Optimizing ML Inference Costs on Amazon EKS with AWS Neuron: A Complete Implementation Guide

Introduction

Running machine learning inference workloads on Amazon EKS can be expensive when using standard instances. Recently, we helped an ML team reduce their inference costs by 80% while improving performance by migrating to AWS Inferentia2 chips.

This guide details our step-by-step implementation process.

Initial Architecture Assessment

Before Optimization

• 100 inference pods running on standard EC2 instances

• Infrastructure cost: $25,000/month

• Average inference latency: 200ms

• Using standard EKS deployment without ML-specific optimizations

  
  

Step 1: Setting Up AWS Neuron Environment

Install Neuron Tools

# Install Neuron CLI tools 
sudo apt-get install aws-neuronx-tools 
# Install Neuron container runtime 
sudo apt-get install aws-neuronx-runtime-lib

Configure Docker for Neuron

FROM public.ecr.aws/neuron/neuron-rtd:latest
COPY requirements.txt .
RUN pip3 install -r requirements.txt
RUN pip3 install torch-neuron neuron-cc
COPY model/ /opt/ml/model/
COPY inference.py .

Step 2: Model Optimization for Inferentia2

Convert Model to Neuron Format

#python
import torch 
import torch_neuron 

# Load your PyTorch model 
model = YourModelClass() model.load_state_dict(torch.load('model.pth')) 

# Convert to Neuron format 
model_neuron = torch.neuron.trace(
    model,
    example_inputs=[torch.zeros([1, 3, 224, 224])],
    compiler_args=['--model-type=pytorch']
)

# Save the compiled model 
model_neuron.save("model_neuron.pt")

Step 3: EKS Cluster Configuration

Create EKS Cluster with Inf2 Support

eksctl create cluster \
    --name ml-inference-cluster \
    --node-type inf2.xlarge \
    --nodes 3 \
    --nodes-min 1 \
    --nodes-max 5 \
    --region us-west-2

Install EKS Device Plugin

# neuron-device-plugin.yaml 
apiVersion: apps/v1 
kind: DaemonSet 
metadata:   
  name: neuron-device-plugin   
  namespace: kube-system 
spec:   
  selector:     
    matchLabels:       
      name: neuron-device-plugin   
    template:     
      metadata:       
        labels:         
          name: neuron-device-plugin     
      spec:       
        containers:       
        - name: neuron-device-plugin
          image: public.ecr.aws/neuron/neuron-device-plugin:latest           securityContext:           
            allowPrivilegeEscalation: false           
            capabilities:             
              drop: ["ALL"]

Step 4: Deployment Configuration

Create Inference Deployment

apiVersion: apps/v1
kind: Deployment
metadata:
  name: ml-inference
spec:
  replicas: 100
  template:
    spec:
      containers:
      - name: inference
        image: your-inference-image:latest
        resources:
          limits:
            aws.amazon.com/neuron: 1
          requests:
            aws.amazon.com/neuron: 1
        env:
        - name: NEURON_RT_NUM_CORES
          value: "1"

Configure HPA for Dynamic Scaling

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: ml-inference-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: ml-inference
  minReplicas: 50
  maxReplicas: 150
  metrics:
  - type: Resource
    resource:
      name: aws.amazon.com/neuron
      target:
        type: Utilization
        averageUtilization: 75

Step 5: Performance Monitoring

Set Up Prometheus Metrics

apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
  name: neuron-monitor
spec:
  endpoints:
  - port: metrics
    interval: 30s
  selector:
    matchLabels:
      app: ml-inference

Grafana Dashboard Configuration

{
  "panels": [
    {
      "title": "Inference Latency",
      "targets": [
        {
          "expr": "histogram_quantile(0.95, sum(rate(inference_latency_bucket[5m])) by (le))"
        }
      ]
    }
  ]
}

Results and Optimizations

Performance Improvements

• Latency reduction: 200ms → 50ms

• Throughput increase: 4x

• Cost reduction: $25K → $5K monthly

  
  

Key Optimization Techniques

1. Batch size optimization

2. Model compilation flags tuning

3. Resource allocation fine-tuning

4. Custom schedulers for pod placement

   
   

Best Practices and Lessons Learned

1. Model Optimization

   • Profile model performance before migration

   • Use neuron-top for runtime analysis

   • Optimize batch sizes for throughput

2. Infrastructure Management

   • Use spot instances for non-critical workloads

   • Implement proper node draining

   • Set up monitoring and alerting

3. Cost Management

   • Tag resources for cost allocation

   • Set up cost anomaly detection

   • Regular performance audits

     
     

Troubleshooting Guide

Common issues and solutions:

1. Compilation errors

neuron-cc --debug compile.log

2. Runtime performance

neuron-monitor --watch

Next Steps

For teams looking to implement similar optimizations:

1. Start with a small pilot deployment

2. Validate performance metrics

3. Gradually migrate production workloads

4. Monitor and optimize continuously

Need help implementing ML optimizations on EKS? Feel free to reach out in the comments or connect on LinkedIn.