Method RunHelper

tensorflow/core/distributed_runtime/rpc/grpc_session.cc:196–281

}

Status GrpcSession::RunHelper(
    const RunOptions& run_options,
    const std::vector<std::pair<string, Tensor>>& inputs,
    const std::vector<string>& output_tensor_names,
    const std::vector<string>& target_node_names, std::vector<Tensor>* outputs,
    RunMetadata* run_metadata, const string& prun_handle) {
  // Convert to proto
  std::unique_ptr<MutableRunStepRequestWrapper> req(
      master_->CreateRunStepRequest());
  std::unique_ptr<MutableRunStepResponseWrapper> resp(
      master_->CreateRunStepResponse());

  *req->mutable_options() = run_options;

  if (run_options.timeout_in_ms() == 0) {
    req->mutable_options()->set_timeout_in_ms(
        options_.config.operation_timeout_in_ms());
  }

  if (!prun_handle.empty()) {
    req->set_partial_run_handle(prun_handle);
  }

  for (const auto& it : inputs) {
    req->add_feed(it.first, it.second);
  }

  // Support long error messages by storing the error code in the response body.
  req->set_store_errors_in_response_body(true);

  // Build an index from fetch tensor name to first index in
  // output_tensor_names.
  std::unordered_map<string, int> output_name_to_offset;
  for (int i = 0; i < output_tensor_names.size(); ++i) {
    const string& name = output_tensor_names[i];
    if (output_name_to_offset.insert(std::make_pair(name, i)).second) {
      req->add_fetch(name);
    }
  }
  for (const string& target : target_node_names) {
    req->add_target(target);
  }

  CallOptions call_options;
  call_options.SetTimeout(req->options().timeout_in_ms());
  TF_RETURN_IF_ERROR(RunProto(&call_options, req.get(), resp.get()));

  // Look for an extended error returned in the response body.
  if (resp->status_code() != error::Code::OK) {
    return Status(resp->status_code(), resp->status_error_message());
  }

  if (!output_tensor_names.empty()) {
    outputs->resize(output_tensor_names.size());
  }

  // Convert response back to Tensors in the correct order.
  for (size_t i = 0; i < resp->num_tensors(); ++i) {