---

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.
• 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 to select the package group and minimal import check.

2. Run the bundled diagnostic when the environment is already installed:

   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 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.