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SQLFlow release 20190927
Echo9573 edited this page Sep 23, 2019
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任务 | 负责人 |
---|---|
Hive平台对接 | @YanXu @WeiChuan @ChenXiang |
AntXGBoost | @LueWei @WenJing |
模型可解释性(SHAP) | @WeiChuan @YangYang |
Clustering Model | @Wenjing @Chenxiang @YanXu |
XGboost | @YanXu @Wuyi |
类型 | 任务 | 负责人 | 状态 | DDL |
---|---|---|---|---|
DOC | issue: #836 | |||
DOC | Add Predict and Analyze in UserGuide | issue: #826 | ||
DOC | Install SQLFlow using Docker | issue: #849 | ||
DOC | Quick Start(can use installation.md) | @wuyi | DONE, issue: #832 | |
DOC | Credit Card Fraud Detection (DNN Classifier) | @wuyi | DONE, issue: #833 | |
DOC | run SQLFlow with MaxCompute | @weichuan | issue: #834 | |
DOC | run SQLFlow with Hive via HiveServer2 | @yanxu | DONE, issue: #835 | |
DOC | dmlc/xgboost tutorial | @yanxu | DONE, PR: 820 | 9.16 |
DOC | Clustering Tutorial | @wenjing | 编写cluster模型的tutorial | 09.23 |
DOC | 优化文档目录结构 | @wuyi | WIP, PR: 821 | |
DOC | SQLFlow 用户文档重新梳理 | @yanxu | Discussion | |
DOC | update home page: sqlflow.org | @weichuan | ||
DOC | write a blog on medium about this release | @wangyi | ||
DEV | Tensorflow & XGBoost 模型训练/预测 在 DiDi 验证全流程 1. 基于公开数据集Iris 2. 基于私有数据集 |
@chenxiang | DataSource=Hive, ModelDir为本地目录的 DNN 模型验证已经通过 DataSource=Hive, ModelDir为本地目录的 XGBoost 模型验证通过. |
09.18 |
DEV | Clustering 模型训练/预测 在 Didi 验证全流程 | @chenxiang | ||
DEV | SQLFlow Playground on Aliyun | |||
DEV | Pick the public dataset for DiDi case | @Jeremy | DONE | 9.13 |
DEV | A Docker image with the public dataset and DiDi demo | @wenjing | issue: 853:TODO: 1)REAMDME.md, 2)cluster test model | 9.23 |
Release | create a tag | @wuyi | branch: release-0.1 | |
Release | show demo | @weichuan @yongfeng |
任务 | 负责人 | 状态 |
---|---|---|
AntXGBoost 对接 hive 权限配置 | @luewei | PR: 729 |
[bug] HiveDBWriter mismatch hive table schema | @yanxu | PR: 756 |
[bug] select prediction result table of antxgboost failed | @yanxu | https://github.com/sql-machine-learning/sqlflow/issues/770 |
AntXGBoost Tutorial | @luewei | PR: 772 |
SHAP codegen & Tutorial | @weiguoz | PR: 798, PR: 801 |
Design clustering | @wenjing | PR: 737 |
xgboost train codegen | @yanxu | issue: 749 |
xgboost pred codegen | @wuyi | PR: 789 |
verify with SHAP | @yanxu | PR: 790 |
Clustering SQLFlow models | @chenxiang | PR: 16 |