On Jetson (Tegra) the integrated GPU is enumerated by NVML
(nvidia-l4t-nvml) and reports its name (e.g. "Orin (nvgpu)"), but the
per-metric NVML queries return NVML_ERROR_NOT_SUPPORTED, so the GPU
plugin only ever showed N/A for proc/mem/temperature.
Add a Tegra sysfs backend (glances/plugins/gpu/cards/tegra.py) and wire
it into the NVIDIA card as a per-metric fallback: when NVML returns None
for a device detected as Tegra (name contains "nvgpu", or the Tegra GPU
sysfs node exists), read:
- proc: /sys/devices/platform/gpu.0/load (per-mille -> percent)
- temperature: the gpu-thermal /sys/class/thermal zone (milli-C -> C)
Memory stays N/A by design: the Tegra GPU shares system RAM, already
reported by the MEM plugin. Scales verified against tegrastats
(GR3D_FREQ and gpu@).
Adds unit tests with committed sysfs fixtures, a NEWS.rst entry and a
docs note.
Verified on JetPack 6.2.1 (L4T R36.4.7) and JetPack 7.2 (L4T R39.2):
identical sysfs node layout and gpu-thermal zone selection on both, with
temperature cross-checked against tegrastats on each.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Design document for CVE-2026-46611 patch: add opt-in Host header
validation to the XML-RPC server via a new xmlrpc_allowed_hosts
config key, with permissive default and startup warning (mirrors
the REST/WebUI mitigation pattern).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
- Add [mpp] section to conf/glances.conf with disable=True
- Add docs/aoa/mpp.rst documentation page and index entry
- Add unit test test_026_mpp with Rockchip MPP test fixtures
- Remove unnecessary hasattr guard on get_refresh()
- Init _last_stats_computed_time to time.time() to avoid stale first reading
- Guard against ZeroDivisionError in IO/network rate calculations
- Use title-case status from pylxd directly
- Add trailing newline to containers.rst
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Introduce a generic Min/Max/Mean (mmm) mechanism at the plugin model level.
When a field in `fields_description` defines `'mmm': True`, the plugin
automatically generates and maintains the following derived fields:
<field>_min
<field>_max
<field>_mean
The computation is handled in the base plugin model to ensure the
feature is reusable across all plugins. Mean is calculated as a
running mean, and min/max are updated on each refresh cycle.
This implementation:
- Keeps the feature opt-in per field
- Avoids hardcoding logic in individual plugins
- Maintains full backward compatibility
- Preserves existing API v4 response structure
- Ensures no regression in existing behavior
Unit tests have been added to validate correct field generation
and update behavior.
