ml2p 0.2.0

Installing

Install ML2P with:

$ pip install ml2p

Overview

ML2P helps manage a machine learning project. You’ll define your project by writing a small YAML file named ml2p.yml:

project: "ml2p-tutorial"
s3folder: "s3://your-s3-bucket/"
models:
  bob: "models.RegressorModel"
defaults:
  image: "XXXXX.dkr.ecr.REGION.amazonaws.com/your-docker-image:X.Y.Z"
  role: "arn:aws:iam::XXXXX:role/your-role"
train:
  instance_type: "ml.m5.large"
deploy:
  instance_type: "ml.t2.medium"
  record_invokes: true

This specifies:

• project: the name of your project

• s3folder: the S3 bucket that will hold the models and data sets for your project

• models: a list of model names and the Python classes that will be used to train the models and make predictions

• defaults:

  • image: the docker image that your project will use for training and prediction

  • role: the AWS role your project will run under

• train:

  • instance_type: the AWS instance type that will be used when training your model

• deploy:

  • instance_type: the AWS instance type that will be used when deploying your model

  • record_invokes: whether to record prediction requests in S3

The name of your project functions as a prefix to the names of SageMaker training jobs, models and endpoints that ML2P creates (since these names are global within a SageMaker account).

ML2P also tags all of the AWS objects it creates with your project name.

Tutorial

See https://ml2p.readthedocs.io/en/latest/tutorial/ for a step-by-step tutorial.