Deploy with TensorRT¶

Technical Resources¶

Quick Reference¶

One-sentence definition: TensorRT is NVIDIA's high-performance deep learning inference library, optimized for deploying AI models on GPUs in edge devices.
Key use cases: Real-time object detection, natural language processing, and computer vision tasks in constrained environments.
Prerequisites: Familiarity with Python, deep learning basics, and basic understanding of NVIDIA GPUs.

Introduction¶

What¶

TensorRT is a deep learning inference library from NVIDIA that supports model optimization and acceleration for deployment on edge devices powered by NVIDIA GPUs.

Why¶

Efficient inference is crucial for real-time AI applications on edge devices. TensorRT achieves this by reducing model size, optimizing runtime performance, and leveraging GPU capabilities.

Where¶

Applications include:
- Healthcare: Image segmentation on portable medical devices.
- Autonomous Systems: Object detection in self-driving cars.
- Retail: Real-time inventory tracking with edge cameras.

Core Concepts¶

What is TensorRT?¶

TensorRT is a framework for optimizing, quantizing, and running deep learning models with high efficiency on NVIDIA hardware.

How TensorRT Works¶

Model Parsing: Load and parse models from frameworks like TensorFlow or PyTorch.
Optimization: Apply techniques like layer fusion, precision calibration, and pruning.
Deployment: Use the optimized model for low-latency, high-throughput inference.

Benefits for Edge AI Deployment¶

High Performance: Accelerated inference on GPUs.
Model Optimization: Reduces memory and compute requirements.
Precision Support: INT8, FP16, and FP32 modes for balancing performance and accuracy.

Visual Architecture¶

Overview of TensorRT Workflow¶

graph TD
A[Pre-trained Model] -->|Export| B[ONNX Format]
B -->|Optimize| C[TensorRT Engine]
C -->|Deploy| D[Edge Device with NVIDIA GPU]

Implementation Details¶

Basic Deployment Example¶

Step 1: Convert Pre-trained Model to ONNX¶

Export a PyTorch model to the ONNX format:

import torch
import onnx

# Load PyTorch model
model = torch.load("model.pth")
model.eval()

# Export to ONNX
dummy_input = torch.randn(1, 3, 224, 224)
onnx.export(model, dummy_input, "model.onnx")

Step 2: Optimize with TensorRT¶

Convert the ONNX model into a TensorRT engine:

import tensorrt as trt

# TensorRT Logger
logger = trt.Logger(trt.Logger.WARNING)

# Load ONNX model
builder = trt.Builder(logger)
network = builder.create_network()
parser = trt.OnnxParser(network, logger)

with open("model.onnx", "rb") as f:
    parser.parse(f.read())

# Build optimized TensorRT engine
config = builder.create_builder_config()
config.max_workspace_size = 1 << 30  # 1GB
engine = builder.build_engine(network, config)

# Save engine
with open("model.trt", "wb") as f:
    f.write(engine.serialize())

Step 3: Deploy on Edge Device¶

Run inference using the TensorRT engine:

import pycuda.driver as cuda
import pycuda.autoinit
import tensorrt as trt

# Load TensorRT engine
runtime = trt.Runtime(trt.Logger(trt.Logger.WARNING))
with open("model.trt", "rb") as f:
    engine = runtime.deserialize_cuda_engine(f.read())

# Allocate buffers and run inference
# (Buffer allocation and inference code here...)