ML2P

ML2P -- or (ML)^2P -- is the minimal lovable machine-learning pipeline and a friendlier interface to AWS SageMaker.

Design goals:

• support the full machine learning lifecyle
• support custom feature engineering
• support building custom models in Python
• provide reproducible training and deployment of models
• support the use of customised base Docker images for training and deployment

Concretely it provides a command line interface and a Python library to assist with:

• S3:

  • Managing training data

• SageMaker:

  • Launching training jobs
  • Deploying trained models
  • Creating notebook instances

• On your local machine or in a SageMaker notebook:

  • Downloading training datasets from S3
  • Training models
  • Loading trained models from SageMaker / S3

Installing

Install ML2P with:

$ pip install ml2p

Mailing list

If you have questions about ML2P, or would like to contribute or have suggestions for improvements, you are welcome to join the project mailing list at https://groups.google.com/g/ml2p and write us a letter there.

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.