There is 4 GPUs on my host mahchine
[root@c3-sa-i2-20151229-buf023 ~]# nvidia-smi
Wed Jul 12 14:27:40 2017
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 375.26 Driver Version: 375.26 |
|-------------------------------+----------------------+----------------------+
| 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 K40m Off | 0000:02:00.0 Off | 0 |
| N/A 23C P8 21W / 235W | 0MiB / 11439MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 1 Tesla K40m Off | 0000:03:00.0 Off | 0 |
| N/A 23C P8 22W / 235W | 0MiB / 11439MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 2 Tesla K40m Off | 0000:83:00.0 Off | 0 |
| N/A 42C P0 105W / 235W | 8336MiB / 11439MiB | 94% Default |
+-------------------------------+----------------------+----------------------+
| 3 Tesla K40m Off | 0000:84:00.0 Off | 0 |
| N/A 23C P8 22W / 235W | 0MiB / 11439MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 2 4148 C python 8330MiB |
+-----------------------------------------------------------------------------+
In docker inspect,I only used 2 GPUs.
"Devices": [
{
"PathOnHost": "/dev/nvidiactl",
"PathInContainer": "/dev/nvidiactl",
"CgroupPermissions": "mrw"
},
{
"PathOnHost": "/dev/nvidia-uvm",
"PathInContainer": "/dev/nvidia-uvm",
"CgroupPermissions": "mrw"
},
{
"PathOnHost": "/dev/nvidia0",
"PathInContainer": "/dev/nvidia0",
"CgroupPermissions": "mrw"
},
{
"PathOnHost": "/dev/nvidia1",
"PathInContainer": "/dev/nvidia1",
"CgroupPermissions": "mrw"
},
{
"PathOnHost": "/dev/fuse",
"PathInContainer": "/dev/fuse",
"CgroupPermissions": "mrw"
}
],
But I can see 4 GPUs in my container.
root@de-3879-ng-1-021909-1176603283-2jpbx:/notebooks# ls /dev | grep nv
nvidia-uvm
nvidia-uvm-tools
nvidia0
nvidia1
nvidia2
nvidia3
nvidiactl
root@de-3879-ng-1-021909-1176603283-2jpbx:/tmp# ./nvidia-smi
Wed Jul 12 06:31:57 2017
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 375.26 Driver Version: 375.26 |
|-------------------------------+----------------------+----------------------+
| 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 K40m Off | 0000:02:00.0 Off | 0 |
| N/A 23C P8 21W / 235W | 0MiB / 11439MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 1 Tesla K40m Off | 0000:03:00.0 Off | 0 |
| N/A 23C P8 22W / 235W | 0MiB / 11439MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 2 Tesla K40m Off | 0000:83:00.0 Off | 0 |
| N/A 41C P0 98W / 235W | 8336MiB / 11439MiB | 66% Default |
+-------------------------------+----------------------+----------------------+
| 3 Tesla K40m Off | 0000:84:00.0 Off | 0 |
| N/A 23C P8 22W / 235W | 0MiB / 11439MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
+-----------------------------------------------------------------------------+
Can I get the device mapping info in docker container?
ex.
host /dev/nvidia0-> container /dev/nvidia0
Can I believe docker inspect info?