wandb docker
Usage
wandb docker [OPTIONS] [DOCKER_RUN_ARGS]... [DOCKER_IMAGE]
Summary
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 existance 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 |
--help | Show this message and exit. |
