> For the complete documentation index, see [llms.txt](https://paperspace.gitbook.io/gradient/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://paperspace.gitbook.io/gradient/explore-train-deploy/workflows.md).

# Gradient Workflows

Gradient Workflows provides a simple way to automate machine learning tasks.

## Overview

Gradient Workflows is a new and powerful way to build-out machine learning applications. Workflows utilize  [GitHub-action](https://docs.github.com/en/actions) style syntax via [YAML](https://en.wikipedia.org/wiki/YAML) files to easily create powerful automation.

Workflows is based on the [Argo runtime engine](https://github.com/argoproj/argo-workflows#what-is-argo-workflows), which is a container-native continuous delivery tool for Kubernetes, and makes it easy to build complex and scalable projects with an arbitrary number of discrete steps. 

![Examining the logs from a simple workflow.](https://3486137344-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-LHZRFUkajubOAmgu6Rd-1459739844%2Fuploads%2FiwTp4BMnzpknI1h6u1Ap%2Freading%20workflow%20logs%20dark.gif?alt=media\&token=3d243bc8-2bb1-4d62-9a0f-afc63133a54d)

Workflows is used by ML Engineers to build-out deterministic machine learning pipelines. 

## Where to start

The best tutorial is the canonical Gradient Workflows Tutorial:

{% content-ref url="/pages/msOg38q02aa10vy23Tq7" %}
[Gradient Workflows Tutorial](/gradient/get-started/tutorials-list/gradient-workflows-tutorial.md)
{% endcontent-ref %}

![Gradient Workflows — Automate from idea to production](https://3486137344-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-LHZRFUkajubOAmgu6Rd%2F-MjfERjgH12to_d9eoHL%2F-MjfE_o5yXArkXSKL0Uf%2FScreen%20Shot%202021-06-16%20at%2011.17.23%20AM.png?alt=media\&token=54de360b-9453-4b24-bd67-0f22c7a72890)

## Key Terminology

* **Workflow**: a named or unnamed entity that belongs to a team and project
  * Named workflows can be re-run with a default `workflow spec`, or be passed a new spec every time
* [**Workflow Spec**](/gradient/explore-train-deploy/workflows/workflow-spec.md): a YAML list of jobs that is converted into an Argo template and run on the Gradient distributed runtime engine.
* [**Job**](/gradient/explore-train-deploy/workflows/workflow-spec.md#jobs): self-contained part of a workflow spec that is similar to an Argo step
  * Jobs can define inputs, outputs, and their own environment variables
  * Jobs can require other jobs via "needs" and collect/pass info between jobs
  * Jobs can be implemented with an action via "use"
* [**Action**](/gradient/explore-train-deploy/workflows/gradient-actions.md): a self-contained, composable set of code building blocks that can perform specific actions within a machine learning project. 
  * Actions can receive parameters (e.g., args, image) within the job step via the "with" argument
  * E.g., `container@v1` action = run a container, load inputs, and produce outputs
* [**Workflow Run**](/gradient/explore-train-deploy/workflows/workflow-spec.md#example-workflow-run-output): the implementation of a workflow
  * The most basic run requires a `workflowId` and `clusterId` - most will also include a workflowSpec, and the inputs to be passed into the workflow
  * The workflow run contains everything needed for the workflow to actually be executed, i.e., what (`workflowId`), where (`clusterId`), how (`workflowSpec`), with ([inputs](/gradient/explore-train-deploy/workflows/workflow-spec.md#inputs), etc.)