ROCmPorter — AI-Powered CUDA to AMD ROCm Migration Agent
Hi AMD Developer Community! 
I’m excited to share ROCmPorter, an open-source developer tool I built to make migrating NVIDIA/CUDA-based projects to the AMD ROCm ecosystem easier, faster, and more transparent.
Live Demo:
GitHub Repository:
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Why I Built ROCmPorter
A large amount of GPU software is tightly coupled to NVIDIA CUDA.
Projects often contain things like:
- “cudaMalloc”
- “cudaMemcpy”
- “torch.cuda”
- CUDA kernels
- “nvcc”
- NVIDIA-specific Docker images
- CUDA-only dependencies
- CUDA build configurations
Migrating such projects to AMD ROCm can require developers to manually inspect hundreds or thousands of lines of code.
My goal with ROCmPorter was simple:
«Give it a GitHub repository, identify CUDA lock-in, measure ROCm portability, generate migration changes, validate them, and help developers move toward AMD ROCm.»
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What ROCmPorter Can Do
Evidence-Driven Repository Scanning
ROCmPorter scans a GitHub repository and identifies CUDA/NVIDIA dependencies throughout the project.
Instead of giving generic warnings, every finding includes:
File → Line Number → Code Snippet → Compatibility Issue → Migration Direction
This makes every result inspectable and easier for developers to understand.
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ROCm Readiness Score
After scanning a repository, ROCmPorter calculates a:
0–100 ROCm Readiness Score
The report also includes:
- Portability risk level
- CUDA/NVIDIA findings
- Compatibility issues
- Migration recommendations
- Migration checklist
- File-level evidence
The idea is to give developers a quick understanding of how difficult a CUDA → ROCm migration may be before they begin modifying the code.
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Hybrid CUDA → HIP Migration Engine
One of the most important design decisions in ROCmPorter was not relying completely on AI.
The migration pipeline combines deterministic transformations with AI-based reasoning.
Migration Flow
GitHub Repository
↓
Static Analysis
↓
ROCm Readiness Score
↓
Deterministic CUDA → HIP Conversion
↓
AI Semantic Migration
↓
Verification
↓
Migration Pull Request
Straightforward CUDA patterns are converted using deterministic mappings.
AI is used only when deeper semantic understanding is required.
This approach helps make the migration process more predictable and easier to review.
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AI-Assisted Migration
For CUDA code that cannot be converted through simple deterministic mappings, ROCmPorter can generate ROCm/HIP migration patches using an AI layer.
The AI migration system is designed around ROCm migration knowledge such as:
- CUDA → HIP API migration
- cuDNN → MIOpen alternatives
- rocThrust
- AMD GPU architecture considerations
- ROCm PyTorch behavior
- GPU portability issues
- Wavefront differences
Generated changes can then be inspected before being applied.
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Verification Before Apply
Generated migration patches go through verification steps before touching the repository.
ROCmPorter supports checks such as:
- Syntax validation
- Drift detection
- Diff verification
- Artifact hashing
- Migration replay checks
The goal is to make AI-assisted migration more auditable rather than blindly accepting generated code.
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ROCm Compile Validation
ROCmPorter also includes ROCm compile validation using:
“hipcc”
Migrated code can be compile-checked inside a ROCm environment through CI.
This moves the workflow beyond simply generating CUDA → HIP code and toward validating whether the generated code can actually compile within the ROCm toolchain.
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One-Click Migration Pull Requests
After analysis and migration, ROCmPorter can generate a repository-wide migration and create a:
CUDA → ROCm Migration Pull Request
This allows developers to review the migration using a familiar GitHub workflow instead of manually copying generated code.
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VS Code Extension
I also built a VS Code extension for ROCmPorter.
It can help developers identify CUDA lock-in directly while coding.
Features include:
- CUDA compatibility diagnostics
- HIP alternatives on hover
- Quick fixes
- CUDA → HIP suggestions
- One-click file hipify
The goal is to help developers think about AMD ROCm portability during development, not only after a project is finished.
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GitHub CI Integration
ROCmPorter can also be integrated into GitHub Actions.
Repositories can automatically receive a ROCm readiness check on pull requests.
This makes it possible to monitor whether new changes increase or decrease AMD ROCm portability over time.
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ROCm Readiness Badge
Projects can display their current ROCm readiness score directly inside their README through a generated badge.
This could help projects communicate how portable their codebase is toward AMD GPU environments.
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Tech Stack
Frontend
- React 19
- Vite
- React Router
Backend
- Python
- FastAPI
- Static analysis engine
- Git repository analysis
AMD / GPU
- AMD ROCm
- HIP
- “hipcc”
- CUDA → HIP migration logic
AI
- Pluggable LLM migration layer
- ROCm migration knowledge base
Authentication & Database
- Supabase
- PostgreSQL
- GitHub OAuth
- Google OAuth
Developer Integration
- GitHub Actions
- VS Code Extension
Deployment
- Vercel
- Render
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Try It Live
You can test ROCmPorter with a public GitHub repository here:
Paste a repository URL and ROCmPorter will analyze the project and generate its ROCm readiness report.
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Source Code
The entire project is availablable at the above top of my post
The project is open source under the MIT License.
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What I Want to Build Next
The next major goal for ROCmPorter is:
Real AMD GPU Execution Validation
Currently, ROCmPorter can analyze, migrate, verify, and perform ROCm compile validation.
The next step is to validate migrated projects by actually executing them on AMD GPU infrastructure.
Other ideas on the roadmap include:
- More CUDA API coverage
- Better cross-file migration reasoning
- Expanded ROCm compatibility rules
- More advanced CI validation
- AMD Developer Cloud integration
- Runtime performance comparison
- Team migration workflows
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Feedback From the AMD Community
This project is still evolving, and I would genuinely appreciate feedback from developers working with:
ROCm, HIP, AMD GPUs, CUDA migration, PyTorch on ROCm, GPU tooling, or HPC workloads.
I would especially love to hear about:
- CUDA migration edge cases ROCmPorter should detect
- Important ROCm compatibility rules I may be missing
- Better ways to validate generated HIP code
- Additional AMD tooling that could be integrated
- Real-world CUDA repositories worth testing
Contribution 
Built for the AMD ROCm ecosystem.