How to import TensorFlow Serving APIs for C++ in Bazel project

TensorFlow and TensorFlow Serving are built using Bazel and developing gRPC clients for TensorFlow Serving requires to import the TensorFlow project. That work is a little bit hard except for Python since the prebuilt package only exists for Python. We don’t know what protobuf files we should import until looking into the protobuf files in the TF and TF-Serving.

In this blog post, I will write about integrating TF-Serving’s gRPC interfaces for C++ in the Bazel project with sample client codes. All codes here are in GitHub (

Set up the environment

Before implementing a client, I launched a sample TF-Serving container in the local environment. I followed the guide here but exposed port for gRPC additionally.

docker pull tensorflow/serving

git clone

docker run -t --rm -p 8501:8501 -p 8500:8500 \
    -v "$TESTDATA/saved_model_half_plus_two_cpu:/models/half_plus_two" \
    -e MODEL_NAME=half_plus_two \

Implement a client

Set up Bazel Workspace

I added the below codes in the WORKSPACE file to add TensorFlow and TensorFlow Serving projects. For the version of TF, I just check the WORKSPACE file of the TensorFlow Serving repository and copied the commit and sha256 hash value.

load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive")

# TensorFlow Serving
    name = "com_github_tensorflow_serving",
    sha256 = "193d2cf959d9444a2837fe13f3efa51127532ad9ee99585903301c1a4a4c21c9",
    strip_prefix = "serving-2.6.2",
    url = "",

load("@com_github_tensorflow_serving//tensorflow_serving:repo.bzl", "tensorflow_http_archive")

# TensorFlow
    name = "org_tensorflow",
    git_commit = "c2363d6d025981c661f8cbecf4c73ca7fbf38caf",
    sha256 = "add5982a3ce3b9964b7122dd0d28927b6a9d9abd8f95a89eda18ca76648a0ae8",

load("@com_github_tensorflow_serving//tensorflow_serving:workspace.bzl", "tf_serving_workspace")


load("@org_tensorflow//tensorflow:workspace3.bzl", "workspace")


load("@org_tensorflow//tensorflow:workspace2.bzl", "workspace")


load("@org_tensorflow//tensorflow:workspace1.bzl", "workspace")


load("@org_tensorflow//tensorflow:workspace0.bzl", "workspace")


Add client codes

After that, I could use TF Serving APIs for my project. To show how to do it, I wrote a sample code to inference the half_plus_two model.

#include "grpcpp/grpcpp.h"
#include "tensorflow_serving/apis/prediction_service.grpc.pb.h"

#define HOST ""
#define MODEL_NAME "half_plus_two"
#define MODEL_SIGNATURE_NAME "serving_default"

int main() {
  tensorflow::serving::PredictRequest predictRequest;
  tensorflow::serving::PredictResponse predictResponse;
  grpc::ClientContext clientContext;

  auto inputMap = predictRequest.mutable_inputs();

  tensorflow::TensorProto inputTensor;
  (*inputMap)["x"] = inputTensor;

  for (const auto& inputPair : *inputMap) {
    std::cout << "Input " << inputPair.first << std::endl;
    auto tensor = inputPair.second;

    for (const auto val : tensor.float_val()) {
      std::cout << "\t" << val;
    std::cout << std::endl;

  std::unique_ptr<tensorflow::serving::PredictionService::Stub> stub =
          grpc::CreateChannel(HOST, grpc::InsecureChannelCredentials()));

  grpc::Status status =
      stub->Predict(&clientContext, predictRequest, &predictResponse);

  if (!status.ok()) {
    std::cerr << "Error code: " << status.error_code()
              << ", message: " << status.error_message() << std::endl;
    return 1;

  for (const auto& outputPair : predictResponse.outputs()) {
    std::cout << "Output " << outputPair.first << std::endl;
    auto tensor = outputPair.second;

    for (const auto val : tensor.float_val()) {
      std::cout << "\t" << val;
    std::cout << std::endl;

To build it, I linked it with prediction_service_cc_proto from the TensorFlow Serving project.

    name = "test_client",
    srcs = [""],
    deps = [

Build and run

Then I could successfully build and run it to inference half_plus_two model via gRPC

$ bazel run //:test_client
INFO: Analyzed target //:test_client (0 packages loaded, 0 targets configured).
INFO: Found 1 target...
Target //:test_client up-to-date:
INFO: Elapsed time: 5.389s, Critical Path: 5.19s
INFO: 3 processes: 1 internal, 2 darwin-sandbox.
INFO: Build completed successfully, 3 total actions
INFO: Build completed successfully, 3 total actions
Input x
        1       2       5
Output y
        2.5     3       4.5
December 15, 2021
Tags: bazel tensorflow