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 BiSheng's enterprise LLM application DevOps platform repository, including FastAPI backend, workflow/RAG/Linsight runtimes, permissions, dual React frontends, deployment, and maintainer 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。
Use this repo skill when a task names BiSheng, 毕昇, dataelement/bisheng, or asks about this enterprise LLM application DevOps platform's backend, workflow engine, knowledge/RAG pipeline, Linsight agent, permissions/tenancy, dual React frontends, Docker deployment, or repository maintenance.
This root skill is a router. Read the focused sub-skill first, then return here for cross-cutting checks.
references/repo-routing-metadata.json is structured metadata for managed repo-skill routing and import checks.python scripts/check_repo_surface.py --repo-root <bisheng-checkout>
For backend work, install from the backend root with the repository-supported lockfile:
cd <bisheng-checkout>/src/backend
uv sync --frozen
uv run python -c "import bisheng, bisheng_langchain; print(getattr(bisheng, '__version__', 'unknown'))"
For frontend work, install and test from the owning app directory only:
cd <bisheng-checkout>/src/frontend/platform && npm install && npm test
cd <bisheng-checkout>/src/frontend/client && npm install && npm run test:ci
Use narrower sub-skill commands before running broad suites; many integration/e2e paths require MySQL, Redis, Milvus, Elasticsearch, MinIO, OpenFGA, or the commercial gateway.
| User task | Read |
|---|---|
| Add or debug FastAPI routes, DDD modules, response envelopes, settings/context, SQLModel DAO/model behavior, backend tests, or error-code handling | backend-core |
| Work on workflow canvas execution, LangGraph graph compilation, node types, callbacks, interruption/resume, workflow Celery tasks, or new workflow nodes | workflow-engine |
| Work on knowledge libraries/spaces, file upload states, document parsing, Load → Transform → Ingest, Milvus + Elasticsearch recall, MinIO artifacts, QA libraries, or knowledge workers | knowledge-rag |
| Work on Linsight task mode, SOP/Skill migration, Redis worker queue, task/session state events, deepagents runtime, built-in tools, or MCP integrations | linsight-mcp |
| Work on JWT auth, RBAC/ReBAC/OpenFGA, PermissionService, tenant isolation, admin scope, approval center, org/SSO sync, commercial gateway, or cursor-pagination permission performance | identity-permissions-tenancy |
| Modify Platform or Client React apps, routes, stores, request wrappers, UI systems, i18n, brand theme, tests, or Vite config | frontend-apps |
| Install, run, deploy, configure, migrate, operate, or maintain the repo, Docker Compose stack, backend scripts, arch guard, uv/npm commands, or SDD workflow | deployment-maintenance |
frontend-apps.workflow-engine; autonomous task-mode execution, SOP/Skill runtime, Redis Linsight worker, and MCP tool wrapping belong to linsight-mcp.knowledge-rag; resource visibility, ReBAC checks, approval modes, tenant filters, and cursor permission performance belong to identity-permissions-tenancy.deployment-maintenance; feature implementation details route to the owning code sub-skill.axios directly in business frontend code; use the app's wrapped request layer.The bundled scripts are read-only inspectors. They accept --repo-root so they can be used with any BiSheng checkout; they do not depend on the checkout used to create this skill.
Use repository-native tests only after selecting the focused surface. Many end-to-end and integration tests require MySQL, Redis, Milvus, Elasticsearch, MinIO, OpenFGA, frontend browsers, or the commercial gateway; prefer the narrow safe test listed by the owning sub-skill before running broad suites.
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。