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。
Use when building, debugging, testing, or maintaining Plotly Dash applications, callbacks, server backends, component packages, renderer internals, and Dash repository workflows.
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。
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.
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。