Choose an image

Specific Deep Learning VM Images images are available to suit your choice of framework and processor. There are currently images supporting TensorFlow, PyTorch, and generic high-performance computing, with versions for both CPU-only and GPU-enabled workflows. To find the image that you want, see the table below.

Deciding on an image family

Choose a Deep Learning VM image family based on the framework and processor that you need. The following table lists the most recent versions of image families, organized by framework type. To get the most recent version of an image, create an instance by referencing an image family with latest in the name. If you need a specific framework version, skip to Supported framework versions.

Framework Processor Image family name(s)
Base GPU common-cu123
common-cu122
common-cu121
common-cu118
common-cu113
common-cu110
CPU common-cpu
TensorFlow Enterprise GPU tf-ent-latest-gpu
CPU tf-ent-latest-cpu
PyTorch GPU pytorch-latest-gpu
CPU pytorch-latest-cpu

Choosing an operating system

For most frameworks, Debian 11 is the default OS. Ubuntu 22.04 images are available for some frameworks. They are denoted by the -ubuntu-2204 suffixes in the image family name (see Listing all available versions). Debian 10 and Debian 9 images have been 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

Some Deep Learning VM 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

You can reuse the same image even if the latest image is newer. This can be useful, for instance, if you are trying to create a cluster and you want to ensure that any images that are used to create new instances are always the same. You should not use the name of the image family in this situation because, if the latest image is updated, you'll have different images on some instances in your cluster.

Instead, you can determine what the exact name of the image is, incorporate the version number, and then use that specific image to spawn new instances in your cluster.

To find out the exact name of the latest image, use the following command in the Google Cloud CLI with your preferred terminal or in Cloud Shell. Replace IMAGE_FAMILY with the image family name for which you want to find out the latest 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.

Supported framework versions

Deep Learning VM supports each framework version based on a schedule to minimize security vulnerabilities. Review the Deep Learning VM 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-cu123 (Python 3.10) CUDA 12.3 GPU (CUDA 12.3) Oct 19, 2024 Oct 19, 2025 common-cu123-VERSION_DATE-debian-11-py310
Base-cu122 (Python 3.10) CUDA 12.2 GPU (CUDA 12.2) June 28, 2024 June 28, 2025 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 1, 2023 Sep 1, 2024 common-cu113-VERSION_DATE-py37
Base-cu110 (Python 3.7) CUDA 11.0 GPU (CUDA 11.0) Sep 1, 2023 Sep 1, 2024 common-cu110-VERSION_DATE-py37
Base-CPU (Python 3.7) Not applicable (N/A) CPU only Sep 1, 2023 Sep 1, 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 Ju1 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 12.2) Nov 14, 2024 Nov 14, 2025 tf-2-15-cu122-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) June 30, 2024 June 30, 2025 tf-2-12-cu113-VERSION_DATE-py310
2.11 (Python 3.10) 2.11.0 CPU only Nov 15, 2022 Nov 15, 2023 tf-2-11-cpu-VERSION_DATE-py310
2.11 (Python 3.10) 2.11.0 GPU (CUDA 11.3) Nov 15, 2022 Nov 15, 2023 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 1, 2023 Sep 1, 2024 tf-2-6-cpu-VERSION_DATE-py37
2.6 (py37) 2.6.5 GPU (CUDA 11.3) Sep 1, 2023 Sep 1, 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
2.3 (Python 3.10) 2.3.0 CUDA 12.1 Apr 24, 2025 Apr 24, 2026 pytorch-2-3-VERSION_DATE-py310
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 Deep Learning VM images using the following gcloud CLI command:

gcloud compute images list \
    --project deeplearning-platform-release \
    --format="value(NAME)" \
    --no-standard-images

Image families are named in the format FRAMEWORK-VERSION-CUDA_VERSION(-experimental), where FRAMEWORK is the target library, VERSION is the framework version, and CUDA_VERSION is the version of the CUDA stack, if present.

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

What's next

Create a new Deep Learning VM instance using the Cloud Marketplace or using the command line.