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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
AuthorAMD
TypeOpen-source GPU Computing Platform
DocumentationROCm Docs
Repositorygithub.com/ROCm
InstallationROCm Installation Guide
Current VersionROCm 7
LicenseMIT 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

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.

A-JEPA AUTOMATA

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

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.

MusKent Commerce OS

MusKent Commerce OS

MusKent is a production-ready autonomous AI system designed to support real commerce operations across revenue, sales, automation, fulfillment, billing, and marketplace workflows. It moves beyond traditional copilots by combining reasoning agents, async execution, tool orchestration, and multimodal input into a unified operating system. At its core, MusKent uses agent-driven decision flows. It continuously evaluates business signals such as revenue performance, marketplace activity, sales trends, and operational state, then determines the next best action using AI. These agents operate within structured workflows, interact with internal tools and external APIs, and execute multi-step tasks while safely degrading to fallback systems when needed. The platform integrates multiple intelligence layers, including AI-powered reasoning for decision-making, specialized compute for ranking and scoring, and voice-based interaction through real-time and batch transcription. This enables a collaborative multi-agent system where different models and providers handle specific tasks like reasoning, analysis, and fallback execution. MusKent is designed for reliability and real-world usage. It supports asynchronous job processing for long-running operations, structured outputs for consistency, provider health awareness, and safe fallback mechanisms to maintain performance even under degraded conditions. From a systems perspective, MusKent delivers intelligent reasoning, agentic workflows, enterprise utility, and multimodal interaction in a single platform. The result is an AI-powered commerce operating system that can analyze, plan, and act across business operations while remaining resilient in production environments.

Apohara PROBANT — Cross-AI Code Verifier

Apohara PROBANT — Cross-AI Code Verifier

Apohara PROBANT is a cross-AI code verification platform. Gemini writes a review; a 12-vendor adversarial ensemble (Claude, GPT, DeepSeek, Kimi, GLM, Qwen, Nemotron, MiniMax, Big-Pickle, Mistral Large, Grok 2, Perplexity Sonar) independently audits the output for prompt injection, vulnerabilities, and logic bugs. Verifiable, not claimed: - 12 vendors via OpenRouter, each in an isolated KV-cache enforced by INV-15 JCRSafetyGate. Paper v3.0 (formal Z3 SMT proof, UNSAT on negation in 10.08 ms) complements v2.0.1 empirical sweep (0/1210 violations). DOI 10.5281/zenodo.20277875. - JBB-Behaviors block rate 93.75% (Wilson 95% CI [86.2%, 97.3%], n=80 holdout). Numbers from logs/*.json, not marketing. - 120+ pytest tests + 15+ measurement JSON logs. - Multi-hardware: AMD MI300X (ROCm 7.2) + NVIDIA H100. Four hardening layers (auditable in repo): 1. Veea LobsterTrap DPI subprocess pre-check — measured: SQLi block 50% (n=20, CI [29.9%, 70.1%] directional), benign FPR 9.8% (n=51). Live demo SQLi returns verdict=blocked in ~25 ms. 2. Prompt envelope nonce-tagged sentinels (Hines et al. arXiv 2403.14720). 3. HMAC-SHA256 verdict ledger chain. verify_chain() catches tampering. 4. NO-HEDGING gate (HARD/SOFT split, judge uncertainty flagged). Distribution: Cursor / Claude Code plugin shipped as VSIX. MCP server (stdio) for Claude Desktop / Cursor / Zed. /v1/verify_stream SSE for live per-vendor results. /dashboard for ops view. Stack: FastAPI/Python 3.11+, React+Vite + Next.js SSR PoC, Apache-2.0, monorepo across 3 GitHub orgs. Live demo BYOK or 5 free/IP/day.

klarixa-tricortex-amd-hackathon

klarixa-tricortex-amd-hackathon

.### Technical Architecture & Core Overview Tricortex is an enterprise-grade, infrastructure-agnostic AI orchestration core engineered to execute complex multi-model reasoning pipelines. Built using the pydantic-ai framework, the system enforces a strict, non-blocking asynchronous lifecycle over autonomous agents. Rather than deploying volatile, free-form execution loops, Tricortex establishes structural operational boundaries through narrow API integration gates, explicit dynamic schema enforcement, and robust human-in-the-loop validation milestones. ### Cross-Platform Hardware Validation To demonstrate absolute operational resilience, the entire orchestration layer has been cross-validated and stress-tested under high-performance AMD compute cluster configurations. This cross-hardware implementation ensures that the system's token routing, latency management loops, and memory distribution handling remain highly stable across varied cloud infrastructures—such as Vultr nodes—preventing critical Out-Of-Memory (OOM) faults during heavy multi-model execution. ### Multi-Model Brain Integration Tricortex decouples the core reasoning layer from a single provider by leveraging a highly adaptive, multi-model backend topography: 1. Google DeepMind Ecosystem: Integrates advanced Gemini models via Google AI Studio to anchor real-time, context-aware routing decisions. 2. Alibaba Qwen Specialized Intelligence: Dynamically injects specialized model intelligence optimized for precise vertical logic across complex domains like finance, law, and medicine. ### Key Architectural Pillars * Asynchronous Emulation: Implements secure, in-memory execution tracking to simulate dense tensor processing latencies natively, ensuring total code reliability. * Model Context Protocol (MCP) Integration: Bridges the gap between remote LLM environments and local system runtime operations, allowing secure context pathway mapping without exposing raw database states.