User-managed notebooks instances are Deep Learning VM Images instances with JupyterLab notebook environments enabled and ready for use. Specific user-managed notebooks images are available to suit your choice of framework and processor. To find the image that you want, see the following table.
Deciding on an image family
Deciding on which user-managed notebooks image family
to use depends on your needs.
The following table lists the default versions of image families,
organized by framework type.
Creating an instance by referencing an image family with
the name ensures that your instance is a supported image family.
If you need a specific framework version that is not shown here,
skip to Listing all available versions.
|Framework||Processor||Image family name(s)|
|TensorFlow Enterprise 2.x||GPU||
|TensorFlow Enterprise 1.x||GPU||
|PyTorch XLA||TPU/GPU/CPU (experimental)||
|Deprecated notebook images||Mixed||
Choosing an operating system
For most frameworks, Debian 10 is the default OS. Ubuntu 18.04
images are available for some frameworks.
They are denoted by the
suffixes in the image family name (see Listing all available
versions). Debian 9 images have been deprecated.
PyTorch and TensorFlow Enterprise (
image families support A100 GPU accelerators.
TensorFlow Enterprise images
TensorFlow Enterprise image families 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.
Use the following table of available TensorFlow images to help you select the image with the version of TensorFlow or TensorFlow Enterprise that you want.
|Version of TensorFlow or TensorFlow Enterprise||Processor||Image family name||Long Term Version Support|
|TensorFlow Enterprise 2.6||GPU||
|TensorFlow Enterprise 2.5||GPU||
|TensorFlow Enterprise 2.3||GPU||
|TensorFlow Enterprise 2.1||GPU||
|TensorFlow Enterprise 1.15||GPU||
Some user-managed notebooks 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.
Specifying an image version
Creating a new user-managed notebooks instance based on the image
family name gives you the most recent image of that version of the framework.
For example, if you create a user-managed notebooks instance
based on the family name
the specific image name might look like
To create multiple user-managed notebooks instances based on the exact same image, use the image name instead of the image family name.
To determine the exact name of the most recent image, use the following
gcloud command-line tool with your preferred terminal or in
Cloud Shell. Replace
IMAGE_FAMILY with the image family name for which you want
to find out the most recent version number.
gcloud compute images describe-from-family IMAGE_FAMILY \ --project deeplearning-platform-release
Look for the
name field in the output and use the image name
given there when creating new instances.
Listing all available versions
If you need a specific framework, CUDA version, or operating system,
you can search
the complete list of available images. To list all available
user-managed notebooks images, use the following
gcloud tool command.
gcloud compute images list \ --project deeplearning-platform-release | grep notebooks
Image families will be in the format
FRAMEWORK is the target library,
VERSION is the framework version, and
CUDA_VERSION is the version of the CUDA stack,
For example, an image from the family
TensorFlow Enterprise 2.3 and CUDA 11.0.
Learn more about Deep Learning VM instances in the Deep Learning VM documentation.