Steps

A step is a reusable blueprint that defines a specific ML workload in your project. Think of it as a template that describes what should happen when you want to run a particular job.

When you actually run a step, you create an execution, a versioned snapshot of that step run with specific inputs and parameters.

💡 One step can generate thousands of executions. Each execution is version-controlled and reproducible.

Why steps matter

Steps give you reproducible, scalable ML workflows. Instead of manually running scripts with different parameters each time, you define the work once and run it as many times as needed.

Common step types include:

• Data preprocessing and feature engineering

• Model training with hyperparameter sweeps

• Model validation and testing

• Batch inference and predictions

• Model deployment to staging/production

Step vs. Execution

Step	Execution
Blueprint/template	Versioned snapshot of the step being run
Defined in valohai.yaml	Created when you run a step
Reusable	Specific run with exact inputs/parameters
Static definition	Has logs, outputs, duration, and lineage

Anatomy of a step

Steps are defined in your project's valohai.yaml file and specify:

• Docker image — the environment your code runs in

• Commands — what gets executed

• Inputs — data files your step needs

• Parameters — configurable values (learning rate, epochs, etc.)

• Environment — compute requirements

Example: Simple training pipeline

---
- step:
    name: preprocess-dataset
    image: python:3.9
    command:
      - pip install numpy valohai-utils
      - python ./preprocess_dataset.py
    inputs:
      - name: dataset
        default: https://valohaidemo.blob.core.windows.net/mnist/mnist.npz

- step:
    name: train-model
    image: tensorflow/tensorflow:2.6.0
    command:
      - pip install valohai-utils
      - python ./train_model.py {parameters}
    parameters:
      - name: epochs
        default: 5
        type: integer
      - name: learning_rate
        default: 0.001
        type: float
    inputs:
      - name: dataset
        default: https://valohaidemo.blob.core.windows.net/mnist/preprocessed_mnist.npz

- step:
    name: evaluate-model
    image: tensorflow/tensorflow:2.6.0
    command:
      - pip install valohai-utils scikit-learn
      - python ./evaluate_model.py
    inputs:
      - name: model
      - name: test_data

Pipeline examples

For production workflows, you might chain steps together:

Data Pipeline: fetch-data → clean-data → feature-engineering

Training Pipeline: train-model → validate-model → register-model

Deployment Pipeline: build-inference-service → deploy-staging → deploy-production

Each step runs independently but can use outputs from previous steps as inputs.

Next steps

Ready to create your first step? Check out:

• Quickstart: Run your first job — hands-on tutorial for beginners

• Pipeline quickstart — connect multiple steps together

• valohai.yaml reference — complete configuration options