{"product_id":"kit-pi-5-graph-neural-network-research-kit","title":"Pi 5 Graph Neural Network Research Kit","description":"\u003ch1\u003ePi 5 Graph Neural Network Research Kit — Train GNNs Locally for Node Classification and Link Prediction\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 10-12 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 Edge AI Model Training\u003c\/span\u003e\n\u003c\/div\u003e\n\n\u003cp\u003eThis kit transforms a Raspberry Pi 5 into a dedicated graph learning workstation. You’ll configure an NVMe SSD, install PyTorch Geometric, and train graph neural networks on datasets like Cora, CiteSeer, and molecular property prediction benchmarks — all on a power-efficient edge device that can run silently in a research nook.\u003c\/p\u003e\n\n\u003ch2\u003eWhat You'll Build\u003c\/h2\u003e\n\u003cp\u003eBy the end of the build, you’ll have a fully operational Pi 5 system capable of running end-to-end GNN training workflows. You’ll load real-world graph datasets, implement message-passing neural networks, and generate metrics for node classification accuracy and link prediction AUC. The NVMe SSD ensures rapid data throughput, making this kit suitable for small-batch research experiments and reproducible edge AI benchmarks.\u003c\/p\u003e\n\n\u003ch2\u003eWhat You'll Learn\u003c\/h2\u003e\n\u003cul\u003e\n  \u003cli\u003eSetting up PyTorch Geometric on ARM64 architecture from source\u003c\/li\u003e\n  \u003cli\u003eDesigning graph convolutional layers for node embedding on citation networks\u003c\/li\u003e\n  \u003cli\u003eTraining GNN models for multi-class node classification with early stopping\u003c\/li\u003e\n  \u003cli\u003ePerforming link prediction on molecular graphs using NVMe-accelerated storage\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 512GB\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\u003eThis kit is purpose-built for B.Tech final-year students in AI\/ML, research interns at IIT, NIT, or IISc labs, and Smart India Hackathon teams tackling graph-based challenges. If you’re preparing a paper on graph representation learning or need a reproducible hardware baseline for edge AI experiments, this kit delivers a pre-integrated environment that’s ready for the rigor of academic evaluation.\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\u003eThe AI companion gives step-by-step guidance, and you can message us on WhatsApp for human backup within hours.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eCan I train large graphs like PubMed on this Pi 5 kit?\u003c\/summary\u003e\u003cp\u003eYes, the 8GB RAM and NVMe allow graphs up to ~100K nodes with batching; for larger graphs, use mini-batch training and the companion will guide you through dataset sampling.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eWhich Python version and PyG version are pre-configured?\u003c\/summary\u003e\u003cp\u003eThe AI companion provides setup scripts for Python 3.11 and PyTorch Geometric 2.5, fully compatible with ARM64; you’ll run the installation during the build to ensure the latest patches.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eIs this kit suitable for submission as a B.Tech final-year project?\u003c\/summary\u003e\u003cp\u003eAbsolutely. Many universities accept hardware-in-the-loop AI projects, and the kit includes documentation and the companion to help you produce a formal report with reproducibility logs.\u003c\/p\u003e\u003c\/details\u003e\n\n\u003cdiv class=\"kit-description\"\u003e\n  \u003cp\u003ePyTorch Geometric on Pi 5 NVMe trains GNNs on molecular and citation graph datasets — node classification and link prediction.\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 512GB\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 Graph Neural Network Research Kit?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"The Pi 5 Graph Neural Network Research Kit includes all components needed: Raspberry Pi 5 8GB, NVMe SSD 512GB, 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 Graph Neural Network Research Kit?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"This kit is designed for Advanced level makers, suitable for ages 18-25. PyTorch Geometric on Pi 5 NVMe trains GNNs on molecular and citation graph datasets — node classification and link prediction. Estimated build time is 10-12 hrs.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Can I buy the Pi 5 Graph Neural Network Research Kit online in India?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Yes, the Pi 5 Graph Neural Network Research Kit is available online at Compoden (compoden.in), India's AI-powered electronics and robotics store. 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