This page helps you choose which container image 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 appropriate container image, see the table below.
The following list of Deep Learning Containers image types is organized by framework type.
|Framework||Processor||Container Image Name(s)|
A list of each Deep Learning Containers release's Python dependencies is available in Cloud Storage at
Replace RELEASE_MILESTONE with the release milestone, such as
For example, the lists for the M88 release are at
TensorFlow Enterprise container images
TensorFlow Enterprise container images provide a Google Cloud optimized distribution of TensorFlow. For more information about TensorFlow Enterprise including which versions are supported, see TensorFlow Enterprise overview.
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, 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="gcr.io/deeplearning-platform-release"
Deep Learning Containers can be pulled and used locally. To do so, see Getting started with a local deep learning container.
- 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.