Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
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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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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
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.
If you are trying to decide where a task belongs, route by intent:
sub-skills/app-development/sub-skills/strategies-and-mods/sub-skills/simulation-and-deployment/sub-skills/datasets-and-examples/sub-skills/repository-maintenance/Work in an isolated Python 3.11 environment and install the Flower packages you need for the task.
flwr and flwr-datasets.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.
scripts/check_flower_install.py checks
package imports, versions, safe CLI help/version output, app-component wiring,
and tiny Flower Datasets partitioner behavior.scripts/catalog_examples.py summarizes the
example app catalog and their dependency surfaces.scripts/check_public_api.py verifies public
exports for __all__-based packages.references/troubleshooting.md for
cross-cutting install, import, CLI, optional-dependency, and runtime issues.references/repo-provenance.md to decide
whether this skill matches the current checkout or should be refreshed.references/repo-routing-metadata.json
for router metadata consumed by repo-skills-router.The inspected runtime packages expose these public roots:
flwr → app, clientapp, serverapp, agentappflwr_datasets → FederatedDataset, partitioner, preprocessor,
metrics, utils, visualizationThe sub-skills contain the detailed API notes, workflows, and troubleshooting for each workflow family.
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Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Guide 3DDFA Python inference, geometry rendering, training/evaluation, and optional C++ ONNX workflows for 3D dense face alignment.
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Routes 3DDFA_V2 face-alignment setup, still-image demos, video tracking, and ONNX benchmarking workflows.
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Operate AB3DMOT 3D multi-object tracking workflows for KITTI and nuScenes data, tracking, evaluation, and visualization.
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Use Hugging Face Accelerate for PyTorch training-loop migration, distributed launch/configuration, DeepSpeed/FSDP/TPU backend setup, big-model inference/offload, checkpointing, tracking, and troubleshooting.
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Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.
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