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 BentoML to author model-serving Services, build and containerize Bentos, run HTTP/gRPC servers and clients, manage models, operate the CLI and BentoCloud, and configure observability or production runtime behavior. Use when tasks mention BentoML, bentoml service.py, bentofile.yaml, Bento build/serve/deploy, BentoCloud, or BentoML model stores.
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 BentoML, a Python framework for building, serving, packaging, and deploying AI/model inference APIs. This root skill is a router: read the focused sub-skill that matches the user’s task, then use its bundled references and scripts.
pip install -U bentoml; optional extras include bentoml[grpc], bentoml[io], bentoml[tracing-otlp], bentoml[monitor-otlp], and framework-specific packages needed by the user’s model.python -c "import bentoml; print(bentoml.__version__)".bentoml --help and bentoml env.references/repo-provenance.md before deciding whether this skill is stale for a current checkout.references/troubleshooting.md for cross-cutting install/import, CLI, environment, optional dependency, and cloud safety issues.| User task | Use this sub-skill | Typical triggers |
|---|---|---|
Write or debug service.py, APIs, tasks, batching, lifecycle hooks, ASGI/Gradio/WebSocket/streaming, IO specs, runtime image hints | sub-skills/service-authoring/SKILL.md | @bentoml.service, @bentoml.api, @bentoml.task, bentoml.importing, service import target, API schema |
Build a Bento, author bentofile.yaml, include/exclude files, configure build dependencies/images, local Bento store, Docker/containerize | sub-skills/packaging-and-containerization/SKILL.md | bentoml build, bentofile.yaml, bentoml.build, containerize, .bentoignore, build context |
| Run a local server or call endpoints using Python clients, curl/OpenAPI, HTTP/gRPC, streaming, WebSocket, server flags | sub-skills/serving-and-clients/SKILL.md | bentoml serve, bentoml.serve, SyncHTTPClient, AsyncHTTPClient, port, host, reload, gRPC |
| Save/load/list/import/export models and choose framework helpers or optional dependencies | sub-skills/model-management/SKILL.md | bentoml.models, model tags, Model Store, sklearn/pytorch/transformers/mlflow/xgboost helpers, missing model |
| Use CLI command families or BentoCloud login/deploy/deployment/secret/api-token/codespace/push/pull workflows | sub-skills/cli-and-cloud/SKILL.md | bentoml deploy, bentoml deployment, bentoml cloud, secrets, tokens, context, cluster, BentoCloud |
| Configure logging, metrics, tracing, monitoring, resources, scaling, gateways, testing, config files, production diagnostics | sub-skills/observability-and-operations/SKILL.md | metrics missing, tracing, config YAML, GPU resources, workers, autoscaling, gateways, production readiness |
sub-skills/service-authoring/SKILL.md; validate the import target before serving.sub-skills/serving-and-clients/SKILL.md; keep server/client debugging separate from service authoring.sub-skills/packaging-and-containerization/SKILL.md; validate bentofile.yaml or bentoml.build(...) options before expensive builds.sub-skills/model-management/SKILL.md when the workflow needs saved model tags, framework helpers, or model inclusion in a Bento.sub-skills/cli-and-cloud/SKILL.md; treat credentialed and destructive operations as explicit user-run or user-approved actions.sub-skills/observability-and-operations/SKILL.md.sub-skills/service-authoring/scripts/create_minimal_service.py generates a starter service file.sub-skills/service-authoring/scripts/validate_service_target.py imports and inspects a service target without serving it.sub-skills/packaging-and-containerization/scripts/validate_bentofile.py statically checks a bentofile.yaml.sub-skills/serving-and-clients/scripts/serve_command_builder.py constructs dry-run bentoml serve commands.sub-skills/model-management/scripts/check_framework_extra.py checks optional framework imports.sub-skills/cli-and-cloud/scripts/inspect_bentoml_cli.py renders safe CLI help without contacting BentoCloud.sub-skills/observability-and-operations/scripts/check_observability_config.py checks local config shapes without external collectors.bentoml build, model save/delete/import/export, local store changes, config writes, and server startup.bentoml deploy, cloud, deployment, secret, api-token, push, pull, Docker/container builds, OTLP/exporter endpoints, and cloud scaling changes.This skill was generated from BentoML source commit 73c4dbead99be6515fa25fcd91e348ac30f5c22e on branch main and installed package version 0.0.0.post1+g73c4dbead. If the current checkout, package version, CLI flags, or public SDK signatures differ, run a DisCo refresh before relying on detailed guidance.
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。