Machine Learning Model Release Definition Templates

hashistack offers a Screwdriver template that deploys immutable instances of Machine Learning models as REST API to AWS. It uses the screwdriver-template-main npm package to assist with template validation, publishing, and tagging.

This release definition contains the following templates:

• Deploying ML models via MLflow Docker container

All templates tag the latest versions with the latest tag.

How to Use the Templates

Using the Template

Create a Screwdriver pipeline that uses this template with the following contents:

---
jobs:
  main:
    requires: [~pr, ~commit]
    template: QubitPi/machine-learning-model-release-definition-template-mlflow-docker@latest
    steps:
      - build-ml-models: ...
    secrets:
      - AWS_ACCESS_KEY_ID
      - AWS_SECRET_ACCESS_KEY
      - AWS_ML_PKRVARS_HCL
      - AWS_ML_TFVARS

The following Screwdriver CD - steps must be implemented:

• build-ml-models - Commands that package up MLflow Python models

The following Screwdriver Secrets needs to be defined before running this template:

• AWS_ACCESS_KEY_ID

• AWS_SECRET_ACCESS_KEY

• AWS_ML_PKRVARS_HCL - A HashiCorp Packer variable values file with the following variable values:

  aws_image_region = "us-east-2"
  ami_name         = "my-ml-ami"
  instance_type    = "t2.small"
  ml_models_path   = "/home/my-nlp-models"
  • aws_image_region is the image region of AWS AMI
  • ami_name is the name of the resulting AMI that will appear when managing AMIs in the AWS console or via APIs. This can be the same across builds, because hashistack will deregister the old AMI with the same name and replace it with the current built one
  • instance_type is the recommended AWS EC2 instance type running this image
  • ml_models_path is the absolute path to the model directory generated by the build-ml-models step above

• AWS_ML_TFVARS - A HashiCorp Terraform variable values file with the following variable values:

  aws_ec2_region   = "us-east-2"
  ami_name            = "my-ml-ami"
  instance_type       = "t2.small"
  ec2_instance_name   = "My Machine Learning REST Service"
  ec2_security_groups = ["ML REST Service Security Group"]
  • aws_ec2_region is the EC2 runtime region
  • ami_name is the name of the published AMI; it must be the same as the ami_name in AWS_ML_PKRVARS_HCL
  • instance_type is the AWS EC2 instance type used for deployed infrastructure
  • ec2_instance_name is the deployed EC2 name as appeared in the instance list of AWS console; it can be arbitrary
  • ec2_security_groups is the AWS Security Group name (yes, not ID, but name...)