README.md

metadata

title: ROCmPort AI
emoji: ⚡
colorFrom: red
colorTo: yellow
sdk: gradio
app_file: app.py
pinned: false
license: mit
short_description: CUDA-to-ROCm migration scanner for PyTorch, HF & vLLM repos

⚡ ROCmPort AI

AMD Developer Hackathon — lablab.ai | Track: AI Agents & Agentic Workflows

ROCmPort AI is a CUDA-to-ROCm migration scanner powered by a three-agent CrewAI pipeline and Qwen3-Coder running on AMD Instinct GPUs. Drop in any CUDA-first PyTorch, Hugging Face, or vLLM repository and get a full AMD readiness report in seconds.

What it does

graph LR
    User([User Repo]) --> Gradio[Gradio UI]
    Gradio --> Pipeline{Pipeline}
    
    subgraph Agentic Workflow
    Pipeline --> Auditor[CUDA Auditor]
    Auditor --> Engineer[ROCm Engineer]
    Engineer --> Reporter[Report Writer]
    end
    
    Reporter --> LLM[(Qwen3-Coder on AMD Instinct)]
    LLM --> Reporter
    
    Pipeline --> Scanner[Deterministic Scanner]
    Scanner --> Patcher[Patcher]
    Patcher --> Artifacts[Artifact Generator]
    
    Reporter --> Final([Migration Artifacts & Patch])
    Artifacts --> Final

Output	Description
AMD Readiness Score	Before/after scores across 5 categories
Findings table	File + line references for every CUDA blocker
ROCm patch diff	Auto-generated unified diff to apply deterministic fixes
Dockerfile.rocm	ROCm-enabled container using vllm/vllm-openai-rocm
AMD Developer Cloud Runbook	Exact validation commands for AMD Instinct GPUs
Migration report	Narrative report (CrewAI + Qwen when configured)
Benchmark schema	Structured result to fill after AMD Developer Cloud run
Artifact ZIP	All outputs bundled for download

Three-agent pipeline

When QWEN_BASE_URL and QWEN_API_KEY are set (pointing to a Qwen3-Coder endpoint on AMD Instinct MI300X via vLLM), three CrewAI agents collaborate:

1. CUDA Migration Auditor — scans every file for blockers using scan_cuda_repository tool
2. ROCm Migration Engineer — generates the patch diff using generate_rocm_patch tool
3. Migration Report Writer — synthesises findings into an actionable Markdown report

Without those env vars the app falls back to the fully deterministic scanner + patcher (which always runs).

Run locally

pip install -r requirements.txt
python app.py

App listens on http://127.0.0.1:7860.

Enable the full CrewAI + Qwen pipeline

# Windows
set QWEN_BASE_URL=https://your-amd-instinct-endpoint/v1
set QWEN_API_KEY=your-token
set QWEN_MODEL=Qwen/Qwen3-Coder-Next-FP8
python app.py

# Linux / macOS
QWEN_BASE_URL=https://your-amd-instinct-endpoint/v1 \
QWEN_API_KEY=your-token \
QWEN_MODEL=Qwen/Qwen3-Coder-Next-FP8 \
python app.py

Tests

python -m pytest tests/ -v

7 tests cover the scanner, pipeline, and CrewAI agent layer.

AMD Benchmark

The data/benchmark_result.json is a transparent pending benchmark schema — not a fabricated result. Run the generated AMD Developer Cloud runbook (shown in the app's Runbook tab) on an AMD Instinct MI300X instance to capture real throughput, latency, and VRAM figures, then replace the file.

Deploy to Hugging Face Spaces

python scripts/deploy_to_hf.py --token hf_... --username YourHFUsername

Tech stack

• AMD Developer Cloud + AMD Instinct MI300X for GPU compute
• ROCm — open-source GPU computing platform
• CrewAI — multi-agent orchestration
• Qwen3-Coder-Next-FP8 — code-specialist LLM on AMD hardware
• vLLM (ROCm build) — high-throughput serving
• Hugging Face — model hub + Space hosting
• Gradio 5 — web UI