Creating a pipeline with valohai-utils


  • This tutorial is a part of our Valohai pipelines series. Make sure you have completed the Valohai fundamentals learning path first.

  • Even though not mandatory, we strongly recommend that you Connect to a Git repository before using pipelines. Using Git is not a Valohai requirement but it will make your workflows more efficient.

In this section you will

  • Learn how to use valohai-utils helper library to build and update pipelines in valohai.yaml.

Previously, you defined the pipeline manually in your valohai.yaml. Alternatively, you can define pipelines using the valohai-utils Toolkit helper library. Start by creating a new file with the following code in it.

Create a separate Python script for your pipeline definition

When creating the valohai.yaml and updating the step information with vh yaml step <filename> the code in the file is only parsed and not executed. For vh yaml pipeline <filename> the main method defined below is actually run. This means that you should have all the libraries that will be imported installed. Thus, it might make more sense to have the pipeline code in a separate file.

from valohai import Pipeline

def main(config) -> Pipeline:

    #Create a pipeline called "utilspipeline".
    pipe = Pipeline(name="utilspipeline", config=config)

    # Define the pipeline nodes.
    preprocess = pipe.execution("preprocess-dataset")
    train = pipe.execution("train-model")

    # Configure the pipeline, i.e. define the edges.

    return pipe

Next, run the command:

vh yaml pipeline <filename>

If you now check your valohai.yaml, you should now have a new pipeline called utilspipeline. Even though otherwise exactly alike, the edges look different for the two pipelines. When creating the pipeline manually, you used the shorthand syntax but valohai-utils doesn’t. Regardless, the pipeline will be built in a similar manner in the UI.

- pipeline:
    name: utilspipeline
    - configuration: {}
      source: preprocess-dataset_1.output.preprocessed_mnist.npz
      target: train-model_1.input.dataset
    - name: preprocess-dataset_1
      actions: []
      override: {}
      step: preprocess-dataset
      type: execution
    - name: train-model_1
      actions: []
      override: {}
      step: train-model
      type: execution

You can now test this pipeline by running an --adhoc pipeline from the command line.

vh pipeline run utilspipeline --adhoc

If you want to run the pipeline in the Valohai UI, remember to push the changes to your Git repository.

git add valohai.yaml
git commit -m "Added pipeline definition"
git push

Instead of the UI, you can of course run the the pipeline from the CLI. Remember to fetch the changes to your Valohai project either by running the fetch command on the CLI or pushing the button in the UI.

vh project fetch
vh pipeline run utilspipeline


Be careful that you are actually reading and updating the right valohai.yaml file when creating pipelines. If you get a message saying that the step is not found, check both the valohai.yaml and the project linked to your working directory.