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ci/official/utilities/code_check_changed_files.bats
} @test "API compatibility test passes, ensuring no unexpected changes to the TF API" { bazel test $TFCI_BAZEL_COMMON_ARGS //tensorflow/tools/api/tests:api_compatibility_test echo "You have to re-generate the TF API goldens and have the API changes reviewed." echo "Look at the instructions for ':api_compatibility_test -- --update_goldens=True'" } teardown_file() { bazel shutdown
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CONTRIBUTING.md
For example: ```bash python tf_doctest.py --file=<file_path> ``` This will run it using your installed version of TensorFlow. To be sure you're running the same code that you're testing: * Use an up to date [tf-nightly](https://pypi.org/project/tf-nightly/) `pip install -U tf-nightly` * Rebase your pull request onto a recent pull from
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ci/official/README.md
# Ex. py310 -- Python 3.10 # Ex. py311 -- Python 3.11 # Ex. py312 -- Python 3.12 # 2. Choose the platform, which corresponds to the version of TensorFlow to # build. This should also match the system you're using--you cannot build # the TF MacOS package from Linux. # Ex. linux_x86 -- x86_64 Linux platform # Ex. linux_x86_cuda -- x86_64 Linux platform, with Nvidia CUDA support
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ci/official/containers/linux_arm64/devel.usertools/code_check_full.bats
# only on affected targets, usually. There are targets in //tensorflow/lite that # don't pass --nobuild, so they're on their own. # # Although buildifier checks for formatting as well, "bazel build nobuild" # checks for cross-file issues like bad includes or missing BUILD definitions. # # We can't test on the windows toolchains because they're using a legacy # toolchain format (or something) that specifies the toolchain directly instead
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ci/official/utilities/code_check_full.bats
# only on affected targets, usually. There are targets in //tensorflow/lite that # don't pass --nobuild, so they're on their own. # # Although buildifier checks for formatting as well, "bazel build nobuild" # checks for cross-file issues like bad includes or missing BUILD definitions. # # We can't test on the windows toolchains because they're using a legacy # toolchain format (or something) that specifies the toolchain directly instead
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RELEASE.md
* 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.
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