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我使用的是Theano後端的keras 1.1.0。爲了使用GPU,我將os.envorn設置爲:Keras和GPU的使用
os.environ['THEANO_FLAGS'] = "device=gpu0"
在導入keras之前。導入keras給我以下信息:
Using gpu device 0: TITAN X (Pascal) (CNMeM is disabled, cuDNN 5105)
/home/library_python/lib/python/Theano-0.8.2-py2.7.egg/theano/sandbox/cuda/__init__.py:600: UserWarning: Your cuDNN version is more recent than the one Theano officially supports. If you see any problems, try updating Theano or downgrading cuDNN to version 5.
warnings.warn(warn)
所以我認爲我正確設置設備。但是,當我用keras訓練模型時,nvidia-smi顯示以下消息:
Fri May 5 13:05:33 2017
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 375.51 Driver Version: 375.51 |
|-------------------------------+----------------------+----------------------+
| 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 GT 730 On | 0000:01:00.0 N/A | N/A |
| 30% 38C P8 N/A/N/A | 56MiB/ 980MiB | N/A Default |
+-------------------------------+----------------------+----------------------+
| 1 TITAN X (Pascal) On | 0000:02:00.0 Off | N/A |
| 36% 63C P2 87W/250W | 373MiB/12189MiB | 35% Default |
+-------------------------------+----------------------+----------------------+
| 2 TITAN X (Pascal) On | 0000:03:00.0 Off | N/A |
| 23% 42C P8 18W/250W | 3MiB/12189MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 Not Supported |
| 1 1520 C /usr/bin/python 155MiB |
| 1 14996 C python 215MiB |
+-----------------------------------------------------------------------------+
它說我只使用215MB的GPU內存。我期待更像10GB的使用。你能告訴我我做錯了什麼嗎?我的GPU設備設置是否正確?
os.environ['THEANO_FLAGS'] = "device=gpu0,floatX=float32,lib.cnmem=1"
我現在用的11.7內存預期:
建議將cnmem剪切到'0.95'。 –
其實,我可以問一下,cnmem = 1在做什麼? – ThePrincess
它基本上預先分配GPU內存。它[觀察](https://datascience.stackexchange.com/questions/13381/how-does-the-cnmem-library-work-and-which-types-of-theano-models-does-it-speed)設置'cnmem'可以提高速度。但是,您不希望阻止GPU的所有內存,因此慣例是將其修剪爲0.95。 –