Deploying Models to Edge Devices

Package, optimize, and deploy ML models to edge hardware using TensorRT, TensorFlow Lite, ONNX Runtime, and containerized deployment strategies.

Model Optimization Pipeline

1. Export from Training Framework

   Export your trained model to an intermediate format: ONNX from PyTorch, SavedModel from TensorFlow, or Core ML from Apple frameworks.

2. Quantization

   Convert FP32 weights to INT8 or FP16. This reduces model size by 2-4x and inference time by 2-3x with minimal accuracy loss on most tasks.

3. Hardware-Specific Compilation

   Use TensorRT for NVIDIA Jetson, Edge TPU Compiler for Coral, or OpenVINO for Intel. These tools fuse layers, optimize memory layout, and generate hardware-specific instructions.

4. Benchmarking

   Measure inference latency, throughput, memory usage, and accuracy on the target device. Compare against your requirements before deploying.

TensorRT Optimization for Jetson

import tensorrt as trt
import torch

# Export PyTorch model to ONNX
model = torch.load("detector.pt")
dummy = torch.randn(1, 3, 640, 640).cuda()
torch.onnx.export(model, dummy, "detector.onnx",
    opset_version=17,
    dynamic_axes={'input': {0: 'batch'}})

# Build TensorRT engine with INT8 quantization
# trtexec --onnx=detector.onnx --saveEngine=detector.engine \
#   --int8 --workspace=4096 --best

Containerized Edge Deployment

Containers provide consistent deployment across diverse edge hardware. Use NVIDIA's L4T-based containers for Jetson or lightweight Alpine-based containers for CPU-only edge devices.

FROM nvcr.io/nvidia/l4t-tensorrt:r8.6.2-runtime

COPY detector.engine /models/
COPY inference_server.py /app/
COPY requirements.txt /app/

RUN pip3 install -r /app/requirements.txt
WORKDIR /app

CMD ["python3", "inference_server.py"]

Runtime Comparison