ion into a step that can be used within a pipeline@pipeline defines a function as a pipeline and within this function, the steps are called and their outputs link them together.

Copy this code into a new file and name it run.py. Then run it with your command line:

$ python run.py

Initiating a new run for the pipeline: simple_ml_pipeline.

Registered new version: (version 2).

Executing a new run.

Using user: hamza@zenml.io

Using stack: default

orchestrator: default

artifact_store: default

Step load_data has started.

Step load_data has finished in 0.385s.

Step train_model has started.

Trained model using 3 data points. Feature sum is 21, label sum is 1

Step train_model has finished in 0.265s.

Run simple_ml_pipeline-2023_11_23-10_51_59_657489 has finished in 1.612s.

Pipeline visualization can be seen in the ZenML Dashboard. Run zenml up to see your pipeline!

Explore the dashboard

Once the pipeline has finished its execution, use the zenml up command to view the results in the ZenML Dashboard. Using that command will open up the browser automatically.

Usually, the dashboard is accessible at http://127.0.0.1:8237/. Log in with the default username "default" (password not required) and see your recently run pipeline. Browse through the pipeline components, such as the execution history and artifacts produced by your steps. Use the DAG visualization to understand the flow of data and to ensure all steps are completed successfully.

For further insights, explore the logging and artifact information associated with each step, which can reveal details about the data and intermediate results.

If you have closed the browser tab with the ZenML dashboard, you can always reopen it by running zenml show in your terminal.

Understanding steps and artifacts