# 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

```yaml
---
- step:
    name: preprocess-dataset
    image: python:3.14
    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](https://docs.valohai.com/getting-started/quickstart) — hands-on tutorial for beginners
* [Pipeline quickstart](https://docs.valohai.com/pipelines) — connect multiple steps together
* [valohai.yaml reference](https://docs.valohai.com/valohai-yaml) — complete configuration options