Platform
W&B Models
W&B Weave
W&B Inference
W&B Training
Release Noteswandb docker [OPTIONS] [DOCKER_RUN_ARGS]... [DOCKER_IMAGE]
Summary
Run your code in a docker container.
W&B docker lets you run your code in a docker image ensuring wandb is
configured. It adds the WANDB_DOCKER and WANDB_API_KEY environment variables
to your container and mounts the current directory in /app by default. You
can pass additional args which will be added to docker run before the
image name is declared, we’ll choose a default image for you if one isn’t
passed:
wandb docker -v /mnt/dataset:/app/data
wandb docker gcr.io/kubeflow-images-public/tensorflow-1.12.0-notebook-cpu:v0.4.0 —jupyter
wandb docker
wandb/deepo:keras-gpu —no-tty —cmd “python train.py —epochs=5”
By default, we override the entrypoint to check for the existence of wandb
and install it if not present. If you pass the —jupyter flag we will
ensure jupyter is installed and start jupyter lab on port 8888. If we
detect nvidia-docker on your system we will use the nvidia runtime. If you
just want wandb to set environment variable to an existing docker run
command, see the wandb docker-run command.
Options
| Option | Description |
|---|---|
--nvidia / --no-nvidia | Use the nvidia runtime, defaults to nvidia if nvidia-docker is present |
--digest | Output the image digest and exit |
--jupyter / --no-jupyter | Run jupyter lab in the container |
--dir | Which directory to mount the code in the container |
--no-dir | Don’t mount the current directory |
--shell | The shell to start the container with |
--port | The host port to bind jupyter on |
--cmd | The command to run in the container |
--no-tty | Run the command without a tty |