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Python Flask 架設機器學習 API

此範例使用鳶尾花朵資料集進行 XGBoost 分類器模型訓練。將模型儲存起來,並使用 Flask 建置 API 介面提供輸入值預測。

Getting Started

Clone Project

你可以在本機直接使用 git 指令 clone 此專案並執行。

git clone https://github.com/uchpython12/tensorflow-ml-docker-flask
cd https://github.com/uchpython12/tensorflow-ml-docker-flask

Docker Build Image

Docker自動打包image,本地端安裝請跳至Installation

docker run過後可直接訪問 localhost:3000.

docker build -t tensorflow-ml-docker-flask .
docker run -p 3000:3000 tensorflow-ml-docker-flask

Installation

此專案下載至桌面後,使用以下指令安裝必要套件。

pip install -r requirements.txt

Running the Project

套件安裝成功後,即可開始執行本專案。

python run.py

running locally! Your app should now be running on localhost:3000.

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