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Sample: System Information Query (Python)

python/1_GettingStarted/systemInfo/README.md

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Sample: System Information Query (Python)

Description

This sample demonstrates how to inspect the CUDA driver, NVML, and every installed GPU through the cuda.core.system module.

cuda.core.system wraps the NVIDIA Management Library (NVML) and can be imported without CUDA being installed or initialized, so it is useful as a lightweight pre-flight check before any CUDA context is created. The script prints driver and NVML versions, the current process name, per-device metadata (name, compute capability, architecture, memory, PCI info, temperature, performance state), and, on multi-GPU systems, the topology and peer-to-peer capabilities between each pair of devices.

What You'll Learn

  • Querying CUDA driver and NVML versions with cuda.core.system
  • Enumerating GPUs without creating a CUDA context
  • Reading per-device metadata exposed by NVML (name, UUID, memory usage, temperature, performance state)
  • Inspecting GPU-to-GPU topology and peer-to-peer (P2P) capabilities

Key Libraries

Key APIs

From cuda.core.system:

  • get_driver_version(), get_driver_version_full(), get_driver_branch() - CUDA driver version tuple and branch string
  • get_nvml_version() - NVML library version
  • get_num_devices() - number of GPUs visible to NVML
  • get_process_name(pid) - process name for a given PID
  • Device(index=...) - NVML-backed device handle (no CUDA context required)
    • name, uuid, cuda_compute_capability, arch, brand
    • memory_info (total, used, free)
    • pci_info (domain, bus, device, bus_id)
    • temperature.sensor(TemperatureSensors.TEMPERATURE_GPU)
    • performance_state
  • get_topology_common_ancestor(dev0, dev1) - GpuTopologyLevel between two devices
  • get_p2p_status(dev0, dev1, GpuP2PCapsIndex.P2P_CAPS_INDEX_READ) - peer-access capability between two devices

Import stable symbols from the top-level cuda.core package (not cuda.core.experimental).

Requirements

  1. NVIDIA Graphics Card with CUDA support
  2. CUDA Drivers installed on your system
  3. CUDA Toolkit installed on your system
  4. Python 3.12 or newer

Hardware

  • One or more NVIDIA GPUs
  • Driver compatible with cuda-python 13.x

Software

  • CUDA Toolkit 13.0 or newer (matches cuda-python 13.x)
  • Python 3.10 or newer
  • cuda-python (>=13.0.0)
  • cuda-core (>=1.0.0)

Installation

Install the required packages from requirements.txt:

bash
cd /path/to/cuda-samples/python/1_GettingStarted/systemInfo
pip install -r requirements.txt

The requirements.txt installs:

  • cuda-python (>=13.0.0)
  • cuda-core (>=1.0.0)

How to Run

Basic usage

bash
cd cuda-samples/python/1_GettingStarted/systemInfo
python systemInfo.py

Skip topology queries

Useful on machines with only one GPU or to shorten the output:

bash
python systemInfo.py --no-topology

Expected Output

Output varies with your hardware. On a machine with two GPUs you should see something like:

======================================================================
Driver / NVML
======================================================================
CUDA driver version: 13.2
CUDA driver version (full): (13, 2, 0)
NVML version: (13, 595, 58, 3)
Driver branch: r595_88
Current process: /usr/bin/python

======================================================================
Devices detected: 2
======================================================================

-- Device 0 --
Name: <Your GPU Name>
UUID: ...
Compute capability: 8.9
Architecture: ADA
Brand: BRAND_GEFORCE
Memory: total=23.99 GiB, used=960.00 KiB, free=23.52 GiB
PCI: domain=0000 bus=41 device=00 id=00000000:41:00.0
Temperature (GPU sensor): 47 C
Performance state: <Pstates.PSTATE_8: 8>

...

======================================================================
GPU topology and peer-to-peer
======================================================================
Device 0 <-> Device 1: topology=TOPOLOGY_HOSTBRIDGE, p2p_read=..., p2p_write=...

Done

Note: Device names, compute capability, temperatures, and topology details will vary based on your GPUs and system.

Files

  • systemInfo.py - Python implementation using cuda.core.system
  • README.md - This file
  • requirements.txt - Sample dependencies

See Also