd
envd context create --name flyte-sandbox --builder tcp --builder-address localhost:30003 --use

You will also need to update your ImageSpec instances to set registry="localhost:30000".

What’s next?

Try out the examples in the Basics section.