Monitor Intel GPUs using OpenTelemetry

Shield360 uses OpenTelemetry to help you monitor Intel GPUs. This includes tracking GPU metrics like temperature, power consumption, energy usage, and clock frequency via the Linux i915/Xe kernel driver.

Note

Intel GPU support requires Linux with the i915 or Xe kernel driver (kernel 5.10+). Metrics are read directly from the kernel’s sysfs/hwmon interface - no additional software or libraries are required. Utilization and memory metrics are not available via this interface; use Intel GPU Top or XPUManager for those.

Collected Metrics

Metric	Description	Requirement
hw.temperature	Die temperature	hwmon temp1_input
hw.power	Current power draw (W)	hwmon power1_average
hw.power.limit	Power cap (W)	hwmon power1_max
hw.energy	Cumulative energy (J)	hwmon energy1_input
hw.gpu.speed	Graphics clock (Hz; clock_domain=graphics)	DRM gt_cur_freq_mhz
hw.fan.speed	Fan speed (RPM)	hwmon fan1_input, kernel 6.16+

Get started

Using the SDK

Collect and send GPU performance metrics directly from your application to an OpenTelemetry endpoint.

Using the Collector

Install the OpenTelemetry GPU Collector as a Docker container to collect and send GPU performance metrics to an OpenTelemetry endpoint.

Using the SDK

Install Shield360

Open your command line or terminal and run:

pip install shield360

Initialize Shield360 in your Application

Python

Zero Code Instrumentation

Perfect for existing applications - no code modifications needed:

Via CLI Arguments

# Configure via CLI arguments
shield360-instrument \
  --service-name my-ai-app \
  --environment production \
  --otlp-endpoint YOUR_OTEL_ENDPOINT \
  python your_app.py

Via Environment Variables

# Configure via environment variables
export OTEL_SERVICE_NAME=my-ai-app
export OTEL_DEPLOYMENT_ENVIRONMENT=production
export OTEL_EXPORTER_OTLP_ENDPOINT=YOUR_OTEL_ENDPOINT

# Run with zero code changes
shield360-instrument python your_app.py

Note

Perfect for: Legacy applications, production systems where code changes need approval, quick testing, or when you want to add observability without touching existing code.

One-Line Instrumentation

Via Function Parameters

import shield360

shield360.init(otlp_endpoint="YOUR_OTEL_ENDPOINT")

Via Environment Variables

Add the following two lines to your application code:

import shield360

shield360.init()

Then, configure the your OTLP endpoint using environment variable:

export OTEL_EXPORTER_OTLP_ENDPOINT=YOUR_OTEL_ENDPOINT

Typescript

One-Line Instrumentation (SDK)

Via Function Parameters

import shield360 from "shield360"

shield360.init({ otlpEndpoint: "YOUR_OTEL_ENDPOINT" })

Via Environment Variables

Add the following two lines to your application code:

import shield360 from "shield360"

shield360.init()

Then, configure the your OTLP endpoint using environment variable:

export OTEL_EXPORTER_OTLP_ENDPOINT=YOUR_OTEL_ENDPOINT

Replace: YOUR_OTEL_ENDPOINT with the URL of your OpenTelemetry backend, such as http://127.0.0.1:4318 if you are using Shield360 and a local OTel Collector.

To send metrics and traces to other Observability tools, refer to the supported destinations.

For more advanced configurations and application use cases, visit the SDK configuration reference or TypeScript SDK reference.

Using the Collector

Pull `otel-gpu-collector` Docker Image

You can quickly start using the OTel GPU Collector by pulling the Docker image:

docker pull ghcr.io/thinkfleetai/otel-gpu-collector:latest

Run `otel-gpu-collector` Docker container

Here’s a quick example showing how to run the container with the required environment variables.

For Intel GPUs, pass the DRM device into the container using --device:

docker run \
    --device /dev/dri:/dev/dri \
    --pid=host \
    -e OTEL_SERVICE_NAME='my-app' \
    -e OTEL_RESOURCE_ATTRIBUTES='deployment.environment=staging' \
    -e OTEL_EXPORTER_OTLP_ENDPOINT="YOUR_OTEL_ENDPOINT" \
    -e OTEL_EXPORTER_OTLP_HEADERS="YOUR_OTEL_HEADERS" \
    ghcr.io/thinkfleetai/otel-gpu-collector:latest

--pid=host is required for per-process GPU attribution (cmdline, PID, zombie state).

For more advanced configurations of the collector, visit the GPU Collector documentation.

Note: If you’ve deployed Shield360 using Docker Compose (docker-compose.yml in the Shield360 distribution), make sure to use the host’s IP address or add OTel GPU Collector to the Docker Compose (docker-compose.yml in the Shield360 distribution):

Docker Compose: Add the following config under `services`

otel-gpu-collector:
  image: ghcr.io/thinkfleetai/otel-gpu-collector:latest
  pid: host
  environment:
    OTEL_SERVICE_NAME: 'my-app'
    OTEL_RESOURCE_ATTRIBUTES: 'deployment.environment=staging'
    OTEL_EXPORTER_OTLP_ENDPOINT: "http://otel-collector:4318"
  devices:
    - /dev/dri:/dev/dri
  depends_on:
    - otel-collector
  restart: always

Host IP: Use the Host IP to connect to OTel Collector

OTEL_EXPORTER_OTLP_ENDPOINT="http://192.168.10.15:4318"

Environment Variables

OTel GPU Collector uses standard OpenTelemetry environment variables for configuration:

Environment Variable	Description	Default Value
OTEL_SERVICE_NAME	Service/application name attached to all metrics	default
OTEL_RESOURCE_ATTRIBUTES	Resource attributes, e.g. deployment.environment=production	deployment.environment=default
OTEL_EXPORTER_OTLP_ENDPOINT	OpenTelemetry OTLP endpoint URL	(required)
OTEL_EXPORTER_OTLP_HEADERS	Headers for authenticating with the OTLP endpoint	Ignore if using Shield360
OTEL_METRIC_EXPORT_INTERVAL	Metric polling interval in milliseconds	10000

Collected Metrics

Details on the types of metrics collected and their descriptions.

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Quickstart: LLM Observability

Production-ready AI monitoring setup in 2 simple steps with zero code changes

Configuration

Configure the Shield360 SDK according to your requirements.

Destinations

Send telemetry to Datadog, Grafana, New Relic, and other observability stacks