AI SDKs
```wiki
- AI SDKs: Server Configuration and Integration
This article details the server configuration required to effectively run and manage AI Software Development Kits (SDKs) within our MediaWiki environment. It's geared towards system administrators and developers looking to integrate AI functionality into existing or new wiki features. Understanding these configurations is crucial for performance, scalability, and security.
Overview
AI SDKs provide pre-built tools and libraries for incorporating Artificial Intelligence capabilities – such as natural language processing, image recognition, and machine learning – into applications. For our MediaWiki instance, these SDKs will primarily be utilized for features like enhanced search, content summarization, and potentially, automated categorization. This guide focuses on the server-side requirements for hosting these SDKs and ensuring their smooth operation alongside the core MediaWiki software. We'll cover hardware, software dependencies, and key configuration settings. Consider reviewing the MediaWiki Installation Guide before proceeding.
Hardware Requirements
The hardware requirements for running AI SDKs are significantly higher than those for basic MediaWiki operation. This is due to the computationally intensive nature of AI algorithms. The following table outlines minimum and recommended specifications:
| Component | Minimum Specification | Recommended Specification |
|---|---|---|
| CPU | Quad-Core Intel Xeon E3 or equivalent AMD processor | Octa-Core Intel Xeon E5 or equivalent AMD processor |
| RAM | 16 GB DDR4 | 32 GB DDR4 ECC |
| Storage | 500 GB SSD | 1 TB NVMe SSD |
| GPU (Optional, but highly recommended) | NVIDIA GeForce GTX 1660 with 6GB VRAM | NVIDIA Tesla T4 or equivalent AMD Radeon Pro |
| Network | 1 Gbps Ethernet | 10 Gbps Ethernet |
It's important to note that GPU acceleration dramatically improves the performance of many AI SDKs, particularly those involved in deep learning. Consider utilizing a dedicated server for AI tasks to avoid impacting the performance of the core MediaWiki application. Refer to the Server Hardware Documentation for detailed information on our current server infrastructure.
Software Dependencies
Several software dependencies are required to successfully run AI SDKs on our servers. These include operating system requirements, programming language runtimes, and specific AI libraries.
| Software | Version | Notes |
|---|---|---|
| Operating System | Ubuntu Server 20.04 LTS | Other Linux distributions may be compatible, but Ubuntu is officially supported. |
| Python | 3.8 or higher | Used by many AI SDKs; ensure virtual environments are utilized. |
| pip | Latest version | Python package installer. |
| TensorFlow | 2.8 or higher | A popular open-source machine learning framework. |
| PyTorch | 1.10 or higher | Another widely used machine learning framework. |
| CUDA Toolkit (if using NVIDIA GPU) | Version compatible with TensorFlow/PyTorch | Required for GPU acceleration. |
| Docker | Latest version | Recommended for containerizing AI SDKs and dependencies. |
Ensure all dependencies are installed and properly configured before attempting to deploy any AI SDKs. We have a Software Repository containing pre-built packages for many of these dependencies. Also, review the Python Configuration Guide for best practices.
Configuration and Integration
Integrating AI SDKs into the MediaWiki environment requires careful configuration. The recommended approach is to utilize a microservices architecture, where the AI SDKs run as separate services and communicate with MediaWiki via APIs. This promotes modularity, scalability, and fault tolerance.
- API Endpoints: Define clear API endpoints for communication between MediaWiki and the AI SDKs. For example, a `/summarize` endpoint could be used to request a content summary from an NLP SDK.
- Authentication and Authorization: Implement robust authentication and authorization mechanisms to secure access to the AI SDKs. Consider using API keys or OAuth 2.0. Refer to the API Security Guidelines.
- Resource Allocation: Carefully allocate resources (CPU, RAM, GPU) to each AI SDK service to prevent resource contention. Docker containers can be used to limit resource usage.
- Logging and Monitoring: Implement comprehensive logging and monitoring to track the performance of the AI SDKs and identify potential issues. Utilize tools like Prometheus and Grafana. See the Server Monitoring Documentation.
- Virtual Environments: Always use Python virtual environments to isolate the dependencies of each AI SDK. This prevents conflicts and ensures reproducibility.
The following table summarizes key configuration parameters:
| Parameter | Description | Default Value |
|---|---|---|
| API Base URL | The base URL for the AI SDK API. | `/api/ai` |
| Authentication Method | The authentication method used to secure the API. | `API Key` |
| Timeout (seconds) | The maximum time allowed for an API request. | `30` |
| Logging Level | The level of detail to include in the logs. | `INFO` |
| GPU Allocation | The percentage of GPU memory allocated to the SDK. | `50%` |
Security Considerations
Running AI SDKs introduces new security considerations. It's crucial to protect against potential vulnerabilities such as:
- Data Privacy: Ensure that sensitive data is not inadvertently exposed to the AI SDKs. Implement appropriate data masking and anonymization techniques.
- Model Poisoning: Protect against malicious attacks that attempt to corrupt the AI models. Regularly audit and validate the models.
- Denial of Service (DoS): Implement rate limiting and other security measures to prevent DoS attacks against the AI SDKs.
- Dependency Vulnerabilities: Regularly scan and update the dependencies of the AI SDKs to address known vulnerabilities. Refer to the Security Patching Policy.
Further Resources
- MediaWiki API Documentation
- Docker Configuration Guide
- Server Security Best Practices
- Troubleshooting Guide
- AI SDK Vendor Documentation (links to specific SDK documentation will go here)
```
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 |
Order Your Dedicated Server
Configure and order your ideal server configuration
Need Assistance?
- Telegram: @powervps Servers at a discounted price
⚠️ *Note: All benchmark scores are approximate and may vary based on configuration. Server availability subject to stock.* ⚠️