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quick_start.md 1.19 KB

# Quick Start

It's quite simple to try out SQLFlow using [SQLFlowPlayground On the Cloud](https://playground.sqlflow.tech/).

1. Click [here](https://playground.sqlflow.tech/) to open our playground website.
1. Login SQLFlow Playground with your GitHub account, just click the authorization button.
1. Select some project to try out SQLFlow.
1. If you want to try SQLFLow on your own computer, please refer to [Deploy SQLFlow on Kubernetes](run/kubernetes.md), or [Run SQLFlow in a VM Playground](https://github.com/sql-machine-learning/playground/blob/master/dev.md#for-end-users).

For advanced usage, you might want to go on reading

- [Language Guide](language_guide.md)
- [SQLFlow Playground](https://github.com/sql-machine-learning/playground/blob/master/dev.md#for-end-users)
- [Run on Kubernetes](run/kubernetes.md)
- [Run on Docker](run/docker.md)
- [Export Trained Models](export_model.md)
- [Run with command-line tool](run/cli.md) or clients ([Python](https://github.com/sql-machine-learning/pysqlflow), Java...) 
- [Run SQLFlow with Different DataSources](/run/docker.md#connect-to-your-own-database) (MySQL, Hive, MaxCompute..)
- [Improve builtin Models](https://github.com/sql-machine-learning/models)

一键复制原始数据按行查看历史

提交于 2021-01-25 22:19 . Adding few more helpful links in quickstart docs (#3119)

Quick Start

It's quite simple to try out SQLFlow using SQLFlowPlayground On the Cloud.

Click here to open our playground website.
Login SQLFlow Playground with your GitHub account, just click the authorization button.
Select some project to try out SQLFlow.
If you want to try SQLFLow on your own computer, please refer to Deploy SQLFlow on Kubernetes, or Run SQLFlow in a VM Playground.

For advanced usage, you might want to go on reading

Language Guide
SQLFlow Playground
Run on Kubernetes
Run on Docker
Export Trained Models
Run with command-line tool or clients (Python, Java...)
Run SQLFlow with Different DataSources (MySQL, Hive, MaxCompute..)
Improve builtin Models

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