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ci/official/envs/rbe
# Enables RBE as long as RBE is available for the selected platform. # The "selected" platform is considered to be # TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX. "Available" means there is # an entry in tensorflow/.bazelrc for build:rbe_TFCI_BAZEL_TARGET... # ^^^^ # This env is only valid when RBE is available on the selected platform, since
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RELEASE.md
* `tf.sparse.segment_sum` `tf.sparse.segment_mean` `tf.sparse.segment_sqrt_n` `SparseSegmentSum/Mean/SqrtN[WithNumSegments]` * Added `sparse_gradient` option (default=false) that makes the gradient of these functions/ops sparse (`IndexedSlices`) instead of dense (`Tensor`), using new `SparseSegmentSum/Mean/SqrtNGradV2` ops. * `tf.nn.embedding_lookup_sparse`
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CONTRIBUTING.md
TensorFlow team members will be assigned to review your pull requests. Once the pull requests are approved and pass continuous integration checks, a TensorFlow team member will apply `ready to pull` label to your change. This means we are working on getting your pull request submitted to our internal repository. After the change has been submitted internally, your pull request will be merged automatically on GitHub.
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.bazelrc
# On linux, we dynamically link small amount of kernels build:linux --config=dynamic_kernels # Make sure to include as little of windows.h as possible build:windows --copt=-DWIN32_LEAN_AND_MEAN build:windows --host_copt=-DWIN32_LEAN_AND_MEAN build:windows --copt=-DNOGDI build:windows --host_copt=-DNOGDI # MSVC (Windows): Standards-conformant preprocessor mode
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tensorflow/BUILD
# configure script unconditionally); otherwise if it is false or undefined, the # build is static and TensorFlow symbols (in Python only) are loaded into the # global symbol table in order to support op registration. This means that # projects building with Bazel and importing TensorFlow as a dependency will not # depend on libtensorflow_framework.so unless they opt in. tf_cc_shared_library( name = "tensorflow_framework",
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ci/official/utilities/code_check_full.bats
cat <<EOF There was a path found connecting //tensorflow/tools/pip_package:wheel to a banned CUDA dependency when '--define framework_shared_object=false' is set. This means that a CUDA target was probably included via an is_static condition, used when targeting platforms like Windows where we build statically instead of dynamically. Here's the output from bazel query: EOF
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