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Learn how to pass output parameters between steps in Argo Workflows, streamlining your data flow for smoother execution.
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How to Effectively Pass Output Parameters in Argo Workflows

Argo Workflows is a powerful tool used for orchestrating complex workflows in Kubernetes, particularly in machine learning and data processing pipelines. However, when it comes to passing parameters between steps, it can become a bit tricky. In this guide, we will address a common challenge: how to pass output parameters from one step to the environment declarations of another step within an Argo Workflow.

The Challenge

Imagine you have a workflow where you need to set up some environmental variables based on initial parameters and then use those variables in subsequent steps. For instance, you might find yourself in a situation where:

You have an initial step that saves parameters (like client and pipeline) to a file.

You attempt to retrieve these parameters in the environment of subsequent steps, but they appear as unresolved placeholders (e.g., {{ steps.setup-env.outputs.parameters.envs.pipeline }}) rather than their actual values.

This situation can lead to frustration as the intended parameters are not being passed correctly.

The Solution

Fortunately, a solution exists, albeit a bit more verbose than expected. We can use a two-step approach where we properly define the outputs and pass them as inputs to the next step. Below is a simplified overview of how to achieve this in your Argo Workflow.

Step 1: Define Your Workflow

Start by creating a new workflow YAML configuration with the necessary specs. Here’s the template for what your workflow might look like:

[[See Video to Reveal this Text or Code Snippet]]

Step 2: Explanation of Each Component

Step Definition: Within the templates section, we define our steps. The first step setup-env generates the parameters you want to set. The second step say-something, retrieves those parameters.

Script for Environmental Setup: In the setup-env template, a Python script creates a JSON file containing your parameters. The output of this script is wrapped in a parameter definition to make it available for downstream tasks.

Passing Parameters: In the second step, we capture the outputs of the first step using the built-in syntax {{ steps.setup-env.outputs.parameters.envs }}. This makes the parameters from the environment setup available as inputs for the next step.

Container Setup: Finally, the say-something template uses those parameters by defining an environment variable that references the config passed from the first step.

Final Thoughts

By employing this two-step method, you can seamlessly pass outputs from one step to environment variables in another, thereby ensuring better data flow throughout your Argo Workflow. While it might take a little extra setup, the result is a more robust and efficient workflow.

If you’re still experiencing issues or have further questions, feel free to leave a comment below! Let’s streamline our workflows together!