Every 1.0s: nvidia-smi Tue Feb 20 12:49:34 2018
Tue Feb 20 12:49:34 2018
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 390.25 Driver Version: 390.25 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 Quadro M1000M Off | 00000000:01:00.0 Off | N/A |
| N/A 59C P0 N/A / N/A | 1895MiB / 2002MiB | 64% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 1166 G /usr/lib/xorg/Xorg 239MiB |
| 0 1864 G compiz 80MiB |
| 0 6755 C python 1408MiB |
| 0 25674 G ...-token=5769296849603E2A1B668201DBB31D6A 149MiB |
+-----------------------------------------------------------------------------+
我是基于 keras+gpu 的深度学习的新从业者。
这是在watch -n 1 nvidia-smi
告诉我什么?
它有什么价值主张,可以用来提高gpu的性能吗?
在背景中,一个小的 Keras 模型正在训练。