Monitor NVIDIA GPUs using OpenTelemetry

Open your command line or terminal and run:

pip install openlit

You can set up OpenLIT in your application using either function arguments directly in your code or by using environment variables.

• Setup using function arguments
• Setup using Environment Variables

import openlit

openlit.init(
  otlp_endpoint="YOUR_OTEL_ENDPOINT", otlp_headers="YOUR_OTEL_HEADERS"
  collect_gpu_stats=True 
)

To send metrics to other Observability tools, refer to the Connections Guide.

For more advanced configurations and application use cases, visit the OpenLIT Python repository.

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

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

You can quickly start using the OTel GPU Collector by pulling the Docker image: Here’s a quick example showing how to run the container with the required environment variables:

docker run --gpus all \
    -e GPU_APPLICATION_NAME='chatbot' \
    -e GPU_ENVIRONMENT='staging' \
    -e OTEL_EXPORTER_OTLP_ENDPOINT="YOUR_OTEL_ENDPOINT" \
    -e OTEL_EXPORTER_OTLP_HEADERS="YOUR_OTEL_HEADERS" \
    ghcr.io/openlit/otel-gpu-collector:latest

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

Note: If you’ve deployed OpenLIT using Docker Compose, make sure to use the host’s IP address or add OTel GPU Collector to the Docker Compose:

otel-gpu-collector:
  image: ghcr.io/openlit/otel-gpu-collector:latest
  environment:
    GPU_APPLICATION_NAME: 'chatbot'
    GPU_ENVIRONMENT: 'staging'
    OTEL_EXPORTER_OTLP_ENDPOINT: "http://otel-collector:4318"
  device_requests:
  - driver: nvidia
    count: all
    capabilities: [gpu]
  depends_on:
  - otel-collector
  restart: always

OTEL_EXPORTER_OTLP_ENDPOINT="http://192.168.10.15:4318"

Environment Variables

OTel GPU Collector supports several environment variables for configuration. Below is a table that describes each variable: