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aeab82ff30
- Added Python samples for CUDA Python 1.0 release - Renamed top-level `Samples` directory to `cpp` to accommodate Python samples.
153 lines
4.6 KiB
Markdown
153 lines
4.6 KiB
Markdown
# Sample: System Information Query (Python)
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## Description
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This sample demonstrates how to inspect the CUDA driver, NVML, and every
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installed GPU through the
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[`cuda.core.system`](https://nvidia.github.io/cuda-python/cuda-core/latest/)
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module.
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`cuda.core.system` wraps the NVIDIA Management Library (NVML) and can be
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imported without CUDA being installed or initialized, so it is useful as a
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lightweight pre-flight check before any CUDA context is created. The script
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prints driver and NVML versions, the current process name, per-device
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metadata (name, compute capability, architecture, memory, PCI info,
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temperature, performance state), and, on multi-GPU systems, the topology
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and peer-to-peer capabilities between each pair of devices.
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## What You'll Learn
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- Querying CUDA driver and NVML versions with `cuda.core.system`
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- Enumerating GPUs without creating a CUDA context
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- Reading per-device metadata exposed by NVML (name, UUID, memory usage,
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temperature, performance state)
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- Inspecting GPU-to-GPU topology and peer-to-peer (P2P) capabilities
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## Key Libraries
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- [`cuda.core.system`](https://nvidia.github.io/cuda-python/cuda-core/latest/) - Python wrapper around NVML
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## Key APIs
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From `cuda.core.system`:
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- `get_driver_version()`, `get_driver_version_full()`, `get_driver_branch()` - CUDA driver version tuple and branch string
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- `get_nvml_version()` - NVML library version
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- `get_num_devices()` - number of GPUs visible to NVML
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- `get_process_name(pid)` - process name for a given PID
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- `Device(index=...)` - NVML-backed device handle (no CUDA context required)
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- `name`, `uuid`, `cuda_compute_capability`, `arch`, `brand`
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- `memory_info` (`total`, `used`, `free`)
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- `pci_info` (`domain`, `bus`, `device`, `bus_id`)
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- `temperature.sensor(TemperatureSensors.TEMPERATURE_GPU)`
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- `performance_state`
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- `get_topology_common_ancestor(dev0, dev1)` - `GpuTopologyLevel` between two devices
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- `get_p2p_status(dev0, dev1, GpuP2PCapsIndex.P2P_CAPS_INDEX_READ)` - peer-access capability between two devices
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Import stable symbols from the top-level `cuda.core` package (not `cuda.core.experimental`).
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## Requirements
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1. **NVIDIA Graphics Card** with CUDA support
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2. **CUDA Drivers** installed on your system
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3. **CUDA Toolkit** installed on your system
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4. **Python 3.12 or newer**
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### Hardware
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- One or more NVIDIA GPUs
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- Driver compatible with `cuda-python` 13.x
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### Software
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- CUDA Toolkit 13.0 or newer (matches `cuda-python` 13.x)
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- Python 3.10 or newer
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- `cuda-python` (>=13.0.0)
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- `cuda-core` (>=0.6.0)
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## Installation
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Install the required packages from `requirements.txt`:
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```bash
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cd /path/to/cuda-samples/python/1_GettingStarted/systemInfo
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pip install -r requirements.txt
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```
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The `requirements.txt` installs:
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- `cuda-python` (>=13.0.0)
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- `cuda-core` (>=0.6.0)
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## How to Run
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### Basic usage
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```bash
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cd cuda-samples/python/1_GettingStarted/systemInfo
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python systemInfo.py
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```
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### Skip topology queries
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Useful on machines with only one GPU or to shorten the output:
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```bash
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python systemInfo.py --no-topology
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```
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## Expected Output
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Output varies with your hardware. On a machine with two GPUs you should see
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something like:
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```
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======================================================================
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Driver / NVML
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======================================================================
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CUDA driver version: 13.2
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CUDA driver version (full): (13, 2, 0)
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NVML version: (13, 595, 58, 3)
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Driver branch: r595_88
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Current process: /usr/bin/python
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======================================================================
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Devices detected: 2
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======================================================================
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-- Device 0 --
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Name: <Your GPU Name>
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UUID: ...
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Compute capability: 8.9
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Architecture: ADA
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Brand: BRAND_GEFORCE
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Memory: total=23.99 GiB, used=960.00 KiB, free=23.52 GiB
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PCI: domain=0000 bus=41 device=00 id=00000000:41:00.0
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Temperature (GPU sensor): 47 C
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Performance state: <Pstates.PSTATE_8: 8>
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...
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======================================================================
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GPU topology and peer-to-peer
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======================================================================
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Device 0 <-> Device 1: topology=TOPOLOGY_HOSTBRIDGE, p2p_read=..., p2p_write=...
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Done
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```
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**Note:** Device names, compute capability, temperatures, and topology
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details will vary based on your GPUs and system.
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## Files
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- `systemInfo.py` - Python implementation using `cuda.core.system`
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- `README.md` - This file
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- `requirements.txt` - Sample dependencies
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## See Also
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- [CUDA Python Documentation](https://nvidia.github.io/cuda-python/)
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- [`cuda.core.system` API reference](https://nvidia.github.io/cuda-python/cuda-core/latest/api.html)
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- [NVML reference](https://docs.nvidia.com/deploy/nvml-api/)
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