Feature

M129 release

• Updated the Dataproc JupyterLab plugin to version 0.1.85.

Feature

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-py310 and corresponding image family name common-cpu-debian-11-py310).
• Images that include conda will be available until at least September 30, 2025. These images now have -conda appended to the name (for example: the image name common-cpu-v20250310-debian-11-py310-conda and corresponding image family name common-cpu-debian-11-py310-conda).
• All TensorFlow images and image families still include conda, but M128 image names and image family names have -conda appended. Specifying images or image families without -conda appended references older images, which also include conda.

Feature

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.

Feature

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.

Feature

M125 release

• TensorFlow 2.17 VM images are now available.

Feature

M124 release

• Pytorch 2.3.0 with CUDA 12.1 and Python 3.10 VM images are now available.

Feature

M123 release

• TensorFlow 2.16 images are now available.

Feature

M122 release

• Updated Nvidia drivers to version 550.90.07 to fix vulnerabilities.

Feature

M121 release

• CUDA 12.2 images are now available.
• Updated TensorFlow 2.15 images from CUDA 12.1 to CUDA 12.2.
• Re-enabled common-gpu Deep Learning VM releases that were erroneously deactivated in M117.
• Updated Nvidia drivers to 550.54.15 to fix an issue where Nvidia drivers failed to install on startup after Debian 11 images upgraded kernel to linux-image-5.10.0-29-cloud-amd64.
• The linux-headers-cloud-amd64 metapackage is now installed for faster driver recompiling on kernel upgrades.
• TensorFlow 2.6 CPU and GPU images are deprecated. There will be no further updates to these images in future releases.

Feature

M120 release

• Upgraded TensorFlow 2.15 images to TensorFlow 2.15.1.
• Added Ubuntu 22.04 support for CPU images, and for GPU images using CUDA 12.1 or higher.

Fixed

M119 release

• Fixed an issue wherein Dataproc extensions caused JupyterLab to crash when remote kernels weren't available.

Feature

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.

Feature

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-gpu is renamed tf-2-12-cu118.

Feature

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-gpu is renamed to tf-2-15-cu121.
• Deprecated the tf-2-15-gpu image family in favor of tf-2-15-cu121.

Feature

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.

Feature

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.

Feature

M113 release

• Miscellaneous bug fixes and improvements in Python 3.10 images.

Feature

M112 release

• CUDA 12.1 VM images are available with the following image names:
• common-cu121-debian-11-py310
• common-cu121-ubuntu-2004-py310
• Miscellaneous bug fixes and improvements.

Feature

M111 release

• PyTorch 2.0 images now include PyTorch XLA 2.0.
• Miscellaneous software updates.

Feature

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.

Deprecated

TensorFlow 2.9 images are deprecated.

Feature

M109 release

• PyTorch 2.0 on Debian 11 with Python 3.10 and CUDA 11.8 images are now available.
• GPU-based Deep Learning VM Images now installs Nvidia drivers with the new open kernel modules if started on an A2 or G2 machine instead of the proprietary kernel modules.
• Miscellaneous software updates.

Feature

M108 update

This update of the M108 release includes the following:

• The following Deep Learning VM images are now available:
  • Tensorflow 2.12 CPU with CUDA 11.8 and Python 3.10 (tf-2-12-cpu-debian-11-py310)
  • Tensorflow 2.12 GPU with CUDA 11.8 and Python 3.10 (tf-2-12-gpu-debian-11-py310)

Feature

M108 release

• The image name common-container-experimental was changed to common-container. The related image family name wasn't changed.
• Miscellaneous software updates.

Feature

M107 release

• Miscellaneous software updates.

Feature

M106 release

• Rolled back a previous change in which Jupyter dependencies were located in a separate Conda environment.
• Miscellaneous software updates.

Feature

M105 release

• The following Deep Learning VM images are now available with Python 3.10 on Debian 11:

  • TensorFlow 2.11 CPU (tf-2-11-cpu-debian-11-py310)
  • TensorFlow 2.11 GPU with Cuda 11.3 (tf-2-11-cu113-debian-11-py310)
  • PyTorch 1.13 with Cuda 11.3 (pytorch-1-13-cu113-debian-11-py310)
  • Base CPU (common-cpu-debian-11-py310)
  • Base GPU with Cuda 11.3 (common-cu113-debian-11-py310)

• The following Deep Learning VM images are now available with Python 3.9 on Debian 11:

  • TensorFlow 2.6 CPU (tf-2-6-cpu-debian-11-py39)
  • TensorFlow 2.6 GPU with Cuda 11.3 (tf-2-6-cu113-debian-11-py39)

• Jupyter-related libraries have been moved to a different Conda environment, separate from the one containing machine learning frameworks and base software libraries.

• Miscellaneous bug fixes and improvements.

Feature

M104 release

• Added the following packages:
  • google-cloud-artifact-registry
  • google-cloud-bigquery-storage
  • google-cloud-language
  • keyring
  • keyrings.google-artifactregistry-auth
• Fixed a bug in which curl could not find the right SSL certificate path by default.

Changed

TensorFlow Enterprise 2.1 has reached the end of its support period. See Version details.

Feature

M103 release

• Upgraded PyTorch to 1.13.1.
• Minor bug fixes and improvements.

Feature

M102 release

• TensorFlow 2.11 is now available.
• PyTorch 1.13 is now available.
• Added support for Jupyter[Lab] Language Server Protocol.
• Regular security patches and package upgrades.

Feature

M101 release

• TensorFlow patch version upgrades:
  • From 2.8.3 to 2.8.4.
  • From 2.9.2 to 2.9.3.
  • From 2.10.0 to 2.10.1.
• TensorFlow 1.15 Deep Learning VM images are now deprecated.
• Regular security patches and package upgrades.