GPU Performance Monitoring
Simple GPU monitoring setup for AI workloads. Track NVIDIA and AMD GPU usage, temperature, and costs with zero code changes using OpenTelemetry.
Get started
OpenLIT SDK
Collect and send GPU performance metrics directly from your application to an OpenTelemetry endpoint.
OpenTelemetry GPU Collector
Install the OpenTelemetry GPU Collector as a Docker container to collect and send GPU performance metrics to an OpenTelemetry endpoint.
Using the SDK
Using the SDK
1
Deploy OpenLIT
1
Git clone OpenLIT repository
2
Start Docker Compose
From the root directory of the OpenLIT Repo, Run the below command:
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Install OpenLIT
Open your command line or terminal and run:
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Initialize OpenLIT in your Application
Not sure which method to choose? Check out Instrumentation Methods to understand the differences.
Using the Collector
Using the Collector
1
Deploy OpenLIT
1
Git clone OpenLIT repository
2
Start Docker Compose
From the root directory of the OpenLIT Repo, Run the below command:
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Pull `otel-gpu-collector` Docker Image
You can quickly start using the OTel GPU Collector by pulling the Docker image:
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Run `otel-gpu-collector` Docker container
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: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:
Docker Compose: Add the following config under `services`
Docker Compose: Add the following config under `services`
Host IP: Use the Host IP to connect to OTel Collector
Host IP: Use the Host IP to connect to OTel Collector
Configuration
Configure the OpenLIT SDK according to you requirements.
Integrations
60+ AI integrations with automatic instrumentation and performance tracking
Destinations
Send elemetry to Datadog, Grafana, New Relic, and other observability stacks
Running in Kubernetes? Try the OpenLIT Operator
Automatically inject instrumentation into existing workloads without modifying pod specs, container images, or application code.