Skip to content
cccskills
FreePublished on GitHub

bentoml

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.

The English summary is being prepared. Showing the original description.

Works withDocker
See how to install

Files included (40)

  • SKILL.md5.7 KB
  • references/repo-provenance.md1.9 KB
  • references/repo-routing-metadata.json413 B
  • references/troubleshooting.md4.0 KB
  • sub-skills/cli-and-cloud/references/bentocloud-workflows.md6.3 KB
  • sub-skills/cli-and-cloud/references/cli-catalog.md6.8 KB
  • sub-skills/cli-and-cloud/references/troubleshooting.md4.5 KB
  • sub-skills/cli-and-cloud/scripts/deployment_config_lint.py6.9 KB
  • sub-skills/cli-and-cloud/scripts/inspect_bentoml_cli.py2.5 KB
  • sub-skills/cli-and-cloud/SKILL.md4.3 KB
  • sub-skills/model-management/references/frameworks.md6.2 KB
  • sub-skills/model-management/references/model-store.md4.3 KB
  • sub-skills/model-management/references/troubleshooting.md4.9 KB
  • sub-skills/model-management/scripts/check_framework_extra.py5.3 KB
  • sub-skills/model-management/scripts/inspect_model_store.py4.3 KB
  • sub-skills/model-management/SKILL.md4.8 KB
  • sub-skills/observability-and-operations/references/configuration.md8.2 KB
  • sub-skills/observability-and-operations/references/observability.md8.2 KB
  • sub-skills/observability-and-operations/references/troubleshooting.md8.4 KB
  • sub-skills/observability-and-operations/scripts/check_observability_config.py13.1 KB
  • sub-skills/observability-and-operations/scripts/resource_plan_template.py9.0 KB
  • sub-skills/observability-and-operations/SKILL.md3.4 KB
  • sub-skills/packaging-and-containerization/references/bentofile-reference.md4.8 KB
  • sub-skills/packaging-and-containerization/references/troubleshooting.md4.1 KB
  • sub-skills/packaging-and-containerization/references/workflows.md3.3 KB
  • sub-skills/packaging-and-containerization/scripts/create_minimal_bentofile.py6.5 KB
  • sub-skills/packaging-and-containerization/scripts/validate_bentofile.py16.0 KB
  • sub-skills/packaging-and-containerization/SKILL.md4.6 KB
  • sub-skills/service-authoring/references/api-reference.md6.1 KB
  • sub-skills/service-authoring/references/troubleshooting.md5.4 KB
  • sub-skills/service-authoring/references/workflows.md4.5 KB
  • sub-skills/service-authoring/scripts/create_minimal_service.py3.8 KB
  • sub-skills/service-authoring/scripts/validate_service_target.py5.7 KB
  • sub-skills/service-authoring/SKILL.md5.8 KB
  • sub-skills/serving-and-clients/references/cli-reference.md4.0 KB
  • sub-skills/serving-and-clients/references/client-reference.md4.0 KB
  • sub-skills/serving-and-clients/references/troubleshooting.md4.0 KB
  • sub-skills/serving-and-clients/scripts/inspect_service_client.py4.4 KB
  • sub-skills/serving-and-clients/scripts/serve_command_builder.py3.4 KB
  • sub-skills/serving-and-clients/SKILL.md7.4 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

BentoML

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.

First Checks

  • Install: 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.
  • Import check: python -c "import bentoml; print(bentoml.__version__)".
  • CLI check: bentoml --help and bentoml env.
  • Provenance check: read references/repo-provenance.md before deciding whether this skill is stale for a current checkout.
  • Troubleshooting: read references/troubleshooting.md for cross-cutting install/import, CLI, environment, optional dependency, and cloud safety issues.

Route The Task

User taskUse this sub-skillTypical triggers
Write or debug service.py, APIs, tasks, batching, lifecycle hooks, ASGI/Gradio/WebSocket/streaming, IO specs, runtime image hintssub-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/containerizesub-skills/packaging-and-containerization/SKILL.mdbentoml 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 flagssub-skills/serving-and-clients/SKILL.mdbentoml serve, bentoml.serve, SyncHTTPClient, AsyncHTTPClient, port, host, reload, gRPC
Save/load/list/import/export models and choose framework helpers or optional dependenciessub-skills/model-management/SKILL.mdbentoml.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 workflowssub-skills/cli-and-cloud/SKILL.mdbentoml deploy, bentoml deployment, bentoml cloud, secrets, tokens, context, cluster, BentoCloud
Configure logging, metrics, tracing, monitoring, resources, scaling, gateways, testing, config files, production diagnosticssub-skills/observability-and-operations/SKILL.mdmetrics missing, tracing, config YAML, GPU resources, workers, autoscaling, gateways, production readiness

Common End-To-End Flow

  1. Author a Service with sub-skills/service-authoring/SKILL.md; validate the import target before serving.
  2. Serve locally and call endpoints with sub-skills/serving-and-clients/SKILL.md; keep server/client debugging separate from service authoring.
  3. Package with sub-skills/packaging-and-containerization/SKILL.md; validate bentofile.yaml or bentoml.build(...) options before expensive builds.
  4. Use sub-skills/model-management/SKILL.md when the workflow needs saved model tags, framework helpers, or model inclusion in a Bento.
  5. Deploy or manage cloud resources with sub-skills/cli-and-cloud/SKILL.md; treat credentialed and destructive operations as explicit user-run or user-approved actions.
  6. Add telemetry, resources, scaling, config, and production validation with sub-skills/observability-and-operations/SKILL.md.

Safe Bundled Helpers

  • 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.

Side-Effect Boundaries

  • Safe by default: import checks, CLI help, static YAML/config validation, command construction, and read-only local store inspection.
  • Potentially mutating: bentoml build, model save/delete/import/export, local store changes, config writes, and server startup.
  • External or credentialed: bentoml deploy, cloud, deployment, secret, api-token, push, pull, Docker/container builds, OTLP/exporter endpoints, and cloud scaling changes.
  • Expensive or environment-specific: framework model downloads, GPU workloads, large image builds, long-running servers, and integration tests requiring Docker/cloud credentials.

Version Notes

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.

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.

The English summary is being prepared. Showing the original description.

VectorSpaceLab/AREX-Skill339Updated Sep 3, 2026

3ddfa

Free

Guide 3DDFA Python inference, geometry rendering, training/evaluation, and optional C++ ONNX workflows for 3D dense face alignment.

The English summary is being prepared. Showing the original description.

VectorSpaceLab/AREX-Skill339Updated Sep 3, 2026

Routes 3DDFA_V2 face-alignment setup, still-image demos, video tracking, and ONNX benchmarking workflows.

The English summary is being prepared. Showing the original description.

VectorSpaceLab/AREX-Skill339Updated Sep 3, 2026

Operate AB3DMOT 3D multi-object tracking workflows for KITTI and nuScenes data, tracking, evaluation, and visualization.

The English summary is being prepared. Showing the original description.

VectorSpaceLab/AREX-Skill339Updated Sep 3, 2026

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.

The English summary is being prepared. Showing the original description.

VectorSpaceLab/AREX-Skill339Updated Sep 3, 2026

acme

Free

Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.

The English summary is being prepared. Showing the original description.

VectorSpaceLab/AREX-Skill339Updated Sep 3, 2026

See all skills by VectorSpaceLab

Report a problem with this skill