【发布时间】:2017-10-28 21:27:00
【问题描述】:
我想使用 nvidia-smi 来监控我的 GPU 以用于我的机器学习/人工智能项目。但是,当我在 cmd、git bash 或 powershell 中运行 nvidia-smi 时,会得到以下结果:
$ nvidia-smi
Sun May 28 13:25:46 2017
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 376.53 Driver Version: 376.53 |
|-------------------------------+----------------------+----------------------+
| GPU Name TCC/WDDM | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GeForce GTX 1070 WDDM | 0000:28:00.0 On | N/A |
| 0% 49C P2 36W / 166W | 7240MiB / 8192MiB | 4% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 7676 C+G ...ost_cw5n1h2txyewy\ShellExperienceHost.exe N/A |
| 0 8580 C+G Insufficient Permissions N/A |
| 0 9704 C+G ...x86)\Google\Chrome\Application\chrome.exe N/A |
| 0 10532 C ...\Anaconda3\envs\tensorflow-gpu\python.exe N/A |
| 0 11384 C+G Insufficient Permissions N/A |
| 0 12896 C+G C:\Windows\explorer.exe N/A |
| 0 13868 C+G Insufficient Permissions N/A |
| 0 14068 C+G Insufficient Permissions N/A |
| 0 14568 C+G Insufficient Permissions N/A |
| 0 15260 C+G ...osoftEdge_8wekyb3d8bbwe\MicrosoftEdge.exe N/A |
| 0 16912 C+G ...am Files (x86)\Dropbox\Client\Dropbox.exe N/A |
| 0 18196 C+G ...I\AppData\Local\hyper\app-1.3.3\Hyper.exe N/A |
| 0 18228 C+G ...oftEdge_8wekyb3d8bbwe\MicrosoftEdgeCP.exe N/A |
| 0 20032 C+G ...indows.Cortana_cw5n1h2txyewy\SearchUI.exe N/A |
+-----------------------------------------------------------------------------+
GPU Memory Usage 列显示每个进程的N/A。此外,列出的进程比我在 Internet 上找到的大多数示例要多得多。这可能是什么原因?
我正在运行华硕的 Nvidia GTX 1070,我的操作系统是 Windows 10 Pro。
【问题讨论】:
-
Windows 上的解决方案:以管理员身份运行 Sysinternals Process Explorer,然后打开“GPU Dedicated”和“GPU Committed”列以查看每个进程的 GPU 内存使用情况。
标签: machine-learning tensorflow gpu nvidia