metamaker 0.1.0

Simple CLI to train and deploy your ML models with AWS SageMaker

metamaker

Simple command line tool to train and deploy your machine learning models with AWS SageMaker

Features

metamaker enables you to:

• Build a docker image for training and inference with poetry and FastAPI
• Train your own machine learning model with SageMaker
• Deploy inference endpoint with SageMaker

Usage

1. Create poetry project and install metamaker

❯ poetry new your_module
❯ cd your_module
❯ poetry add git+https://github.com/altescy/metamaker#main

2. Define scripts for traning and inference in main.py

from pathlib import Path
from typing import Any, Dict

from metamaker import MetaMaker

from your_module import Model, Input, Output

app = MetaMaker[Model, Input, Output]()

@app.trainer
def train(
    dataset_path: Path,
    artifact_path: Path,
    hyperparameters: Dict[str, Any],
) -> None:
    model = Model(**hyperparameters)
    model.train(dataset_path / "train.csv")
    model.save(artifact_path / "model.tar.gz")

@app.loader
def load(artifact_path: Path) -> Model:
    return Model.load(artifact_path / "model.tar.gz")

@app.predictor
def predict(model: Model, data: Input) -> Output:
    return model.predict(data)

3. Write metamaker configs in metamaker.yaml

handler: main:app
dataset_path: s3://your-bucket/path/to/dataset/
artifact_path: s3://your-bucket/path/to/artifacts/
hyperparameter_path: ./hparams.yaml

image:
  name: metamaker
  includes:
    - your_module/
    - main.py
  excludes:
    - __pycache__/
    - '*.py[cod]'

training:
  execution_role: arn:aws:iam::xxxxxxxxxxxx:role/SageMakerExecutionRole
  instance:
    type: ml.m5.large
    count: 1

inference:
  endpoint_name: your_endpoint
  instance:
    type: ml.t2.meduim
    count: 1

4. Build docker image and push to ECR

metamaker build --deploy .

5. Train your model with SageMaker and deploy endpoint

metamker sagemaker train --deploy