# kubeflow-fairing

**Repository Path**: garlong/kubeflow-fairing

## Basic Information

- **Project Name**: kubeflow-fairing
- **Description**: github太烂，搬过来看
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-07-31
- **Last Updated**: 2021-08-27

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

<img src="https://www.kubeflow.org/images/logo.svg" width="100">

## Overview of Kubeflow Fairing

Kubeflow Fairing is a Python package that streamlines the process of building,
training, and deploying machine learning (ML) models in a hybrid cloud
environment. By using Kubeflow Fairing and adding a few lines of code, you can
run your ML training job locally or in the cloud, directly from Python code or
a Jupyter notebook. After your training job is complete, you can use Kubeflow
Fairing to deploy your trained model as a prediction endpoint.

## Use Kubeflow Fairing SDK

To install the SDK:
  ```
  pip install kubeflow-fairing
  ```

To quick start, you can run the [E2E MNIST sample](examples/mnist).

## Documentation

```
没有文档，基本看ipynb文件，
examples/pytorch/mnist/mnist.ipynb
examples/aws/backend.ipynb做镜像的接口的例子
examples/distributed-training/main.pyexamples的例子
examples/gcp/hello-world-gpu-pytorch.py，就hello-world
examples/kubeflow-gke/main.py """Trains and Evaluates the MNIST network using a feed dictionary.""" tensorflow
examples/kubeflow/main.py """Trains and Evaluates the MNIST network using a feed dictionary.""" tensorflow
examples/kubeflow/main_ocp.py """Trains and Evaluates the MNIST network using a feed dictionary.""" tensorflow ocp
examples/kubernetes-in-cluster-builder/main.py 和上面一样，不过是cluster的
examples/kubernetes/main.py MNIST tensorflow
examples/lightgbm ibm的，Dockerfile有用到google的什么cloud,gcr。可以不用，去掉这个
examples/mnist TFjob的，训练和上KFserving，运行预测推理，删除清理等
examples/notebook XGBoost训练评估，用GCPServingDeployer进行gcp部署，远端训练
examples/prediction/HousingTrain.py 房价预测模型训练，评估，保存
examples/prediction/HousingServe.py 部署seldon
examples/prediction/xgboost-gkeop.ipynb 部署kubeflow,gcp部署，http访问
examples/prediction/xgboost-high-level-apis.ipynb notebook里提交kubeflow训练任务，发布服务,跑预测，删除服务。
examples/prediction/xgboost-train-and-deploy-in-gcp-from-local-notebook.ipynb KF remote训练，上KF服务
examples/pytorch/mnist/mnist.ipynb kubeflow开job训练pytorch卷积神经网络，可以用cuda
examples/simple/main.py 弹子复流，单容器训练。
examples/train_job_api/main.ipynb 指定内存cpu数，提交训练任务。
```

To learn how Kubeflow Fairing streamlines the process of training and deploying
ML models in the cloud, read the [Kubeflow Fairing
documentation][fairing-overview]. 

To learn the Kubeflow Fairing SDK API, read the [HTML documentation][html-doc].

[fairing-overview]: https://www.kubeflow.org/docs/components/fairing/fairing-overview/
[html-doc]: https://kubeflow-fairing.readthedocs.io/en/latest/index.html