GPU¶
The JupyterHub AMI includes NVIDIA drivers and multiple versions of CUDA to support the GPUs available on AWS and the different data science libraries, no extra configuration is needed.
| Software | Version |
|---|---|
NVIDIA Drivers |
495.29.05 |
For example, after launching AMI in a g4dn.xlarge instance:
Terminal
$ nvidia-smi
ubuntu@ip-172-31-27-202:~$ nvidia-smi
Tue May 16 21:58:15 2023
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 530.30.02 Driver Version: 530.30.02 CUDA Version: 12.1 |
|-----------------------------------------+----------------------+----------------------+
| 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 44C P0 27W / 70W| 2248MiB / 15360MiB | 0% Default |
| | | N/A |
+-----------------------------------------+----------------------+----------------------+
+---------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=======================================================================================|
| 0 N/A N/A 3003 C /opt/python/3.10-rapids/bin/python 2144MiB |
| 0 N/A N/A 3058 C /opt/python/3.10/bin/python 100MiB |
+---------------------------------------------------------------------------------------+
Cuda¶
The different versions of Cuda to support Tensorflow and PyTorch are installed as conda packages and are available on the respective Python installations.