Run the first flow

After you have configured access to the platform, let's run a simple Metaflow flow to confirm that everything works.

tip

If you are new to Metaflow, take a quick look at the Metaflow documentation. All features of Metaflow work on Outerbounds.

Save the following code snippet in a file, hello.py

from metaflow import FlowSpec, step

class HelloFlow(FlowSpec):

    @step
    def start(self):
        print("Hello world! 👋")
        self.next(self.end)

    @step
    def end(self):
        pass

if __name__ == "__main__":
    HelloFlow()

and execute it as follows:

# python hello.py run

If you see Hello World! in the output, congratulations! You just ran your first flow successfully.

In the output, you should see a link to the Runs view which shows all executions, both prototyping and production, taking place on the platform. You can click the latest HelloFlow run to explore information about the run that you just executed.

Hello artifacts

Extend the above example with the following lines, storing data in self. variables that are persisted automatically as artifacts which are a core concept of Metaflow.

from metaflow import FlowSpec, step, card

class HelloFlow(FlowSpec):

    @card
    @step
    def start(self):
        self.greeting = "Hello world!"
        self.x = 1
        self.next(self.end)

    @card
    @step
    def end(self):
        print(self.greeting)
        self.x += 10
        print("x is", self.x)


if __name__ == "__main__":
    HelloFlow()

Note how we refer to the variables self.x and self.greeting both in the start and end steps. By design, this doesn't seem that special but notably the steps could execute on separate cloud instances, as we will see when executing the code in the cloud. The data is moved automatically between steps and stored for later inspection.

Run the flow as before

# python hello.py run

Check the the Runs view again for the latest run. Navigate to the start task and observe a new card section in the task view, as shown in this clip:

The card section is produced by the @card decorator that allows you to attach custom visualizations in the UI. Note how the card shows how the value of x changes between the start and end steps - Metaflow versions data automatically. You can use this feature to track metrics, models, dataframes, or any other data across prototypes and production runs.

If you are feeling adventurous, you can explore various options for visualizing artifacts through cards. Feel free to hack HelloFlow freely - you can't break anything!

Hello artifacts​

Hello artifacts