Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
日本語の概要は準備中です。原文の説明を表示しています。
Route Flower repository tasks for app authoring, strategies and mods, local simulation and deployment, Flower Datasets and examples, and repository maintenance.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
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.
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
日本語の概要は準備中です。原文の説明を表示しています。
Guide 3DDFA Python inference, geometry rendering, training/evaluation, and optional C++ ONNX workflows for 3D dense face alignment.
日本語の概要は準備中です。原文の説明を表示しています。
Routes 3DDFA_V2 face-alignment setup, still-image demos, video tracking, and ONNX benchmarking workflows.
日本語の概要は準備中です。原文の説明を表示しています。
Operate AB3DMOT 3D multi-object tracking workflows for KITTI and nuScenes data, tracking, evaluation, and visualization.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.
日本語の概要は準備中です。原文の説明を表示しています。