#BentoML Heroku deployment tool

Prerequisites

• An active Heroku account configured on the machine with AWS CLI installed and configured
  • Install instruction: https://devcenter.heroku.com/articles/heroku-cli#getting-started
  • Login Heroku CLI: $heroku login
• Docker is installed and running on the machine.
  • Install instruction: https://docs.docker.com/install
• Install required python packages
  • $ pip install -r requirements.txt

Deployment operations

Create a deployment

Use command line

$ python deploy.py <Bento_bundle_path> <Deployment_name> <Config_JSON default is heroku_config.json>

Example:

$ MY_BUNDLE_PATH=${bentoml get IrisClassifier:latest --print-location -q)
$ python deploy.py $MY_BUNDLE_PATH my_first_deployment heroku_config.json

Use Python API

from deploy import deploy_heroku

deploy_heroku(BENTO_BUNDLE_PATH, DEPLOYMENT_NAME, CONFIG_JSON)

Available options

• dyno_counts: Amount of dyno running for the deployment, see https://devcenter.heroku.com/articles/dyno-types#default-scaling-limits for more information.
• dyno_type: Heroku dyno(instance) type, see https://devcenter.heroku.com/articles/dyno-types for more information

Update a deployment

Use command line

$ python update.py <Bento_bundle_path> <Deployment_name> <Config_JSON>

Use Python API

from update import update_heroku

update_heroku(BENTO_BUNDLE_PATH, DEPLOYMENT_NAME, CONFIG_JSON)

Get a deployment's status and information

Use command line

$ python describe.py <Deployment_name>

Use Python API

from describe import describe_heroku

describe_heroku(DEPLOYMENT_NAME)

Delete a deployment

Use command line

$ python delete.py <Deployment_name>

Use Python API

from delete import delete_heroku

delete_heroku(DEPLOYMENT_NAME)