Choose a virtual machine image

User-managed notebooks instances are Deep Learning VM Images instances with JupyterLab notebook environments that are enabled and ready to 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.

Decide on an image family

To ensure that your instance uses a supported image family, create an instance by referencing an image family with -notebooks in the name. The following table lists the default versions of image families, organized by framework type. If you need a specific framework version that isn't shown here, see Supported framework versions.

Framework Processor Image family names
Base GPU common-cu110-notebooks
common-cu113-notebooks
common-cu118-notebooks
common-cu121-notebooks
CPU common-cpu-notebooks
TensorFlow Enterprise GPU tf-ent-2-13-cu113-notebooks
PyTorch GPU pytorch-2-2-cu121-notebooks
R CPU (experimental) r-4-1-cpu-experimental-notebooks

Choose an operating system

Debian 11 is the default OS for most frameworks. Ubuntu 20.04 images are available for some frameworks. Ubuntu 20.04 images are denoted by the -ubuntu-2004 suffixes in the image family name (see List all available versions). Debian 10 and Debian 9 images are deprecated.

PyTorch and TensorFlow Enterprise image families support A100 GPU accelerators.

TensorFlow Enterprise images

TensorFlow Enterprise image families provide a Google Cloud optimized distribution of TensorFlow. For more information about TensorFlow Enterprise including which versions are supported, see TensorFlow Enterprise overview.

Experimental images

The table of image families shows the user-managed notebooks image families that are experimental. Experimental images are supported on a best-effort basis and might not receive refreshes on each new release of the framework.

Specify an image version

When you use an image family name to create a user-managed notebooks instance, you get 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 tf-ent-2-13-cu113-notebooks, the specific image name might look like tf-ent-2-13-cu113-notebooks-v20230716.

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, run the following command by using the Google Cloud CLI in your preferred terminal or in Cloud Shell. Replace IMAGE_FAMILY with the image family name for which you want the most recent version number.

gcloud compute images describe-from-family IMAGE_FAMILY \
    --project deeplearning-platform-release

In the output, look for the name field, and use that image name when you create instances.

Supported framework versions

Vertex AI supports each framework version based on a schedule to minimize security vulnerabilities. Review the Vertex AI framework support policy to understand the implications of the end-of-support and end-of-availability dates.

If you need a specific framework or CUDA version, see the following tables. To find a specific VERSION_DATE for an image, see Listing the available versions.

Base versions

ML framework version Current patch version Supported accelerators End of patch and support date End of availability date Image family name
Base-CPU (Python 3.10 / Debian 11) Not applicable (N/A) CPU only Jul 1, 2024 Jul 1, 2025 common-cpu-VERSION_DATE-debian-11
Base-cu121 (Python 3.10) CUDA 12.1 GPU (CUDA 12.1) Feb 28, 2024 Feb 28, 2025 common-cu121-VERSION_DATE-debian-11-py310
Base-cu118 (Python 3.10) CUDA 11.8 GPU (CUDA 11.8) >Jul 1, 2024 >Jul 1, 2025 common-cu118-VERSION_DATE-debian-11-py310
Base-cu113 (Python 3.10) CUDA 11.3 GPU (CUDA 11.3) Jan 1, 2024 Jan 1, 2025 common-cu113-VERSION_DATE-debian-11-py310
Base-cu113 (Python 3.7) CUDA 11.3 GPU (CUDA 11.3) Sep 18, 2023 Sep 18, 2024 common-cu113-VERSION_DATE-py37
Base-cu110 (Python 3.7) CUDA 11.0 GPU (CUDA 11.0) Sep 18, 2023 Sep 18, 2024 common-cu110-VERSION_DATE-py37
Base-CPU (Python 3.7) Not applicable (N/A) CPU only Sep 18, 2023 Sep 18, 2024 common-cpu-VERSION_DATE-debian-10

TensorFlow versions

ML framework version Current patch version Supported accelerators End of patch and support date End of availability date Image family name
2.15 (Python 3.10) 2.15.0 CPU only Nov 14, 2024 Nov 14, 2025 tf-2-15-cpu-VERSION_DATE-py310
2.15 (Python 3.10) 2.15.0 GPU (CUDA 12.1) Nov 14, 2024 Nov 14, 2025 tf-2-15-cu121-VERSION_DATE-py310
2.14 (Python 3.10) 2.14.0 CPU only Sep 26, 2024 Sep 26, 2025 tf-2-14-cpu-VERSION_DATE-py310
2.14 (Python 3.10) 2.14.0 GPU (CUDA 11.8) Sep 26, 2024 Sep 26, 2025 tf-2-14-cu118-VERSION_DATE-py310
2.13 (Python 3.10) 2.13.0 CPU only Jul 5, 2024 Jul 5, 2025 tf-2-13-cpu-VERSION_DATE-py310
2.13 (Python 3.10) 2.13.0 GPU (CUDA 11.8) Jul 5, 2024 Jul 5, 2025 tf-2-13-cu118-VERSION_DATE-py310
2.12 (Python 3.10) 2.12.0 CPU only June 30, 2024 June 30, 2025 tf-2-12-cpu-VERSION_DATE-py310
2.12 (Python 3.10) 2.12.0 GPU (CUDA 11.8) Jan 18, 2024 Jan 18, 2025 tf-2-12-cu113-VERSION_DATE-py310
2.11 (Python 3.10) 2.11.0 CPU only Nov 15, 2023 Nov 15, 2024 tf-2-11-cpu-VERSION_DATE-py310
2.11 (Python 3.10) 2.11.0 GPU (CUDA 11.3) Nov 15, 2023 Nov 15, 2024 tf-2-11-cu113-VERSION_DATE-py310
2.11 2.11.0 CPU only Nov 15, 2023 Nov 15, 2024 tf-2-11-cpu-VERSION_DATE-py37
2.11 2.11.0 GPU (CUDA 11.3) Nov 15, 2023 Nov 15, 2024 tf-2-11-cu113-VERSION_DATE-py37
2.10 2.10.1 CPU only Nov 15, 2023 Nov 15, 2024 tf-2-10-cpu-VERSION_DATE-py37
2.10 2.10.1 GPU (CUDA 11.3) Nov 15, 2023 Nov 15, 2024 tf-2-10-cu113-VERSION_DATE-py37
2.9 2.9.3 CPU only Nov 15, 2023 Nov 15, 2024 tf-2-9-cpu-VERSION_DATE-py37
2.9 2.9.3 GPU (CUDA 11.3) Nov 15, 2023 Nov 15, 2024 tf-2-9-cu113-VERSION_DATE-py37
2.8 2.8.4 CPU only Nov 15, 2023 Nov 15, 2024 tf-2-8-cpu-VERSION_DATE-py37
2.8 2.8.4 GPU (CUDA 11.3) Nov 15, 2023 Nov 15, 2024 tf-2-8-cu113-VERSION_DATE-py37
2.6 (py39) 2.6.5 CPU only Aug 10, 2024 Aug 10, 2025 tf-2-6-cpu-VERSION_DATE-py39
2.6 (py39) 2.6.5 GPU (CUDA 11.3) Aug 10, 2024 Aug 10, 2025 tf-2-6-cu110-VERSION_DATE-py39
2.6 (py37) 2.6.5 CPU only Sep 18, 2023 Sep 18, 2024 tf-2-6-cpu-VERSION_DATE-py37
2.6 (py37) 2.6.5 GPU (CUDA 11.3) Sep 18, 2023 Sep 18, 2024 tf-2-6-cu110-VERSION_DATE-py37
2.3 2.3.4 CPU only Sep 1, 2023 Sep 1, 2024 tf-2-3-cpu
2.3 2.3.4 GPU (CUDA 11.3) Sep 1, 2023 Sep 1, 2024 tf-2-3-cu110-VERSION_DATE

PyTorch versions

ML framework version Current patch version Supported accelerators End of patch and support date End of availability date Image family name
2.2 (Python 3.10) 2.2.0 CUDA 12.1 Jan 30, 2025 Jan 30, 2026 pytorch-2-2-VERSION_DATE-py310
2.1 (Python 3.10) 2.1.0 CUDA 12.1 Oct 4, 2024 Oct 4, 2025 pytorch-2-1-VERSION_DATE-py310
2.0 (Python 3.10) 2.0.0 CUDA 11.8 Mar 15, 2024 Mar 15, 2025 pytorch-2-0-VERSION_DATE-py310
1.13 (Python 3.10) 1.13.1 CUDA 11.3 Dec 8, 2023 Dec 8, 2024 pytorch-1-13-VERSION_DATE-py310
1.13 1.13.1 CUDA 11.3 Dec 8, 2023 Dec 8, 2024 pytorch-1-13-VERSION_DATE-py37
1.12 1.12.1 CUDA 11.3 Sep 1, 2023 Sep 1, 2024 pytorch-1-12-VERSION_DATE-py310

List all available versions using gcloud CLI

You can also list all available Vertex AI images using the following gcloud CLI command:

gcloud compute images list \
    --project deeplearning-platform-release | grep notebooks

Image family names are listed in the following format:

FRAMEWORK-VERSION-CUDA_VERSION(-experimental)-notebooks

  • FRAMEWORK: the target library
  • VERSION: the framework version
  • CUDA_VERSION: the version of the CUDA stack, if present.

For example, an image from the family tf-ent-2-13-cu113-notebooks has TensorFlow Enterprise 2.13 and CUDA 11.3.

What's next