We are building an interface that helps users with a situation by the use of an AI that asks questions on the basis of a code which the aspirant has written, asked from a diverse pack of frequently asked coding questions. Then the AI asks the aspirant questions related to his code and some staple questions. At the end of this experience, the user receives his interview profile showing him his flaws in answering questions, his fluency, and his ability to handle the situation verbally. This can be done on various levels and be stored for future scrutiny by the user. We can also provide a growth curve that helps the aspirant to judge his progress.
The use of the ambe config to build and test aarch64 is not needed. The ambe config will be removed in the future. Making cpu_arm64_pip.sh and cpu_arm64_nonpip.sh more similar for easier future maintenance.
TensorFlow 2.12.0
Release 2.12.0
TensorFlow
Breaking Changes
Build, Compilation and Packaging
Removed redundant packages tensorflow-gpu and tf-nightly-gpu. These packages were removed and replaced with packages that direct users to switch to tensorflow or tf-nightly respectively. Since TensorFlow 2.1, the only difference between these two sets of packages was their names, so there is no loss of functionality or GPU support. See https://pypi.org/project/tensorflow-gpu for more details.
tf.function:
tf.function now uses the Python inspect library directly for parsing the signature of the Python function it is decorated on. This change may break code where the function signature is malformed, but was ignored previously, such as:
Using functools.wraps on a function with different signature
Using functools.partial with an invalid tf.function input
tf.function now enforces input parameter names to be valid Python identifiers. Incompatible names are automatically sanitized similarly to existing SavedModel signature behavior.
Parameterless tf.functions are assumed to have an empty input_signature instead of an undefined one even if the input_signature is unspecified.
tf.types.experimental.TraceType now requires an additional placeholder_value method to be defined.
tf.function now traces with placeholder values generated by TraceType instead of the value itself.
Experimental APIs tf.config.experimental.enable_mlir_graph_optimization and tf.config.experimental.disable_mlir_graph_optimization were removed.
Major Features and Improvements
Support for Python 3.11 has been added.
Support for Python 3.7 has been removed. We are not releasing any more patches for Python 3.7.
tf.lite:
Add 16-bit float type support for built-in op fill.
Coordination service now works with dtensor.initialize_accelerator_system, and enabled by default.
Add tf.experimental.dtensor.is_dtensor to check if a tensor is a DTensor instance.
tf.data:
Added support for alternative checkpointing protocol which makes it possible to checkpoint the state of the input pipeline without having to store the contents of internal buffers. The new functionality can be enabled through the experimental_symbolic_checkpoint option of tf.data.Options().
Added a new rerandomize_each_iteration argument for the tf.data.Dataset.random() operation, which controls whether the sequence of generated random numbers should be re-randomized every epoch or not (the default behavior). If seed is set and rerandomize_each_iteration=True, the random() operation will produce a different (deterministic) sequence of numbers every epoch.
The use of the ambe config to build and test aarch64 is not needed. The ambe config will be removed in the future. Making cpu_arm64_pip.sh and cpu_arm64_nonpip.sh more similar for easier future maintenance.
Release 2.12.0
Breaking Changes
Build, Compilation and Packaging
Removed redundant packages tensorflow-gpu and tf-nightly-gpu. These packages were removed and replaced with packages that direct users to switch to tensorflow or tf-nightly respectively. Since TensorFlow 2.1, the only difference between these two sets of packages was their names, so there is no loss of functionality or GPU support. See https://pypi.org/project/tensorflow-gpu for more details.
tf.function:
tf.function now uses the Python inspect library directly for parsing the signature of the Python function it is decorated on. This change may break code where the function signature is malformed, but was ignored previously, such as:
Using functools.wraps on a function with different signature
Using functools.partial with an invalid tf.function input
tf.function now enforces input parameter names to be valid Python identifiers. Incompatible names are automatically sanitized similarly to existing SavedModel signature behavior.
Parameterless tf.functions are assumed to have an empty input_signature instead of an undefined one even if the input_signature is unspecified.
tf.types.experimental.TraceType now requires an additional placeholder_value method to be defined.
tf.function now traces with placeholder values generated by TraceType instead of the value itself.
Experimental APIs tf.config.experimental.enable_mlir_graph_optimization and tf.config.experimental.disable_mlir_graph_optimization were removed.
Major Features and Improvements
Support for Python 3.11 has been added.
Support for Python 3.7 has been removed. We are not releasing any more patches for Python 3.7.
tf.lite:
Add 16-bit float type support for built-in op fill.
Coordination service now works with dtensor.initialize_accelerator_system, and enabled by default.
Add tf.experimental.dtensor.is_dtensor to check if a tensor is a DTensor instance.
tf.data:
Added support for alternative checkpointing protocol which makes it possible to checkpoint the state of the input pipeline without having to store the contents of internal buffers. The new functionality can be enabled through the experimental_symbolic_checkpoint option of tf.data.Options().
Added a new rerandomize_each_iteration argument for the tf.data.Dataset.random() operation, which controls whether the sequence of generated random numbers should be re-randomized every epoch or not (the default behavior). If seed is set and rerandomize_each_iteration=True, the random() operation will produce a different (deterministic) sequence of numbers every epoch.
Added a new rerandomize_each_iteration argument for the tf.data.Dataset.sample_from_datasets() operation, which controls whether the sequence of generated random numbers used for sampling should be re-randomized every epoch or not. If seed is set and rerandomize_each_iteration=True, the sample_from_datasets() operation will use a different (deterministic) sequence of numbers every epoch.
tf.test:
... (truncated)
Commits
8e2b665 Merge pull request #61094 from tensorflow/venkat-patch-444
02478f0 Fix unit test failure caused by numpy update
2cd9b41 Merge pull request #61082 from tensorflow/venkat-patch-333
7995c95 Updating Simplified retry logic to DNS cache
29479ed Merge pull request #60872 from tensorflow/r2.12-c45a6c0b1cb
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netlify[bot]
commented
3 months ago
Bumps tensorflow from 2.11.1 to 2.12.1.
Release notes
Sourced from tensorflow's releases.
... (truncated)
Changelog
Sourced from tensorflow's changelog.
... (truncated)
Commits
8e2b665Merge pull request #61094 from tensorflow/venkat-patch-44402478f0Fix unit test failure caused by numpy update2cd9b41Merge pull request #61082 from tensorflow/venkat-patch-3337995c95Updating Simplified retry logic to DNS cache29479edMerge pull request #60872 from tensorflow/r2.12-c45a6c0b1cbe76a933Simplified retry logic to DNS cache76addf7Merge pull request #60850 from elfringham/non_pip_fix05987a8[Linaro:ARM_CI] Fix permissions for running nonpip tests23724d2Merge pull request #60842 from elfringham/r2.12496730bLimit typing_extensions to less than 4.6.0 until it worksDependabot 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