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content/guides/bake/index.md

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## Prerequisites
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- A recent version of Docker is installed on your machine.
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- Git is installed for cloning repositories.
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- You have a recent version of Docker installed on your machine.
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- You have Git installed for cloning repositories.
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- You're using the [containerd](/manuals/desktop/containerd.md) image store.
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## Introduction
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This guide uses an example project to demonstrate how Docker Buildx Bake can
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streamline your build and test workflows. This repository includes both a
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Dockerfile and `docker-bake.hcl`, giving you a ready-to-use setup to try out
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Bake commands.
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streamline your build and test workflows. The repository includes both a
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Dockerfile and a `docker-bake.hcl` file, giving you a ready-to-use setup to try
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out Bake commands.
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Start by cloning the example repository:
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## Testing and linting
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Bake isn't just for defining build configurations, and running builds. You can
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Bake isn't just for defining build configurations and running builds. You can
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also use Bake to run your tests, effectively using BuildKit as a task runner.
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Running your tests in containers is great for ensuring reproducible results.
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This section shows how to add two types of tests:
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```
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> [!TIP]
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> Using the `type=cacheonly` ensures that the build output is effectively
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> Using `type=cacheonly` ensures that the build output is effectively
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> discarded; the layers are saved to BuildKit's cache, but Buildx will not
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> attempt to load the result to the Docker Engine's image store.
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>
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Dockerfile.
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```console
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$ docker buildx bake bake test
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$ docker buildx bake test
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[+] Building 1.2s (6/6) FINISHED
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=> [internal] load local bake definitions
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...
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The `matrix` attribute defines the variants to build ("release" and "debug").
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The `name` attribute defines how the matrix gets expanded into multiple
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distinct build targets. In this case, it's relatively simple. The matrix
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attribute expands the build into two workflows: `image-release` and
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`image-debug`, each using different configuration parameters.
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distinct build targets. In this case, the matrix attribute expands the build
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into two workflows: `image-release` and `image-debug`, each using different
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configuration parameters.
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Next, when building the development variant, we'll pass in a `BUILD_TAGS`
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argument with the value of the matrix variable, which we'll later consume in
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the Dockerfile.
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Next, define a build argument named `BUILD_TAGS` which takes the value of the
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matrix variable.
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```diff {title="docker-bake.hcl"}
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target = "image"
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Exporting build artifacts like binaries can be useful for deploying to
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environments without Docker or Kubernetes. For example, if your programs are
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meant to be run on user's local machine.
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meant to be run on a user's local machine.
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> [!TIP]
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> The techniques discussed in this section can be applied not only to build
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into your projects, you can simplify your Docker builds, make your build
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configuration portable, and wrangle complex configurations more easily.
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Experiment with different configurations and extend your Bake files to match
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Experiment with different configurations and extend your Bake files to suit
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your project's needs. You might consider integrating Bake into your CI/CD
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pipelines to automate builds, testing, and artifact deployment. The flexibility
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and power of Buildx Bake can significantly improve your development and

content/guides/jupyter.md

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### Share your volume
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This example uses the Docker Desktop [Volumes Backup & Share](https://hub.docker.com/extensions/docker/volumes-backup-extension) extension. Alternatively, in the CLI you can [back up the volume](/engine/storage/volumes/#back-up-a-volume) and then [push it using the ORAS CLI](/manuals/docker-hub/oci-artifacts.md#push-a-volume).
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1. Install the Volumes Backup & Share extension.
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1. Open the Docker Dashboard and select **Extensions**.
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2. Search for `Volumes Backup & Share`.
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3. In the search results select **Install** for the extension.
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2. Open the **Volumes Backup & Share** extension in the Docker Dashboard.
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3. Next to the **jupyter-data** volume, select the **Export volume** icon.
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4. In the **Export content** window, select **Registry**.
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5. In the text box under **Registry**, specify your Docker ID and a name for the
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volume. For example, `YOUR-USERNAME/jupyter-data`.
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6. Select **Export**.
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7. Verify that you exported the volume to Docker Hub.
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This example uses the Docker Desktop graphical user interface. Alternatively, in the command line interface you can [back up the volume](/engine/storage/volumes/#back-up-a-volume) and then [push it using the ORAS CLI](/manuals/docker-hub/oci-artifacts.md#push-a-volume).
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1. Sign in to Docker Desktop.
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2. In the Docker Dashboard, select **Volumes**.
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3. Select the **jupyter-data** volume by selecting the name.
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4. Select the **Exports** tab.
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5. Select **Quick export**.
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6. For **Location**, select **Registry**.
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7. In the text box under **Registry**, specify your Docker ID, a name for the
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volume, and a tag. For example, `YOUR-USERNAME/jupyter-data:latest`.
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8. Select **Save**.
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9. Verify that you exported the volume to Docker Hub.
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1. Go to [Docker Hub](https://hub.docker.com).
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2. Select **Repositories**.
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3. View the **Last pushed** time for your repository.
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Other users can now download and import your volume. To import the volume and then run it with your image:
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1. In the Volumes Backup & Share extension, select **Import into new volume**.
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2. In the **Import into a new volume** window, select **Registry**.
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3. In the text box under **Registry**, specify your Docker ID and the repository
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name for the volume. For example, `YOUR-USERNAME/jupyter-data`.
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4. In **Volume name**, specify the name you want to give the
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volume. This example uses `jupyter-data` as the name.
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5. Select **Import**.
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6. In a terminal, run `docker run` to run your image with the imported volume.
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1. Sign in to Docker Desktop.
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2. In the Docker Dashboard, select **Volumes**.
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3. Select **Create** to create a new volume.
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4. Specify a name for the new volume. For this example, use `jupyter-data-2`.
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5. Select **Create**.
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6. In the list of volumes, select the **jupyter-data-2** volume by selecting the
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name.
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7. Select **Import**.
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8. For **Location**, select **Registry**.
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9. In the text box under **Registry**, specify the same name as the repository
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that you exported your volume to. For example,
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`YOUR-USERNAME/jupyter-data:latest`.
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10. Select **Import**.
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11. In a terminal, run `docker run` to run your image with the imported volume.
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Replace `YOUR-USER-NAME` with your Docker ID.
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```console
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$ docker run --rm -p 8889:8888 -v jupyter-data:/home/jovyan/work YOUR-USER-NAME/my-jupyter-image start-notebook.py --NotebookApp.token='my-token'
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$ docker run --rm -p 8889:8888 -v jupyter-data-2:/home/jovyan/work YOUR-USER-NAME/my-jupyter-image start-notebook.py --NotebookApp.token='my-token'
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```
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## Summary

content/manuals/security/for-admins/access-tokens.md

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> [!WARNING]
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>
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> Organization access tokens aren't currently compatible with Docker Build Cloud. If you
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> are using Docker Build Cloud, you must use personal access tokens instead.
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> Organization access tokens aren't currently compatible with the following services:
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>
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> - Docker Build Cloud
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> - Docker Scout
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>
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> If you use these services, you must use personal access tokens instead.
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An organization access token (OAT) is like a [personal access token
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(PAT)](/security/for-developers/access-tokens/), but an OAT is associated with

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