This page describes how to create and set up a local deep learning container. This guide expects you to have basic familiarity with Docker.
Before you begin
Complete the following steps to set up a Google Cloud account, enable the required APIs, and install and activate the required software.
In the Google Cloud Console, go to the Manage resources page and select or create a project.
-
If you're using a Linux-based operating system, such as Ubuntu or Debian, add your username to the
docker
group so that you can run Docker without usingsudo
:sudo usermod -a -G docker ${USER}
You may need to restart your system after adding yourself to the
docker
group. Open Docker. To ensure that Docker is running, run the following Docker command, which returns the current time and date:
docker run busybox date
Use
gcloud
as the credential helper for Docker:gcloud auth configure-docker
Optional: If you want to run the container using GPU locally, install
nvidia-docker
.
Create your container
Follow these steps to create your container.
To view a list of containers available:
gcloud container images list \ --repository="gcr.io/deeplearning-platform-release"
You may want to go to Choosing a container to help you select the container that you want.
If you don't need to use a GPU-enabled container, enter the following code example. Replace tf-cpu.1-13 with the name of the container that you want to use.
docker run -d -p 8080:8080 -v /path/to/local/dir:/home/jupyter \ gcr.io/deeplearning-platform-release/tf-cpu.1-13
If you want to use a GPU-enabled container, enter the following code example. Replace tf-gpu.1-13 with the name of the container that you want to use.
docker run --runtime=nvidia -d -p 8080:8080 -v /path/to/local/dir:/home/jupyter \ gcr.io/deeplearning-platform-release/tf-gpu.1-13
This command starts up the container in detached mode, mounts the local
directory /path/to/local/dir
to /home/jupyter
in the container, and maps
port 8080 on the container to port 8080 on your local machine. The
container is preconfigured to start a JupyterLab server, which you can
visit at http://localhost:8080
.
What's next
- Learn more about how to work with containers in the Docker documentation.