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ci/official/wheel_test/README.md
current build. It utilizes the `_api/v2/api_packages.txt` list of packages from the local wheel file specified in the `requirements_lock_<python_version>.txt`. Packages are imported one by one in alphabetical order during runtime. The test doesn't identify package's order-dependent issues; for instance, importing "tf.foo" followed by "tf.bar" won't reveal that "tf.bar" depends on "tf.foo" being imported first.
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ci/official/containers/linux_arm64/devel.usertools/aarch64.bazelrc
# use the wheel's TensorFlow installation instead of the one made available # through bazel. This must be done in a different root directory, //bazel_pip/..., # because "import tensorflow" run from the root directory would instead import # the folder instead of the venv package. # # Pass --config=pip to run the same suite of tests. If you want to run just one # test for investigation, you'll need --config=pip_venv instead, and then you
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CONTRIBUTING.md
airtime before a decision is made regarding whether they are to be migrated to the core. * As every PR requires several CPU/GPU hours of CI testing, we discourage submitting PRs to fix one typo, one warning,etc. We recommend fixing the same issue at the file level at least (e.g.: fix all typos in a file, fix all compiler warnings in a file, etc.) * Tests should follow the
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.github/ISSUE_TEMPLATE/tflite-converter-issue.md
- TensorFlow installation (pip package or built from source): - TensorFlow library (version, if pip package or github SHA, if built from source): ### 2. Code Provide code to help us reproduce your issues using one of the following options: #### Option A: Reference colab notebooks
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ci/official/containers/linux_arm64/devel.usertools/aarch64_clang.bazelrc
# use the wheel's TensorFlow installation instead of the one made available # through bazel. This must be done in a different root directory, //bazel_pip/..., # because "import tensorflow" run from the root directory would instead import # the folder instead of the venv package. # # Pass --config=pip to run the same suite of tests. If you want to run just one # test for investigation, you'll need --config=pip_venv instead, and then you
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SECURITY.md
implement, and therefore security issues deriving from their absence are not considered a vulnerability in TensorFlow. ### Resource allocation A denial of service caused by one model could bring down the entire server, but we don't consider this as a vulnerability, given that models can exhaust resources in many different ways and solutions exist to prevent this from
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ci/official/containers/linux_arm64/devel.usertools/code_check_full.bats
bazel query --keep_going 'kind(py_test, //tensorflow/python/...) - attr("tags", "no_pip|manual", //tensorflow/python/...)' | grep -v -f $BATS_TEST_TMPDIR/ignore_deps_for_these_packages | paste -sd "+" - > $BATS_TEST_TMPDIR/deps # Find all one-step dependencies of those tests which are from //tensorflow # (since external deps will come from Python-level pip dependencies), # excluding dependencies and files that are known to be unneccessary.
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RELEASE.md
policy with `tf.data.experimental.service.ShardingPolicy` enum. It can be one of `OFF` (equivalent to today's `"parallel_epochs"` mode), `DYNAMIC` (equivalent to today's `"distributed_epoch"` mode), or one of the static sharding policies: `FILE`, `DATA`, `FILE_OR_DATA`, or `HINT` (corresponding to values of `tf.data.experimental.AutoShardPolicy`).
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ci/official/utilities/code_check_full.bats
# Find all one-step dependencies of those tests which are from //tensorflow # (since external deps will come from Python-level pip dependencies), # excluding dependencies and files that are known to be unneccessary.
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tensorflow/BUILD
# # This setting is currently experimental. The 'v2' implementation does _not_ # correspond to a particular, finalized design; rather, it relates to # developing one. # # The current aim of the 'v2' implementation is to allow 'unused' ops and # kernels to be discarded by the linker (to the benefit of binary size). bool_flag( name = "enable_registration_v2",
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