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Deep Learning Containers publishes containers and virtual machine images to simplify the
configuration of your machine learning (ML) workloads. These images contain the
operating system, the ML frameworks, drivers, and other libraries. We publish
new versions of images regularly to include new patches, security updates, and
features. Each image provided by Deep Learning Containers provides support for a
specific minor version of an ML framework.
This allows you time to update and test your code
when moving from one framework version to another. You should always test your
jobs and models thoroughly when switching to a new framework version, regardless
of whether it's a major or minor update.
For all services, subscribe to the Deep Learning Containers release notes page
for announcements about new version releases for your containers, images, and
frameworks.
Securing your workloads on Deep Learning Containers is a shared responsibility. While
Deep Learning Containers regularly publishes new versions of images to address
security vulnerabilities, you are responsible for tasks such as the following:
Manually upgrading to the latest version.
Ensuring that you properly configured your services to use the latest version.
During the supported period for an ML framework version, we will publish new
image versions regularly. The updates may include the following:
Patch updates for supported frameworks. For example, if we support
TensorFlow 2.7, and TensorFlow releases 2.7.1 to address bugs, we will
release a new image version.
Security updates for supported frameworks.
Non-breaking updates to other packages and software installed on the image.
Updates to dependencies that have reached end-of-support. For example, if an
image has Python 3.7 installed and it reaches the end-of-support date, we
will release a new image version. If the change in dependency may be a
breaking change, we will update Choose a container image
to indicate the change in the dependency.
Once published, an image version is immutable and does not change. You should
always use the latest image version, as older versions may have security
vulnerabilities or other critical bugs.
Support policy schedule
Support periods for each framework version follows this schedule:
End-of-patch and support date: After this date, Deep Learning Containers will no
longer publish new image versions for that framework version. Existing
resources that have been deployed to Deep Learning Containers continue to function.
After this date, we recommend you plan to switch to a more recent framework
version.
To receive troubleshooting support from Deep Learning Containers, you may be asked
to upgrade to a framework version that is within the supported time period.
End-of-availability date: After this date, you can no longer use images
for this framework version. Services may block the creation of new resources
using these images, and the images will no longer be available for download.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-08-29 UTC."],[[["\u003cp\u003eDeep Learning Containers offers pre-configured container and virtual machine images to streamline machine learning workload setup, including the OS, ML frameworks, drivers, and libraries.\u003c/p\u003e\n"],["\u003cp\u003eNew image versions are regularly released by Deep Learning Containers to provide patches, security updates, and new features, always supporting a specific minor version of an ML framework.\u003c/p\u003e\n"],["\u003cp\u003eUsers are responsible for manually upgrading to the latest image versions and ensuring their services are configured to use them, as securing workloads is a shared responsibility.\u003c/p\u003e\n"],["\u003cp\u003eDuring the supported period of an ML framework, Deep Learning Containers regularly updates images with patches, security updates, non-breaking package updates, and dependency upgrades.\u003c/p\u003e\n"],["\u003cp\u003eEach framework version has an end-of-patch and support date after which new image versions are no longer published, as well as an end-of-availability date when those images are no longer usable.\u003c/p\u003e\n"]]],[],null,["# Deep Learning Containers framework support policy\n\nDeep Learning Containers publishes containers and virtual machine images to simplify the\nconfiguration of your machine learning (ML) workloads. These images contain the\noperating system, the ML frameworks, drivers, and other libraries. We publish\nnew versions of images regularly to include new patches, security updates, and\nfeatures. Each image provided by Deep Learning Containers provides support for a\nspecific minor version of an ML framework.\n\nThis allows you time to update and test your code\nwhen moving from one framework version to another. You should always test your\njobs and models thoroughly when switching to a new framework version, regardless\nof whether it's a major or minor update.\n\nFor all services, subscribe to the [Deep Learning Containers release notes](/deep-learning-containers/docs/release-notes) page\nfor announcements about new version releases for your containers, images, and\nframeworks.\n\nFor the list of supported framework versions, see [Choose a container image](/deep-learning-containers/docs/choosing-container#deciding).\n\nShared responsibility\n---------------------\n\nSecuring your workloads on Deep Learning Containers is a shared responsibility. While\nDeep Learning Containers regularly publishes new versions of images to address\nsecurity vulnerabilities, you are responsible for tasks such as the following:\n\n- Manually upgrading to the latest version.\n\n- Ensuring that you properly configured your services to use the latest version.\n\nFor more information, see [Shared responsibility](/deep-learning-containers/docs/shared-responsibility).\n\nSupport policy for framework versions\n-------------------------------------\n\nDuring the supported period for an ML framework version, we will publish new\nimage versions regularly. The updates may include the following:\n\n- Patch updates for supported frameworks. For example, if we support\n TensorFlow 2.7, and TensorFlow releases 2.7.1 to address bugs, we will\n release a new image version.\n\n- Security updates for supported frameworks.\n\n- Non-breaking updates to other packages and software installed on the image.\n\n- Updates to dependencies that have reached end-of-support. For example, if an\n image has Python 3.7 installed and it reaches the end-of-support date, we\n will release a new image version. If the change in dependency may be a\n breaking change, we will update [Choose a container image](/deep-learning-containers/docs/choosing-container#deciding)\n to indicate the change in the dependency.\n\nOnce published, an image version is immutable and does not change. You should\nalways use the latest image version, as older versions may have security\nvulnerabilities or other critical bugs.\n\n### Support policy schedule\n\nSupport periods for each framework version follows this schedule:\n\n- **End-of-patch and support date:** After this date, Deep Learning Containers will no\n longer publish new image versions for that framework version. Existing\n resources that have been deployed to Deep Learning Containers continue to function.\n After this date, we recommend you plan to switch to a more recent framework\n version.\n\n To receive troubleshooting support from Deep Learning Containers, you may be asked\n to upgrade to a framework version that is within the supported time period.\n- **End-of-availability date:** After this date, you can no longer use images\n for this framework version. Services may block the creation of new resources\n using these images, and the images will no longer be available for download.\n\nWhat's next\n-----------\n\n- Review the [list of supported framework versions](/deep-learning-containers/docs/choosing-container#deciding)."]]
