TensorFlow与Kubernetes/Docker结合使用实践(4)

同样我可以使用nvidia-smi查看GPU使用情况

[root@A01-R06-I184-22 ~]# nvidia-smi Tue Oct 11 00:10:28 2016 +------------------------------------------------------+ | NVIDIA-SMI 352.39 Driver Version: 352.39 | |-------------------------------+----------------------+----------------------+ | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | |===============================+======================+======================| | 0 Tesla K20c Off | 0000:02:00.0 Off | 0 | | 30% 26C P0 48W / 225W | 4540MiB / 4799MiB | 2% Default | +-------------------------------+----------------------+----------------------+ | 1 Tesla K20c Off | 0000:04:00.0 Off | 0 | | 30% 31C P0 48W / 225W | 4499MiB / 4799MiB | 0% Default | +-------------------------------+----------------------+----------------------+ | 2 Tesla K20c Off | 0000:83:00.0 Off | 0 | | 30% 25C P8 26W / 225W | 11MiB / 4799MiB | 0% Default | +-------------------------------+----------------------+----------------------+ | 3 Tesla K20c Off | 0000:84:00.0 Off | 0 | | 30% 24C P8 25W / 225W | 11MiB / 4799MiB | 0% Default | +-------------------------------+----------------------+----------------------+ +-----------------------------------------------------------------------------+ | Processes: GPU Memory | | GPU PID Type Process name Usage | |=============================================================================| | 0 132460 C python 4524MiB | | 1 132460 C python 4484MiB | +-----------------------------------------------------------------------------+

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