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ci/official/envs/rbe
EOF exit 1 fi TFCI_BAZEL_COMMON_ARGS="$TFCI_BAZEL_COMMON_ARGS --config rbe_$TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX" # These flags share the user's gcloud credentials with the container, so that bazel # inside the container can authenticate. Note: TF's CI does not have any credential # stored here.
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ci/official/debug_tfci.sh
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ci/official/containers/linux_arm64/devel.usertools/setup_venv_test.sh
# See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== set -euxo pipefail # Run this from inside the tensorflow github directory. # Usage: setup_venv_test.sh venv_and_symlink_name "glob pattern for one wheel file" # Example: setup_venv_test.sh bazel_pip "/tf/pkg/*.whl" #
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tensorflow/api_template.__init__.py
from tensorflow.python.tools import module_util as _module_util from tensorflow.python.util.lazy_loader import KerasLazyLoader as _KerasLazyLoader # Make sure code inside the TensorFlow codebase can use tf2.enabled() at import. _os.environ["TF2_BEHAVIOR"] = "1" from tensorflow.python import tf2 as _tf2 _tf2.enable() # API IMPORTS PLACEHOLDER # WRAPPER_PLACEHOLDER
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ci/official/requirements_updater/README.md
as a rough example of the steps below. All the files referenced below are located in the same directory as this README, unless indicated otherwise. 1) Add the new version to the `VERSIONS` variable inside `tensorflow/tools/toolchains/python/python_repo.bzl`. \ While this isn't necessary for running the updater, it is required for actually using the new version with Tensorflow.
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ci/official/utilities/setup_macos.sh
gcloud auth activate-service-account --key-file="${GOOGLE_APPLICATION_CREDENTIALS}" fi set -x # When cross-compiling with RBE, we need to copy the macOS sysroot to be # inside the TensorFlow root directory. We then define them as a filegroup # target inside "tensorflow/tools/toolchains/cross_compile/cc" so that Bazel # can register it as an input to compile/link actions and send it to the remote # VMs when needed.
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SECURITY.md
Since models are practically programs that TensorFlow executes, using untrusted models or graphs is equivalent to running untrusted code. If you need to run untrusted models, execute them inside a [**sandbox**](https://developers.google.com/code-sandboxing). Memory corruptions in TensorFlow ops can be recognized as security issues only if they are reachable and exploitable through production-grade, benign models.
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tensorflow/c/experimental/filesystem/modular_filesystem_registration.cc
// * increased security as plugins will not be able to alter function table // after loading up. Thus, malicious plugins can't alter functionality to // probe for gadgets inside core TensorFlow. We can even protect the area // of memory where the copies reside to not allow any more writes to it // after all copies are created. template <typename T>
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tensorflow/c/experimental/filesystem/filesystem_interface.h
void* plugin_filesystem; } TF_Filesystem; typedef struct TF_TransactionToken { void* token; TF_Filesystem* owner; } TF_TransactionToken; // The named union is needed here (as opposed to // inside the `TF_Filesystem_Option_Value` struct) // as MSVC does not recognize `typeof`. typedef union TF_Filesystem_Option_Value_Union { int64_t int_val; double real_val; struct { char* buf; int buf_length;
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RELEASE.md
* `conversion_params` is now deprecated inside `TrtGraphConverterV2` in favor of direct arguments: `max_workspace_size_bytes`, `precision_mode`, `minimum_segment_size`, `maximum_cached_engines`, `use_calibration` and `allow_build_at_runtime`. * Added a new parameter called `save_gpu_specific_engines` to the `.save()` function inside `TrtGraphConverterV2`. When `False`, the
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