Metadata-Version: 2.1
Name: metamaker
Version: 0.1.0
Summary: Simple CLI to train and deploy your ML models with AWS SageMaker
Home-page: https://github.com/altescy/metamaker
License: MIT
Keywords: python,machine-learning,sagemaker
Author: altescy
Author-email: altescy@fastmail.com
Requires-Python: >=3.8,<4.0
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Requires-Dist: boto3 (>=1.19.9,<2.0.0)
Requires-Dist: fastapi (>=0.70.0,<0.71.0)
Requires-Dist: pydantic (>=1.8.2,<2.0.0)
Requires-Dist: pyyaml (>=5.0,<6.0)
Requires-Dist: sagemaker[local] (>=2.68.0,<3.0.0)
Requires-Dist: uvicorn (>=0.15.0,<0.16.0)
Description-Content-Type: text/markdown

metamaker
=========

[![Actions Status](https://github.com/altescy/metamaker/workflows/CI/badge.svg)](https://github.com/altescy/metamaker/actions/workflows/main.yaml)
[![License](https://img.shields.io/github/license/altescy/metamaker)](https://github.com/altescy/metamaker/blob/master/LICENSE)

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](https://python-poetry.org/) and [FastAPI](https://fastapi.tiangolo.com/)
- 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`

```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`

```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
```

