# How to Use Custom Containers in Gradient Workflows (private) Workflows automate machine learning tasks, combining GPU instances with an expressive syntax to generate production-ready machine learning pipelines with a few lines of code. Gradient uses containers to create consistent, reproducible environments for your workloads. Containers define the runtime and dependencies that Gradient uses when running your code. You can use one of Gradient’s preconfigured containers, a public container image, or your own private image. ## Add a Container Registry You can store credentials for private container registries in Gradient so they can be reused across your environment. In the [Gradient console](https://cloud.digitalocean.com), in the top-right corner, click your profile image, select **Team Settings**, and then click the **Containers** tab. From the **Containers** page, you can add, edit, or delete registry connections. ### Create a Container Registry Connection To create a registry connection, click **Add Container Registry**, select the registry type, and then type the required credentials for that registry. After saving, you can reference the registry by name in your configuration file or container spec. ### Configure Container Registry The following examples show how to connect different container registries to Gradient. Each example outlines the credentials required for that registry and how to reference the registry name in your container configuration. ## DigitalOcean Container Registry If your image is stored at `registry.digitalocean.com/your-registry-name/image:tag`, type your container registry information like this: - **Display name:** `My-Example-DO-Registry` - **Registry name:** `your-registry-name` - **API token:** Your DigitalOcean API token Then, reference it in your configuration like this: ```yaml image: registry.digitalocean.com/your-registry-name/image:tag containerRegistry: My-Example-DO-Registry ``` ## Docker Hub If your image is at `example-repository/streamlit-app:latest`, type your container registry information like this: - **Display name:** `My-Example-DockerHub-Repository` - **Namespace:** `example-repository` - **Username:** Your Docker Hub username - **Password:** Your Docker Hub password Then, reference it in your configuration like this: ```yaml image: example-repository/streamlit-app:latest containerRegistry: My-Example-DockerHub-Repository ``` Learn more about [Docker registries](https://docs.docker.com/get-started/overview/#docker-registries). ## Google Container Registry (GCR) If your image is at `us-west1-docker.pkg.dev/project-name/repo-name/image:tag`, type your container registry information like this: - **Display name:** `My-Example-GCR` - **Region:** `us-west1` - **Project:** `project-name` - **JSON Key:** Your service account JSON key For reference, see [GCR authentication docs](https://cloud.google.com/container-registry/docs/advanced-authentication#json-key). Then, reference it in your configuration like this: ```yaml image: us-west1-docker.pkg.dev/project-name/repo-name/image:tag containerRegistry: My-Example-GCR ``` ## GitHub Container Registry (GHCR) If your image is at `ghcr.io/example-org/streamlit-app:latest`, type your container registry information like this: - **Display name:** `My-Example-GHCR` - **Organization:** `example-org` - **Email:** GitHub account email - **Password:** Personal access token Then, reference it in your configuration like this: ```yaml image: ghcr.io/example-org/streamlit-app:latest containerRegistry: My-Example-GHCR ``` ## Other (for Azure or generic registries) If your image is at `example-registry.azurecr.io/streamlit-app:latest`, type your container registry information like this: - **Display name:** `My-Example-ACR` - **Registry URL:** `https://example-registry.azurecr.io` - **Namespace:** `/` - **Username:** `_json_key` - **Password:** Azure Service Principal ID For reference, see [Azure docs](https://learn.microsoft.com/en-us/azure/container-registry/container-registry-auth-service-principal#create-a-service-principal). Then, reference it in your configuration like this: ```yaml image: example-registry.azurecr.io/streamlit-app:latest containerRegistry: My-Example-ACR ``` ### Use Your Container You can specify a container image directly in your YAML spec to define the environment for each step. To use a public TensorFlow container, specify a container image like this: ```yaml image: tensorflow/tensorflow:2.7.0-gpu-jupyter ``` Gradient then automatically pulls and manages this image when your Workflow runs. To learn more, see the [Workflow spec reference](https://docs.digitalocean.com/products/paperspace/workflows/reference/workflow-spec/index.html.md). ## Build a Custom Container You can build a custom Docker container and push it to a container registry, such as Docker Hub, DigitalOcean Container Registry, or NGC, for use in Gradient. Custom containers let you define the exact runtime, dependencies, and environment needed for your workloads. **Tip**: You don’t need to build a container from scratch. Many ready-to-use images are available on [Docker Hub](https://hub.docker.com) and [NGC](https://ngc.nvidia.com/catalog/landing). You can use these as a starting point or modify an existing image to suit your requirements. Before you use a custom container, you need to [create a machine](https://docs.digitalocean.com/products/paperspace/machines/how-to/create/index.html.md) with [Docker CE](https://github.com/docker/docker-ce), [NVIDIA Docker](https://github.com/NVIDIA/nvidia-docker), and NVIDIA drivers installed. These tools ensure your image is compatible with the GPU runtimes and dependencies that Gradient uses when running containers. If your local environment doesn’t meet these requirements, you can use a [Paperspace Linux-based machine](https://docs.digitalocean.com/products/paperspace/machines/how-to/create/index.html.md) instead. ### Access Your Docker Hub To access your Docker Hub, run the following command with your Docker Hub credentials like this: ```shell docker login -u -p ``` ### Create a Dockerfile After accessing your Docker Hub, create a Dockerfile to define your container’s environment, dependencies, and startup commands like this: ```shell git clone https://github.com/gradient-ai/tensorflow-python cd tensorflow-python ``` In this example, the image is a Paperspace-built TensorFlow 2.0 container optimized for GPU workloads. You can modify the Dockerfile to include any additional dependencies or setup steps your application requires. ### Build the Image In the same directory as your Dockerfile, build your image with the following command: ```shell docker build -t test-container . ``` Then, tag your image before pushing it to a registry like this: ```shell docker tag test-container /test-container:latest ``` ### Push the Image To push the image to your container registry, run the following command: ```shell docker push /test-container:latest ``` ### Reference Your Custom Container After your container is pushed to a registry, reference it in your configuration file or container spec like this: ```yaml image: /test-container:latest containerRegistry: ``` Gradient then automatically pulls and runs the container in your environment. **Note**: Private containers require registry credentials to be stored in Gradient before use. ### Bring Your Custom Container to Workflows You can use custom containers in Gradient Workflows to control the runtime environment for each step. This lets you include specific frameworks, libraries, or dependencies that your workflow needs. When defining your Workflow step: - For **public images**, specify the container path using the `image` key like this: ```yaml image: paperspace/gradient-sdk ``` - For **private images**, specify both the `image` and `containerRegistry` keys to reference your saved registry credentials like this: ```yaml image: registry.digitalocean.com/example-registry/my-image:latest containerRegistry: My-Example-DO-Registry ``` The value of `containerRegistry` must match the name of the registry connection you created in the **Create a Registry Connection** section above. Learn more about structuring and managing Workflow steps in the [Workflows overview](https://docs.digitalocean.com/products/paperspace/workflows/index.html.md).