ROCm

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ROCm

ROCm (Radeon Open Compute) is AMD's open-source software platform for GPU-accelerated computing. It is the AMD equivalent of NVIDIA's CUDA and provides a complete stack for running AI, machine learning, and HPC workloads on AMD GPUs. ROCm supports major ML frameworks including PyTorch, TensorFlow, JAX, and ONNX Runtime, and includes the HIP (Heterogeneous-compute Interface for Portability) programming model for writing GPU code that runs on both AMD and NVIDIA hardware.

General
Author	AMD
Type	Open-source GPU Computing Platform
Documentation	ROCm Docs
Repository	github.com/ROCm
Installation	ROCm Installation Guide
Current Version	ROCm 7
License	MIT and Apache 2.0

Start building with ROCm

ROCm gives you a complete software stack to run AI training and inference workloads on AMD GPUs. It integrates directly with PyTorch, TensorFlow, and JAX so most standard pipelines run with minimal changes from a CUDA environment. Hugging Face Optimum-AMD and vLLM both support ROCm, making it straightforward to run transformer inference and fine-tuning jobs on AMD hardware. Check out the community-built AMD Use Cases and Applications to see what developers are running on ROCm today.

ROCm Tutorials

Documentation and Resources

ROCm Documentation Full reference for installation, APIs, and libraries
ROCm Installation Guide Step-by-step setup for supported Linux distributions
ROCm GitHub Open-source repositories, examples, and issue tracking
HIP SDK SDK for writing portable GPU code for both AMD and NVIDIA hardware
AMD Developer Hub Guides, training videos, and cloud credits

Framework Support

PyTorch Full support for training and inference, including integration with Hugging Face Accelerate and PEFT
TensorFlow GPU-accelerated training and inference on AMD hardware
JAX Supported via the ROCm JAX build
ONNX Runtime Cross-framework model deployment on AMD GPUs
Hugging Face Optimum-AMD Optimized inference and fine-tuning pipelines for transformer models
vLLM High-throughput LLM serving with a ROCm backend

Libraries

hipBLAS BLAS implementation for AMD GPUs
MIOpen Deep learning primitives library for AMD GPUs
rocRAND Random number generation for AMD hardware
hipSPARSE Sparse matrix operations on AMD GPUs
rocBLAS BLAS implementation optimized for AMD Instinct accelerators

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amd AMD ROCm AI technology Hackathon projects

Discover innovative solutions crafted with amd AMD ROCm AI technology, developed by our community members during our engaging hackathons.

CogniCore

CogniCore is an AI-powered enterprise platform that transforms meetings into production-ready operational workflows. Instead of relying on manual documentation, fragmented communication, and multiple disconnected tools, CogniCore automatically captures conversations, understands business and technical context, extracts requirements, identifies risks, and generates actionable tasks in real time. Through its integrated systems, CogniScribe and CogniOPS, the platform orchestrates planning, governance, development workflows, and AI-assisted execution from a single interface. By reducing miscommunication, accelerating project delivery, and minimizing operational overhead, CogniCore enables organizations to move seamlessly from discussion to implementation with speed, accuracy, scalability, and enterprise-grade automation.

RavenAI

RavenAI democratizes enterprise-grade cybersecurity for SMBs, startups, and developers who can't afford $10K/year tools like Qualys. The problem: 43% of cyberattacks target small businesses, but existing scanners are slow, technical, and require security experts. Founders need instant answers, not 50-page PDF reports. Our solution: A Streamlit-based AI scanner that delivers SSL certificate analysis, security header audits, and domain threat intelligence in 30 seconds. Enter any domain → get instant security grade + AI-generated explanation of risks + copy-paste fix instructions. No cybersecurity degree required. Built with Python, OpenSSL, Whois, Requests, and HuggingFace LLM for threat analysis. Using AMD ROCm GPU credits to power an AI agent that auto-tests XSS/SQLi payloads and generates compliance reports 10x faster than traditional scanners. Freemium model: 3 free scans/day, $29/mo for unlimited + API access. Security shouldn't require a $150K CISO. RavenAI gives every founder a security team in their browser.

AluminatiEye

AluminatiEye is a GPU Cloud Intelligence Oracle built to help AI teams make smarter infrastructure decisions in an increasingly complex GPU market. Today, AI builders face fragmented cloud providers, constantly changing GPU pricing, infrastructure shortages, and limited visibility into which provider is the best fit for a workload. Teams often spend hours comparing vendors, researching companies, monitoring pricing, and evaluating risk before deploying models. AluminatiEye creates a unified intelligence layer across the GPU ecosystem. The platform collects and analyzes data from multiple GPU cloud providers and public sources to generate actionable infrastructure insights. Key capabilities include: • Live Pricing – Tracks GPU pricing across multiple cloud vendors in real time. • Arbitrage Detection – Finds cost-saving opportunities between providers. • Market Intelligence – Aggregates news, sentiment, regulations, and competitive signals. • Risk Scores – Evaluates providers based on reliability, growth, uptime, and market health. • Cost Calculator – Estimates infrastructure spending. • Recommender – Suggests optimal GPUs and providers for training, fine-tuning, inference, and image generation workloads. • Oracle Engine – Combines all signals into a single recommendation. Built using Bright Data's web intelligence infrastructure, AluminatiEye transforms raw infrastructure data into strategic recommendations that help organizations reduce costs, mitigate risk, and make faster infrastructure decisions. Our vision is to become the intelligence layer for the GPU economy, giving founders, engineers, researchers, and AI teams a single source of truth for cloud infrastructure decisions.

A-JEPA AUTOMATA

High Level Overview Automata is a production-grade AI agent platform that combines live web intelligence with formal verification — making it the first system where AI-driven business decisions are mathematically auditable before execution. The core problem: enterprise teams can't trust AI agents acting on web data because there's no proof the reasoning is sound. Automata solves this with a three-layer stack. Layer 1 — Web Intelligence Intake (Bright Data): The Bright Data MCP Server and Web Scraper API feed structured live data — competitor pricing, regulatory filings, LinkedIn hiring signals, SERP trends — directly into the ingestion pipeline. Web Unlocker handles bot-protected sources. All intakes logged to a Blake2b-hashed append-only audit trail from the first byte. Layer 2 — Agentic Orchestration: A FastAPI backend with async workers processes ingested signals. The Go CLI harness runs named analysis flows — sorry scan, interconnect map, signal diff — and exposes structured JSON for downstream AI agents. A proof watcher tracks theorem and proof-completion metrics per file in real time, ensuring that the logic layer never silently regresses. Layer 3 — Formal Verification : Every intelligence claim that triggers an action passes through an Automata state machine. The proof_completion metric — theorems minus sorry-count divided by theorem-count — gates whether a decision is certified or flagged for human review. No sorry-equivalent proof, no downstream action. This is provable trust, not probabilistic trust. Infrastructure: Docker Compose stack with Postgres, Redis, Alembic migrations, Grafana/Loki observability, nginx reverse proxy, and an inotify-based file watcher. Deployable on ROCm hardware. Track coverage: GTM Intelligence (competitor and buying-signal monitoring), Finance & Market Intelligence (pricing and filing pipelines), Security & Compliance (regulatory change detection with proof-gated alerts). A single coherent system spanning all three tracks.

Apohara CONSILIUM — Agent Governance OS

THE PROBLEM. Italian banks face dual regulatory urgency: DORA (mandatory since Jan 17 2025 for 22,000+ EU financial entities — UniCredit, Intesa Sanpaolo as G-SIBs) + EU AI Act Article 14 (enforceable Aug 2 2026, fines up to €35M or 7% of global revenue). When Banca d'Italia asks "why did your AI decide X?", the bank needs tamper-evident, third-party-signed audit evidence. Existing tools (Galileo $73M, Lakera $30M, Patronus $20M, Credo AI) don't generate court-grade compliance evidence from production runtime. THE SOLUTION. CONSILIUM is a 3-tier open-source platform: (A) OSS Apache-2.0 entry — 9-vendor adversarial LLM ensemble + 78-rule deterministic judge layer + INV-15 Z3 SMT formal proof (UNSAT in 10.08ms). (B) Governance OS core — 4-stage SOAR pipeline + 6 compliance framework dashboards (EU AI Act, NIST AI RMF, ISO 42001, SOC 2, GDPR, NIST 800-53) + HMAC-SHA256 verdict chain + STIX 2.1 export. (D) CAICEP module — RFC 3161 TSA-timestamped verdict chain via freetsa.org (live evidence today) + roadmap to court-admissible attestation Q3 2026 via eIDAS QTSP partnership (Actalis Italia). LIVE EVIDENCE. apohara.dev/consilium/verify — interactive demo: paste any prompt → 9-vendor decision. Click any of 3 demo verdicts → verify RFC 3161 timestamp against freetsa.org independently. api.apohara.dev shows 10+ SOAR endpoints live, /v1/verdicts/{hash}/verify-timestamp returns valid:true with real Freetsa.org-signed token (1312 bytes, signed 2026-05-19T12:21:50Z). BUSINESS VALUE. TAM AI governance $3.59B by 2033 (36% CAGR). SAM EU regulated industries $400-800M by 2027. Initial wedge: Italian G-SIBs + Milan Fintech District (200+ companies) = $15-30M ACV in 12 months. Revenue: OSS free + Cloud Pro $299-999/mo + Business $2-5K/mo + Enterprise+CAICEP $25-200K/year. Exit reference: Cisco acquired Robust Intelligence Aug 2024 (~$350M, 451 Research). Built solo by Pablo M. Suarez (UNT, Argentina) in 8 days for Milan AI Week 2026.