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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/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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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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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/upload.sh
# $TF_VER_FULL will resolve to e.g. "2.15.0-rc2". Since $TF_VER_FULL comes # from get_versions.sh, which must be run *after* update_version.py, FINAL_URI # can't be set inside the rest of the _upload envs. FINAL_URI="$TFCI_ARTIFACT_FINAL_GCS_URI/$TF_VER_FULL" gsutil -m rsync -d -r "$DOWNLOADS" "$FINAL_URI" # Also mirror the latest-uploaded folder to the "latest" directory.
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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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configure.py
) if get_var( environ_cp, 'TF_OVERRIDE_EIGEN_STRONG_INLINE', 'Eigen strong inline', True, ('Would you like to override eigen strong inline for some C++ ' 'compilation to reduce the compilation time?'), 'Eigen strong inline overridden.', 'Not overriding eigen strong inline, ' 'some compilations could take more than 20 mins.'): # Due to a known MSVC compiler issue
Python - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Mon Apr 15 18:25:36 GMT 2024 - 53.8K bytes - Viewed (1) -
.bazelrc
# See https://developer.nvidia.com/cuda-gpus#compute # `compute_XY` enables PTX embedding in addition to SASS. PTX # is forward compatible beyond the current compute capability major # release while SASS is only forward compatible inside the current # major release. Example: sm_80 kernels can run on sm_89 GPUs but # not on sm_90 GPUs. compute_80 kernels though can also run on sm_90 GPUs.
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tensorflow/BUILD
) config_setting( name = "using_rocm_hipcc", define_values = {"using_rocm_hipcc": "true"}, ) config_setting( name = "override_eigen_strong_inline", define_values = {"override_eigen_strong_inline": "true"}, visibility = ["//visibility:public"], ) # This flag specifies whether TensorFlow 2.0 API should be built instead
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tensorflow/c/eager/tape.h
}; // A deque-backed stack, whose element references are not invalidated by // pushes and pops at the back. std::stack<AccumulatorCallState> call_state_; }; // Template instantiations here inline bool IsDtypeTrainable(DataType dtype) { switch (dtype) { case DT_HALF: case DT_BFLOAT16: case DT_FLOAT: case DT_DOUBLE: case DT_COMPLEX64: case DT_COMPLEX128:
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