Configuring BentoML#

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.

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.yaml bentoml serve iris_classifier: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.yaml#

 api_server:
   workers: 4
   timeout: 60
   http:
     port: 6000

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.

Overrding configuration with environment variables#

Users can also override configuration fields with environment variables. by defining an oneline value of a “flat” JSON via BENTOML_CONFIG_OPTIONS:

$ BENTOML_CONFIG_OPTIONS='runners.pytorch_mnist.resources."nvidia.com/gpu"[0]=0 runners.pytorch_mnist.resources."nvidia.com/gpu"[1]=2' \
         bentoml serve pytorch_mnist_demo:latest --production

Which the override configuration will be intepreted as:

runners:
 pytorch_mnist:
   resources:
     nvidia.com/gpu: [0, 2]

Note

For fields that represents a iterable type, the override array must have a space separating each element:

Configuration override environment variable

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