Choose a container image

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

This page helps you to choose which container image you want to use.

Choose a container image type

Each container image provides a Python 3 environment and includes the selected data science framework (such as PyTorch or TensorFlow), Conda, the NVIDIA stack for GPU images (CUDA, cuDNN, NCCL2), and many other supporting packages and tools. To find the container image that you want, see the table below.

The following list of Deep Learning Containers image types is organized by framework type.

Framework Processor Container Image Name(s)
Base GPU
TensorFlow Enterprise 2.x GPU
TensorFlow Enterprise 1.x GPU
PyTorch GPU
R CPU (experimental)
Scikit-learn CPU (experimental)
XGBoost CPU (experimental)
Deprecated images Mixed

Included dependencies

Lists of the Python dependencies that are included in each release are available on Cloud Storage at


Replace RELEASE_MILESTONE with the release miletone, such as m88. For example, the lists for the M88 release are at gs://deeplearning-platform-release/installed-dependencies/containers/m88/.

TensorFlow Enterprise container images

TensorFlow Enterprise container images provide you with a Google Cloud optimized distribution of TensorFlow, and specific versions of the TensorFlow Enterprise distribution also include Long Term Version Support. To learn more about TensorFlow Enterprise, read the TensorFlow Enterprise overview.

Experimental images

Some Deep Learning Containers image families are experimental, as indicated by the table of image families. Experimental images are supported on a best-effort basis, and may not receive refreshes on each new release of the framework.

Listing all available versions

If you need a specific framework or CUDA version, you can search the complete list of available container images. To list all available Deep Learning Containers images, use the following command in the Google Cloud CLI with your preferred terminal or in Cloud Shell.

gcloud container images list --repository=""

Using locally

Deep Learning Containers can be pulled and used locally. To do so, see Getting started with a local deep learning container.

What's next

  • Read the Deep Learning Containers overview to learn more about what is pre-installed on container images.
  • Get started with Deep Learning Containers by walking through the How-to guides, which provide instructions on how to build and push deep learning container images.