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On-Premise to AWS Migration with GPU Acceleration
How we helped Searcly migrate their infrastructure to AWS, implementing GPU-powered machine learning capabilities with real-time monitoring.
Client Overview
About Searcly
Industry
Search & ML Platform
Challenge
Migrate to cloud with GPU support
Solution
AWS EKS with GPU-enabled nodes
The Challenge
Scaling Beyond On-Premise Limitations
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GPU Resource Constraints
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Limited on-premise GPU resources were bottlenecking machine learning workloads. Scaling up required significant capital investment and long procurement cycles.
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Scaling Limitations
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Unable to handle traffic spikes or scale ML workloads dynamically. Fixed infrastructure couldn't adapt to varying computational demands.
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Real-time Processing Needs
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Growing demand for real-time inference capabilities requiring low-latency GPU processing that on-premise infrastructure couldn't deliver efficiently.
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Lack of Real-time Monitoring
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Limited visibility into infrastructure performance and ML workload metrics made it difficult to optimize resource utilization and troubleshoot issues.
Technical Implementation
Cloud-Native GPU Infrastructure
1. Migration Planning & Assessment
Infrastructure Assessment
- Documented existing on-premise architecture
- Identified GPU workload requirements
- Capacity planning for AWS resources
- Data migration strategy development
AWS Architecture Design
Multi-Account Strategy
Separate accounts for dev, staging, and production
Network Design
VPC with public/private subnets across AZs
GPU Instance Selection
p3.2xlarge and g4dn.xlarge for different workloads
High-Bandwidth Networking
Enhanced networking for GPU communication
2. AWS Foundation with Terraform
Infrastructure as Code Implementation
We used Terraform to provision GPU-enabled EKS node groups with automatic driver installation and configuration. The infrastructure was designed for elasticity and cost optimization.
GPU Node Configuration
- • Mixed instance types (g4dn.xlarge, g4dn.2xlarge)
- • Auto-scaling from 1 to 10 nodes
- • NVIDIA driver auto-installation
- • GPU-specific taints and labels
Automation Features
- • User data scripts for setup
- • Container toolkit configuration
- • Docker runtime GPU support
- • Health check validation
EKS Cluster
- • Multi-AZ control plane
- • Mixed instance types
- • GPU-enabled node groups
- • Auto-scaling configuration
Storage
- • EFS for shared storage
- • S3 for model storage
- • EBS GP3 volumes
- • Lifecycle policies
Networking
- • Enhanced networking
- • VPC endpoints
- • Private subnets
- • NAT gateways
3. GPU-Enabled Kubernetes Platform
NVIDIA Device Plugin Implementation
GPU Resource Management
- • Automatic GPU discovery
- • Resource allocation per pod
- • GPU sharing capabilities
- • Health monitoring
Scheduling Configuration
- • Node selectors for GPU workloads
- • Taints and tolerations
- • Priority classes
- • Resource quotas
GPU Pod Configuration
ML workloads were configured with precise GPU resource requests and node affinity to ensure optimal scheduling and resource utilization.
- • 1 GPU per training pod
- • 16GB memory allocation
- • GPU-specific node selection
- • CUDA device configuration
- • Toleration for GPU taints
Auto-scaling Configuration
Implemented sophisticated auto-scaling based on GPU utilization metrics to optimize costs while maintaining performance.
- • Scales from 2 to 20 replicas
- • GPU utilization target: 80%
- • 60-second stabilization window
- • Aggressive scale-up policy
- • Custom GPU metrics integration
4. Zero-Downtime Data Migration
Phased Migration Approach
Phase 1: Stateless Services
Migrated API services and stateless workloads first
- • Containerized microservices
- • Load balancer configuration
- • DNS preparation
Phase 2: Database Migration
Used AWS DMS for minimal downtime migration
- • PostgreSQL replication setup
- • Redis snapshot migration
- • Data validation
Phase 3: GPU Workloads
Migrated ML training and inference services
- • Model transfer to S3
- • GPU driver validation
- • Performance benchmarking
Phase 4: Complete Cutover
Final migration and decommissioning
- • DNS switch to AWS
- • Traffic validation
- • On-premise shutdown
5. Real-Time Monitoring Implementation
Observability Stack
Prometheus & Grafana
GPU metrics collection and visualization
- • GPU utilization tracking
- • Memory usage monitoring
- • Temperature alerts
CloudWatch Integration
AWS native monitoring
- • EKS cluster metrics
- • Custom application metrics
- • Cost tracking dashboards
Distributed Tracing
End-to-end request tracking
- • AWS X-Ray integration
- • OpenTelemetry setup
- • Performance analysis
GPU Metrics Dashboard
Real-time Metrics
GPU, Memory, Temperature
ML Performance
Inference latency, throughput
Cost Analysis
Per-workload cost tracking
Alerts
Proactive issue detection
Custom GPU Metrics Collection
We configured Prometheus to collect detailed GPU metrics, enabling precise monitoring and alerting for ML workloads.
Metrics Collection
- • GPU utilization percentage
- • Memory usage and allocation
- • Temperature monitoring
- • Power consumption tracking
Configuration Features
- • Kubernetes service discovery
- • Pod label-based targeting
- • NVIDIA GPU metric filtering
- • Custom relabeling rules
6. Machine Learning Infrastructure
ML Pipeline Architecture
Training Pipeline
- • Kubeflow orchestration
- • Distributed training
- • Hyperparameter tuning
- • Model versioning
Model Serving
- • GPU-accelerated inference
- • Auto-scaling based on load
- • A/B testing framework
- • Model monitoring
Optimization
- • CUDA optimization
- • Batch processing
- • Memory management
- • Multi-GPU support
Training Optimization
Distributed Training
Multi-GPU training with Horovod reduced training time by 10x
Mixed Precision
FP16 training for faster computation without accuracy loss
Spot Instances
70% cost reduction for training workloads
Inference Optimization
TensorRT Optimization
3x inference speed improvement with NVIDIA TensorRT
Dynamic Batching
Improved GPU utilization and reduced latency
Model Caching
Reduced cold start times to under 1 second
Results Achieved
Transformational Performance Gains
Performance Improvements
Operational Benefits
Cost Management
Instances for training
For predictable workloads
GPU resources in dev
Cost optimization achieved
"We built our operations on AWS from scratch using Terraform and EKS, and Fizyonops' guidance was critical throughout the process. Thanks to their managed services, we now monitor infrastructure performance in real time."
Sezai Yıldırım
Searcly
Key Takeaways
Lessons from GPU Cloud Migration
GPU Optimization Critical
Proper GPU utilization and optimization techniques like mixed precision training and TensorRT can dramatically improve performance and reduce costs.
Monitoring is Essential
Real-time GPU monitoring and custom metrics are crucial for optimizing utilization and catching issues early in ML workloads.
Cost Management Strategy
Combining spot instances for training, reserved instances for inference, and proper resource sharing can significantly reduce GPU costs.
Ready to Migrate to the Cloud?
Let's discuss how we can help you achieve a seamless cloud migration with GPU support.