> ## Documentation Index
> Fetch the complete documentation index at: https://wb-21fd5541-evaltables.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

> Run W&B inside Docker containers by configuring API keys, environment variables, and local file storage.

# Docker

## Docker integration

W\&B can store a pointer to the Docker image that your code ran in, letting you restore a previous experiment to the exact environment it ran in. The W\&B Python SDK (`wandb`) looks for the `WANDB_DOCKER` environment variable to persist this state. W\&B provides a few helpers that automatically set this state.

The following sections describe how to set the `WANDB_DOCKER` environment variable in different environments, from local development through Kubernetes-based training.

### Local development

`wandb docker` is a command that starts a Docker container, passes in wandb environment variables, mounts your code, and ensures wandb is installed. By default, the command uses a Docker image with TensorFlow, PyTorch, Keras, and Jupyter installed. You can use the same command to start your own Docker image: `wandb docker my/image:latest`. The command mounts the current directory into the `/app` directory of the container. You can change this with the `--dir` flag.

### Production

The `wandb docker-run` command is provided for production workloads. It's a drop-in replacement for `nvidia-docker` that wraps the `docker run` command and adds your credentials and the `WANDB_DOCKER` environment variable to the call. If you don't pass the `--runtime` flag and `nvidia-docker` is available on the machine, this also ensures the runtime is set to nvidia.

### Kubernetes

If you run your training workloads in Kubernetes and the Kubernetes API is exposed to your pod (which is the case by default), W\&B queries the API for the digest of the Docker image and automatically sets the `WANDB_DOCKER` environment variable.

## Restore the training environment

Once the `WANDB_DOCKER` environment variable is set during a run, you can use it to reproduce the original training environment later.

If a run was instrumented with the `WANDB_DOCKER` environment variable, calling `wandb restore username/project:run_id` checks out a new branch restoring your code, then launches the exact Docker image used for training pre-populated with the original command.