# Gradient Actions

Gradient Actions are composable building blocks for creating reproducible machine learning Workflows. Actions use the `uses` and `with` syntax to specify how a job step executes.

## container

```yaml
uses: container@v1
with:
  image: bash:5
  args: ["echo", "hello", "world"]
```

The Gradient Action called `container@v1` allows you to use an arbitrary Docker container image (in this case the lightweight `bash` container image) and pass arguments directly to it.

## script

```yaml
uses: script@v1
with:
  script: |-
    echo 'hello world'
    echo $RANDOM
  image: bash:5
```

If you want to run multiple commands, the `script@v1` action allows you to pass a `script` in a [literal-style HereDoc](https://lzone.de/cheat-sheet/YAML#yaml-heredoc-multiline-strings) denoted by `|-`. The pipe character will preserve newlines and the dash will remove extra newlines after the block.

*Note:* The image you provide will need to have `bash` available in its PATH.

## git-checkout

```yaml
outputs:
  repo:
    type: volume
uses: git-checkout@v1
with:
  url: https://github.com/user/my-public-repo
  ref: 46aa59d6ecc3720ffe2454a6d9d360e6ce75acce # Optional git ref
  path: /outputs/repo # Optional, defaults to exactly one output volume or dataset
```

In this example, the Gradient Action `git-checkout@v1` clones the public GitHub URL `https://github.com/user/my-public-repo` at ref `46aa...` into a volume named `repo`. The cloned files are accessible at `/outputs/<output-name>` (in this case, `/outputs/repo`), and subsequent jobs that specify the checkout job's volume as an input can also access the repository files as read-only at `/inputs/<input-name>`.

```yaml
inputs:
  repo: checkout-job.outputs.repo
uses: container@v1
with:
  image: busybox
  args: ['ls', '/inputs/repo']
```

*Note:* To clone a private repository, add your username as a parameter, [set a Gradient secret](/gradient/get-started/managing-projects/using-secrets.md#set-a-secret) with a [GitHub access token](https://docs.github.com/en/github/authenticating-to-github/creating-a-personal-access-token) value, and add a password parameter:

```yaml
outputs:
  repo:
    type: volume
uses: git-checkout@v1
with:
  url: https://github.com/user/my-private-repo
  username: paperspace
  password: secret:MY_SECRET_NAME
```

You can also use `path` to pick an output target:

```yaml
outputs:
  repo:
    type: volume
  ds:
    type: dataset
    with:
      ref: my-dataset
uses: git-checkout@v1
with:
  url: https://github.com/user/my-public-repo
  ref: 46aa59d6ecc3720ffe2454a6d9d360e6ce75acce # Optional git ref
  path: /outputs/repo/subfolder
```

## s3-download

```yaml
outputs:
  s3:
    type: volume
uses: s3-download@v1
with:
  url: s3://bucket/path/
  access-key: MYACCESSKEY
  secret-access-key: secret:MY_SECRET_NAME
```

The `s3-download@v1` Gradient Action copies the contents of an Amazon S3 bucket into an output (in this example, the volume is named `s3`). Subsequent jobs that specify an input that reference the `s3-download` job's volume output can access the downloaded files within that job at `/inputs/<input-name>`.

*Note:* `access-key` and `secret-access-key` are required parameters, and the latter must be a [Gradient secret](/gradient/get-started/managing-projects/using-secrets.md#set-a-secret). Optional parameters include `region` (for AWS buckets), `endpoint` (for non-AWS buckets), and `path` (to disambiguate target outputs or to download to a subfolder).

## model-create

```yaml
inputs:
  model:
    type: dataset
    with:
      ref: dsr8k5qzn401lb5:klfoyy9 # Example dataset ref
outputs:
  model-id:
    type: string
uses: create-model@v1
with:
  name: my-model-name
  type: Tensorflow # Tensorflow, ONNX, or Custom
```

In this example, the `create-model@v1` action takes a dataset input named `model` and outputs a string ID (named `model-id`) that references a [Gradient model](/gradient/data/models.md). With this reference, the created model can be tested, edited, or deployed in future jobs.


---

# 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://paperspace.gitbook.io/gradient/explore-train-deploy/workflows/gradient-actions.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.