A minimum-lovable machine-learning pipeline, built on top of AWS SageMaker.
Project description
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.
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