microservice-ML-API-kubernetes：使用kubernetes的机器学习微服务，这是一个用于自动化容器化

[](https://app.circleci.com/pipelines/github/Gwtm-00/microservice-ML-API-kubernetes) # Operationalize a Machine Learning Microservice API ### Project Overview A pre-trained, `sklearn` model that has been trained to predict housing prices in Boston according to several features, such as average rooms in a home and data about highway access, teacher-to-pupil ratios, and so on. You can read more about the data, which was initially taken from Kaggle, on the data source site. This project tests your ability to operationalize a Python flask app—in a provided file, app.py—that serves out predictions (inference) about housing prices through API calls. This project could be extended to any pre-trained machine learning model, such as those for image recognition and data labeling. ### Environment Setup * Create a virtual environment `python3 -m venv ~/.devops` * Activate it `source ~/.devops/bin/activate` ### Kuberneted Environment setup * Install minkube * `minikube start` ### Running Script * Start server : `python app.py` * Run in Docker: `./run_docker.sh` * Run in Kubernetes: `./run_kubernetes.sh` ### Files * `app.py` ---> contains app logic and flask module * `requirements.txt` ---> contains list of third-party libraries and framework used * `run_docker.sh` ---> builds docker image * `upload_docker.sh` ---> Uploads docker image to docker hub * `run_kubernetes.sh` ---> runs the container in kubernetes cluster * `Makefile` ---> Takes care of installing dependencies and linting * `make_prediction.sh` ---> forwards input to the running server * `.circleci` ---> contains config file for circel CI