# Abstract

Try to use [MLflow](https://mlflow.org) platform to log PyTorch model training, and pull production model from model registry to run inference⛩

# Requirements

* MacOS 12.5
* Docker 20.10

# Dirs

* **service**
  * House MLflow service data, including MLflow artifacts, backend store and model registry
* **env**
  * **mlflow.yaml**
    * conda env yaml to run this repo

# Files

* **docker-compose.yaml**
  * a yaml to apply docker-compose to start MLflow service with basic configuration (run ```docker-compose -f docker-compose.yaml up```)
* **test_pytorch_m1.py**
  * a script to test PyTorch on Apple M1 platform with GPU acceleration
* **train.py**
  * a sample code to apply PyTorch to train a small neural network to predict fortune with MLflow logging
* **predict.py**
  * a sample code to call registered model to predict testing data and save model to local file system
* **get_registered_model_via_rest_api.py**
  * a script to test MLflow REST api
* **log_unsupported_model.py**
  * a sample script to apply mlflow.pyfunc to package unsupported ml model which can be logged and registered by mlflow

###### tags: `MLOps`