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tensorflow/c/eager/unified_api_test.cc
PartialTensorShape shape; TF_RETURN_IF_ERROR(inputs[0]->Shape(&shape)); if (shape.dims() != 0) { return errors::InvalidArgument( "Tensor expected to have scalar shape found rank: ", shape.dims()); } return absl::OkStatus(); } TEST_P(UnifiedAPI, TestTensorShapeScalar) { if (UseFunction() && UseMlir()) { // TODO(b/173074167): Remove this.
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ci/official/containers/linux_arm64/Dockerfile
COPY cuda.packages.txt /cuda.packages.txt RUN /setup.sources.sh && /setup.packages.sh /devel.packages.txt # Install various tools. # - bats: bash unit testing framework # NOTE: v1.6.0 seems to have a bug that made "git" in setup_file break # - bazelisk: always use the correct bazel version # - buildifier: clean bazel build deps # - buildozer: clean bazel build deps
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tensorflow/c/eager/c_api_unified_experimental.cc
} // end namespace tensorflow // ============================================================================= // Public C API entry points // // These are only the generic entry points for the C API. This file does not // have any visibility into the graph/eager implementation and is only providing // C bindings to the abstract classes defined in the // c_api_unified_experimental_internal.h header. //
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ci/official/containers/linux_arm64/devel.usertools/aarch64_clang.bazelrc
build --linkopt="-Wl,--undefined-version" # Prevent double-compilation of some TF code, ref. b/183279666 (internal) # > TF's gen_api_init_files has a genrule to run the core TensorFlow code # > on the host machine. If we don't have --distinct_host_configuration=false, # > the core TensorFlow code will be built once for the host and once for the # > target platform.
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tensorflow/c/eager/c_api_experimental.h
// Causes the calling thread to block till all ops dispatched in this executor // have been executed. Note that "execution" here refers to kernel execution / // scheduling of copies, etc. Similar to sync execution, it doesn't guarantee // that lower level device queues (like GPU streams) have been flushed. // // This call may not block for execution of ops enqueued concurrently with this // call.
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ci/official/utilities/rename_and_verify_wheels.sh
fi # VERY basic check to ensure the [and-cuda] package variant is installable. # Checks TFCI_BAZEL_COMMON_ARGS for "gpu" or "cuda", implying that the test is # relevant. All of the GPU test machines have CUDA installed via other means, # so I am not sure how to verify that the dependencies themselves are valid for # the moment. if [[ "$TFCI_BAZEL_COMMON_ARGS" =~ gpu|cuda ]]; then
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tensorflow/c/c_api.cc
return true; } // Copies `src_graph` into `dst_graph`. Any node in `src_graph` with input // `src_inputs[i]` will have that input replaced with `dst_inputs[i]`. `prefix` // will be prepended to copied node names. `control_deps` are nodes in // `dst_graph` that the copied `src_graph` nodes will have control dependencies // on. `return_nodes` are nodes in `src_graph`, and the new corresponding nodes
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tensorflow/c/eager/unified_api_testutil.cc
// track of indices in the model's outputs are nullptr in this set. // The FunctionDef only outputs the non-null tensors. We later pad the // function op outputs to have nullptrs at the `null_indices`. absl::flat_hash_set<int> null_indices; { AbstractContextPtr func_ctx(BuildFunction(fn_name)); std::vector<AbstractTensorHandle*> func_inputs;
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RELEASE.md
`allow_build_at_runtime`. * Added a new parameter called `save_gpu_specific_engines` to the `.save()` function inside `TrtGraphConverterV2`. When `False`, the `.save()` function won't save any TRT engines that have been built. When `True` (default), the original behavior is preserved.
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tensorflow/c/eager/tape.h
// have all gradients available. This set usually is a subset of the set of // targets (not all since targets which have outputs in the tape will not have // gradients available initially). // // Then we repeatedly pop an entry from the stack, run its backprop, and update // the gradients of its inputs. Once we have computed all gradients for a single
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