【发布时间】：2025-12-29 04:35:15
【问题描述】：
我有 Kubuntu 18.04 和 Anaconda 5.2 64。我安装了 CUDA 驱动程序和 keras-gpu 和 tensorflow-gpu（自动也安装了 tensorflow）。
以下代码
from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
import tensorflow as tf
print('Tensorflow: ', tf.__version__)
给出输出
2018-07-29 12:14:06.821996: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
2018-07-29 12:14:06.880569: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:897] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2018-07-29 12:14:06.880910: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1392] Found device 0 with properties:
name: GeForce GTX 980 major: 5 minor: 2 memoryClockRate(GHz): 1.2155
pciBusID: 0000:01:00.0
totalMemory: 3.95GiB freeMemory: 2.72GiB
2018-07-29 12:14:06.880924: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1471] Adding visible gpu devices: 0
2018-07-29 12:14:07.058984: I tensorflow/core/common_runtime/gpu/gpu_device.cc:952] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-07-29 12:14:07.059012: I tensorflow/core/common_runtime/gpu/gpu_device.cc:958] 0
2018-07-29 12:14:07.059017: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0: N
2018-07-29 12:14:07.059114: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1084] Created TensorFlow device (/device:GPU:0 with 2430 MB memory) -> physical GPU (device: 0, name: GeForce GTX 980, pci bus id: 0000:01:00.0, compute capability: 5.2)
[name: "/device:CPU:0"
device_type: "CPU"
memory_limit: 268435456
locality {
}
incarnation: 18195666940796676435
, name: "/device:GPU:0"
device_type: "GPU"
memory_limit: 2548367360
locality {
bus_id: 1
links {
}
}
incarnation: 7016427886680347829
physical_device_desc: "device: 0, name: GeForce GTX 980, pci bus id: 0000:01:00.0, compute capability: 5.2"
]
Using TensorFlow backend.
Tensorflow: 1.9.0
所以看起来 keras 使用的是 tensorflow CPU 而不是 GPU（使用 DeepBach，我的一个 CPU 内核是 100%）？我做错了什么？
如何找出 keras/DeepBach 正在使用哪个设备？ nvidia-smi 在使用 keras/DeepBach 进行训练期间显示没有 GPU 利用率。如何告诉 keras/DeepBach 使用 GPU 而不是 CPU？
CUDA 似乎已安装：
$ nvcc -V
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2017 NVIDIA Corporation
Built on Fri_Sep__1_21:08:03_CDT_2017
Cuda compilation tools, release 9.0, V9.0.176
$ nvidia-smi
Sun Jul 29 12:10:28 2018
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 390.48 Driver Version: 390.48 |
|-------------------------------+----------------------+----------------------+
| 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 980 Off | 00000000:01:00.0 On | N/A |
| 4% 62C P0 47W / 180W | 1160MiB / 4040MiB | 1% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 1113 G /usr/lib/xorg/Xorg 562MiB |
| 0 1385 G kwin_x11 152MiB |
| 0 1395 G /usr/bin/krunner 2MiB |
| 0 1399 G /usr/bin/plasmashell 167MiB |
| 0 26801 G ...-token=2DD4BBFEA86302FEC3C179E07D55C897 267MiB |
+-----------------------------------------------------------------------------+
【问题讨论】：
-
您的日志没有说明正在使用哪个设备，您需要更具体地说明问题。 GPU也被TF正确检测到了，所以我看不出有什么问题。
-
评论不是为了提出新问题，而是为了澄清你的问题。
标签： python tensorflow keras anaconda