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June 02, 2021
May 14, 2021
- Added TensorFlow Enterprise 2.5 containers. Note this is an Enterprise version but not a Long Term Support (LTS) version.
May 13, 2021
- Updated cuDNN from 8.0.4 to 8.0.5.
May 05, 2021
- 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
- Added Horovod to TensorFlow GPU containers.
- Regular package refreshment and bug fixes.
March 31, 2021
March 05, 2021
Upgraded tensorflow-cloud to 0.1.13.
Regular package refreshment and bug fixes.
February 19, 2021
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
- SWIFT 0.12 (experimental) containers are available.
January 25, 2021
AI Platform Deep Learning Containers is now generally available.
Python 2 is no longer supported in Deep Learning Containers. Read more about Python 2 support on Google Cloud.
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.