Configuration¶

BentoML starts with an out-of-the-box configuration that works for common use cases. For advanced users, many features can be customized through configuration. Both BentoML CLI and Python APIs can be customized by the configuration. Configuration is best used for scenarios where the customizations can be specified once and applied to the entire team.

Note

BentoML configuration underwent a major redesign in release 0.13.0. Previous configuration set through the bentoml config CLI command is no longer compatible with the configuration releases in 0.13.0 and above. Please see legacy configuration property mapping table below to upgrade configuration to the new format.

BentoML configuration is defined by a YAML file placed in a directory specified by the BENTOML_CONFIG environment variable. The example below starts the bento server with configuration defined in ~/bentoml_configuration.yaml

$ BENTOML_CONFIG=~/bentoml_configuration.yml bentoml serve IrisClassifier:latest

Users only need to specify a partial configuration with only the properties they wish to customize instead of a full configuration schema. In the example below, the microbatching workers count is overridden to 4. Remaining properties will take their defaults values.

BentoML Configuration¶

bento_server:
    port: 6000
    timeout: 120
    backlog: 1024

Throughout the BentoML documentation, features that are customizable through configuration are demonstrated like the example above. For a full configuration schema including all customizable properties, refer to the BentoML configuration template defined in default_configuration.yml.

Docker Deployment¶

Configuration file can be mounted to the Docker container using the -v option and specified to the BentoML runtime using the -e environment variable option.

$ docker run -v /local/path/configuration.yml:/home/bentoml/configuration.yml -e BENTOML_CONFIG=/home/bentoml/configuration.yml