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setup-and-backends

Install, import, optional dependency, backend-selection, and environment diagnostics guidance for ART users.

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VectorSpaceLab/AREX-Skill
Última atividade na origem
26 de agosto de 2026 às 16:31
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SKILL.md
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name
setup-and-backends
description
Install, import, optional dependency, backend-selection, and environment diagnostics guidance for ART users.
disable-model-invocation
true
metadata
{"disco-role":"operating"}
license
MIT
# setup-and-backends Use this sub-skill before any ART workflow when the user needs to install ART, verify imports, choose CPU/GPU packages, or resolve optional-backend failures. ## Use this for - Installing the public distribution `adversarial-robustness-toolbox` and importing the Python module `art`. - Choosing a minimal CPU-capable stack or adding optional backend families for PyTorch, TensorFlow/Keras, boosted trees, GPy, image helpers, or TensorBoard logging. - Running a safe install/import diagnostic with [`scripts/inspect_art_install.py`](scripts/inspect_art_install.py). - Diagnosing ImportError, missing optional dependencies, TensorFlow/NumPy/ml-dtypes conflicts, CPU-only wheels, no-CUDA hosts, and version mismatches. ## Start here 1. Read [`references/install-and-backends.md`](references/install-and-backends.md) to select the package group and minimal import check. 2. Run the bundled diagnostic when the environment is already installed: ```bash python scripts/inspect_art_install.py --json ``` 3. If the user is on CPU and uses a PyTorch ART estimator or PyTorch preprocessor, explicitly pass `device_type="cpu"`. ART's `PyTorchClassifier` constructor defaults `device_type="gpu"`; being explicit prevents confusing CPU-only or no-CUDA diagnoses. 4. If imports fail, use [`references/troubleshooting.md`](references/troubleshooting.md) before changing framework versions. ## Route away from this sub-skill - Estimator/model wrapper construction, `clip_values`, label shapes, gradient availability, and black-box vs white-box wrapper choice -> sibling `estimators-and-models`. - Evasion attacks, preprocessing defences, adversarial training, and attack budgets -> sibling `evasion-and-preprocessing`. - Poisoning, backdoor, inference/privacy, extraction, and detectors -> sibling `poisoning-inference-extraction`. - Metrics, evaluation objects, SummaryWriter workflow details, certification, and verification -> sibling `evaluation-and-certification`. ## Guardrails - Do not run the original repository's tests, examples, notebooks, or maintainer scripts for setup diagnosis. Use the bundled diagnostic and tiny user-owned checks only. - Do not install broad `all` extras by default. Start with the core package plus only the backend family required by the user's selected workflow. - Treat GPU as optional acceleration unless the user's workflow explicitly requires GPU-only dependencies. A CPU-capable ART workflow should not require CUDA packages. - Keep install commands and troubleshooting self-contained; do not depend on a source checkout being available.
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