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-notebookscommon-cu113-notebookscommon-cu118-notebookscommon-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 22.04
images are available for some frameworks.
Ubuntu 22.04 images are denoted by the -ubuntu-2204
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-cu122 (Python 3.10) | CUDA 12.2 | GPU (CUDA 12.2) | Jan 8, 2025 | Jan 8, 2026 | common-cu122-VERSION_DATE-debian-11-py310 |
| 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 |
|---|---|---|---|---|---|
| 2.17 (Python 3.10) | 2.17.0 | CPU only | Jul 11, 2025 | Jul 11, 2026 | tf-2-17-cpu-VERSION_DATE-py310 |
| 2.17 (Python 3.10) | 2.17.0 | GPU (CUDA 12.3) | Jul 11, 2025 | Jul 11, 2026 | tf-2-17-cu123-VERSION_DATE-py310 |
| 2.16 (Python 3.10) | 2.16.2 | CPU only | Jun 28, 2025 | Jun 28, 2026 | tf-2-16-cpu-VERSION_DATE-py310 |
| 2.16 (Python 3.10) | 2.16.2 | GPU (CUDA 12.3) | Jun 28, 2025 | Jun 28, 2026 | tf-2-16-cu123-VERSION_DATE-py310 |
| 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 | Sep 1, 2023 | Sep 1, 2024 | tf-2-6-cpu-VERSION_DATE-py39 |
| 2.6 (py39) | 2.6.5 | GPU (CUDA 11.3) | Sep 1, 2023 | Sep 1, 2024 | 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 libraryVERSION: the framework versionCUDA_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
- Use the Google Cloud console to Create a user-managed notebooks instance with default properties
- Use the Google Cloud CLI to Create a user-managed notebooks instance
- Learn more about Deep Learning VM instances