Bumpstensorflow from 2.5.2 to 2.11.1.
Release notes
Sourced fromtensorflow's releases.
TensorFlow 2.11.1
Release 2.11.1
Note: TensorFlow 2.10 was the last TensorFlow release that supported GPU on native-Windows. Starting with TensorFlow 2.11, you will need to install TensorFlow in WSL2, or install tensorflow-cpu and, optionally, try the TensorFlow-DirectML-Plugin.
- Security vulnerability fixes will no longer be patched to this Tensorflow version. The latest Tensorflow version includes the security vulnerability fixes. You can update to the latest version (recommended) or patch security vulnerabilities yourselfsteps. You can refer to therelease notes of the latest Tensorflow version for a list of newly fixed vulnerabilities. If you have any questions, please create a GitHub issue to let us know.
This release also introduces several vulnerability fixes:
TensorFlow 2.11.0
Release 2.11.0
Breaking Changes
Thetf.keras.optimizers.Optimizer base class now points to the new Keras optimizer, while the old optimizers have been moved to thetf.keras.optimizers.legacy namespace.
If you find your workflow failing due to this change, you may be facing one of the following issues:
- Checkpoint loading failure. The new optimizer handles optimizer state differently from the old optimizer, which simplifies the logic of checkpoint saving/loading, but at the cost of breaking checkpoint backward compatibility in some cases. If you want to keep using an old checkpoint, please change your optimizer to
tf.keras.optimizer.legacy.XXX (e.g.tf.keras.optimizer.legacy.Adam). - TF1 compatibility. The new optimizer,
tf.keras.optimizers.Optimizer, does not support TF1 any more, so please use the legacy optimizertf.keras.optimizer.legacy.XXX. We highly recommendmigrating your workflow to TF2 for stable support and new features. - Old optimizer API not found. The new optimizer,
tf.keras.optimizers.Optimizer, has a different set of public APIs from the old optimizer. These API changes are mostly related to getting rid of slot variables and TF1 support. Please check the API documentation to find alternatives to the missing API. If you must call the deprecated API, please change your optimizer to the legacy optimizer. - Learning rate schedule access. When using a
tf.keras.optimizers.schedules.LearningRateSchedule, the new optimizer'slearning_rate property returns the current learning rate value instead of aLearningRateSchedule object as before. If you need to access theLearningRateSchedule object, please useoptimizer._learning_rate. - If you implemented a custom optimizer based on the old optimizer. Please set your optimizer to subclass
tf.keras.optimizer.legacy.XXX. If you want to migrate to the new optimizer and find it does not support your optimizer, please file an issue in theKeras GitHub repo. - Errors, such as
Cannot recognize variable.... The new optimizer requires all optimizer variables to be created at the firstapply_gradients() orminimize() call. If your workflow calls the optimizer to update different parts of the model in multiple stages, please calloptimizer.build(model.trainable_variables) before the training loop. - Timeout or performance loss. We don't anticipate this to happen, but if you see such issues, please use the legacy optimizer, and file an issue in the Keras GitHub repo.
The old Keras optimizer will never be deleted, but will not see any new feature additions. New optimizers (for example,tf.keras.optimizers.Adafactor) will only be implemented based on the newtf.keras.optimizers.Optimizer base class.
tensorflow/python/keras code is a legacy copy of Keras since the TensorFlow v2.7 release, and will be deleted in the v2.12 release. Please remove any import oftensorflow.python.keras and use the public API withfrom tensorflow import keras orimport tensorflow as tf; tf.keras.
Major Features and Improvements
... (truncated)
Changelog
Sourced fromtensorflow's changelog.
Release 2.11.1
Note: TensorFlow 2.10 was the last TensorFlow release that supported GPU on native-Windows. Starting with TensorFlow 2.11, you will need to install TensorFlow in WSL2, or install tensorflow-cpu and, optionally, try the TensorFlow-DirectML-Plugin.
- Security vulnerability fixes will no longer be patched to this Tensorflow version. The latest Tensorflow version includes the security vulnerability fixes. You can update to the latest version (recommended) or patch security vulnerabilities yourselfsteps. You can refer to therelease notes of the latest Tensorflow version for a list of newly fixed vulnerabilities. If you have any questions, please create a GitHub issue to let us know.
This release also introduces several vulnerability fixes:
Release 2.11.0
Breaking Changes
tf.keras.optimizers.Optimizer now points to the new Keras optimizer, andold optimizers have moved to thetf.keras.optimizers.legacy namespace.If you find your workflow failing due to this change,you may be facing one of the following issues:
- Checkpoint loading failure. The new optimizer handles optimizerstate differently from the old optimizer, which simplies the logic ofcheckpoint saving/loading, but at the cost of breaking checkpointbackward compatibility in some cases. If you want to keep using an oldcheckpoint, please change your optimizer to
tf.keras.optimizers.legacy.XXX (e.g.tf.keras.optimizers.legacy.Adam). - TF1 compatibility. The new optimizer does not support TF1 any more,so please use the legacy optimizer
tf.keras.optimizer.legacy.XXX.We highly recommend to migrate your workflow to TF2 for stablesupport and new features. - API not found. The new optimizer has a different set of public APIsfrom the old optimizer. These API changes are mostly related togetting rid of slot variables and TF1 support. Please check the API
... (truncated)
Commits
a3e2c69 Merge pull request#60016 from tensorflow/fix-relnotes13b85dc Fix release notes48b18db Merge pull request#60014 from tensorflow/disable-test-that-oomseea48f5 Disable a test that results in OOM+segfaulta632584 Merge pull request#60000 from tensorflow/venkat-patch-393dea7a Update RELEASE.mda2ba9f1 Updating Release.md with Legal Language for Release Notesfae41c7 Merge pull request#59998 from tensorflow/fix-bad-cherrypick-again2757416 Fix bad cherrypickc78616f Merge pull request#59992 from tensorflow/fix-2.11-build- Additional commits viewable incompare view
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Bumpstensorflow from 2.5.2 to 2.11.1.
Release notes
Sourced fromtensorflow's releases.
... (truncated)
Changelog
Sourced fromtensorflow's changelog.
... (truncated)
Commits
a3e2c69Merge pull request#60016 from tensorflow/fix-relnotes13b85dcFix release notes48b18dbMerge pull request#60014 from tensorflow/disable-test-that-oomseea48f5Disable a test that results in OOM+segfaulta632584Merge pull request#60000 from tensorflow/venkat-patch-393dea7aUpdate RELEASE.mda2ba9f1Updating Release.md with Legal Language for Release Notesfae41c7Merge pull request#59998 from tensorflow/fix-bad-cherrypick-again2757416Fix bad cherrypickc78616fMerge pull request#59992 from tensorflow/fix-2.11-buildDependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting
@dependabot rebase.Dependabot commands and options
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@dependabot rebasewill rebase this PR@dependabot recreatewill recreate this PR, overwriting any edits that have been made to it@dependabot mergewill merge this PR after your CI passes on it@dependabot squash and mergewill squash and merge this PR after your CI passes on it@dependabot cancel mergewill cancel a previously requested merge and block automerging@dependabot reopenwill reopen this PR if it is closed@dependabot closewill close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually@dependabot ignore this major versionwill close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself)@dependabot ignore this minor versionwill close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself)@dependabot ignore this dependencywill close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)You can disable automated security fix PRs for this repo from theSecurity Alerts page.