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gfxgraph-testing

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Mis à jour17 mai 2026 à 06:17

Test gfxGRAPH on real hardware and confirm it is functioning after install or configuration changes.

Installation

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Source

carlosfundora

carlosfundora/gfxGRAPH

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Métiers associésSOC

Basé sur la classification professionnelle SOC

Analystes en assurance qualité des logiciels et testeursProfessions informatiques et mathématiques·SOC 15-1253

SKILL.md

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name	gfxgraph-testing
description	Test gfxGRAPH on real hardware and confirm it is functioning after install or configuration changes.

gfxGRAPH Testing

Goal Confirm gfxGRAPH is functioning on the target machine after install or reconfiguration.

When to use this skill

After installing gfxGRAPH.
After changing configuration or replay modes.
When you need to confirm the package actually works on a real ROCm GPU.
When you need a pass/fail answer for the current install state.

Do not use this skill for

Preparing the environment for install.
Applying configuration changes.
Debugging unrelated ROCm failures before the install is proven.

How to use it

Run the relevant Python integration tests from the repo.
Run a direct smoke check with gfxgraph.health_check().
Confirm the runtime reports the expected GPU and replay behavior.
If a failure appears, capture the exact error before changing anything else.

Preferred checks

python3 -m pytest tests/test_torch_integration.py -v
python3 -c "import gfxgraph; gfxgraph.enable(); print(gfxgraph.health_check())"
gfxgraph.stats() after a real capture/replay path

Test rules

Use real hardware when available.
Do not rely on mocks for GPU availability in the success path.
Confirm both importability and runtime behavior.
Prefer a minimal smoke test first, then expand to the repo's integration test path.

Pass criteria

import gfxgraph succeeds.
gfxgraph.health_check() reports ok: True.
The runtime sees the intended ROCm GPU.

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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.

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Benchmark gfxGRAPH internals, run the public benchmark suite, and compare performance between Python and Rust.

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Guide and resources for deployment and development using the ROCm/AMD ATOM (AiTer Optimized Model) inference backend.

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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.

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Verifies whether a requested ROCm-DS workflow is officially supported, source-build feasible, or experimental on the target system.

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Migrates pandas-like workflows to hipDF and cudf.pandas style acceleration, auditing for unsupported features.

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name	gfxgraph-testing
description	Test gfxGRAPH on real hardware and confirm it is functioning after install or configuration changes.

gfxGRAPH Testing

Goal Confirm gfxGRAPH is functioning on the target machine after install or reconfiguration.

When to use this skill

After installing gfxGRAPH.
After changing configuration or replay modes.
When you need to confirm the package actually works on a real ROCm GPU.
When you need a pass/fail answer for the current install state.

Do not use this skill for

Preparing the environment for install.
Applying configuration changes.
Debugging unrelated ROCm failures before the install is proven.

How to use it

Run the relevant Python integration tests from the repo.
Run a direct smoke check with gfxgraph.health_check().
Confirm the runtime reports the expected GPU and replay behavior.
If a failure appears, capture the exact error before changing anything else.

Preferred checks

python3 -m pytest tests/test_torch_integration.py -v
python3 -c "import gfxgraph; gfxgraph.enable(); print(gfxgraph.health_check())"
gfxgraph.stats() after a real capture/replay path

Test rules

Use real hardware when available.
Do not rely on mocks for GPU availability in the success path.
Confirm both importability and runtime behavior.
Prefer a minimal smoke test first, then expand to the repo's integration test path.

Pass criteria

import gfxgraph succeeds.
gfxgraph.health_check() reports ok: True.
The runtime sees the intended ROCm GPU.