Edge AI/ML

Model Optimization

TensorFlow Lite Deployment

apiVersion: apps/v1
kind: Deployment
metadata:
  name: edge-inference
spec:
  replicas: 1
  selector:
    matchLabels:
      app: edge-ml
  template:
    spec:
      containers:
      - name: inference
        image: tensorflow/serving:latest
        resources:
          limits:
            cpu: "2"
            memory: "4Gi"
            nvidia.com/gpu: "1"
        volumeMounts:
        - name: model-store
          mountPath: /models
        env:
        - name: MODEL_NAME
          value: edge_model
        - name: MODEL_BASE_PATH
          value: /models

ONNX Runtime Optimization

Edge Configuration

apiVersion: v1
kind: ConfigMap
metadata:
  name: onnx-config
data:
  config.json: |
    {
      "optimization_level": "all",
      "graph_optimization_level": "ORT_ENABLE_ALL",
      "inter_op_num_threads": 4,
      "intra_op_num_threads": 4,
      "execution_mode": "sequential",
      "memory": {
        "enable_memory_arena": true,
        "arena_extend_strategy": "kNextPowerOfTwo"
      }
    }

Model Serving

Triton Inference Server

apiVersion: serving.kubeflow.org/v1beta1
kind: InferenceService
metadata:
  name: edge-model-server
spec:
  predictor:
    minReplicas: 1
    maxReplicas: 3
    containers:
    - name: triton
      image: nvcr.io/nvidia/tritonserver:24.02-py3
      args:
        - --model-repository=/models
        - --strict-model-config=false
      resources:
        limits:
          cpu: "4"
          memory: "8Gi"
          nvidia.com/gpu: "1"
      volumeMounts:
        - mountPath: /models
          name: model-store

Best Practices

1. Model Optimization

   • Quantization

   • Pruning

   • Layer fusion

   • Kernel optimization

2. Resource Management

   • GPU sharing

   • Memory efficiency

   • Power optimization

   • Thermal management

3. Monitoring

   • Inference latency

   • Model accuracy

   • Resource usage

   • Health metrics

4. Deployment Strategy

   • Rolling updates

   • A/B testing

   • Model versioning

   • Fallback handling