c++ tensorflow接口GPU使用

c++中tensorflow接口GPU使用情况，通过一个小代码看下:

with tf.Session() as sess:
    x = tf.placeholder(tf.float32, [None, 32], name="x")
    y = tf.placeholder(tf.float32, [None, 8], name="y")

    w1 = tf.Variable(tf.truncated_normal([32, 16], stddev=0.1))
    b1 = tf.Variable(tf.constant(0.0, shape=[16]))

    w2 = tf.Variable(tf.truncated_normal([16, 8], stddev=0.1))
    b2 = tf.Variable(tf.constant(0.0, shape=[8]))

    a = tf.nn.tanh(tf.nn.bias_add(tf.matmul(x, w1), b1))
    y_out = tf.nn.tanh(tf.nn.bias_add(tf.matmul(a, w2), b2), name="y_out")
    cost = tf.reduce_sum(tf.square(y-y_out), name="cost")
    optimizer = tf.train.AdamOptimizer().minimize(cost, name="train")

    init = tf.initialize_variables(tf.all_variables(), name='init_all_vars_op')
    tf.train.write_graph(sess.graph_def,
                         './',
                         'mlp.pb', as_text=False)

c++代码，好像目前的高版本的tensorflow c++接口不再需要用sess.close,看源码知道析构函数会自动释放，强制性，其中TF_CHECK_OK是个好东西。实际还有其他很多小问题：

https://stackoverflow.com/questions/48701880/why-is-tensorflow-using-my-gpu-when-the-device-is-set-to-the-cpu

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Since this question is tagged with C++. The solution is

tensorflow::Session *sess;
tensorflow::SessionOptions options;

tensorflow::ConfigProto* config = &options.config;
// disabled GPU entirely
(*config->mutable_device_count())["GPU"] = 0;
// place nodes somewhere
config->set_allow_soft_placement(true);
See the example here. And my other post, how TensorFlow places the nodes.

edit: There is GitHub issue. You can try:

#include <stdlib.h>
setenv("CUDA_VISIBLE_DEVICES", "", 1);

#include "tensorflow/core/public/session.h"
#include "tensorflow/core/graph/default_device.h"
using namespace tensorflow;

int main(int argc, char* argv[]) {

    std::string graph_definition = "mlp.pb";
    Session* session;
    GraphDef graph_def;
    SessionOptions opts;
    std::vector<Tensor> outputs; // Store outputs
    TF_CHECK_OK(ReadBinaryProto(Env::Default(), graph_definition, &graph_def));

    // Set GPU options
    graph::SetDefaultDevice("/gpu:0", &graph_def);
    opts.config.mutable_gpu_options()->set_per_process_gpu_memory_fraction(0.5);
    opts.config.mutable_gpu_options()->set_allow_growth(true);

    // create a new session
    TF_CHECK_OK(NewSession(opts, &session));

    // Load graph into session
    TF_CHECK_OK(session->Create(graph_def));

    // Initialize our variables
    TF_CHECK_OK(session->Run({}, {}, {"init_all_vars_op"}, nullptr));

    Tensor x(DT_FLOAT, TensorShape({100, 32}));
    Tensor y(DT_FLOAT, TensorShape({100, 8}));
    auto _XTensor = x.matrix<float>();
    auto _YTensor = y.matrix<float>();

    _XTensor.setRandom();
    _YTensor.setRandom();

    for (int i = 0; i < 10; ++i) {

        TF_CHECK_OK(session->Run({{"x", x}, {"y", y}}, {"cost"}, {}, &outputs)); // Get cost
        float cost = outputs[0].scalar<float>()(0);
        std::cout << "Cost: " <<  cost << std::endl;
        TF_CHECK_OK(session->Run({{"x", x}, {"y", y}}, {}, {"train"}, nullptr)); // Train
        outputs.clear();
    }


    return 0;
}

使用serving格式设置：

#include <iostream>
#include <vector>
#include "tensorflow/cc/saved_model/loader.h"
#include "tensorflow/core/framework/graph.pb.h"
#include "tensorflow/core/protobuf/meta_graph.pb.h"
#include  "tensorflow/cc/saved_model/tag_constants.h"

#include <tensorflow/core/public/session.h>
#include <tensorflow/core/graph/default_device.h>
#include <tensorflow/core/graph/graph_def_builder.h>

using namespace std;
using namespace tensorflow;

int main(int argc ,char *argv[]) {

    string modelpath;

    if(argc<2){
         modelpath = "/Users/xxxx/Documents/python/credit-nlp-ner/bi-lstm-crf/model/1";


    }else{
        modelpath=argv[1];
    }

    tensorflow::SessionOptions sess_options;

   //   tensorflow::ConfigProto config=sess_options.config;
//    config.mutable_gpu_options()->set_visible_device_list("0");
//    config.mutable_gpu_options()->set_allow_growth(true);
//    config.set_allow_soft_placement(true);

    tensorflow::RunOptions run_options;


    tensorflow::SavedModelBundle bundle;

    TF_CHECK_OK(tensorflow::LoadSavedModel(sess_options, run_options, modelpath, {tensorflow::kSavedModelTagServe}, &bundle));



    tensorflow::MetaGraphDef graph_def = bundle.meta_graph_def;




    std::unique_ptr<tensorflow::Session>& session = bundle.session;

    GraphDef graphdef;
    session->Create(graphdef);
    //gpu设置
    graph::SetDefaultDevice("/gpu:0", &graphdef);
    sess_options.config.mutable_gpu_options()->set_per_process_gpu_memory_fraction(0.5);
    sess_options.config.mutable_gpu_options()->set_allow_growth(true);



    vector<int> vec={7997, 1945, 8471, 14127, 17565, 7340, 20224, 17529, 3796, 16033, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
    int ndim=vec.size();
    Tensor x(tensorflow::DT_INT32, tensorflow::TensorShape({1, ndim})); // New Tensor shape [1, ndim]
    auto x_map = x.tensor<int, 2>();
    for (int j = 0; j < ndim; j++) {
        x_map(0, j) = vec[j];
    }
    std::vector<std::pair<string, tensorflow::Tensor>> inputs;
    inputs.push_back(std::pair<std::string, tensorflow::Tensor>("input_x", x));

    Tensor keep_prob(tensorflow::DT_FLOAT, tensorflow::TensorShape({1}));
    keep_prob.vec<float>()(0) = 1.0f;

    inputs.push_back(std::pair<std::string, tensorflow::Tensor>("keep_prob", keep_prob));



    Tensor tensor_out(tensorflow::DT_INT32, TensorShape({1,ndim}));
    std::vector<tensorflow::Tensor> outputs={{ tensor_out }};
    TF_CHECK_OK(session->Run(inputs, {"crf_pred/ReverseSequence_1"}, {}, &outputs));


    for(int i=0;i<40;++i) {
        std::cout << outputs[0].matrix<int>()(0,i)<<" ";
    }





    return 0;

}

可以简单的看看效果，

c++ tensorflow接口GPU使用

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