Deep Learning Containers release notes

This page documents production updates to Deep Learning Containers. You can periodically check this page for announcements about new or updated features, bug fixes, known issues, and deprecated functionality.

For more information about Deep Learning Containers versions and features, ask a question about Deep Learning Containers on Stack Overflow or join the google-dl-platform Google group to discuss Deep Learning Containers. Learn more about getting support from the community.

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.

To get the latest product updates delivered to you, add the URL of this page to your feed reader, or add the feed URL directly.

November 20, 2024

M126 release

  • Base CUDA 12.3 container images are now available.
  • Base CUDA 12.4 container images are now available.
  • PyTorch 2.4.0 with CUDA 12.4 and Python 3.10 container images are now available.
  • Upgraded R from 4.4.1 to 4.4.2 for R container images.

September 26, 2024

M125 release

  • TensorFlow 2.17 container images are now available.

August 20, 2024

M124 release

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

July 16, 2024

M123 release

  • Hugging Face Text Generation Inference 2.1 GPU container images are now available.

June 21, 2024

M122 release

  • TensorFlow 2.16 container images are now available.
  • PyTorch Inference 2.2 GPU container images are now available.
  • PyTorch Inference 2.2 CPU container images are now available.

May 17, 2024

M121 release

  • Updated the R CPU container image from R 4.3 to R 4.4. The R 4.3 container image is deprecated. There will be no further updates to this image in future releases.

April 25, 2024

M120 release

  • Upgraded TensorFlow 2.15 container images to TensorFlow 2.15.1.
  • Added CUDA-specific release tags for all TensorFlow and PyTorch container images, for example, us-docker.pkg.dev/deeplearning-platform-release/gcr.io/tf-cu121.2-15.

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

  • PyTorch 2.1.0 with CUDA 12.1 and Python 3.10 container images are now available.
  • PyTorch 2.2.0 with CUDA 12.1 and Python 3.10 container images are now available.

February 20, 2024

M117 release

  • Fixed an issue wherein the latest container had a deprecation-public-image tag. In this release and future releases, this tag will only be on the deprecated containers.
  • Fixed a problem wherein the user couldn't access the vulnerabilities result of each container.

January 19, 2024

M115 release

  • TensorFlow 2.15 with CUDA 12.1 and Python 3.10 container images are now available.
  • TensorFlow 2.14 with CUDA 11.8 and Python 3.10 container images are now available.

December 14, 2023

M114 release

  • Starting with this release, Python 3.7 is no longer available.
  • Upgraded R to 4.3 on Python 3.10 containers.
  • Fixed an issue where the PySpark-BigQuery connector didn't work properly on Python 3.10 PySpark container.

November 16, 2023

M113 release

  • Miscellaneous bug fixes and improvements in Python 3.10 container images.

October 10, 2023

M112 release

  • Miscellaneous bug fixes and improvements.

September 14, 2023

M111 release

  • PyTorch 2.0 container 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 container images are deprecated.

June 26, 2023

M109 release

  • PyTorch 2.0 with Python 3.10 and CUDA 11.8 container images are now available.
  • Miscellaneous software updates.

May 09, 2023

M108 update

This update of the M108 release includes the following:

  • The following Deep Learning Containers images are now available:
    • Tensorflow 2.12 CPU with CUDA 11.8 and Python 3.10 (us-docker.pkg.dev/deeplearning-platform-release/gcr.io/tf-cpu.2-12.py310:latest)
    • Tensorflow 2.12 GPU with CUDA 11.8 and Python 3.10 (us-docker.pkg.dev/deeplearning-platform-release/gcr.io/tf-gpu.2-12.py310:latest)

May 04, 2023

M108 release

  • Miscellaneous software updates.

April 13, 2023

M107 release

  • Miscellaneous software updates.

April 06, 2023

M106 release

  • Miscellaneous software updates.

March 31, 2023

M105 release

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

    • TensorFlow 2.11 CPU (us-docker.pkg.dev/deeplearning-platform-release/gcr.io/tf-cpu.2-11.py310:latest)
    • TensorFlow 2.11 GPU with Cuda 11.3 (us-docker.pkg.dev/deeplearning-platform-release/gcr.io/tf-gpu.2-11.py310:latest)
    • PyTorch 1.13 with Cuda 11.3 (us-docker.pkg.dev/deeplearning-platform-release/gcr.io/pytorch-gpu.1-13.py310:latest)
    • Base CPU (us-docker.pkg.dev/deeplearning-platform-release/gcr.io/base-cpu.py310:latest)
    • Base GPU with Cuda 11.3 (us-docker.pkg.dev/deeplearning-platform-release/gcr.io/base-cu113.py310:latest)

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

    • TensorFlow 2.6 CPU (us-docker.pkg.dev/deeplearning-platform-release/gcr.io/tf-cpu.2-6.py39:latest)
    • TensorFlow 2.6 GPU with Cuda 11.3 (us-docker.pkg.dev/deeplearning-platform-release/gcr.io/tf-gpu.2-6.py39:latest)

  • Miscellaneous bug fixes and improvements.

March 16, 2023

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.

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

January 30, 2023

M103 release

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

December 15, 2022

M102 release

  • TensorFlow 2.11 is now available.
  • PyTorch 1.13 is now available.
  • Regular security patches and package upgrades.

December 09, 2022

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 Containers images are now deprecated.
  • Regular security patches and package upgrades.

November 08, 2022

M100 release

  • Regular package updates.

November 02, 2022

M99 release

  • Fixed a bug where Jupyter widgets through ipywidgets were causing errors and not displaying.
  • Regular package updates.

October 18, 2022

M98 release

  • Upgraded JupyterLab from 3.2 to 3.4.
  • Upgraded R from 4.1 to 4.2.
  • Miscellaneous bug and display fixes.
  • Regular package updates.

September 29, 2022

M97 release

  • Regular package updates.

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.
  • 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.
  • Regular package refreshment and bug fixes.

July 06, 2022

M94 release

  • Added support for PyTorch 1.12.
  • Added more system libraries to the R Deep Learning Containers image.

May 27, 2022

M93 release

  • Starting with this release, information on known vulnerabilities for Deep Learning Containers images is now available on Cloud Storage.

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.
  • Deep Learning Containers are now available on Artifact Registry.

March 21, 2022

M91 release

  • PyTorch 1.11 and PyTorch XLA 1.11 are now available in both Deep Learning Containers and Deep Learning VM Images.
  • Fixed an R package installation issue for R Deep Learning Containers and Vertex AI Workbench.

February 28, 2022

M90 release

  • CUDA has been upgraded from 11.3.0 to 11.3.1 to address some NCCL issues.
  • VSlim GPU TensorFlow containers are available and have a significantly smaller size.
  • TensorFlow 2.7 containers are re-released.

February 02, 2022

M89 release

December 20, 2021

M88 release

December 06, 2021

M87 release

  • TensorFlow 2.x container image names are available in two formats: the current standard, which includes a tf- prefix, and the previous standard, which includes a tf2- prefix. For example, both gcr.io/deeplearning-platform-release/tf-gpu.2-7 and gcr.io/deeplearning-platform-release/tf2-gpu.2-7 are available although they are the same container images. Starting within approximately six months, releases of TensorFlow 2 container images will only be named with the current standard.

November 18, 2021

M86 release

  • Upgraded all Ubuntu 18.04 LTS Deep Learning Container 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

  • 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

M82 release

  • Released CUDA11.3 container images.
  • The Vertex SDK for Python is available across all deep learning environment products; it was previously available only in TensorFlow images.
  • Theia IDE (experimental) images were refreshed. PyTorch has been removed from Theia IDE images.

October 12, 2021

M81 release

  • Upgraded R to 4.1.
  • Fixed bug that prevented R kernels from working properly.

September 24, 2021

Starting with the M80 image release, all environments will include JupyterLab 3.x by default. To continue using an existing environment's JupyterLab 1.x version, disable auto-upgrade (if enabled) and do not manually upgrade the environment to a new environment version. To create new instances using older images that have JupyterLab 1.x installed, see creating specific versions of instances.

M80 release

  • Updated JupyterLab from 1.x to 3.x.
  • Added Jupytext.

September 09, 2021

M79 release

  • Updated Pytorch 1.9 containers (they were not refreshed in the last release).
  • Updated Theia IDE (experimental) containers.
  • Node.js is pinned to >=12.14.1,<13.
  • M79 is the last release version that has JupyterLab 1.x installed. For the next release (M80), JupyterLab will be upgraded to 3.x for all Deep Learning VM Images, Deep Learning Containers, and Notebooks.
  • Fixed a bug in which the home folder in custom container VMs was owned by the root instead of Jupyter.

August 18, 2021

M78 release

  • Updated TensorFlow Enterprise patch version 2.3.3 to 2.3.4.

TensorFlow Enterprise 2.5

  • TensorFlow Enterprise 2.5 Deep Learning Containers are now deprecated.

August 11, 2021

M77 release

TensorFlow Enterprise 2.6.0 is now available and includes Long Term Version Support.

August 02, 2021

M76 release

July 15, 2021

M75 release

  • Enhanced environment configurations so it is easier to install additional frameworks in CUDA containers.

June 22, 2021

M73 release

  • Upgraded TensorFlow Enterprise 2.1.3 to 2.1.4.
  • Upgraded TensorFlow Enterprise 2.3.2 to 2.3.3.
  • Miscellaneous bug fixes and updates.

June 17, 2021

M72 release

  • Added PyTorch 1.9 and PyTorch/XLA 1.9 containers.

June 02, 2021

M71 release

May 14, 2021

M70 release

  • Added TensorFlow Enterprise 2.5 containers. Note this is an Enterprise version but not a Long Term Support (LTS) version.

May 13, 2021

M69 release

  • Updated cuDNN from 8.0.4 to 8.0.5.

May 05, 2021

M68 release

  • Upgraded R containers from 3.6 to 4.0.
  • Added xai-tabular-widget onto all TensorFlow containers.
  • Miscellaneous bug fixes and updates.

April 19, 2021

M67 release

  • Added Horovod to TensorFlow GPU containers.
  • Regular package refreshment and bug fixes.

March 31, 2021

M66 release

March 05, 2021

M65 release

  • Upgraded tensorflow-cloud to 0.1.13.

  • Regular package refreshment and bug fixes.

February 19, 2021

M64 release

  • Upgraded TensorFlow 2.4 to 2.4.1.

  • Upgraded TFX and Fairness Indicators from 0.26.0 to 0.27.0.

  • Miscellaneous bug fixes and updates.

Swift For TensorFlow

  • The Swift For TensorFlow project is entering archive mode. Containers will be deprecated and will no longer receive updates after this release.

February 08, 2021

M63 release

January 25, 2021

General Availability

AI Platform Deep Learning Containers is now generally available.

Python 2

Python 2 is no longer supported in Deep Learning Containers. Read more about Python 2 support on Google Cloud.

M62 release

  • Upgraded TensorFlow 2.3 to 2.3.2

  • Upgraded TensorFlow 2.1 to 2.1.3

  • Miscellaneous bug fixes and updates

December 16, 2020

Added TensorFlow 2.4 Deep Learning Containers images.

October 28, 2020

  • Added PyTorch 1.6 CUDA 11 environments that support A100 GPU accelerators. This special PyTorch build provides another option to add to our A100-compatible TensorFlow Enterprise builds.

August 17, 2020

TensorFlow Enterprise 2.3 environments are now available. These environments include support for A100 GPU accelerators, CUDA 11, and TensorFloat-32 (TF32).

January 08, 2020

TensorFlow Enterprise environments are now available. Use TensorFlow Enterprise with Deep Learning Containers.

June 24, 2019

AI Platform Deep Learning Containers is now available in beta. AI Platform Deep Learning Containers lets you quickly prototype with a portable and consistent environment for developing, testing, and deploying your AI applications.

Visit the AI Platform Deep Learning Containers overview and the guide to getting started with a local deep learning container.