W&B can store a pointer to the Docker image that your code ran in, giving you the ability to restore a previous experiment to the exact environment it was run in. The wandb library looks for the WANDB_DOCKER environment variable to persist this state. We provide a few helpers that automatically set this state.
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.
wandb-docker-run command is provided for production workloads. It's meant to be a dropin replacement for
nvidia-docker. It's a simple wrapper to the
docker run command that adds your credentials and the WANDB_DOCKER environment variable to the call. If you do not pass the "--runtime" flag and
nvidia-docker is available on the machine, this also ensures the runtime is set to nvidia.
If you run your training workloads in Kubernetes and the k8s API is exposed to your pod (which is the case by default). wandb will query the API for the digest of the docker image and automatically set the WANDB_DOCKER environment variable.
If a run was instrumented with the WANDB_DOCKER environment variable, calling
wandb restore username/project:run_id will checkout a new branch restoring your code then launch the exact docker image used for training pre-populated with the original command.