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Configure or reconfigure gfxGRAPH runtime behavior, environment variables, and replay modes after installation.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
| name | gfxgraph-configure |
| description | Configure or reconfigure gfxGRAPH runtime behavior, environment variables, and replay modes after installation. |
Goal Set up or adjust gfxGRAPH runtime behavior after installation.
gfxgraph.stats() or gfxgraph.health_check().GFXGRAPH_REPLAY_MODE=standard|adaptive|hotGFXGRAPH_REPLAY_HOT_MODE=1GFXGRAPH_VRAM_CAPHGB_LOG_LEVELHSA_OVERRIDE_GFX_VERSIONGFXGRAPH_REPLAY_MODE between standard, adaptive, and hot.GFXGRAPH_VRAM_CAP if capture pressure is too high.Develop and extend gfxGRAPH internals — add gap bridges, fix HIP/ROCm issues, debug graph capture failures, and understand the architecture. Covers BridgedCUDAGraph, ShapeBucketPool, ConditionalGraph, native bridge, and the monkey-patch system. USE FOR: extend gfxGRAPH, add new gap bridge, fix gfxGRAPH bug, understand gfxGRAPH architecture, modify BridgedCUDAGraph, add gfxGRAPH feature, debug graph capture internals, improve gfxGRAPH performance, gfxGRAPH C++ native bridge, write gfxGRAPH tests. DO NOT USE FOR: just using gfxGRAPH in a project (use gfxgraph-integration), general ROCm issues.
Benchmark gfxGRAPH internals, run the public benchmark suite, and compare performance between Python and Rust.
Guide and resources for deployment and development using the ROCm/AMD ATOM (AiTer Optimized Model) inference backend.
Validates and benchmarks ROCm-DS component ports against their CPU baselines (e.g., pandas vs hipDF). Use this to generate parity tests, verify correctness, and measure the performance speedups of GPU-accelerated code.
Verifies whether a requested ROCm-DS workflow is officially supported, source-build feasible, or experimental on the target system.
Migrates pandas-like workflows to hipDF and cudf.pandas style acceleration, auditing for unsupported features.