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README.md
for machine learning. It has a comprehensive, flexible ecosystem of [tools](https://www.tensorflow.org/resources/tools), [libraries](https://www.tensorflow.org/resources/libraries-extensions), and [community](https://www.tensorflow.org/community) resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications.
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tensorflow/c/eager/c_api.h
TF_Status* status); // Some TF ops need a step container to be set to limit the lifetime of some // resources (mostly TensorArray and Stack, used in while loop gradients in // graph mode). Calling this on a context tells it to start a step. TF_CAPI_EXPORT extern void TFE_ContextStartStep(TFE_Context* ctx);
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tensorflow/c/eager/gradients.cc
std::unordered_map<int64_t, TapeTensor> sources_that_are_targets; for (int i = 0; i < target_tensor_ids.size(); ++i) { int64_t target_id = target_tensor_ids[i]; if (sources_set.find(target_id) != sources_set.end()) { auto tensor = targets[i]; sources_that_are_targets.insert( std::make_pair(target_id, TapeTensor(tensor))); } }
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tensorflow/c/BUILD
"//tensorflow/core:test_main", "//tensorflow/core/kernels:array", "//tensorflow/core/kernels:control_flow_ops", "//tensorflow/core/kernels:math", "//tensorflow/core/platform:resource_loader", ], ) tf_cc_test( name = "c_api_experimental_test", size = "medium", srcs = ["c_api_experimental_test.cc"], data = [ "testdata/tf_record",
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tensorflow/c/c_api_experimental_test.cc
: const_cast<TF_Tensor**>(input_tensors.data()), /*input_tensors_as_shapes*/ nullptr, /*input_resource_shapes_and_types*/ nullptr, &output_shapes, /*output_resource_shapes_and_types*/ nullptr, status_); CHECK_EQ(TF_OK, TF_GetCode(status_)) << TF_Message(status_); CHECK_EQ(output_shapes->num_items, 1);
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tensorflow/c/eager/immediate_execution_context.h
virtual void ClearCachesAndThreadExecutors() = 0; // Initialize the step resource container for a training step. This is used // in current TF runtime. For tfrt, it is used by fallback op handler. virtual void StartStep() = 0; // Destroy the step resource container for a training step. virtual void EndStep() = 0;
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tensorflow/c/eager/tape.h
std::unordered_map<int64_t, TapeTensor> sources_that_are_targets; for (int grad_index = 0, end = grad.size(); grad_index < end; ++grad_index) { Gradient* grad_tensor = grad[grad_index]; if (grad_tensor != nullptr) { int64_t tensor_id = vspace_.TensorId(grad_tensor); targets.push_back(tensor_id); if (sources_set.find(tensor_id) != sources_set.end()) { sources_that_are_targets.emplace(
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tensorflow/c/c_api_experimental.h
// `output_resource_shapes_and_types`. The caller is responsible for freeing the // memory in these buffers by calling `TF_DeleteShapeAndTypeList`. TF_CAPI_EXPORT extern void TFE_InferShapes( TFE_Op* op, TF_ShapeAndTypeList* input_shapes, TF_Tensor** input_tensors, TF_ShapeAndTypeList* input_tensor_as_shapes, TF_ShapeAndTypeList** input_resource_shapes_and_types,
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tensorflow/c/c_api_experimental.cc
TF_ShapeAndTypeList* input_tensors_as_shapes, TF_ShapeAndTypeList** input_resource_shapes_and_types, TF_ShapeAndTypeList** output_shapes, TF_ShapeAndTypeList*** output_resource_shapes_and_types, TF_Status* status) { using tensorflow::NodeDef; using tensorflow::OpRegistrationData;
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tensorflow/c/eager/c_api_cluster_test.cc
ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); // The device of var_handle0 is local device which is the same before and // after cluster update. Remove resource with valid device should succeed. TFE_Op* op = TFE_NewOp(ctx, "DestroyResourceOp", status); ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); TFE_OpAddInput(op, var_handle0, status);
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