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Framework Support Policy

Amazon Deep Learning AMIs(DLAMIs) simplify image configuration for deep learning workloads and are optimized with the latest frameworks, hardware, drivers, libraries, and operating systems. This page details the framework support policy for DLAMIs. For a list of available DLAMIs, see Release Notes for DLAMI.

Supported Frameworks

Reference the following Amazon Deep Learning AMI Framework Support Policy table to check which frameworks and versions are actively supported.

Refer to End of patch to check how long Amazon supports current versions that are actively supported by the origin framework’s maintenance team. Frameworks and versions are available in single-framework DLAMIs, or multi-framework DLAMIs.

Refer to the release notes for more details on specific images:

Frequently Asked Questions

What framework versions get security patches?

If the framework version is labeled Supported in the Amazon Deep Learning AMI Framework Support Policy table, it gets security patches.

What images does Amazon publish when new framework versions are released?

We publish new DLAMIs soon after new versions of TensorFlow and PyTorch are released. This includes major versions, major-minor versions, and major-minor-patch versions of frameworks. We also update images when new versions of drivers and libraries become available. For more information on image maintenance, see When does active support for my framework version end?

What images get new SageMaker/Amazon features?

New features typically release in the latest version of DLAMIs for PyTorch and TensorFlow. Refer to the release notes for a specific image for details on new SageMaker or Amazon features. For a list of available DLAMIs, see Release Notes for DLAMI. For more information on image maintenance, see When does active support for my framework version end?

How is current version defined in the Supported Frameworks table?

The current version in the Amazon Deep Learning AMI Framework Support Policy table refers to the newest framework version that Amazon makes available on GitHub. Each latest release includes updates to the drivers, libraries, and relevant packages in the DLAMI. For information on image maintenance, see When does active support for my framework version end?

What if I am running a version that is not in the Supported Frameworks table?

If you are running a version that is not in the Amazon Deep Learning AMI Framework Support Policy table, you may not have the most updated drivers, libraries, and relevant packages. For a more up-to-date version, we recommend that you upgrade to one of the supported frameworks available using the latest DLAMI of your choice. For a list of available DLAMIs, see Release Notes for DLAMI.

Do DLAMIs support previous versions of TensorFlow?

No. We support the latest patch version of each framework’s latest major version released 365 days from its initial GitHub release as stated in the Amazon Deep Learning AMI Framework Support Policy table. For more information, see What if I am running a version that is not in the Supported Frameworks table?

How can I find the latest patched image for a supported framework version?

To use a DLAMI with the latest framework version, retrieve the DLAMI ID and use it to launch the DLAMI using the EC2 Console. For sample Amazon CLI commands to retrieve the Amazon Deep Learning AMI ID, refer to the DLAMI release notes page single-framework DLAMI release notes. The framework version that you choose must be labeled Supported in the Amazon Deep Learning AMI Framework Support Policy table.

How frequently are new images released?

Providing updated patch versions is our highest priority. We routinely create patched images at the earliest opportunity. We monitor for newly patched framework versions (ex. TensorFlow 2.9 to TensorFlow 2.9.1) and new minor release versions (ex. TensorFlow 2.9 to TensorFlow 2.10) and make them available at the earliest opportunity. When an existing version of TensorFlow is released with a new version of CUDA, we release a new DLAMI for that version of TensorFlow with support for the new CUDA version.

Will my instance be patched in place while my workload is running?

No. Patch updates for DLAMI are not “in-place” updates.

You must turn on a new EC2 instance, migrate your workloads and scripts, and then turn off your previous instance.

What happens when a new patched or updated framework version is available?

Regularly check the release notes page for your image. We encourage you to upgrade to new patched or updated frameworks when they are available. For a list of available DLAMIs, see Release Notes for DLAMI.

Are dependencies updated without changing the framework version?

We update dependencies without changing the framework version. However, if a dependency update causes an incompatibility, we create an image with a different version. Be sure to check the Release Notes for DLAMI for updated dependency information.

When does active support for my framework version end?

DLAMI images are immutable. Once they are created they do not change. There are four main reasons why active support for a framework version ends:

Framework version (patch) upgrades

If you have a DLAMI workload based on TensorFlow 2.7.0 and TensorFlow releases version 2.7.1 on GitHub, then Amazon releases a new DLAMI with TensorFlow 2.7.1. The previous images with 2.7.0 are no longer actively maintained once the new image with TensorFlow 2.7.1 is released. The DLAMI with TensorFlow 2.7.0 does not receive further patches. The DLAMI release notes page for TensorFlow 2.7 is then updated with the latest information. There is no individual release note page for each minor patch.

New DLAMIs created due to patch upgrades are designated with a new AMI ID.

Amazon security patches

If you have a workload based on an image with TensorFlow 2.7.0 and Amazon makes a security patch, then a new version of the DLAMI is released for TensorFlow 2.7.0. The previous version of the images with TensorFlow 2.7.0 is no longer actively maintained. For more information, see Will my instance be patched in place while my workload is running? For steps on finding the latest DLAMI, see How can I find the latest patched image for a supported framework version?

New DLAMIs created due to patch upgrades are designated with a new AMI ID.

End of patch date (Aging out)

DLAMIs hit their end of patch date 365 days after the GitHub release date.

For multi-framework DLAMIs, when one of the framework versions is updated, a new DLAMI with the updated version is required. The DLAMI with the old framework version is no longer actively maintained.

Dependency end-of-support

If you are running a workload on a TensorFlow 2.7.0 DLAMI image with Python 3.6 and that version of Python is marked for end-of-support, then all DLAMI images based on Python 3.6 will no longer be actively maintained. Similarly, if an OS version like Ubuntu 16.04 is marked for end-of-support, then all DLAMI images that are dependent on Ubuntu 16.04 will no longer be actively maintained.

Will images with framework versions that are no longer actively maintained be patched?

No. Images that are no longer actively maintained will not have new releases.

How do I use an older framework version?

To use a DLAMI with an older framework version, retrieve the DLAMI ID and use it to launch the DLAMI using the EC2 Console. For Amazon CLI commands to retrieve the AMI ID, refer to the release notes page in the single-framework DLAMI release notes.

How do I stay up-to-date with support changes in frameworks and their versions?

Stay up-to-date with DLAMI frameworks and versions using the Amazon Deep Learning AMI Framework Support Policy table, the DLAMI release notes.

Do I need a commercial license to use the Anaconda Repository?

Anaconda shifted to a commercial licensing model for certain users. Actively maintained DLAMIs have been migrated to the publicly available open-source version of Conda (conda-forge) from the Anaconda channel.