Building Bentos

Bento is a standardized file archive format in BentoML that describes how to load and run a bentoml.Service defined by the user. It includes code that instantiates the bentoml.Service instance, as well as related configurations, data/model files, and dependencies.

A Bento can be built with the bentoml build command with the bentofile.yaml configuration file. Here’s an example of that process from the quickstart guide (

service: "service:svc"
description: "file: ./"
   owner: bentoml-team
   stage: demo
   - "*.py"
      - scikit-learn
      - pandas

The service field is the python module that holds the bentoml.Service instance.

Built bentos are added the local bento store and can be managed with both Python APIs and CLI.

> bentoml list # list all bentos in the store
> bentoml get iris_classifer:latest # get the description of the bento

The build options by default work for the most common cases but can be further customized by calling the set_build_options() function on the service. Let’s explore the available options. See documentation for in-depth details of build options.

Configuring files to include

In the example above, the *.py is including every Python file in the working directory.

You can also include other wildcard and directory matching.


   - "data/"
   - "**/*.py"
   - "config/*.json"

If the include field is not specified, BentoML, by default, will include every file in the working directory. Try to limit the amount of files that are included in your bento. For example, if unspecified, or if * is specified, all git versioning in the directory could be included in the bento by accident.

Configuring files to exclude

If the user needs to include a lot of files, another approach is to only specify which files to be ignored.

The exclude keyword argument specifies the pathspecs (similar to the .gitignore files) of the Python modules or data files to be excluded in the build. The pathspecs are relative the current working directory. Users can also opt to place a .bentoignore file in the directory where bentoml build is run to achieve the same file exclusion during build. If not explicitly specified, nothing is excluded from the build. Exclude is applied after include.

This is what a .bentoignore file would look like.


Build your Bento

To build a Bento, simply run the following command from your project directory that contains your bentofile.yaml:

bentoml build

By default, build will include all files in current working directory, besides the files specified in the .bentoignore file in the same directory. It will also automatically infer all PyPI packages that are required by the service code, and pin down the version used in current environment.

The version of the bento to be built can be specified by the --version keyword argument. If not explicitly specified, the version is automatically generated based on the timestamp of the build combined with random bytes.

By default the bentofile.yaml is used as the build configuration, but you may also specify a custom bentofile using the --bentofile parameter.

Bento Format

BentoML is a standard file format that describes how to load and run a bentoml.Service defined by the user. It includes code that instantiates the bentoml.Service instance, as well as related configurations, data/model files, and dependencies.

service: "service:svc"
description: "file: ./"
   owner: bentoml-team
   stage: demo
   - "*.py"
      - scikit-learn
      - pandas


The service parameter is a required field which must specify where the service code is located and under what variable name the service is instantiated in the code itself, separated by a colon. If either parameters is incorrect, the bento will not be built properly. BentoML uses this convention to find the service, inspect it and then determine which models should be packed into the bento.

<Your Service .py file>:<Variable Name of Service in .py file>


The keyword argument sets the description of the Bento service. The contents will be used to create the file in the bento archive. If not explicitly specified, the build to first look for the presence of a in the current working directory and set the contents of the file as the description.


The labels argument is a key value mapping which sets labels on the bento so that you can add your own custom descriptors to the bento

Additional Models

The build automatically identifies the models and their versions to be built into the bento based on the service definition. The service definition loads runners through the framework specific load_runner() function, the build will identify the model through the tag provided in the arguments. Use the additional_models` keyword argument to include models tags that are used in customer runners.

Python Packages

Whether you’re using pip or conda, you can specify which Python packages to include in your Bento by configuring them in bentofile.yaml.

Python Options

There are two ways to specify packages in the Bentofile. First, we can list packages like below. When left without a version, pip will just use the latest release.

     - numpy
     - "matplotlib==3.5.1"

The user needs to put all required python packages for the Bento Service in a requirements.txt. For a project, you can run pip freeze > requirements.txt to generate a requirements file to load with BentoML.

  requirements_txt: "requirements.txt"

Additionally, there are more fields that can help manage larger projects.

  requirements_txt: "requirements.txt"
  lock_packages: False
  index_url: ""
  no_index: False
  trusted_host: "localhost"
     - ""
     - ""
  pip_args: "--quiet"
     - "./libs/my_package.whl"




The path to a custom requirements.txt file


Packages to include in this bento


Whether to lock the packages or not


Inputs for the –index-url pip argument


Whether to include the –no-index pip argument


List of trusted hosts used as inputs using the –trusted-host pip argument


List of links to find as inputs using the –find-links pip argument


List of extra index urls as inputs using the pip argument


Any additional pip arguments that you would like to add when installing a package


List of paths to wheels to include in the bento

Package Locking

By default, when the BentoML service generates package requirements from the Bentofile, the package versions will be locked for easier reproducibility. BentoML uses pip-tools to lock the packages.

If the requirements.txt includes locked packages, or a configuration you need, set the lock_packages field to False.

Pip Wheels

If you’re maintaining a private pip wheel, it can be included with the wheels field.

If the wheel is hosted on a local network without TLS, you can indicate that the domain is safe to pip with the trusted_host field.

Conda Options

Similarly to PyPi, you can use Conda to handle dependencies.

     - "scikit-learn==1.2.0"
     - numpy
     - nltk
     - "conda-forge"

Here, we need the conda-forge repository to install numpy with conda. The channels field let’s us specify that to the BentoML service.

In a preexisting environment, running conda export will generate an environment.yml file to be included in the environment_yml field.

  environment_yml: "environment.yml"

Conda Fields




Path to a conda environment file to copy into the bento. If specified, this file will overwrite any additional option specified


Custom conda channels to use. If not specified will use “defaults”


Custom conda dependencies to include in the environment


The specific “pip” conda dependencies to include

Docker Options

BentoML makes it easy to deploy a Bento to a Docker container.

Here’s a basic Docker options configuration.

  distro: debian
  gpu: True
  python_version: "3.8.9"
  setup_script: ""

For the distro options, you can choose from 5.

  • debian

  • amazonlinux2

  • alpine

  • ubi8

  • ubi7

This config can be explored from BentoML’s Docker page.

The gpu field instructs BentoML to select a Docker base image that contains NVIDIA drivers and cuDNN library.

For further Docker development, you can also use a setup_script for the container. This script will run during the docker build process, as Docker containerizes the image.

For example, with NLP projects you can preinstall NLTK data with:

Docker Fields




Configure the particular os distribution on the Docker image [“debian”, “amazonlinux2”, “alpine”, “ubi8”, “ubi7”]


Specify which python to include on the Docker image [“3.7”, “3.8”, “3.9”]


Determine if your container will have a gpu. This is not compatible with certain distros


If you want to use the latest main branch from the BentoML repo in your bento


Is a python or shell script that executes during docker build time


Is a user-provided custom docker base image. This will override all other custom attributes of the image


The bentofile.yaml is essential when generating a Bento, and can be as simple or in-depth as you need. All configuration can be included in the single file, or split with other smaller requirements files.