GPU¶

The JupyterHub AMI includes NVIDIA drivers and multiple versions of CUDA to support the GPUs available on AWS and the different data science library, no extra configuration is needed.

Software	Version
`NVIDIA Drivers`	`495.29.05`
`CUDA`	`11.0`
`CUDA`	`11.1`
`CUDA`	`11.2`
`CUDA`	`11.3`
`CUDA`	`11.4`
`CUDA`	`11.5`

For example, after launching AMI in a g4dn.xlarge instance run nvidia-smi.

Terminal

$ nvidia-smi
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 495.29.05    Driver Version: 495.29.05    CUDA Version: 11.5     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  Tesla T4            On   | 00000000:00:1E.0 Off |                    0 |
| N/A   26C    P8     8W /  70W |      0MiB / 15109MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+