Principle:Bentoml BentoML Bento Building
| Sources | Domains | Last Updated |
|---|---|---|
| BentoML, BentoML Building Bentos | ML_Serving, Build_System, Artifact_Management | 2026-02-13 15:00 GMT |
Overview
Packaging a BentoML service with its dependencies into a self-contained, versioned, immutable distributable artifact known as a Bento.
Description
A Bento is a versioned, immutable artifact containing service code, model references, environment specifications, and metadata. The build process resolves all dependencies, locks package versions, and produces a reproducible package suitable for containerization or cloud deployment. This is the central packaging step in the BentoML workflow, bridging development-time code with deployment-time artifacts.
Usage
Invoke the build process either via the CLI (bentoml build) or programmatically (bentoml.bentos.build() / bentoml.bentos.build_bentofile()). The build reads the service module, resolves dependencies, copies files, and stores the resulting Bento in the local BentoStore.
Theoretical Basis
The Bento build process is analogous to Docker image layering but specialized for ML serving. Key parallels include:
- Immutability -- once built, a Bento is a fixed snapshot of code, dependencies, and configuration
- Versioning -- each Bento receives a unique tag (name:version) for tracking and reproducibility
- Layer composition -- the Bento assembles code files, dependency locks, model references, and environment specs into a structured directory
The build process follows the Reproducible Build principle:
- Dependency locking -- Python packages are resolved and pinned to exact versions at build time
- File snapshotting -- source files are copied into the Bento, isolating from future working directory changes
- Model referencing -- model artifacts are referenced by tag, ensuring consistent model versions
This design enables several deployment patterns:
- Build once, deploy many -- the same Bento can be deployed to different environments (local, cloud, edge)
- Rollback capability -- previous Bento versions remain in the store for quick rollback
- CI/CD integration -- builds can be triggered in pipelines with deterministic outputs
The distinction between build() (fully programmatic) and build_bentofile() (YAML-driven) supports both scripted automation and human-friendly configuration workflows.