{"product_id":"kit-pi-5-continual-learning-research-platform","title":"Pi 5 Continual Learning Research Platform","description":"\u003ch1\u003eRaspberry Pi 5 Continual Learning Research Platform — Benchmark EWC Against Fine-Tuning\u003c\/h1\u003e\n\n\u003cp class=\"value-summary\"\u003eEvery part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.\u003c\/p\u003e\n\n\u003cdiv class=\"specs-strip\"\u003e\n  \u003cspan\u003e\u003cstrong\u003eDifficulty:\u003c\/strong\u003e Advanced\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eBuild Time:\u003c\/strong\u003e 8-10 hrs\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eAge:\u003c\/strong\u003e 18-25\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eSkill:\u003c\/strong\u003e Continual Learning Research\u003c\/span\u003e\n\u003c\/div\u003e\n\n\u003cp\u003eThis kit transforms a Raspberry Pi 5 into a dedicated testbed for investigating catastrophic forgetting in neural networks. You’ll implement Elastic Weight Consolidation (EWC) and benchmark its performance against naive fine-tuning across sequential image classification tasks, all on a low-power device that mimics the constraints of real-world edge deployment.\u003c\/p\u003e\n\n\u003ch2\u003eWhat You'll Build\u003c\/h2\u003e\n\u003cp\u003eYou’ll assemble a compact, autonomous research node with a Pi 5 and high-speed NVMe storage. The setup runs PyTorch-based continual learning experiments directly on the device, allowing you to measure accuracy drops, log Fisher information matrices, and reproduce EWC’s forgetting mitigation in a reproducible environment — no cloud GPU dependency needed.\u003c\/p\u003e\n\n\u003ch2\u003eWhat You'll Learn\u003c\/h2\u003e\n\u003cul\u003e\n  \u003cli\u003eIntegrate NVMe SSD via the M.2 HAT+ and configure fast storage to eliminate I\/O bottlenecks during model training\u003c\/li\u003e\n  \u003cli\u003eImplement the Elastic Weight Consolidation loss function, including on-the-fly computation of Fisher information from previous task data\u003c\/li\u003e\n  \u003cli\u003eDesign a sequential task benchmark, measuring accuracy degradation after fine-tuning and quantifying the EWC gain\u003c\/li\u003e\n  \u003cli\u003eSet up a headless, always-on machine learning environment on Pi 5 with PyTorch, torchvision, and monitoring tools for long-duration runs\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch2\u003eKit Contents\u003c\/h2\u003e\n\u003ctable\u003e\n  \u003cthead\u003e\u003ctr\u003e\n\u003cth\u003eComponent\u003c\/th\u003e\n\u003cth\u003eQuantity\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n  \u003ctbody\u003e\n    \u003ctr\u003e\n\u003ctd\u003eRaspberry Pi 5 8GB\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eNVMe SSD 256GB\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003ePi 5 M.2 HAT+\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eUSB-C PSU\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n  \u003c\/tbody\u003e\n\u003c\/table\u003e\n\n\u003ch2\u003eWhy Buy This Kit Instead of Sourcing Parts Separately\u003c\/h2\u003e\n\u003ctable\u003e\n  \u003cthead\u003e\u003ctr\u003e\n\u003cth\u003eFactor\u003c\/th\u003e\n\u003cth\u003eSourcing Separately\u003c\/th\u003e\n\u003cth\u003eCompoden Kit\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n  \u003ctbody\u003e\n    \u003ctr\u003e\n\u003ctd\u003eCompatibility checks\u003c\/td\u003e\n\u003ctd\u003eYou verify every part\u003c\/td\u003e\n\u003ctd\u003ePre-tested as a system\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eBuild support\u003c\/td\u003e\n\u003ctd\u003eForums and scattered tutorials\u003c\/td\u003e\n\u003ctd\u003eAI companion trained on this exact project\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eTime to first working build\u003c\/td\u003e\n\u003ctd\u003eDays of debugging\u003c\/td\u003e\n\u003ctd\u003eHours, with step-by-step guidance\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eShipping coordination\u003c\/td\u003e\n\u003ctd\u003eMultiple sellers, multiple delays\u003c\/td\u003e\n\u003ctd\u003eOne shipment from Bengaluru in 3-5 days\u003c\/td\u003e\n\u003c\/tr\u003e\n  \u003c\/tbody\u003e\n\u003c\/table\u003e\n\n\u003ch2\u003eWho This Kit Is For\u003c\/h2\u003e\n\u003cp\u003eDesigned for B.Tech ECE and EEE final-year students tackling continual learning capstones, Smart India Hackathon participants building edge AI prototypes, and researchers at IIT, NIT, VIT, or BITS Pilani who need an isolated test platform for EWC vs fine-tuning benchmarks. If you’re exploring federated learning with non-IID data on resource-constrained nodes, this kit gives you a reproducible hardware baseline.\u003c\/p\u003e\n\n\u003ch2\u003eBuilt and Backed by Compoden\u003c\/h2\u003e\n\u003cp\u003eEvery Compoden kit ships with an AI build companion trained on this exact project — accessible via a QR code on the box, with WhatsApp and email backup. We've spent 10 years building projects for makers, schools, and institutions across India. If a part fails because of a manufacturing defect, replace it free within 7 days.\u003c\/p\u003e\n\n\u003cdetails\u003e\u003csummary\u003eWhat if I get stuck during the build?\u003c\/summary\u003e\u003cp\u003eScan the QR code to chat with our AI companion, which walks you through each step. If you need a human, drop a WhatsApp message; we respond within a business day.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eHow do I install the ML frameworks for EWC experiments?\u003c\/summary\u003e\u003cp\u003eThe AI companion provides an environment setup script that installs PyTorch, torchvision, and all required Python libraries on the Pi 5’s OS, ensuring compatibility with the M.2 HAT+ drivers.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eCan I use my own datasets for sequential tasks?\u003c\/summary\u003e\u003cp\u003eAbsolutely. The companion includes a reference two-task benchmark (CIFAR-10 and SVHN), but you can swap in your own datasets and adjust the EWC implementation guided by our documentation.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eDoes the kit require a monitor or keyboard?\u003c\/summary\u003e\u003cp\u003eNo. You operate the Pi 5 headlessly over SSH. The companion details Wi‑Fi setup and remote access so you can run experiments from your laptop.\u003c\/p\u003e\u003c\/details\u003e\n\n\u003cdiv class=\"kit-description\"\u003e\n  \u003cp\u003eElastic Weight Consolidation prevents catastrophic forgetting on Pi 5 — benchmark against naive fine-tuning on sequential tasks.\u003c\/p\u003e\n  \u003ch4\u003eWhat's in this kit\u003c\/h4\u003e\n  \u003cul\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/raspberry-pi-5-model-b-8gb-high-performance-single-board-computer\"\u003eRaspberry Pi 5 8GB\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/official-raspberry-pi-m2-hat-nvme-ssd-add-on-board-for-pi-5\"\u003eNVMe SSD 256GB\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/raspberry-pi-5-pcie-to-m2-nvme-ssd-expansion-board-by-elecrow\"\u003ePi 5 M.2 HAT+\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/raspberry-pi-4-official-power-supply-5v-3a-usb-c-compoden\"\u003eUSB-C PSU\u003c\/a\u003e\u003c\/li\u003e\n  \u003c\/ul\u003e\n\u003c\/div\u003e\n\n\u003cscript type=\"application\/ld+json\"\u003e\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"FAQPage\",\n  \"mainEntity\": [\n    {\n      \"@type\": \"Question\",\n      \"name\": \"What is included in the Pi 5 Continual Learning Research Platform?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"The Pi 5 Continual Learning Research Platform includes all components needed: Raspberry Pi 5 8GB, NVMe SSD 256GB, Pi 5 M.2 HAT+, USB-C PSU and more. Everything is pre-tested for compatibility and shipped from Bengaluru, India.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"What skill level is required for the Pi 5 Continual Learning Research Platform?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"This kit is designed for Advanced level makers, suitable for ages 18-25. Elastic Weight Consolidation prevents catastrophic forgetting on Pi 5 — benchmark against naive fine-tuning on sequential tasks. Estimated build time is 8-10 hrs.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Can I buy the Pi 5 Continual Learning Research Platform online in India?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Yes, the Pi 5 Continual Learning Research Platform is available online at Compoden (compoden.in), India's AI-powered electronics and robotics store. Ships from Bengaluru in 1-5 business days across India.\"\n      }\n    }\n  ]\n}\n\u003c\/script\u003e\n\n\u003cscript type=\"application\/ld+json\"\u003e\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"Product\",\n  \"name\": \"Pi 5 Continual Learning Research Platform\",\n  \"description\": \"Elastic Weight Consolidation prevents catastrophic forgetting on Pi 5 — benchmark against naive fine-tuning on sequential tasks.\",\n  \"sku\": \"CDN-KIT-2566\",\n  \"brand\": {\"@type\": \"Brand\", \"name\": \"Compoden\"},\n  \"offers\": {\n    \"@type\": \"Offer\",\n    \"url\": \"https:\/\/compoden.in\/products\/kit-pi-5-continual-learning-research-platform\",\n    \"priceCurrency\": \"INR\",\n    \"price\": \"35700\",\n    \"availability\": \"https:\/\/schema.org\/InStock\",\n    \"seller\": {\"@type\": \"Organization\", \"name\": \"Compoden\"}\n  },\n  \"category\": \"Edge AI \u0026 Computer Vision\"\n}\n\u003c\/script\u003e","brand":"Compoden","offers":[{"title":"Default Title","offer_id":53469369860461,"sku":"CDN-KIT-2566","price":42130.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0999\/3997\/5533\/files\/kit-pi-5-continual-learning-research-platform.png?v=1781948417","url":"https:\/\/compoden.com\/products\/kit-pi-5-continual-learning-research-platform","provider":"Compoden","version":"1.0","type":"link"}