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tensorflow/c/BUILD
":c_api_internal", ":c_api_macros_hdrs", ":checkpoint_reader", ":tf_buffer", ":tf_buffer_internal", "//tensorflow/c/eager:c_api", "//tensorflow/c/eager:c_api_internal", "//tensorflow/c/eager:tfe_context_internal", "//tensorflow/c/eager:tfe_op_internal", "//tensorflow/c/eager:tfe_tensorhandle_internal", "//tensorflow/compiler/jit:flags",
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tensorflow/c/eager/c_api_experimental.h
limitations under the License. ==============================================================================*/ #ifndef TENSORFLOW_C_EAGER_C_API_EXPERIMENTAL_H_ #define TENSORFLOW_C_EAGER_C_API_EXPERIMENTAL_H_ #include "tensorflow/c/c_api.h" #include "tensorflow/c/c_api_macros.h" #include "tensorflow/c/eager/c_api.h" #ifdef __cplusplus extern "C" { #endif
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tensorflow/c/eager/BUILD
"//tensorflow/core/common_runtime/eager:attr_builder", "//tensorflow/core/common_runtime/eager:context", "//tensorflow/core/common_runtime/eager:copy_to_device_node", "//tensorflow/core/common_runtime/eager:eager_executor", "//tensorflow/core/common_runtime/eager:eager_operation", "//tensorflow/core/common_runtime/eager:execute",
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tensorflow/c/eager/c_api_unified_experimental.cc
// 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. // // ============================================================================= using tensorflow::AbstractFunction;
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tensorflow/BUILD
# cc_shared_library to work. "//tensorflow/c/eager:c_api_experimental", "//tensorflow/c/eager:c_api_internal", "//tensorflow/c/eager:dlpack", "//tensorflow/c/eager:tape", "//tensorflow/c/eager:tfe_context_internal", "//tensorflow/c/eager:tfe_op_internal", "//tensorflow/c/eager:tfe_tensorhandle_internal", "//tensorflow/c/experimental/gradients",
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tensorflow/c/eager/c_api_test_util.cc
return false; } tensorflow::ServerDef GetServerDef(const string& job_name, int num_tasks) { tensorflow::ServerDef server_def; server_def.set_protocol("grpc"); server_def.set_job_name(job_name); server_def.set_task_index(0); tensorflow::ClusterDef* cluster_def = server_def.mutable_cluster(); tensorflow::JobDef* job_def = cluster_def->add_job(); job_def->set_name(job_name);
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tensorflow/c/c_api_experimental.cc
TF_CheckpointReader* reader = new TF_CheckpointReader(filename, status); if (!status->status.ok()) { TF_DeleteCheckpointReader(reader); return nullptr; } const auto& m = reader->GetVariableToDataTypeMap(); for (auto it = m.begin(); it != m.end(); ++it) reader->variable_list.push_back(it->first); std::sort(reader->variable_list.begin(), reader->variable_list.end()); return reader; }
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RELEASE.md
based server to register tf.function methods and a GRPC client to invoke remote registered methods. RPC APIs are intended for multi-client setups i.e. server and clients are started in separate binaries independently. * Example usage to create server: ```python server = tf.distribute.experimental.rpc.Server.create("grpc", "127.0.0.1:1234")
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ci/official/utilities/code_check_full.bats
//tensorflow/core:image_testdata //tensorflow/core/kernels/cloud:bigquery_reader_ops //tensorflow/python:extra_py_tests_deps //tensorflow/python:mixed_precision //tensorflow/python:tf_optimizer //tensorflow/python:compare_test_proto_py //tensorflow/python/framework:test_file_system.so //tensorflow/python/debug:grpc_tensorflow_server.par //tensorflow/python/feature_column:vocabulary_testdata
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.github/workflows/update-rbe.yml
# The "digest" that allows us to pull an image is not the digest as # returned by the API, but a sha256sum of the entire chunk of image # metadata. gcr.io helpfully includes it in the header of the response # as docker-content-digest: sha256:[digest]. Note we use egrep to # match exactly sha256:<hash> because curl may include a ^M symbol at # the end of the line.
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