Object recognition
Object Recognition Server Configuration
This article details the server configuration required for robust object recognition capabilities within our MediaWiki environment. This functionality relies on dedicated hardware and software components working in concert to process visual data and identify objects within images and videos. This guide is aimed at newcomers to the server infrastructure and will cover hardware requirements, software installation, and initial configuration steps. Please review the System Requirements page before proceeding.
Hardware Requirements
Object recognition is computationally intensive. The following hardware is *required* for acceptable performance. Meeting the minimum specifications is crucial for avoiding performance bottlenecks and ensuring accurate results.
| Component | Minimum Specification | Recommended Specification | Notes |
|---|---|---|---|
| CPU | Intel Xeon E5-2680 v4 (14 cores) | Intel Xeon Gold 6248R (24 cores) | Higher core counts drastically improve processing speed. |
| RAM | 64 GB DDR4 ECC | 128 GB DDR4 ECC | Sufficient RAM is essential to handle large datasets. |
| GPU | NVIDIA Tesla P40 (24 GB VRAM) | NVIDIA A100 (80 GB VRAM) | GPUs are vital for accelerating model inference. |
| Storage | 2 TB NVMe SSD (OS & Software) | 4 TB NVMe SSD (OS & Software) | Fast storage reduces loading times. |
| Network | 10 Gigabit Ethernet | 25 Gigabit Ethernet | High-bandwidth network connectivity is needed for data transfer. |
Software Installation
The object recognition pipeline relies on a specific set of software packages. We utilize Ubuntu Server 20.04 LTS as our base operating system. Ensure your server meets the Operating System Requirements before installation.
1. **CUDA Toolkit:** Install the NVIDIA CUDA Toolkit (version 11.7 or higher) to enable GPU acceleration. Follow the official NVIDIA documentation for installation: CUDA Installation Guide. 2. **cuDNN:** Download and install the cuDNN library (version 8.x or higher) corresponding to your CUDA version. This library provides optimized primitives for deep neural networks: cuDNN Download. 3. **Python:** Install Python 3.8 or higher. Consider using a virtual environment: Python Virtual Environments. 4. **TensorFlow/PyTorch:** Choose either TensorFlow or PyTorch as your deep learning framework. Both are supported, but TensorFlow is currently the primary framework. Installation instructions can be found at TensorFlow Installation or PyTorch Installation. 5. **Object Detection Model:** Download a pre-trained object detection model (e.g., YOLOv5, Faster R-CNN). The Model Repository contains currently supported models. 6. **MediaWiki Extension:** Install the custom MediaWiki extension that interfaces with the object recognition pipeline. Details are found at ObjectRecognitionExtension.
Configuration Details
After installation, several configuration steps are necessary to integrate the object recognition server with the MediaWiki platform.
1. **API Key:** Generate an API key for secure communication between MediaWiki and the object recognition server. Store this key securely: API Key Management. 2. **Model Path:** Configure the path to the downloaded object detection model in the extension's configuration file (`/etc/mediawiki-extensions/ObjectRecognition/config.ini`). 3. **GPU Allocation:** Ensure the object recognition process has access to the GPU. This may require configuring environment variables or using `nvidia-smi` to monitor GPU usage: GPU Monitoring. 4. **Queue Management:** Implement a queue system (e.g., Redis, RabbitMQ) to handle incoming requests from MediaWiki. This prevents overloading the server. See Queueing Systems. 5. **Firewall Configuration:** Configure the firewall to allow communication on the necessary ports. See Firewall Configuration.
Performance Tuning
Optimizing performance is critical for a responsive user experience. Consider the following:
| Tuning Parameter | Description | Recommended Value |
|---|---|---|
| Batch Size | Number of images processed simultaneously. | 8-16 |
| Confidence Threshold | Minimum confidence score for object detection. | 0.5-0.7 |
| Non-Maximum Suppression (NMS) Threshold | Threshold for removing duplicate detections. | 0.4-0.6 |
| Model Precision | Use FP16 or INT8 precision for faster inference (if supported by GPU). | FP16 or INT8 |
Troubleshooting
Common issues and their solutions:
| Problem | Possible Solution |
|---|---|
| GPU Out of Memory | Reduce batch size, use lower precision, or upgrade GPU. |
| Slow Processing Speed | Optimize model, increase GPU allocation, or upgrade hardware. |
| Incorrect Detections | Retrain the model with a larger dataset or adjust the confidence threshold. |
| Connection Errors | Verify API key, network connectivity, and firewall settings. |
For further assistance, consult the Troubleshooting Guide or contact the System Administrators. Remember to review the Security Considerations before deploying this configuration to a production environment.
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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.* ⚠️