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部署与运维 · SKILL.md

flower

flower

【部署与运维】Route Flower repository tasks for app authoring, strategies and mods, local simulation and deployment, Flower Datasets and examples, and repository maintenance.

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技能说明

GitHub

name: flower description: "Route Flower repository tasks for app authoring, strategies and mods, local simulation and deployment, Flower Datasets and examples, and repository maintenance." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Flower

Use this repo skill for the Flower ecosystem: the flwr framework, Flower Datasets, example app patterns, local simulation and deployment workflows, and repo-maintenance commands for the Flower checkout itself.

Start here

If you are trying to decide where a task belongs, route by intent:

  • App authoring and customization → sub-skills/app-development/
  • Strategies, aggregation, mods, and checkpoint-aware behavior → sub-skills/strategies-and-mods/
  • Local simulation, SuperLink/SuperNode, CLI, and deployment routing → sub-skills/simulation-and-deployment/
  • Flower Datasets and example app patterns → sub-skills/datasets-and-examples/
  • Contributor commands, public API exposure, protobufs, migrations, and docs → sub-skills/repository-maintenance/

Install and inspect

Work in an isolated Python 3.11 environment and install the Flower packages you need for the task.

  • For ordinary package use and inspection, install flwr and flwr-datasets.
  • For simulation or deployment workflows, add the documented optional runtime pieces only when the task actually needs them.
  • For maintainer tasks, use the repository's dev commands in the package-owned project directory rather than the runtime inspection environment.

A minimal smoke check is:

python -c "import flwr, flwr_datasets; print(flwr.__version__, flwr_datasets.__version__)"

For a more complete read-only smoke, run scripts/check_flower_install.py.

Useful runtime helpers

Read next

Package surfaces

The inspected runtime packages expose these public roots:

  • flwr → app, clientapp, serverapp, agentapp
  • flwr_datasets → FederatedDataset, partitioner, preprocessor, metrics, utils, visualization

The sub-skills contain the detailed API notes, workflows, and troubleshooting for each workflow family.

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