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ci/official/requirements_updater/WORKSPACE
# buildifier: disable=load-on-top workspace(name = "requirements_updater") # buildifier: disable=load-on-top load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive") http_archive( name = "bazel_skylib", sha256 = "74d544d96f4a5bb630d465ca8bbcfe231e3594e5aae57e1edbf17a6eb3ca2506", urls = [
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tensorflow/c/experimental/gradients/nn_grad_test.cc
return ops::BiasAdd(ctx, inputs[0], inputs[1], &outputs[0], "NHWC", "BiasAdd"); } class CppGradients : public ::testing::TestWithParam<std::tuple<const char*, bool, bool>> { protected: void SetUp() override { TF_StatusPtr status(TF_NewStatus()); TF_SetTracingImplementation(std::get<0>(GetParam()), status.get()); status_ = StatusFromTF_Status(status.get());
C++ - Registered: Tue Mar 26 12:39:09 GMT 2024 - Last Modified: Wed Feb 28 13:53:47 GMT 2024 - 8.3K bytes - Viewed (0) -
tensorflow/c/eager/unified_api_testutil.h
// outputs = model(inputs) Status RunModel(Model model, AbstractContext* ctx, absl::Span<AbstractTensorHandle* const> inputs, absl::Span<AbstractTensorHandle*> outputs, bool use_function); Status BuildImmediateExecutionContext(bool use_tfrt, AbstractContext** ctx); // Return a tensor handle with given type, values and dimensions. template <class T, TF_DataType datatype>
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tensorflow/c/c_api_test.cc
// Serialize to GraphDef. GraphDef graph_def; ASSERT_TRUE(GetGraphDef(graph, &graph_def)); // Validate GraphDef is what we expect. bool found_placeholder = false; bool found_scalar_const = false; bool found_add = false; for (const auto& n : graph_def.node()) { if (IsPlaceholder(n)) { EXPECT_FALSE(found_placeholder); found_placeholder = true;
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Mon Apr 15 03:35:10 GMT 2024 - 96.9K bytes - Viewed (3) -
RELEASE.md
* Added support for `tflite_convert` command line tool in 2.0. * Post-training quantization tool supports quantizing weights shared by multiple operations. The models made with versions of this tool will use INT8 types for weights and will only be executable interpreters from this version onwards. * Post-training quantization tool supports fp16 weights and GPU delegate
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tensorflow/BUILD
load( "//tensorflow/python/tools/api/generator:api_gen.bzl", "get_compat_files", "get_nested_compat_files", ) load( "//tensorflow/python/tools/api/generator:api_init_files.bzl", "TENSORFLOW_API_INIT_FILES", ) load( "//tensorflow/python/tools/api/generator:api_init_files_v1.bzl", "TENSORFLOW_API_INIT_FILES_V1", ) load("//tensorflow/python/tools/api/generator2:generate_api.bzl", "generate_apis")
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CONTRIBUTING.md
your changes by running the command: ```bash tensorflow/tools/ci_build/ci_build.sh CPU tensorflow/tools/ci_build/ci_sanity.sh ``` This will catch most license, Python coding style and BUILD file issues that may exist in your changes. #### Running unit tests There are two ways to run TensorFlow unit tests. 1. Using tools and libraries installed directly on your system. Refer to the
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.github/workflows/arm-cd.yml
CI_DOCKER_BUILD_EXTRA_PARAMS="--build-arg py_major_minor_version=${{ matrix.pyver }} --build-arg is_nightly=${is_nightly} --build-arg tf_project_name=${tf_project_name}" \ ./tensorflow/tools/ci_build/ci_build.sh cpu.arm64 bash tensorflow/tools/ci_build/rel/ubuntu/cpu_arm64_test_build.sh - name: Upload pip wheel to PyPI
Others - Registered: Tue May 07 12:40:20 GMT 2024 - Last Modified: Tue Mar 05 10:24:16 GMT 2024 - 3K bytes - Viewed (1) -
tensorflow/c/eager/graph_function.h
absl::StatusOr<core::RefCountPtr<FunctionRecord>> GetFunctionRecord() override { return func_record_.GetNewRef(); } // For LLVM style RTTI. static bool classof(const AbstractFunction* ptr) { return ptr->getKind() == kGraph; } private: core::RefCountPtr<FunctionRecord> func_record_; }; } // namespace graph } // namespace tracing
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tensorflow/c/c_api.cc
unsigned char value) { desc->node_builder.Attr(attr_name, static_cast<bool>(value)); } void TF_SetAttrBoolList(TF_OperationDescription* desc, const char* attr_name, const unsigned char* values, int num_values) { std::unique_ptr<bool[]> b(new bool[num_values]); for (int i = 0; i < num_values; ++i) { b[i] = values[i]; }
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