BentoML

intermediate

ML model serving and deployment framework.

Overview

BentoML is an open-source machine learning model serving framework that simplifies the deployment of ML models to production environments. Originally developed by Atalaya Tech and first released in 2019, BentoML addresses the critical gap between model development and deployment by providing a unified platform for packaging, versioning, and serving ML models from various frameworks including scikit-learn, PyTorch, TensorFlow, and XGBoost. The framework emphasizes performance optimization through features like adaptive batching, model parallelization, and automatic scaling capabilities. This Docker deployment creates a containerized BentoML model server that can host and serve multiple ML models simultaneously. The configuration establishes a persistent environment where models can be built, packaged into 'bentos' (deployment artifacts), and served through REST APIs with automatic OpenAPI documentation generation. BentoML handles the complex infrastructure concerns like request batching, concurrent processing, and resource management while providing a clean Python API for model integration. Data scientists and ML engineers working in production environments will find this setup particularly valuable when transitioning from Jupyter notebooks to scalable model serving infrastructure. The containerized approach ensures consistent model behavior across different deployment targets while BentoML's built-in monitoring and logging capabilities provide visibility into model performance and usage patterns in production workloads.

Key Features

Multi-framework model support with unified APIs for scikit-learn, PyTorch, TensorFlow, XGBoost, and other popular ML libraries
Adaptive micro-batching that automatically groups individual requests to optimize GPU utilization and inference throughput
Built-in model versioning and artifact management with immutable bento packaging for reproducible deployments
Automatic OpenAPI schema generation with interactive Swagger UI for testing and documentation of model endpoints
High-performance async serving architecture with configurable worker processes and resource allocation
Custom runner framework for advanced model serving patterns including ensemble models and multi-stage pipelines
Integrated metrics collection and logging with support for Prometheus monitoring and distributed tracing
Production-ready features including health checks, graceful shutdowns, and automatic request timeout handling

Common Use Cases

1ML model serving for recommendation engines in e-commerce platforms requiring low-latency inference
2Computer vision model deployment for real-time image classification and object detection applications
3NLP model hosting for chatbots, sentiment analysis, and text processing services in enterprise applications
4Financial services fraud detection models requiring high-throughput batch processing and real-time scoring
5A/B testing environments for comparing multiple model versions with traffic splitting capabilities
6Edge deployment preparation where models need containerized packaging for Kubernetes or cloud-native platforms
7Research environments requiring rapid prototyping and deployment of experimental ML models with version control

Prerequisites

Docker Engine 20.10+ and Docker Compose v2 with at least 4GB available RAM for model loading and inference
Basic understanding of machine learning model serving concepts and REST API consumption patterns
Python development environment for building and testing models before containerized deployment
Port 3000 available on the host system for the BentoML API server and Swagger documentation interface
Familiarity with at least one supported ML framework (scikit-learn, PyTorch, TensorFlow) for model integration
Understanding of Docker volume management for persistent model storage and bento artifact organization

For development & testing. Review security settings, change default credentials, and test thoroughly before production use. See Terms

docker-compose.yml

1services: 
2  bentoml: 
3    image: bentoml/model-server:latest
4    container_name: bentoml
5    restart: unless-stopped
6    volumes: 
7      - bentoml_home:/home/bentoml
8      - ./bentos:/bentos
9    ports: 
10      - "3000:3000"
11    environment: 
12      BENTOML_HOME: /home/bentoml
13
14volumes: 
15  bentoml_home:

.env Template

.env

1# Build bento with: bentoml build

Usage Notes

1Docs: https://docs.bentoml.org/
2API at http://localhost:3000, Swagger UI at http://localhost:3000/docs
3Build bento: bentoml build - creates from service.py + bentofile.yaml
4Containerize: bentoml containerize my_service:latest
5Save models: bentoml.sklearn.save_model('model', trained_model)
6Adaptive batching and auto-scaling built-in for production

Quick Start

terminal

1# 1. Create the compose file
2cat > docker-compose.yml << 'EOF'
3services:
4  bentoml:
5    image: bentoml/model-server:latest
6    container_name: bentoml
7    restart: unless-stopped
8    volumes:
9      - bentoml_home:/home/bentoml
10      - ./bentos:/bentos
11    ports:
12      - "3000:3000"
13    environment:
14      BENTOML_HOME: /home/bentoml
15
16volumes:
17  bentoml_home:
18EOF
19
20# 2. Create the .env file
21cat > .env << 'EOF'
22# Build bento with: bentoml build
23EOF
24
25# 3. Start the services
26docker compose up -d
27
28# 4. View logs
29docker compose logs -f

One-Liner

Run this command to download and set up the recipe in one step:

terminal

1curl -fsSL https://docker.recipes/api/recipes/bentoml/run | bash

Troubleshooting

ImportError: No module named 'bentoml': Ensure your model building environment has BentoML installed with pip install bentoml before creating bento packages
Port 3000 already in use: Change the port mapping in docker-compose.yml from '3000:3000' to '3001:3000' or stop conflicting services
Model loading timeout errors: Increase container memory allocation and add BENTOML_RUNNER_TIMEOUT environment variable with higher values for large models
Permission denied accessing /bentos volume: Fix directory ownership with sudo chown -R 1000:1000 ./bentos or create the directory before container startup
High memory usage during inference: Configure adaptive batching parameters in your service definition or limit concurrent requests with BENTOML_MAX_CONCURRENCY
Swagger UI not displaying model schema: Verify your service.py includes proper input/output type annotations and rebuild the bento with bentoml build

Community Notes

Loading notes...

Download Recipe Kit

Get all files in a ready-to-deploy package

Includes docker-compose.yml, .env template, README, and license

Components

bentoml

Related Recipes

Ad Space

Shortcuts: C CopyF FavoriteD Download