NVIDIA Triton Inference Server

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High-performance inference server for ML models.

Overview

NVIDIA Triton Inference Server is a high-performance, open-source inference serving platform developed by NVIDIA for deploying machine learning models at scale in production environments. Originally built to handle NVIDIA's internal ML workloads, Triton supports multiple deep learning frameworks including TensorRT, ONNX Runtime, TensorFlow, PyTorch, and custom backends, making it framework-agnostic while optimizing performance across different model types. The server provides concurrent model execution, dynamic batching, and model ensemble capabilities that maximize GPU utilization and throughput. This Triton deployment configuration leverages GPU acceleration through Docker's NVIDIA runtime integration, exposing three distinct service endpoints for different use cases. The HTTP REST API on port 8000 handles standard web-based inference requests, while the gRPC interface on port 8001 provides low-latency, high-throughput communication for performance-critical applications. Port 8002 serves Prometheus metrics for comprehensive monitoring and observability of inference performance, model statistics, and resource utilization. ML engineers, AI platform teams, and organizations deploying production inference workloads will benefit from this configuration's ability to serve multiple models simultaneously with automatic scaling and load balancing. The model repository structure supports versioning and A/B testing scenarios, while the multi-protocol support enables integration with diverse client applications ranging from web services to real-time streaming applications requiring microsecond-level response times.

Key Features

Multi-framework model serving supporting TensorRT, ONNX, TensorFlow, PyTorch, and custom backends
Dynamic request batching that automatically groups individual requests to maximize GPU throughput
Model ensemble capabilities allowing complex inference pipelines with preprocessing and postprocessing
Concurrent model execution enabling multiple models to run simultaneously on shared GPU resources
Model versioning and A/B testing support through structured repository organization
Built-in performance optimization including CUDA kernel fusion and memory pool management
Real-time inference metrics and health monitoring through Prometheus integration
Multi-protocol support with HTTP REST, gRPC, and C API for diverse client integration patterns

Common Use Cases

1Computer vision applications requiring real-time object detection, classification, and segmentation
2Natural language processing services for text classification, sentiment analysis, and language translation
3Recommendation engines serving personalized content with sub-100ms response times
4Edge AI deployments where multiple models need to share limited GPU resources efficiently
5MLOps pipelines requiring model versioning, canary deployments, and performance monitoring
6High-frequency trading systems using ML models for market prediction and risk assessment
7Autonomous vehicle inference stacks processing multiple sensor inputs through different neural networks

Prerequisites

NVIDIA GPU with CUDA Compute Capability 6.0 or higher and minimum 4GB VRAM
NVIDIA Docker runtime (nvidia-docker2) installed and configured on the host system
Docker Compose version 3.8+ with GPU device support enabled
Minimum 8GB system RAM for model loading and request queuing buffers
Pre-trained models in supported formats (TensorRT .plan, ONNX .onnx, TensorFlow SavedModel, PyTorch .pt)
Understanding of model repository structure and Triton configuration files (config.pbtxt)

For development & testing. Review security settings, change default credentials, and test thoroughly before production use. See Terms

docker-compose.yml

1services: 
2  triton: 
3    image: nvcr.io/nvidia/tritonserver:23.10-py3
4    container_name: triton
5    restart: unless-stopped
6    command: tritonserver --model-repository=/models
7    volumes: 
8      - ./models:/models
9    ports: 
10      - "8000:8000"
11      - "8001:8001"
12      - "8002:8002"
13    deploy: 
14      resources: 
15        reservations: 
16          devices: 
17            - driver: nvidia
18              count: all
19              capabilities: [gpu]

.env Template

.env

1# Place models in ./models directory

Usage Notes

1Docs: https://docs.nvidia.com/deeplearning/triton-inference-server/
2HTTP REST API at http://localhost:8000/v2/models/{model}/infer
3gRPC endpoint at localhost:8001 for high-performance inference
4Prometheus metrics at http://localhost:8002/metrics
5Model repository structure: models/{model_name}/{version}/model.plan
6Supports TensorRT, ONNX, TensorFlow, PyTorch backends

Quick Start

terminal

1# 1. Create the compose file
2cat > docker-compose.yml << 'EOF'
3services:
4  triton:
5    image: nvcr.io/nvidia/tritonserver:23.10-py3
6    container_name: triton
7    restart: unless-stopped
8    command: tritonserver --model-repository=/models
9    volumes:
10      - ./models:/models
11    ports:
12      - "8000:8000"
13      - "8001:8001"
14      - "8002:8002"
15    deploy:
16      resources:
17        reservations:
18          devices:
19            - driver: nvidia
20              count: all
21              capabilities: [gpu]
22EOF
23
24# 2. Create the .env file
25cat > .env << 'EOF'
26# Place models in ./models directory
27EOF
28
29# 3. Start the services
30docker compose up -d
31
32# 4. View logs
33docker compose logs -f

One-Liner

Run this command to download and set up the recipe in one step:

terminal

1curl -fsSL https://docker.recipes/api/recipes/nvidia-triton/run | bash

Troubleshooting

Error 'nvidia-smi has failed because it couldn't communicate with the NVIDIA driver': Install NVIDIA drivers and verify nvidia-docker2 runtime configuration
Model fails to load with 'Invalid argument: model configuration': Create proper config.pbtxt file with correct input/output tensor specifications for your model
Out of memory errors during inference: Reduce max_batch_size in model configuration or implement request queuing with smaller batch sizes
gRPC client connection refused on port 8001: Verify firewall settings allow gRPC traffic and client is using correct protobuf definitions
Prometheus metrics showing high queue times: Enable dynamic batching or increase instance_group count in model configuration
TensorRT models fail to load with 'engine incompatible': Rebuild TensorRT engines on target GPU architecture or use ONNX format for portability

Community Notes

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