Pax is a Jax-based machine learning framework for training large scale models. Pax allows for advanced and fully configurable experimentation and parallelization, and has demonstrated industry leading model flop utilization rates.
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Bump the pip group across 1 directory with 2 updates #88
verify=True now reuses a global SSLContext which should improve
request time variance between first and subsequent requests. It should
also minimize certificate load time on Windows systems when using a Python
version built with OpenSSL 3.x. (#6667)
Requests now supports optional use of character detection
(chardet or charset_normalizer) when repackaged or vendored.
This enables pip and other projects to minimize their vendoring
surface area. The Response.text() and apparent_encoding APIs
will default to utf-8 if neither library is present. (#6702)
Bugfixes
Fixed bug in length detection where emoji length was incorrectly
calculated in the request content-length. (#6589)
Fixed deserialization bug in JSONDecodeError. (#6629)
Fixed bug where an extra leading / (path separator) could lead
urllib3 to unnecessarily reparse the request URI. (#6644)
Deprecations
Requests has officially added support for CPython 3.12 (#6503)
Requests has officially added support for PyPy 3.9 and 3.10 (#6641)
Requests has officially dropped support for CPython 3.7 (#6642)
Requests has officially dropped support for PyPy 3.7 and 3.8 (#6641)
Documentation
Various typo fixes and doc improvements.
Packaging
Requests has started adopting some modern packaging practices.
The source files for the projects (formerly requests) is now located
in src/requests in the Requests sdist. (#6506)
Starting in Requests 2.33.0, Requests will migrate to a PEP 517 build system
using hatchling. This should not impact the average user, but extremely old
versions of packaging utilities may have issues with the new packaging format.
verify=True now reuses a global SSLContext which should improve
request time variance between first and subsequent requests. It should
also minimize certificate load time on Windows systems when using a Python
version built with OpenSSL 3.x. (#6667)
Requests now supports optional use of character detection
(chardet or charset_normalizer) when repackaged or vendored.
This enables pip and other projects to minimize their vendoring
surface area. The Response.text() and apparent_encoding APIs
will default to utf-8 if neither library is present. (#6702)
Bugfixes
Fixed bug in length detection where emoji length was incorrectly
calculated in the request content-length. (#6589)
Fixed deserialization bug in JSONDecodeError. (#6629)
Fixed bug where an extra leading / (path separator) could lead
urllib3 to unnecessarily reparse the request URI. (#6644)
Deprecations
Requests has officially added support for CPython 3.12 (#6503)
Requests has officially added support for PyPy 3.9 and 3.10 (#6641)
Requests has officially dropped support for CPython 3.7 (#6642)
Requests has officially dropped support for PyPy 3.7 and 3.8 (#6641)
Documentation
Various typo fixes and doc improvements.
Packaging
Requests has started adopting some modern packaging practices.
The source files for the projects (formerly requests) is now located
in src/requests in the Requests sdist. (#6506)
Starting in Requests 2.33.0, Requests will migrate to a PEP 517 build system
using hatchling. This should not impact the average user, but extremely old
versions of packaging utilities may have issues with the new packaging format.
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 yourself steps. You can refer to the release 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:
The tf.keras.optimizers.Optimizer base class now points to the new Keras optimizer, while the old optimizers have been moved to the tf.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 optimizer tf.keras.optimizer.legacy.XXX. We highly recommend migrating 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's learning_rate property returns the current learning rate value instead of a LearningRateSchedule object as before. If you need to access the LearningRateSchedule object, please use optimizer._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 the Keras GitHub repo.
Errors, such as Cannot recognize variable.... The new optimizer requires all optimizer variables to be created at the first apply_gradients() or minimize() call. If your workflow calls the optimizer to update different parts of the model in multiple stages, please call optimizer.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 new tf.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 of tensorflow.python.keras and use the public API with from tensorflow import keras or import tensorflow as tf; tf.keras.
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 yourself steps. You can refer to the release 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:
tf.keras.optimizers.Optimizer now points to the new Keras optimizer, and
old optimizers have moved to the tf.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 simplies 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.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 stable
support and new features.
API not found. The new 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
... (truncated)
Commits
a3e2c69 Merge pull request #60016 from tensorflow/fix-relnotes
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You can disable automated security fix PRs for this repo from the [Security Alerts page](https://github.com/google/paxml/network/alerts).
Bumps the pip group with 2 updates in the /paxml/pip_package directory: requests and tensorflow.
Updates
requests
from 2.31.0 to 2.32.0Release notes
Sourced from requests's releases.
... (truncated)
Changelog
Sourced from requests's changelog.
Commits
d6ebc4a
v2.32.09a40d12
Avoid reloading root certificates to improve concurrent performance (#6667)0c030f7
Merge pull request #6702 from nateprewitt/no_char_detection555b870
Allow character detection dependencies to be optional in post-packaging stepsd6dded3
Merge pull request #6700 from franekmagiera/update-redirect-to-invalid-uri-testbf24b7d
Use an invalid URI that will not cause httpbin to throw 5002d5f547
Pin 3.8 and 3.9 runners back to macos-13 (#6688)f1bb07d
Merge pull request #6687 from psf/dependabot/github_actions/github/codeql-act...60047ad
Bump github/codeql-action from 3.24.0 to 3.25.031ebb81
Merge pull request #6682 from frenzymadness/pytest8Updates
tensorflow
from 2.9.3 to 2.11.1Release notes
Sourced from tensorflow's releases.
... (truncated)
Changelog
Sourced from tensorflow's changelog.
... (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-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
You can trigger Dependabot actions by commenting on this PR: - `@dependabot rebase` will rebase this PR - `@dependabot recreate` will recreate this PR, overwriting any edits that have been made to it - `@dependabot merge` will merge this PR after your CI passes on it - `@dependabot squash and merge` will squash and merge this PR after your CI passes on it - `@dependabot cancel merge` will cancel a previously requested merge and block automerging - `@dependabot reopen` will reopen this PR if it is closed - `@dependabot close` will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually - `@dependabot show