Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
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Use when building, debugging, testing, or maintaining Plotly Dash applications, callbacks, server backends, component packages, renderer internals, and Dash repository workflows.
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Dash is Plotly's Python framework for building reactive web applications with Python layouts, React component bundles, and server-side or clientside callbacks. Use this repo skill when a task involves Dash application code, callback routing, Dash server backends, component wrapper generation, the Dash renderer, or Dash repo test/build workflows.
For ordinary app use:
python -m pip install dash
python - <<'PY'
import dash
from dash import Dash, html, dcc, Input, Output
print(dash.__version__)
app = Dash(__name__)
app.layout = html.Div([dcc.Input(id="name"), html.Div(id="out")])
@app.callback(Output("out", "children"), Input("name", "value"))
def update(value):
return value or ""
print(app.layout.to_plotly_json()["type"], len(app.callback_map))
PY
Choose extras only when the workflow needs them:
| Need | Install |
|---|---|
| Selenium/browser testing fixtures | python -m pip install "dash[testing]" |
| Diskcache background callbacks | python -m pip install "dash[diskcache]" |
| Celery background callbacks | python -m pip install "dash[celery]" plus a broker/result backend |
| FastAPI backend and WebSocket callbacks | python -m pip install "dash[fastapi]" |
| Quart backend | python -m pip install "dash[quart]" |
async def callbacks on Flask | python -m pip install "dash[async]" |
| Component generation helpers | install Dash from a checkout with the development dependencies needed by the component package |
Use sub-skills/app-callback-workflows/SKILL.md when the task mentions:
Dash(...), layout trees, html, dcc, dash_table, component IDs, assets,
URL prefixes, Jupyter display, CSP hashes, or pages.@app.callback, @dash.callback, Input, Output, State, MATCH, ALL,
ALLSMALLER, ctx, callback_context, PreventUpdate, no_update, or
Patch.window.dash_clientside.Start with its callbacks and layouts reference, then use its smoke scripts for layout/callback/page checks.
Use sub-skills/server-backends-and-async/SKILL.md when the task mentions:
async def callbacks, dash[async], websocket_callbacks, websocket=True,
persistent=True, ctx.websocket, set_props, or WebSocket disconnects.background=True, DiskcacheManager, CeleryManager, progress, running,
cancel, or callback caching.configure_mcp_server.Run its backend inspection script for a safe optional-extra report before promising backend coverage.
Use sub-skills/component-renderer-development/SKILL.md when the task mentions:
dash-generate-components, dash-update-components, renderer, component
metadata, generated Python wrappers, R/Julia wrapper generation, or built-in
component packages.Component, to_plotly_json, _children_props, _js_dist, _css_dist,
serve_locally, external_url, dev_package_path, or resource fingerprints.dash-renderer, callback queues, setProps, crawlLayout, paths,
window.dash_component_api, SharedWorker/WebSocket client, React versions, or
JSX runtime problems.Use its CLI-check scripts before running heavier Node or component builds.
Use sub-skills/testing-and-maintenance/SKILL.md when the task mentions:
pytest, dash_duo, Selenium, ChromeDriver, Percy, renderer tests, component
tests, npm run build, npm run lint, Black/flake8/pylint/eslint/prettier,
or contribution policy.Follow its rule: never run all integration tests by default. Use focused files and preserve enough output to see tracebacks and assertion context.
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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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Routes 3DDFA_V2 face-alignment setup, still-image demos, video tracking, and ONNX benchmarking workflows.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Operate AB3DMOT 3D multi-object tracking workflows for KITTI and nuScenes data, tracking, evaluation, and visualization.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.