Question

我有一台配备8个GPU的机器（4个GPU GTX 1080 Ti的11 Gb de RAM和4个RTX 1080），并且无法获得张量流以正确（或完全）使用它们。

当我这样做

from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())

它打印

[name: "/device:CPU:0"
device_type: "CPU"
memory_limit: 268435456
locality {
}
incarnation: 5295519098812813462
, name: "/device:XLA_GPU:0"
device_type: "XLA_GPU"
memory_limit: 17179869184
locality {
}
incarnation: 12186007115805339517
physical_device_desc: "device: XLA_GPU device"
, name: "/device:XLA_GPU:1"
device_type: "XLA_GPU"
memory_limit: 17179869184
locality {
}
incarnation: 17706271046686153881
physical_device_desc: "device: XLA_GPU device"
, name: "/device:XLA_GPU:2"
device_type: "XLA_GPU"
memory_limit: 17179869184
locality {
}
incarnation: 14710290295129432533
physical_device_desc: "device: XLA_GPU device"
, name: "/device:XLA_GPU:3"
device_type: "XLA_GPU"
memory_limit: 17179869184
locality {
}
incarnation: 1381213064943868400
physical_device_desc: "device: XLA_GPU device"
, name: "/device:XLA_GPU:4"
device_type: "XLA_GPU"
memory_limit: 17179869184
locality {
}
incarnation: 12093982778662340719
physical_device_desc: "device: XLA_GPU device"
, name: "/device:XLA_GPU:5"
device_type: "XLA_GPU"
memory_limit: 17179869184
locality {
}
incarnation: 682960671898108683
physical_device_desc: "device: XLA_GPU device"
, name: "/device:XLA_GPU:6"
device_type: "XLA_GPU"
memory_limit: 17179869184
locality {
}
incarnation: 9901240111105546679
physical_device_desc: "device: XLA_GPU device"
, name: "/device:XLA_GPU:7"
device_type: "XLA_GPU"
memory_limit: 17179869184
locality {
}
incarnation: 8442134369143872649
physical_device_desc: "device: XLA_GPU device"
, name: "/device:XLA_CPU:0"
device_type: "XLA_CPU"
memory_limit: 17179869184
locality {
}
incarnation: 1687638086072792879
physical_device_desc: "device: XLA_CPU device"
].

如果我尝试将GPU用于任何事物，则nvidia-smi表示它们已被占用，但以0％的速度运行，并且任务的速度表明tensorflow只是在使用CPU。在其他具有相同设置的机器上，它也会同时打印'/device:GPU:2'和'/device:XLA_GPU:2'（例如），并且tensorflow可以毫无问题地使用它们。

我已经看到了类似的问题和解决方案，但似乎没有任何作用。

Answer 1

最有可能您安装了不兼容的CUDA。如果您使用pip安装tensorflow，请检查https://www.tensorflow.org/install/gpu以查看tensorflow版本和相应的CUDA版本（也为cudnn版本）。确保您已安装correct版的tensorflow，CUDA和cudnn。或者，您可以选择从源代码构建张量流，但我对此经验不足，可以自己使用它进行搜索：）祝您好运！

Tensorflow只看到XLA_GPU并且不能使用它们

1 个答案: