This repository guides you for deploying transformer models using NVIDIA's Triton Inference Server. It includes scripts for converting models and setting up a monitoring environment with Prometheus, Grafana, and Loki.
Triton Inference Server: Serves models efficiently.
Model Conversion: Scripts to convert Hugging Face models to ONNX, ready for Triton.
Observability: Built-in with Prometheus, Grafana, and Loki for monitoring.
Make sure Docker, Docker Compose, and Python are installed on your system.
Clone this repository and navigate to its directory. Start the setup with:
bash scripts/up.sh
This script will:
Convert a Hugging Face model to ONNX format. Set up the Triton server and monitoring tools using Docker Compose.
Testing the web server:
Run python demo/test-triton.py to test the model on the Triton server.
Monitoring: Access Grafana at http://localhost:3000 for dashboards and Prometheus at http://localhost:9090 for metrics. Log in with the default credentials (user, pwd both are admin) for grafana or ones you set to see the dashboards.
The system uses Docker Compose to run several services:
Triton Server: Hosts the AI model and handles requests for model predictions.
Loki: Collects and stores logs from the Triton server.
Promtail: Sends logs from the server to Loki.
Prometheus: Gathers and keeps track of performance metrics from the Triton server.
Grafana: Shows data from Prometheus and Loki in visual format for easy understanding and action.
Before you import the dashboard.json file into Grafana, you must configure the data sources for Prometheus and Loki.
Log into Grafana:
http://localhost:3000 the default admin password and username is used for the demo. they are both admin. Login to grafana and then you will be prompted to change password when you login.Add Data Source:
Configure Prometheus:
http://prometheus:9090.Configure Loki:
http://loki:3100.Go to Create > Import:
Import the Dashboard:
grafana/dashboard.json file or paste the JSON directly into the import window.Select Data Sources:
Save the Dashboard:

4 commits
Python
69.0%
Shell
31.0%
This repository guides you for deploying transformer models using NVIDIA's Triton Inference Server. It includes scripts for converting models and setting up a monitoring environment with Prometheus, Grafana, and Loki.
Triton Inference Server: Serves models efficiently.
Model Conversion: Scripts to convert Hugging Face models to ONNX, ready for Triton.
Observability: Built-in with Prometheus, Grafana, and Loki for monitoring.
Make sure Docker, Docker Compose, and Python are installed on your system.
Clone this repository and navigate to its directory. Start the setup with:
bash scripts/up.sh
This script will:
Convert a Hugging Face model to ONNX format. Set up the Triton server and monitoring tools using Docker Compose.
Testing the web server:
Run python demo/test-triton.py to test the model on the Triton server.
Monitoring: Access Grafana at http://localhost:3000 for dashboards and Prometheus at http://localhost:9090 for metrics. Log in with the default credentials (user, pwd both are admin) for grafana or ones you set to see the dashboards.
The system uses Docker Compose to run several services:
Triton Server: Hosts the AI model and handles requests for model predictions.
Loki: Collects and stores logs from the Triton server.
Promtail: Sends logs from the server to Loki.
Prometheus: Gathers and keeps track of performance metrics from the Triton server.
Grafana: Shows data from Prometheus and Loki in visual format for easy understanding and action.
Before you import the dashboard.json file into Grafana, you must configure the data sources for Prometheus and Loki.
Log into Grafana:
http://localhost:3000 the default admin password and username is used for the demo. they are both admin. Login to grafana and then you will be prompted to change password when you login.Add Data Source:
Configure Prometheus:
http://prometheus:9090.Configure Loki:
http://loki:3100.Go to Create > Import:
Import the Dashboard:
grafana/dashboard.json file or paste the JSON directly into the import window.Select Data Sources:
Save the Dashboard:

4 commits
Python
69.0%
Shell
31.0%