This product or feature is covered by the
Pre-GA Offerings Terms of the Google Cloud Platform
Terms of Service. Pre-GA products and features may have limited support, and changes to
pre-GA products and features may not be compatible with other pre-GA versions.
For more information, see the
launch stage descriptions.
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 AI Platform Deep Learning Containers image types is
organized by framework type.
Container Image Name(s)
TensorFlow Enterprise 2.x
TensorFlow Enterprise 1.x
Note: Deep Learning Containers that do not include a version number (for example,
pytorch-gpu) refer to the latest release version of that framework.
If your workload requires a specific framework version, specify that version. TensorFlow Enterprise container images
TensorFlow Enterprise container images provide you with a Google Cloud
optimized distribution of TensorFlow with
Long Term Version
To learn more about TensorFlow Enterprise, read the
TensorFlow Enterprise overview. 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
with your preferred terminal or in
gcloud command-line tool
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. What's next
Deep Learning Containers overview to
learn more about what is pre-installed on container images. Get started with Deep Learning Containers by walking through
which provide instructions on how to build and push deep learning container