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Deep Learning VM Images provides 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 VM 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.
The Deep Learning VM release notes are a comprehensive log of all the
changes, updates, and new features that are rolled out. They are essential for
anyone using these images to stay informed about the latest developments and
to manage their machine learning environments effectively.
Subscribe to the Deep Learning VM release notes page
for announcements about deprecations and new version releases for your images
and frameworks.
Securing your workloads on Deep Learning VM is a shared responsibility. While
Deep Learning VM 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 List of all available versions
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 earlier versions may have security
vulnerabilities or other critical bugs.
Support policy schedule
Deep Learning VM supports images for a specific window of time. It is
a common practice in the industry since components, including some
open source components, have to be managed to ensure security and performance.
For Deep Learning VM, the support policy revolves around two key dates:
End-of-patch and support date: After this date, Deep Learning VM
will no longer publish new image versions for that specific framework version.
This means no more patch updates, security fixes, or non-breaking updates.
Existing resources that have been deployed to Deep Learning VM
will continue to function, but it's recommended to plan your
migration to a newer, supported framework version.
To receive troubleshooting support from Deep Learning VM, 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.
Image deprecation
When images reach the End-of-patch and support date, they are deprecated.
Deprecation means that these images are removed from public visibility and
it's encouraged to use supported images to help ensure security and performance.
However, if you must use a deprecated image, see Use an image after
deprecation.
[[["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 2025-09-04 UTC."],[[["\u003cp\u003eDeep Learning VM offers pre-configured containers and virtual machine images equipped with operating systems, ML frameworks, drivers, and libraries, regularly updated for patches, security enhancements, and new features.\u003c/p\u003e\n"],["\u003cp\u003eEach image supports a specific minor version of an ML framework, allowing users to update and test their code when transitioning between versions, emphasizing the need for thorough testing.\u003c/p\u003e\n"],["\u003cp\u003eSecurity for Deep Learning VM is a shared responsibility, requiring users to manually upgrade to the latest image versions and ensure proper service configuration to leverage the latest updates.\u003c/p\u003e\n"],["\u003cp\u003eDeep Learning VM provides ongoing support for ML framework versions through regular image updates, including patch updates, security fixes, non-breaking package updates, and dependency upgrades until the end-of-patch date.\u003c/p\u003e\n"],["\u003cp\u003eAfter the end-of-patch date, new image versions for a framework version will not be published, and after the end-of-availability date, the images will no longer be usable, so staying up-to-date is essential.\u003c/p\u003e\n"]]],[],null,["# Deep Learning VM framework support policy\n\nDeep Learning VM Images provides 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 VM 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\nThe Deep Learning VM release notes are a comprehensive log of all the\nchanges, updates, and new features that are rolled out. They are essential for\nanyone using these images to stay informed about the latest developments and\nto manage their machine learning environments effectively.\nSubscribe to the [Deep Learning VM release notes](/deep-learning-vm/docs/release-notes) page\nfor announcements about deprecations and new version releases for your images\nand frameworks.\n\nSee also the [list of supported framework versions](/deep-learning-vm/docs/images#supported-frameworks).\n\nShared responsibility\n---------------------\n\nSecuring your workloads on Deep Learning VM is a shared responsibility. While\nDeep Learning VM 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-vm/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\n 2.7.1 to address bugs, we will 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 [List of all available versions](/deep-learning-vm/docs/images#supported-frameworks)\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 earlier versions may have security\nvulnerabilities or other critical bugs.\n\n### Support policy schedule\n\nDeep Learning VM supports images for a specific window of time. It is\na common practice in the industry since components, including some\nopen source components, have to be managed to ensure security and performance.\nFor Deep Learning VM, the support policy revolves around two key dates:\n\n- **End-of-patch and support date:** After this date, Deep Learning VM\n will no longer publish new image versions for that specific framework version.\n This means no more patch updates, security fixes, or non-breaking updates.\n Existing resources that have been deployed to Deep Learning VM\n will continue to function, but it's recommended to plan your\n migration to a newer, supported framework version.\n\n To receive troubleshooting support from Deep Learning VM, you may be\n asked to upgrade to a framework version that is within the supported\n 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\n### Image deprecation\n\nWhen images reach the **End-of-patch and support date** , they are deprecated.\nDeprecation means that these images are removed from public visibility and\nit's encouraged to use supported images to help ensure security and performance.\nHowever, if you must use a deprecated image, see [Use an image after\ndeprecation](/deep-learning-vm/docs/images#use-image-after-deprecation).\n\nWhat's next\n-----------\n\n- Review the [list of supported framework versions](/deep-learning-vm/docs/images#supported-frameworks)."]]
