update file description and reference

README.md

@@ -1,3 +1,31 @@
-# test_mlflow
+# Abstract
 
-測試使用MLflow紀錄Pytorch模型訓練，以及從Model registry中拉取Production model進行推論。
+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
+
+# Dir
+
+* **service**
+  * House MLflow service data, including MLflow artifacts, backend store and model registry
+
+# Files
+
+* **conda.yaml**
+  * conda env yaml to run this repo
+* **start_mlflow_server.sh**
+  * a script to start MLflow server with basic configuration
+* **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
+
+
+###### tags: `MLOps`

test_pytorch_m1.py

@@ -1,8 +1,14 @@
+# test_pytorch_m1.py
+# Ref: https://towardsdatascience.com/installing-pytorch-on-apple-m1-chip-with-gpu-acceleration-3351dc44d67c
+#
+# author: deng
+# date  : 20230301
+
 import torch
 import math
 
-print('this ensures that the current MacOS version is at least 12.3+')
+print('This ensures that the current MacOS version is at least 12.3+')
 print(torch.backends.mps.is_available())
 
-print('\nthis ensures that the current current PyTorch installation was built with MPS activated.')
+print('\nThis ensures that the current current PyTorch installation was built with MPS activated.')
 print(torch.backends.mps.is_built())