# Building Pipelines

> 💡 **About this tutorial:** We use YOLOv8 as a practical example to demonstrate Valohai's features. You don't need computer vision knowledge—the patterns you learn here apply to any ML framework. This tutorial focuses on chaining multiple steps into a pipeline while ensuring proper versioning and tracking of your ML workflows.

Connect your execution steps into automated workflows. Pipelines let you chain training, evaluation, and inference jobs with automatic data handoffs between them.

### Core Concepts

**Nodes** — Individual execution steps in your pipeline\
**Edges** — Data connections between nodes (outputs → inputs, metrics → parameters, parameters → parameters)

Your pipeline is fully defined in `valohai.yaml`. No separate orchestration needed.

### Define a Pipeline

Add this pipeline definition to your `valohai.yaml`:

```yaml
- pipeline:
    name: train-and-predict
    nodes:
      - name: train
        step: yolo
        type: execution
      - name: inference
        step: inference
        type: execution
        override:
          inputs:
            - name: model
            - name: images
              default: https://ultralytics.com/images/bus.jpg
    edges:
    - [train.output.best.onnx, inference.input.model]
```

#### What's happening here?

1. **Two nodes**: `train` runs your YOLO training step, `inference` runs predictions
2. **One edge**: Connects the trained model output to the inference input
3. **Override**: Sets default values for the inference step

> 💡 *The edge syntax `[source.output.filename, target.input.name]` automatically passes files between steps.*

### Run Your Pipeline

Execute the pipeline from your terminal:

```shell
vh pipeline run train-and-predict --adhoc
```

The `--adhoc` flag runs using your local `valohai.yaml` without committing to Git first.

### Monitor Pipeline Progress

Open the Valohai UI to track your pipeline:

1. **Click nodes** to view individual job details, logs, and metrics
2. **Check outputs** in the top-right corner for pipeline-level results

When the training job completes, inference automatically starts with the trained model.

### Related Content

* [Add conditional logic to pipelines](/pipelines/dynamic-conditions.md)
* [Debug failed pipeline nodes](https://github.com/valohai/dokuhai/blob/main/how-to/debugging/pipeline-failures/README.md)


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.valohai.com/getting-started/intro/pipelines.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.