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name: bentoml description: "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." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

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 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

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.