Question

我正在尝试在装有Ubuntu（18.04）的笔记本电脑上执行机器学习项目，这需要GPU。我的笔记本电脑具有NVIDIA Geforce MX-150（2GB）GPU，并且已安装以下组件。

Kernel                : 5.3.0-53-generic
GCC                   : 7.5.0
Nvidia Driver Version : 440.33
CUDA Version          : 10.0
cuDNN Version         : 7.4.1.5-1+cuda10.0 
Python                : 3.6.3
tensorflow            : 1.14.0
tensorflow-gpu        : 1.14.0

下面是nvidia-smi命令的输出

nvidia-smi

+-----------------------------------------------------------------------------+
Sat May 23 03:36:54 2020       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 440.33.01    Driver Version: 440.33.01    CUDA Version: 10.2     |
|-------------------------------+----------------------+----------------------+
| 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 MX150       On   | 00000000:01:00.0 Off |                  N/A |
| N/A   57C    P0    N/A /  N/A |    320MiB /  2002MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+

|Processes:                                                        GPU Memory |
|  GPU       PID   Type   Process name                             Usage      |
|=============================================================================|
|    0      1054      G   /usr/lib/xorg/Xorg                            20MiB |
|    0      1204      G   /usr/bin/gnome-shell                          46MiB |
|    0      1509      G   /usr/lib/xorg/Xorg                            89MiB |
|    0      1680      G   /usr/bin/gnome-shell                          76MiB |
|    0      2078      G   ...AAAAAAAAAAAACAAAAAAAAAA= --shared-files    84MiB |
+-----------------------------------------------------------------------------+

在检查GPU可用性时，我从python得到以下消息：

import tensorflow as tf

tf.test.is_gpu_available()
2020-05-23 03:22:00.113303: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU
supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA

2020-05-23 03:22:00.139002: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] 
CPU Frequency: 1800000000 Hz

2020-05-23 03:22:00.139733: I tensorflow/compiler/xla/service/service.cc:168] XLA service 
0x55bf899c6a30 executing computations on platform Host. Devices:

2020-05-23 03:22:00.139791: I tensorflow/compiler/xla/service/service.cc:175]   
StreamExecutor device (0): Host, Default Version

False

我的图形卡支持CUDA，可以通过下面的链接进行验证：

https://www.geforce.com/hardware/notebook-gpus/geforce-mx150/specifications/

在仔细检查了所有相关软件的兼容性之后，我完成了所有这些安装，但是现在我仍然无法成功使用我的GPU。谁能告诉我我的GPU或处理器出了什么问题，为什么不能识别GPU？

Answer 1

Linux或Ubuntu开发人员本质上无法检查封闭代码（专有）驱动器。我的建议，请注意不要使用封闭源视频卡，并尝试使下载完成

使用NVIDIA Geforce MX-150 GPU的笔记本电脑无法使用GPU

1 个答案: