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RELEASE.md
as `tf.tpu.experimental.embedding.serving_embedding_lookup` which can take arbitrary rank of dense and sparse tensor. For ragged tensor, though the input tensor remains to be rank 2, the activations now can be rank 2 or above by specifying the output shape in the feature config or via the build method. * Add
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tensorflow/c/eager/abstract_tensor_handle.h
// the tensor handle can be an error and return non-OK status. virtual tensorflow::Status TensorHandleStatus() const; // Returns tensor shape. If tensor has unknown rank, shape remains untouched. virtual tensorflow::Status Shape( tensorflow::PartialTensorShape* shape) const = 0; // Returns tensor (full) type.
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tensorflow/c/eager/c_api.cc
return nullptr; } std::vector<std::unique_ptr<tensorflow::Device>> devices; status->status = tensorflow::DeviceFactory::AddDevices( opts->session_options.options, "/job:localhost/replica:0/task:0", &devices); if (!status->status.ok()) return nullptr; std::unique_ptr<tensorflow::DeviceMgr> device_mgr( new tensorflow::DynamicDeviceMgr(std::move(devices)));
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tensorflow/c/c_api_experimental_test.cc
job { name: "worker" tasks { key: 0 value: "tpuserver:0" } tasks { key: 1 value: "localhost:1" } } } job_name: "worker" task_index: 1 protocol: "grpc" )"); TF_Status* status = TF_NewStatus(); TF_Buffer* result = TFE_GetServerDef(expected_text_proto.c_str(), status); EXPECT_EQ(TF_GetCode(status), TF_OK); ServerDef actual;
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tensorflow/c/eager/c_api_unified_experimental_graph.cc
TF_RETURN_IF_ERROR( operation->SetOpName(absl::StrCat("_input_", inputs_.size()).c_str())); TF_RETURN_IF_ERROR(operation->SetAttrType("dtype", dtype)); if (!shape.unknown_rank()) { TF_RETURN_IF_ERROR(operation->SetAttrShape( "shape", reinterpret_cast<int64_t*>(shape.dim_sizes().data()), shape.dims())); } int num_outputs = 1;
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tensorflow/c/c_api_experimental.cc
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tensorflow/c/eager/c_api_cluster_test.cc
namespace { using ::tensorflow::string; void ReplaceTaskInServerDef(tensorflow::ServerDef* server_def, int task_index) { tensorflow::JobDef* job_def = server_def->mutable_cluster()->mutable_job(0); int port = tensorflow::testing::PickUnusedPortOrDie(); job_def->mutable_tasks()->at(task_index) = tensorflow::strings::StrCat("localhost:", port); } void CheckTFE_TensorHandleHasFloats(TFE_TensorHandle* handle,
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tensorflow/c/eager/parallel_device/parallel_device_lib_test.cc
ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get()); std::vector<std::string> devices{ "/job:localhost/replica:0/task:0/device:CPU:0", "/job:localhost/replica:0/task:0/device:CPU:1"}; ParallelDevice parallel_device(std::move(devices)); std::unique_ptr<TFE_Op, decltype(&TFE_DeleteOp)> handle_op(
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Thu Jul 08 23:47:35 GMT 2021 - 15.3K bytes - Viewed (0) -
.github/workflows/stale-issues.yml
only-labels: "stat:awaiting response" stale-issue-message: > This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you. close-issue-message: > This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.
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tensorflow/c/c_api_experimental.h
// down, collective ops may hang. TF_CAPI_EXPORT extern void TFE_CollectiveOpsCheckPeerHealth( TFE_Context* ctx, const char* task, int64_t timeout_in_ms, TF_Status* status); // Information about the shape of a Tensor and its type. struct TF_ShapeAndType { // Number of dimensions. -1 indicates unknown rank. int num_dims; // Array of dimensions. -1 indicates unknown dim. int64_t* dims;
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