Get started with a local deep learning container

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 resourcespage and select or create a project.

Note: If you don't plan to keep the resources you create in this tutorial, create a new project instead of selecting an existing project. After you finish, you can delete the project, removing all resources associated with the project and tutorial.

Go to Manage resources
Install and initialize the gcloud CLI .
Install Docker .

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 using sudo :
```
 sudo  
usermod  
-a  
-G  
docker  
 ${ 
 USER 
 } 
 
```
Caution: The docker group is equivalent to the root user. See Docker's documentation for details on how this affects the security of your system.

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 .