You can see the latest product updates for all of Google Cloud on the Google Cloud page, browse and filter all release notes in the Google Cloud console, or programmatically access release notes in BigQuery.
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April 13, 2026
Images with Nvidia 570 drivers have been deprecated as Nvidia officially stopped supporting these versions. You can use images with Nvidia 580 drivers instead.
April 08, 2026
M132 release
- Ubuntu 22.04 and Ubuntu 24.04 images with Nvidia 580 drivers and CUDA 12.9 stack are now available for Common and PyTorch 2.9 images.
December 08, 2025
M131 release
- Ubuntu 24.04 images with CUDA 12.8 and PyTorch 2.7 stack are now available for Common and PyTorch images.
July 31, 2025
M130 release
- Ubuntu 22.04 images with CUDA 12.8 and PyTorch 2.7 stack are now available for Common and PyTorch images.
July 14, 2025
The following framework versions have reached their end of patch and support dates:
- Base versions with CUDA 12.4 and earlier
- Tensorflow versions 2.17 and earlier
- PyTorch versions 2.3 and earlier
To view the end of patch and support dates, see Supported framework versions.
For more information on what the end of patch and support date means, see Support policy schedule.
If you must use an image after deprecation against Google security recommendations and at your own risk, see After deprecation.
April 16, 2025
M129 release
- Updated the Dataproc JupyterLab plugin to version 0.1.85.
March 12, 2025
M128 release
- Except for TensorFlow images, new images don't include conda. This change was made to improve size, performance, and vulnerability management. The existing image names and image family names now point to images and image families that don't include conda (for example: the image name
common-cpu-v20250310-debian-11-py310and corresponding image family namecommon-cpu-debian-11-py310). - Images that include conda will be available until at least September 30, 2025. These images now have
-condaappended to the name (for example: the image namecommon-cpu-v20250310-debian-11-py310-condaand corresponding image family namecommon-cpu-debian-11-py310-conda). - All TensorFlow images and image families still include conda, but M128 image names and image family names have
-condaappended. Specifying images or image families without-condaappended references older images, which also include conda.
January 16, 2025
M127 release
- Fixed an issue related to ownership of the home directory when using authorized ssh keys.
The following framework versions have reached their end of patch and support dates:
- Tensorflow versions 2.15 and earlier
- PyTorch versions 2.1 and earlier
- Base versions with CUDA 12.2 and earlier
To view the end of patch and support dates, see Supported framework versions. To create a VM instance using an image family that has reached its end of patch and support date, you must specify an image from the image family when you create the VM instance. To list images from an image family name after its end of patch and support date, include the --show-deprecated flag in your gcloud compute images list command, or select Show deprecated images when creating an instance in the Google Cloud console.
November 20, 2024
M126 release
- CUDA 12.4 VM images are now available.
- PyTorch 2.4.0 with CUDA 12.4 and Python 3.10 VM images are now available.
- Upgraded R from 4.4.1 to 4.4.2 for R VM images.
One or more framework versions have reached their end of patch and support dates. To view end of patch and support dates, see Supported framework versions. To create a VM instance using an image family that has reached its end of patch and support date, you must specify an image from the image family when you create the VM instance. To list images from an image family name after its end of patch and support date, include the --show-deprecated flag in your gcloud compute images list command, or select Show deprecated images when creating an instance in the Google Cloud console.
March 29, 2024
M119 release
- Fixed an issue wherein Dataproc extensions caused JupyterLab to crash when remote kernels weren't available.
March 18, 2024
M118 release
- Restored legacy gpu image families for TensorFlow 2.12 through 2.14, and for PyTorch 2.0.
- PyTorch 2.1.0 with CUDA 12.1 and Python 3.10 VM images are now available.
- PyTorch 2.2.0 with CUDA 12.1 and Python 3.10 VM images are now available.
- R images (Experimental) updated to R 4.3.3.
- Updated Nvidia drivers of older Deep Learning VM images to R535.
February 28, 2024
M117 release
- Added the CUDA version (CUDA 11.8) to the TensorFlow 2.12, 2.13, and 2.14 image names and image family names. For example,
tf-2-12-gpuis renamedtf-2-12-cu118.
February 08, 2024
M116 release
- Added the CUDA version to the TensorFlow 2.15 image family name, for this release and future releases. For example,
tf-2-15-gpuis renamed totf-2-15-cu121. - Deprecated the
tf-2-15-gpuimage family in favor oftf-2-15-cu121.
January 19, 2024
M115 release
- TensorFlow 2.15 with CUDA 12.1 and Python 3.10 images are now available.
- TensorFlow 2.14 with CUDA 11.8 and Python 3.10 images are now available.
December 14, 2023
M114 release
- Starting with this release, Debian 10 Python 3.7 images are no longer available.
- Upgraded R to 4.3 on Debian 11 Python 3.10 images.
November 16, 2023
M113 release
- Miscellaneous bug fixes and improvements in Python 3.10 images.
October 10, 2023
M112 release
- CUDA 12.1 VM images are available with the following image names:
common-cu121-debian-11-py310common-cu121-ubuntu-2004-py310- Miscellaneous bug fixes and improvements.
September 14, 2023
M111 release
- PyTorch 2.0 images now include PyTorch XLA 2.0.
- Miscellaneous software updates.
August 10, 2023
M110 release
- Added support for TensorFlow 2.13 with Python 3.10 on Debian 11.
- Added support for TensorFlow 2.8 with Python 3.10 on Debian 11.
- Miscellaneous software updates.
TensorFlow 2.9 images are deprecated.
September 29, 2022
M97 release
- Improved the startup time for Ubuntu GPU images.
- Regular package updates.
Proxy registration fails if the Service Account attached to the VM does not have the compute.instances.get permission
September 20, 2022
M96 release
- TensorFlow 2.10.0 is now available.
- TensorFlow patch updates for 2.9.2 and 2.8.3 are now available.
- The PyTorch patch update for 1.12.1 is now available.
- The Diagnostic tool supports DNS resolution check.
- Docker is updated to 20.10.
- Miscellaneous bug fixes.
August 12, 2022
M95 release
- Tensorflow has been updated to 2.9.1, 2.8.1, and 2.6.5 to include upstream changes.
- Updated to the latest NVIDIA driver version: 510.47.03.
- The latest NVIDIA driver version does not support K80 GPUs. To use K80 GPUs, you must use an M94 or earlier environment.
- Fixed bug in which the user is prompted with the warning
JupyterLab build is suggestedon startup for TensorFlow Deep Learning VMs. - Regular package refreshment and bug fixes.
n1-standard-1 Compute instances that use the tensorflow-gpu family fail to boot if they were created with a single disk and no accelerator.
Please use the tf-latest-cpu image family for instances without accelerators, or increase the machine type to at least n1-standard-2.
July 06, 2022
M94 release
- Added support for PyTorch 1.12.
- Added more system libraries to the R Deep Learning VM image.
May 27, 2022
M93 release
- Fixed a bug that prevented kernels from shutting down properly in Vertex AI Workbench managed notebooks.
May 16, 2022
M92 release
- TensorFlow Enterprise 2.9 is now available. Note that this TensorFlow Enterprise version does not include Long Term Version Support.
- Starting with PyTorch 1.11, PyTorch environments now support XLA by default.
- TensorFlow Enterprise patch releases: 2.6.4 and 2.8.1.
- Fixed an issue in the Cloud Storage backup and restore feature. This fix helps prevent the deletion of local files after a reboot when the VM loses connectivity to the configured Cloud Storage backup bucket.
December 20, 2021
M88 release
- To help address the Apache Log4j 2 vulnerability, H2O has been updated to 3.34.0.6 in the R and Theia images. This addresses CVE-2021-44228 and CVE-2021-45046. See the official H2O update.
- As previously announced in the M87 release and M71 release, the previous format of TensorFlow 2.x image names,
tf2-xxx-2-y-zzz, is unavailable starting with this release. Please use the current format oftf-xxx-2-y-zzzfor image names.
- Images from the M88 release mistakenly have M87 metadata stored in the images. For example, the welcome message upon terminal login for the base CPU image shows "Version: common-cpu.m87". This mistaken metadata is also shown in the
versionfield in notebookCustom metadata. Users can verify they are actually using the M88 images by looking forv20211219in the image name of the boot disk. After clicking the image, users can also verify if the image has the labelrelease : m88. Other than the mistaken metadata, users can use the M88 images as normal.
- Starting with this release, the Python packages that are installed on each image are listed in files that are available on Cloud Storage.
December 06, 2021
M87 release
- Added Artifact Registry's Python keyring authentication library to deep learning vm environments.
- The M87 release is the last release in which TensorFlow 2.x image names are available in two formats: the current standard,
tf-xxx-2-y-zzzand the previous standard,tf2-xxx-2-y-zzz. For example, bothtf-ent-2-7-cpuandtf2-ent-2-7-cpuare available although they are the same images. The next release will only contain TensorFlow 2 images named with the current standard, as originally announced in the M71 release notes from June 2021.
November 18, 2021
M86 release
- Upgraded all Ubuntu 18.04 LTS Deep Learning VM images to Ubuntu 20.04 LTS (see What is an Ubuntu LTS release?).
- Released PyTorch/XLA 1.10.
- Upgraded TensorFlow Enterprise image to the latest patch version: 2.6.2
- Deprecated CUDA 10.x environments.
- Locked JupyterLab version to 3.2.
November 08, 2021
M85 release
- CUDA 11.3 Debian-10 image is available.
- Regular package refreshment and bug fixes.
November 05, 2021
M84 release
- TensorFlow Enterprise 2.7 is now available with CUDA 11.3 support. Note that this TensorFlow Enterprise version does not include Long Term Version Support.
October 28, 2021
M83 release
- PyTorch 1.10 is now available.
October 26, 2021
- Theia IDE (experimental) images were refreshed. PyTorch has been removed from Theia IDE images.
M82 release
- The Vertex SDK for Python is available across all deep learning environment products; it was previously available only in TensorFlow images.
October 12, 2021
M81 release
- Upgraded R to 4.1.
- Improved Cloud Storage sync logic so that only newer files sync.
- Fixed bug that prevented R kernels from working properly.
December 16, 2020
M60 release
- Added TensorFlow 2.4 Deep Learning VM Images
November 12, 2020
M59 release
- Miscellaneous bug fixes.
tensorflow_enterprise_addonpackage is renamed totensorflow-cloud
October 27, 2020
M58 release
- Added PyTorch 1.6 CUDA 11 images that support A100 GPU accelerators. This special PyTorch build provides another option to add to our A100-compatible TensorFlow Enterprise builds.
- Added the PyTorch/XLA package.
- Added the Swift for TensorFlow framework.
- Added the Ubuntu 18.04 OS.
- TensorFlow Enterprise updated to 2.3.1 from 2.3.0.
- Debian 10 is now the default OS for Deep Learning VM images.
June 23, 2020
M50 release
Miscellaneous bug fixes.
June 11, 2020
M49 release
TensorFlow Enterprise images updated to 1.15.3 and 2.1.1.
The tensorflow-enterprise-addons package is now available in all deep learning environments.
XGBoost, MXNet, R, PyTorch, CNTK, and Caffe images have been updated with library upgrades and bug fixes.
May 18, 2020
M48 release
TensorFlow 2.2 images have been added. The new TensorFlow 2.2 image families are tf2-2-2-cpu and tf2-2-2-cu101. See the available image families.
May 12, 2020
M47 release
Fixed an OS login issue under single user mode for a user external to an organization.
Fixed a git extensions plugin issue in TensorFlow 2 images.
January 21, 2020
M41 release
TensorFlow Enterprise 2.1 images are now available.
MXNet upgraded to 1.5.1.
PyTorch upgraded to 1.4.0.
XGBoost upgraded to 0.90.
June 20, 2019
M27.1 release updates
TensorFlow upgraded to: 1.14.0.
TensorFlow 2.0 upgraded to: Beta 1.
Miscellaneous bug fixes.
June 17, 2019
M27 release
New ML framework added: CNTK 2.7 from Microsoft.
New ML framework added: Caffe 1.0 BVLC from UC Berkeley.
Updated TensorFlow 2.0 Beta0.
Miscellaneous bug fixes.
May 29, 2019
M26 release
RAPIDS updated to 0.7.
Faster driver installation time for common TensorFlow and PyTorch images.
You can now use Deep Learning VMs without a public IP address if you have enabled Google Private Access.
Miscellaneous bug fixes.
May 03, 2019
M25 release
New image added: CUDA 10.1.
PyTorch upgraded to 1.1.0.
fastai upgraded to 1.0.52.
MXNet upgraded to 1.4.0 (and now based on CUDA 10.0 images).
Chainer upgraded to 5.4.0.
April 26, 2019
M24 release
We now support two authorization modes in the new release: single user mode and service account mode3.
rpy2 is now pre-installed in the R image.
A plugin for editing metadata of cells is now pre-installed.
jupyterlab-celltags JupyterLab extension is now pre-installed.
Fixed bug with sudo (now you can use sudo from the JupyterLab terminal).
Downloading files from JupyterLab file browser is now working.
March 15, 2019
M22 release
Tensorflow upgraded to version 1.13.
Due to incompatibilities between Tensorflow 1.13 (which requires Numpy 1.16.2 or greater) and the latest Intel optimized version of Numpy (which is 1.15) we are not using the intel optimized versions of Numpy and Scipy for this release.
Fairing now preinstalled.
More descriptive serial logs to help customers debug common issues.
cookiecutter and seaborn now preinstalled.
Misc bug fixes.
February 21, 2019
M20 release
TensorFlow and Pytorch GPU images switch between CPU-only/GPU-enabled binaries at startup depending on whether GPUs are attached.
Due to incompatibilities between the latest kernel update (Debian 9.8) and Docker, we have put a hold on the kernel updates for this release (that is, apt-mark hold linux-image-4.9.0-8-amd64). If you require the latest kernel, you can run sudo apt-mark unhold linux-image-4.9.0-8-amd64 && sudo apt upgrade, but we cannot guarantee that Docker or our direct JupyterLab link from Marketplace will function correctly if you force the upgrade.
SSH is not disabled during NVIDIA driver installation on GPU images.
January 29, 2019
M19 release
New TensorFlow 2.0 (experimental) flavor is added.
New experimental ability to use Deep Learning VMs with special Web proxy, instead of SSHing to the VM.
January 14, 2019
M16 release
New MXNet 1.3 (experimental) flavor is added.
December 19, 2018
General Availability
Launched the new 1.0 version of AI Platform Deep Learning VM Image.
fastai updated to the latest version 1.0.38.
M15 release
BigQuery magic plugin now preloaded all the time.
Jupyter SQL integration now pre-installed and SQL plugin now preloaded.
TensorFlow images now include bazel pre-installed.
Python Dataproc client now pre-installed on all our images.
December 10, 2018
M14 release
Fixed bug that was resulting in a broken Git UI in some cases.
Fast.Ai updated to 1.0.36.
December 05, 2018
M13 release
Integrates fix for speed regression in linear models when using TensorFlow with Intel® MKL DNN.
Adds Git-Jupyter integration.
November 20, 2018
M12 release
Chainer is now upgraded to 5.0.0 (and CuPy to 5.0.0).
CuDNN updated to 7.4.
TensorRT5 updated to GA.
XGBoost updated to 0.81.
Images now have papermill pre-installed.
Ability to change Jupyter UI that is running on the port 8080, currently supported: Lab and Notebook.
November 13, 2018
M11.1 release
Fixed an issue where users were locked out of apt after startup due to a package needing configuration. If you are using an M11 image and are experiencing issues with apt, please either recreate your VM or run sudo dpkg --configure -a to clear the lock.
November 08, 2018
M11 release
All GPU images install NVIDIA driver 410.72.
TensorFlow updated to v1.12.0.
PyTorch 0.4 image now uses conda for package management.
October 23, 2018
M10 release
PyTorch 1.0 updated to the latest build as of October 23.
fastai updated to 1.0.12.
fastai course materials are now available at $HOME/tutorials/fastai/.
Chainer UI updated to 0.6.0.
Chainer MN updated to 1.3.1.
Fixed a bug that was causing Intel packages to be overwritten.
October 10, 2018
M9 release
Intel Optimized Python packages are installed in all distributions:
- NumPy
- SciPy
scikit-learn- TensorFlow (when applicable)
PyTorch 1.0 (Experimental) images include support for [conda](https://conda.io/) and [fastai](http://fast.ai/).
Chainer updated from v4.4.0 to v4.5.0.