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

Showing content from http://cloud.google.com/deep-learning-containers/docs/derivative-container below:

Create a derivative container | Deep Learning Containers

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

This page describes how to create a derivative container based on one of the standard available Deep Learning Containers images.

To complete the steps in this guide, you can use either Cloud Shell or any environment where the Google Cloud CLI is installed.

Before you begin, make sure you have completed the following steps.

1. Complete the set up steps in the Before you begin section of Getting started with a local deep learning container.

2. Make sure that billing is enabled for your Google Cloud project.

   Learn how to enable billing

3. Enable the Artifact Registry API.

   Enable the API

To create a derivative container, you'll use a process similar to this:

1. Create the initial Dockerfile and run modification commands.

   To start, you create a Deep Learning Containers container using one of the available image types. Then use conda, pip, or Jupyter commands to modify the container image for your needs.

2. Build and push the container image.

   Build the container image, and then push it to somewhere that is accessible to your Compute Engine service account.

Use the following commands to select a Deep Learning Containers image type and make a small change to the container image. This example shows how to start with a TensorFlow image and updates the image with the latest version of TensorFlow. Write the following commands to the Dockerfile:

FROM us-docker.pkg.dev/deeplearning-platform-release/gcr.io/tf-gpu:latest
# Uninstall the container's TensorFlow version and install the latest version
RUN pip install --upgrade pip && \
    pip uninstall -y tensorflow && \
    pip install tensorflow

Use the following commands to build and push the container image to Artifact Registry, where it can be accessed by your Google Compute Engine service account.

Create and authenticate the repository:

export PROJECT=$(gcloud config list project --format "value(core.project)")
gcloud artifacts repositories create REPOSITORY_NAME \
    --repository-format=docker \
    --location=LOCATION
gcloud auth configure-docker LOCATION-docker.pkg.dev

Replace the following:

• LOCATION: The regional or multi-regional location of the repository, for example us. To view a list of supported locations, run the command gcloud artifacts locations list.
• REPOSITORY_NAME: The name of the repository that you want to create, for example my-tf-repo.

Then, build and push the image:

export IMAGE_NAME="LOCATION-docker.pkg.dev/${PROJECT}/REPOSITORY_NAME/tf-custom:v1"
docker build . -t $IMAGE_NAME
docker push $IMAGE_NAME

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 a derivative container from a standard Deep Learning Containers image, using either Cloud Shell or an environment with the Google Cloud CLI installed."],["The process involves creating an initial Dockerfile and executing modification commands, such as using conda, pip, or Jupyter commands, to customize the container image."],["Before starting, ensure you have completed the necessary setup steps, including enabling billing for your Google Cloud project and the Artifact Registry API."],["After modifying the container, you need to build it and push the resulting image to a repository, such as Artifact Registry, that is accessible to your Compute Engine service account."],["The example provided shows how to take a tensorflow image, and modify the container by uninstalling the current version and installing the latest version of Tensorflow."]]],[]]

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