artifacts/check2/migration_report.md

ROCmPort AI Migration Report: cuda_first_repo

AMD Readiness Score

• Before deterministic fixes: 51/100
• After deterministic fixes: 100/100

Category	Before	After
Code portability	0	100
Environment readiness	0	100
Serving readiness	90	100
Benchmark readiness	65	100
Deployment readiness	100	100

Findings

Severity	Category	Location	Finding	Suggested fix
high	Benchmark readiness	benchmarks/benchmark.py:6	NVIDIA-specific GPU inspection command found.	Use rocm-smi for AMD GPU monitoring and benchmark metadata collection.
high	Environment readiness	Dockerfile:1	Dockerfile uses an NVIDIA CUDA base image.	Use vllm/vllm-openai-rocm:latest for vLLM serving or rocm/pytorch:latest for PyTorch workloads.
medium	Environment readiness	Dockerfile:8	NVIDIA container environment variable found.	Use HIP_VISIBLE_DEVICES or ROCR_VISIBLE_DEVICES for AMD GPU targeting.
high	Code portability	infer.py:6	torch.device is hardcoded to CUDA.	Use torch.device("cuda" if torch.cuda.is_available() else "cpu"); ROCm PyTorch reports AMD GPUs through torch.cuda.
high	Code portability	infer.py:11	PyTorch tensor or module is moved with a hardcoded .cuda() call.	Replace .cuda() with .to(_rocmport_device) and define a runtime device abstraction.
high	Code portability	infer.py:12	Tensor or module transfer hardcodes the CUDA device string.	Replace .to("cuda") with .to(_rocmport_device).
low	Code portability	infer.py:19	CUDA availability check may confuse ROCm users because PyTorch ROCm still uses the torch.cuda namespace.	Keep the API call but document that it covers AMD GPUs under ROCm PyTorch.
medium	Environment readiness	scripts/serve_vllm.sh:4	CUDA_VISIBLE_DEVICES is used for GPU selection.	Use HIP_VISIBLE_DEVICES or ROCR_VISIBLE_DEVICES for AMD GPU targeting.
high	Environment readiness	scripts/serve_vllm.sh:5	NVIDIA-specific GPU inspection command found.	Use rocm-smi for AMD GPU monitoring and benchmark metadata collection.
low	Serving readiness	scripts/serve_vllm.sh:6	vLLM serving command found without explicit ROCm container guidance.	Run vLLM inside vllm/vllm-openai-rocm with /dev/kfd, /dev/dri, host IPC, and video group access.

Generated Artifacts

• rocm_patch.diff contains deterministic MVP fixes.
• Dockerfile.rocm uses the ROCm-enabled vLLM container.
• amd_developer_cloud_runbook.md documents the validation path.
• benchmark_result.json records the AMD benchmark schema and status.

Qwen Agent Notes

Qwen endpoint was not configured. The report uses deterministic scanner output only.

Remaining Risks

• CUDA C++ kernels, custom Triton kernels, and CUDA-only binary dependencies require manual review.
• Uploaded repositories are not executed inside the Space; live validation belongs on AMD Developer Cloud.
• ROCm performance depends on model, batch shape, vLLM version, ROCm version, and GPU instance configuration.