edge-deployment

name	edge-deployment
description	Edge deployment strategies including mobile optimization, embedded systems, TFLite, Core ML, and resource-constrained inference.

Edge Deployment

Deploying ML models to edge devices.

Edge Deployment Landscape

┌─────────────────────────────────────────────────────────────┐
│                   EDGE DEPLOYMENT TARGETS                    │
├─────────────────────────────────────────────────────────────┤
│                                                              │
│  MOBILE              EMBEDDED           IOT/SENSORS         │
│  ──────              ────────           ───────────         │
│  iOS (Core ML)       Raspberry Pi       Arduino/ESP32       │
│  Android (TFLite)    NVIDIA Jetson      Microcontrollers    │
│  React Native        Google Coral       FPGA boards         │
│                                                              │
│  CONSTRAINTS:                                                │
│  ├── Memory: 256MB - 8GB                                    │
│  ├── Compute: CPU/GPU/NPU                                   │
│  ├── Power: Battery/USB/Wall                                │
│  ├── Connectivity: Always/Sometimes/Never                   │
│  └── Latency: 1ms - 100ms                                   │
│                                                              │
└─────────────────────────────────────────────────────────────┘

TensorFlow Lite

Model Conversion

import tensorflow as tf

# Basic conversion
converter = tf.lite.TFLiteConverter.from_saved_model('saved_model/')
tflite_model = converter.convert()

# With optimizations
converter.optimizations = [tf.lite.Optimize.DEFAULT]

# Float16 quantization
converter.target_spec.supported_types = [tf.float16]

# Full integer quantization
def representative_dataset():
    for data in calibration_data:
        yield [data.astype(np.float32)]

converter.representative_dataset = representative_dataset
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
converter.inference_input_type = tf.int8
converter.inference_output_type = tf.int8

# Save model
with open('model.tflite', 'wb') as f:
    f.write(converter.convert())

TFLite Inference

import numpy as np
import tensorflow as tf

# Load model
interpreter = tf.lite.Interpreter(model_path='model.tflite')
interpreter.allocate_tensors()

# Get input/output details
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

# Inference
def predict(input_data):
    interpreter.set_tensor(input_details[0]['index'], input_data)
    interpreter.invoke()
    return interpreter.get_tensor(output_details[0]['index'])

# With delegate (GPU acceleration)
delegate = tf.lite.experimental.load_delegate('libedgetpu.so.1')
interpreter = tf.lite.Interpreter(
    model_path='model_edgetpu.tflite',
    experimental_delegates=[delegate]
)

Core ML (iOS)

PyTorch to Core ML

import coremltools as ct
import torch

# Export to Core ML
model.eval()
example_input = torch.rand(1, 3, 224, 224)
traced_model = torch.jit.trace(model, example_input)

mlmodel = ct.convert(
    traced_model,
    inputs=[ct.TensorType(shape=example_input.shape, name="image")],
    outputs=[ct.TensorType(name="predictions")],
    compute_precision=ct.precision.FLOAT16,
    minimum_deployment_target=ct.target.iOS15
)

# Add metadata
mlmodel.author = "Your Name"
mlmodel.short_description = "Image classifier"
mlmodel.input_description["image"] = "Input image"
mlmodel.output_description["predictions"] = "Class probabilities"

mlmodel.save("Model.mlpackage")

Swift Integration

import CoreML
import Vision

class ModelInference {
    let model: VNCoreMLModel

    init() throws {
        let config = MLModelConfiguration()
        config.computeUnits = .all  // Use Neural Engine
        let mlModel = try MyModel(configuration: config)
        self.model = try VNCoreMLModel(for: mlModel.model)
    }

    func predict(image: CGImage, completion: @escaping ([String: Double]) -> Void) {
        let request = VNCoreMLRequest(model: model) { request, error in
            guard let results = request.results as? [VNClassificationObservation] else { return }
            let predictions = Dictionary(
                uniqueKeysWithValues: results.prefix(5).map { ($0.identifier, Double($0.confidence)) }
            )
            completion(predictions)
        }

        let handler = VNImageRequestHandler(cgImage: image)
         handler.perform([request])
    }
}

NVIDIA Jetson

TensorRT Optimization

import tensorrt as trt

def build_engine(onnx_path, engine_path, precision='fp16'):
    logger = trt.Logger(trt.Logger.INFO)
    builder = trt.Builder(logger)
    network = builder.create_network(
        1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
    )
    parser = trt.OnnxParser(network, logger)

    # Parse ONNX
    with open(onnx_path, 'rb') as f:
        parser.parse(f.read())

    # Build config
    config = builder.create_builder_config()
    config.max_workspace_size = 1 << 30  # 1GB

    if precision == 'fp16':
        config.set_flag(trt.BuilderFlag.FP16)
    elif precision == 'int8':
        config.set_flag(trt.BuilderFlag.INT8)
        config.int8_calibrator = EntropyCalibrator(calibration_data)

    # Build engine
    engine = builder.build_engine(network, config)

    with open(engine_path, 'wb') as f:
        f.write(engine.serialize())

    return engine

# Inference with TensorRT
class TRTInference:
    def __init__(self, engine_path):
        logger = trt.Logger(trt.Logger.WARNING)
        with open(engine_path, 'rb') as f:
            self.engine = trt.Runtime(logger).deserialize_cuda_engine(f.read())
        self.context = self.engine.create_execution_context()

     ():
        
        

DeepStream Pipeline

# DeepStream config for video inference
# config_infer_primary.txt
"""
[property]
gpu-id=0
net-scale-factor=0.0039215697906911373
model-file=resnet18.onnx
model-engine-file=resnet18.engine
labelfile-path=labels.txt
batch-size=4
network-mode=2  # FP16
num-detected-classes=80
interval=0
gie-unique-id=1
process-mode=1
network-type=0
cluster-mode=2
maintain-aspect-ratio=1
symmetric-padding=1
"""

# Python pipeline
import gi
gi.require_version('Gst', '1.0')
from gi.repository import Gst

Gst.init(None)
pipeline = Gst.parse_launch("""
    filesrc location=video.mp4 !
    decodebin !
    nvvideoconvert !
    nvinfer config-file-path=config.txt !
    nvdsosd !
    nvegltransform !
    nveglglessink
""")
pipeline.set_state(Gst.State.PLAYING)

Microcontroller Deployment

TensorFlow Lite Micro

#include "tensorflow/lite/micro/all_ops_resolver.h"
#include "tensorflow/lite/micro/micro_interpreter.h"
#include "model_data.h"

// Allocate tensor arena
constexpr int kTensorArenaSize = 10 * 1024;
uint8_t tensor_arena[kTensorArenaSize];

void setup() {
    // Set up model
    const tflite::Model* model = tflite::GetModel(model_data);

    // Set up resolver
    tflite::AllOpsResolver resolver;

    // Build interpreter
    tflite::MicroInterpreter interpreter(
        model, resolver, tensor_arena, kTensorArenaSize
    );
    interpreter.AllocateTensors();

    // Get input tensor
    TfLiteTensor* input = interpreter.input(0);

    // Fill input and invoke
    // input->data.f[0] = sensor_value;
    interpreter.Invoke();

    // Get output
    TfLiteTensor* output = interpreter.output(0);
    float prediction = output->data.f[0];
}

Model Optimization for Edge

def optimize_for_edge(model, target_size_mb=10, target_latency_ms=50):
    """Optimize model for edge deployment."""
    optimizations = []

    # 1. Quantization
    quantized = quantize_dynamic(model, {nn.Linear, nn.Conv2d}, dtype=torch.qint8)
    if get_size_mb(quantized) <= target_size_mb:
        optimizations.append(('quantization', quantized))

    # 2. Pruning
    pruned = prune_model(model, amount=0.5)
    if get_size_mb(pruned) <= target_size_mb:
        optimizations.append(('pruning', pruned))

    # 3. Knowledge distillation
    student = create_smaller_model(model)
    distilled = distill(teacher=model, student=student)
    if get_size_mb(distilled) <= target_size_mb:
        optimizations.append(('distillation', distilled))

    # Evaluate each
    results = []
    for name, opt_model in optimizations:
        latency = measure_latency(opt_model)
        accuracy = evaluate(opt_model)
        if latency <= target_latency_ms:
            results.append({
                'method': name,
                'latency': latency,
                'accuracy': accuracy,
                'size_mb': get_size_mb(opt_model)
            })

    return sorted(results, key=lambda x: x['accuracy'], reverse=True)

Offline Inference

class OfflineInferenceManager:
    def __init__(self, model_path, cache_dir='./cache'):
        self.model = load_model(model_path)
        self.cache_dir = cache_dir
        self.pending_queue = []

    def predict(self, input_data, priority='normal'):
        """Run inference locally."""
        return self.model(input_data)

    def predict_with_fallback(self, input_data, cloud_endpoint=None):
        """Try cloud first, fall back to local."""
        try:
            if self._is_online() and cloud_endpoint:
                return self._cloud_predict(input_data, cloud_endpoint)
        except Exception:
            pass

        return self.predict(input_data)

    def queue_for_sync(self, input_data, result):
        """Queue predictions for later sync."""
        self.pending_queue.append({
            'input': input_data,
            'result': result,
            'timestamp': time.time()
        })

    def sync_when_online(self, endpoint):
        """Sync queued predictions when connectivity restored."""
         .pending_queue  ._is_online():
            item = .pending_queue.pop()
            requests.post(endpoint, json=item)

Commands

• /omgdeploy:edge - Edge deployment
• /omgoptim:quantize - Quantization
• /omgdeploy:package - Package for target

Best Practices

1. Profile on target device early
2. Use hardware-specific frameworks
3. Quantize to int8 when possible
4. Implement offline fallbacks
5. Monitor battery and thermal impact