NNI

Use this repo skill when the user asks about Microsoft NNI / Neural Network Intelligence: AutoML experiments, hyperparameter optimization, neural architecture search, model compression, feature engineering, nnictl, trial APIs, search spaces, training services, or NNI optional dependency failures.

Start Here

1. Identify the user-facing workflow from the request: HPO experiment, NAS, compression, feature engineering/utilities, or install/runtime troubleshooting.
2. Run scripts/check_nni_environment.py --format text when the user reports import, nnictl, package metadata, or optional dependency problems.
3. Route to the focused sub-skill before writing detailed code or configs.
4. Use bundled validators for static checks instead of running training, cloud services, notebooks, benchmarks, or downloads by default.
5. Read references/repo-provenance.md before deciding whether this skill is current for a different NNI checkout.

Route By Task

User task or signal	Use
nnictl, experiment YAML, ExperimentConfig, Experiment, trial code, search-space JSON, tuners, assessors, training services	sub-skills/hpo-experiments/
ModelSpace, LayerChoice, InputChoice, NAS evaluator, NAS strategy, NasExperiment, fixed architecture export	sub-skills/nas/
pruning, quantization, distillation, compression config_list, pruners, quantizers, evaluators, speedup/export	sub-skills/model-compression/
GBDTSelector, FeatureGradientSelector, nni.trace, nni.dump, concrete tracing, graph/profiler/flop utilities	sub-skills/feature-engineering-and-utilities/
import/install failures, package version, missing optional stacks, pkg_resources, broad NNI task classification	this root skill plus references/troubleshooting.md

Quick Install And Import Checks

For normal users, start from a released package:

python -m pip install nni
python -c "import nni; print(nni.__version__)"
nnictl --help

For source-development workflows, NNI packaging can involve generated frontend assets in addition to Python modules. If editable installation from a checkout fails on missing node/frontend package material, either build the frontend according to the source-build docs or use the Python source tree for read-only API inspection while treating Web UI assets as unavailable.

Sub-skills

HPO Experiments

Read sub-skills/hpo-experiments/SKILL.md when the request mentions HPO, experiment configs, search spaces, nnictl, trial metric reporting, tuners, assessors, or training services.

Useful bundled entry points:

• sub-skills/hpo-experiments/references/workflows.md for CLI and Python experiment recipes.
• sub-skills/hpo-experiments/references/api-reference.md for trial, tuner, assessor, and search-space API facts.
• sub-skills/hpo-experiments/references/cli-reference.md for nnictl command families.
• sub-skills/hpo-experiments/scripts/validate_search_space.py for dependency-free JSON search-space validation.

NAS

Read sub-skills/nas/SKILL.md when the request mentions NAS model spaces, mutables, evaluators, strategies, model export/freeze, or torch / pytorch_lightning import failures while doing NAS.

Useful bundled entry points:

• sub-skills/nas/references/workflows.md for model-space, evaluator, strategy, and fixed-architecture workflows.
• sub-skills/nas/references/api-reference.md for key object names and optional dependency boundaries.
• sub-skills/nas/scripts/check_nas_optional_deps.py for safe optional-stack diagnostics.

Model Compression

Read sub-skills/model-compression/SKILL.md when the request mentions NNI pruning, quantization, distillation, config_list, evaluator wrappers, speedup, or export.

Useful bundled entry points:

• sub-skills/model-compression/references/workflows.md for pruning, quantization, distillation, evaluator, and speedup flows.
• sub-skills/model-compression/references/api-reference.md for config-list and compression API contracts.
• sub-skills/model-compression/scripts/validate_config_list.py for dependency-free JSON config-list validation.

Feature Engineering And Utilities

Read sub-skills/feature-engineering-and-utilities/SKILL.md when the request mentions feature selectors, trace serialization, concrete tracing, graph inspection, FLOP/profiler helpers, or utility import problems.

Useful bundled entry points:

• sub-skills/feature-engineering-and-utilities/references/workflows.md for selector and utility recipes.
• sub-skills/feature-engineering-and-utilities/references/api-reference.md for selected import paths and data expectations.
• sub-skills/feature-engineering-and-utilities/scripts/check_optional_utilities.py for safe utility-stack diagnostics.

Cross-cutting Troubleshooting

Read references/troubleshooting.md when the request is about installation, package metadata, nnictl entry points, pkg_resources, missing optional dependencies, source-build frontend assets, version mismatch, or deciding which optional stack is required.

Use these defaults:

• Do not install broad optional extras unless the user explicitly needs many optional algorithms; prefer the specific extra or package named by the failing workflow.
• Do not run remote/cloud training services, notebooks, benchmarks, full NAS searches, compression training, or dataset downloads without explicit user approval.
• Treat torch, pytorch_lightning, LightGBM, Transformers, DeepSpeed, TensorRT, ONNX, and cloud credentials as optional workflow dependencies, not base NNI requirements.
• Prefer static validators and import diagnostics before suggesting expensive runtime execution.

Provenance And Refresh

Read references/repo-provenance.md if a user asks whether this skill matches a current NNI checkout. If the commit, package version, source roots, public entry points, documentation, examples, or tests have changed, refresh the repo skill before relying on detailed guidance.