Running a Sample Workload#
Running a Sample Workload with Docker#
After you install and configure the toolkit and install an NVIDIA GPU Driver, you can verify your installation by running a sample workload.
Run a sample CUDA container:
sudo docker run --rm --runtime=nvidia --gpus all ubuntu nvidia-smiYour output should resemble the following output:
+-----------------------------------------------------------------------------+ | NVIDIA-SMI 535.86.10 Driver Version: 535.86.10 CUDA Version: 12.2 | |-------------------------------+----------------------+----------------------+ | 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 34C P8 9W / 70W | 0MiB / 15109MiB | 0% Default | | | | N/A | +-------------------------------+----------------------+----------------------+ +-----------------------------------------------------------------------------+ | Processes: | | GPU GI CI PID Type Process name GPU Memory | | ID ID Usage | |=============================================================================| | No running processes found | +-----------------------------------------------------------------------------+
Running a Sample Workload with Podman#
After you install and configure the toolkit (including generating a CDI specification) and install an NVIDIA GPU Driver, you can verify your installation by running a sample workload.
Run a sample CUDA container:
podman run --rm --security-opt=label=disable \ --device=nvidia.com/gpu=all \ ubuntu nvidia-smi
Your output should resemble the following output:
+-----------------------------------------------------------------------------+ | NVIDIA-SMI 535.86.10 Driver Version: 535.86.10 CUDA Version: 12.2 | |-------------------------------+----------------------+----------------------+ | 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 34C P8 9W / 70W | 0MiB / 15109MiB | 0% Default | | | | N/A | +-------------------------------+----------------------+----------------------+ +-----------------------------------------------------------------------------+ | Processes: | | GPU GI CI PID Type Process name GPU Memory | | ID ID Usage | |=============================================================================| | No running processes found | +-----------------------------------------------------------------------------+
Running Sample Workloads with containerd or CRI-O#
These runtimes are more common with Kubernetes than desktop computing. Refer to About the NVIDIA GPU Operator in the NVIDIA GPU Operator documentation for more information.