AI in New Caledonia
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- AI in New Caledonia: Server Configuration
This article details the server configuration currently deployed to support Artificial Intelligence (AI) initiatives in New Caledonia. It's designed for newcomers to the server infrastructure and provides a comprehensive overview of the hardware, software, and network components involved. This configuration is optimized for machine learning tasks, particularly those related to environmental monitoring and resource management.
Overview
The AI infrastructure in New Caledonia is designed as a distributed system, leveraging both on-premise servers and cloud resources. This hybrid approach provides scalability, redundancy, and cost-effectiveness. The core of the on-premise infrastructure resides within a secure data center in Nouméa. The primary functions supported include data ingestion, model training, model deployment, and real-time inference. Data Security is paramount, and all systems adhere to strict data privacy regulations. Network Topology is designed for high bandwidth and low latency.
Hardware Specifications
The on-premise server infrastructure consists of several key components. The following table details the specifications of the primary compute servers.
| Server Role | CPU | RAM | Storage | GPU |
|---|---|---|---|---|
| Compute Node 1 (Training) | 2x Intel Xeon Gold 6338 | 512 GB DDR4 ECC | 8 TB NVMe SSD (RAID 0) | 4x NVIDIA A100 (80GB) |
| Compute Node 2 (Inference) | 2x Intel Xeon Silver 4310 | 256 GB DDR4 ECC | 4 TB NVMe SSD (RAID 1) | 2x NVIDIA T4 |
| Data Ingestion Server | 2x Intel Xeon E-2388G | 64 GB DDR4 ECC | 16 TB HDD (RAID 5) | None |
| Database Server | 2x AMD EPYC 7763 | 1 TB DDR4 ECC | 32 TB SSD (RAID 10) | None |
These servers are housed in a climate-controlled rack with redundant power supplies and network connections. Power Redundancy is a critical design consideration.
Software Stack
The software stack is built around a Linux-based operating system (Ubuntu Server 22.04 LTS) and utilizes a range of open-source tools. Operating System Details can be found on the internal wiki.
| Component | Version | Purpose |
|---|---|---|
| Operating System | Ubuntu Server 22.04 LTS | Base operating system |
| Python | 3.10 | Primary programming language for AI models |
| TensorFlow | 2.12 | Machine learning framework |
| PyTorch | 2.0 | Machine learning framework |
| CUDA Toolkit | 12.2 | GPU acceleration library |
| Docker | 20.10 | Containerization platform |
| Kubernetes | 1.27 | Container orchestration |
| PostgreSQL | 15 | Database management system |
All software is regularly updated to address security vulnerabilities and benefit from performance improvements. Security Patching Procedures are documented internally.
Network Configuration
The network infrastructure is designed for high throughput and low latency, crucial for transferring large datasets and facilitating real-time inference.
| Component | Specification | Purpose |
|---|---|---|
| Core Switch | Cisco Catalyst 9500 Series | High-speed switching |
| Server Network Interface Cards (NICs) | 100 GbE | High-bandwidth connectivity |
| Firewall | FortiGate 600F | Network security |
| Load Balancer | HAProxy | Distribute traffic across servers |
| Internet Connection | 10 Gbps Dedicated Line | External connectivity |
The network is segmented using VLANs to isolate different components and enhance security. VLAN Configuration details are available to authorized personnel. Regular Network Monitoring is conducted to ensure optimal performance and identify potential issues.
Data Storage and Management
Data is stored in a combination of local NVMe SSDs and a centralized object storage system connected to the cloud. The PostgreSQL database stores metadata and model parameters. Data Backup Procedures are in place to ensure data durability. A dedicated team manages the data pipeline, ensuring data quality and availability. Data Governance Policies are strictly enforced.
Future Considerations
Future upgrades include the addition of more GPU-accelerated servers and the expansion of the cloud storage capacity. We are also exploring the use of edge computing devices for real-time data processing closer to the source. Edge Computing Roadmap provides more details.
Server Maintenance Schedule will be updated regularly. Please consult the Troubleshooting Guide for common issues. Contact Information for the server team is available on the internal wiki.
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Intel-Based Server Configurations
| Configuration | Specifications | Benchmark |
|---|---|---|
| Core i7-6700K/7700 Server | 64 GB DDR4, NVMe SSD 2 x 512 GB | CPU Benchmark: 8046 |
| Core i7-8700 Server | 64 GB DDR4, NVMe SSD 2x1 TB | CPU Benchmark: 13124 |
| Core i9-9900K Server | 128 GB DDR4, NVMe SSD 2 x 1 TB | CPU Benchmark: 49969 |
| Core i9-13900 Server (64GB) | 64 GB RAM, 2x2 TB NVMe SSD | |
| Core i9-13900 Server (128GB) | 128 GB RAM, 2x2 TB NVMe SSD | |
| Core i5-13500 Server (64GB) | 64 GB RAM, 2x500 GB NVMe SSD | |
| Core i5-13500 Server (128GB) | 128 GB RAM, 2x500 GB NVMe SSD | |
| Core i5-13500 Workstation | 64 GB DDR5 RAM, 2 NVMe SSD, NVIDIA RTX 4000 |
AMD-Based Server Configurations
| Configuration | Specifications | Benchmark |
|---|---|---|
| Ryzen 5 3600 Server | 64 GB RAM, 2x480 GB NVMe | CPU Benchmark: 17849 |
| Ryzen 7 7700 Server | 64 GB DDR5 RAM, 2x1 TB NVMe | CPU Benchmark: 35224 |
| Ryzen 9 5950X Server | 128 GB RAM, 2x4 TB NVMe | CPU Benchmark: 46045 |
| Ryzen 9 7950X Server | 128 GB DDR5 ECC, 2x2 TB NVMe | CPU Benchmark: 63561 |
| EPYC 7502P Server (128GB/1TB) | 128 GB RAM, 1 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (128GB/2TB) | 128 GB RAM, 2 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (128GB/4TB) | 128 GB RAM, 2x2 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (256GB/1TB) | 256 GB RAM, 1 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (256GB/4TB) | 256 GB RAM, 2x2 TB NVMe | CPU Benchmark: 48021 |
| EPYC 9454P Server | 256 GB RAM, 2x2 TB NVMe |
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⚠️ *Note: All benchmark scores are approximate and may vary based on configuration. Server availability subject to stock.* ⚠️