我正在使用动态链接的tensorflow库在c ++代码上运行神经网络。
2018-06-07 19:03:10.578031: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1084] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9168 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:17:00.0, compute capability: 6.1)
2018-06-07 19:03:10.615271: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1084] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:1 with 9822 MB memory) -> physical GPU (device: 1, name: GeForce GTX 1080 Ti, pci bus id: 0000:65:00.0, compute capability: 6.1)
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 384.90 Driver Version: 384.90 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GeForce GTX 108... Off | 00000000:17:00.0 Off | N/A |
| 0% 54C P2 106W / 250W | 10734MiB / 11172MiB | 33% Default |
+-------------------------------+----------------------+----------------------+
| 1 GeForce GTX 108... Off | 00000000:65:00.0 On | N/A |
| 1% 54C P2 65W / 250W | 10655MiB / 11171MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 22230 C ./demo 10723MiB |
| 1 1264 G /usr/lib/xorg/Xorg 309MiB |
| 1 3230 G compiz 235MiB |
| 1 22230 C ./demo 10095MiB |
| 1 25625 G unity-control-center 3MiB |
+-----------------------------------------------------------------------------+
代码
tf::Session* session_ptr;
auto options = tf::SessionOptions();
options.config.mutable_gpu_options()->set_visible_device_list("0");
auto status = NewSession(tf::SessionOptions(), &session_ptr);
session.reset(tensorflow::NewSession(options));
似乎没有阻止在所有gpus中设置设备的张量流。
我知道您可以使用CUDA_VISIBLE_DEVICES环境变量，但我需要在运行时执行此操作。我还需要在同一个gpu上运行这个程序的几个实例（可能是4个）
有没有办法做到这一点？
我也尝试过使用
tf::GraphDef graph_def;
auto status1 = ReadBinaryProto(tf::Env::Default(), tf_model, &graph_def);
tensorflow::graph::SetDefaultDevice("0",&graph_def);
还为两个gpu分配内存......