Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Showing content from http://cloud.google.com/deep-learning-containers/docs/getting-started-local below:

Get started with a local deep learning container | Deep Learning Containers

Stay organized with collections Save and categorize content based on your preferences.

This page describes how to create and set up a local deep learning container. This guide expects you to have basic familiarity with Docker.

Complete the following steps to set up a Google Cloud account, enable the required APIs, and install and activate the required software.

1. In the Google Cloud Console, go to the Manage resources page 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

2. Install and initialize the gcloud CLI.

3. 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.

4. Open Docker. To ensure that Docker is running, run the following Docker command, which returns the current time and date:

   docker run busybox date
   

5. Use gcloud as the credential helper for Docker:

   gcloud auth configure-docker
   

6. Optional: If you want to run the container using GPU locally, install nvidia-docker.

Follow these steps to create your container.

1. 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.

2. 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.

• Learn more about how to work with containers in the Docker documentation.

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.

Last updated 2025-08-07 UTC.

[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-08-07 UTC."],[[["This guide details the process of creating and setting up a local deep learning container, requiring basic Docker knowledge."],["The setup involves creating or selecting a Google Cloud project, installing and initializing the gcloud CLI, and installing Docker, with specific instructions for Linux users to avoid using `sudo`."],["Users can choose from available deep learning containers using a command to list them or visit the \"Choosing a container\" page, then using a command to either use a cpu container, or a gpu-enabled container."],["The container is launched in detached mode, mounting a local directory to the container and mapping a port, which then allows the user to use a preconfigured JupyterLab server."],["Optionally, for those requiring GPU acceleration, the guide suggests installing `nvidia-docker`, and using the appropriate container creation command."]]],[]]

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4