Remove unmaintained jvm readme and dev scripts. (#9395)

demo/README.md

@@ -145,7 +145,7 @@ Send a PR to add a one sentence description:)
 ## Tools using XGBoost
 
 - [BayesBoost](https://github.com/mpearmain/BayesBoost) - Bayesian Optimization using xgboost and sklearn API
-- [FLAML](https://github.com/microsoft/FLAML) - An open source AutoML library 
+- [FLAML](https://github.com/microsoft/FLAML) - An open source AutoML library
 designed to automatically produce accurate machine learning models with low computational cost. FLAML includes [XGBoost as one of the default learners](https://github.com/microsoft/FLAML/blob/main/flaml/model.py) and can also be used as a fast hyperparameter tuning tool for XGBoost ([code example](https://microsoft.github.io/FLAML/docs/Examples/AutoML-for-XGBoost)).
 - [gp_xgboost_gridsearch](https://github.com/vatsan/gp_xgboost_gridsearch) - In-database parallel grid-search for XGBoost on [Greenplum](https://github.com/greenplum-db/gpdb) using PL/Python
 - [tpot](https://github.com/rhiever/tpot) - A Python tool that automatically creates and optimizes machine learning pipelines using genetic programming.

jvm-packages/README.md

@@ -3,161 +3,15 @@
 [![Documentation Status](https://readthedocs.org/projects/xgboost/badge/?version=latest)](https://xgboost.readthedocs.org/en/latest/jvm/index.html)
 [![GitHub license](http://dmlc.github.io/img/apache2.svg)](../LICENSE)
 
-[Documentation](https://xgboost.readthedocs.org/en/latest/jvm/index.html) |
+[Documentation](https://xgboost.readthedocs.org/en/stable/jvm/index.html) |
 [Resources](../demo/README.md) |
 [Release Notes](../NEWS.md)
 
-XGBoost4J is the JVM package of xgboost. It brings all the optimizations
-and power xgboost into JVM ecosystem.
+XGBoost4J is the JVM package of xgboost. It brings all the optimizations and power xgboost
+into JVM ecosystem.
 
-- Train XGBoost models in scala and java with easy customizations.
-- Run distributed xgboost natively on jvm frameworks such as
-Apache Flink and Apache Spark.
+- Train XGBoost models in scala and java with easy customization.
+- Run distributed xgboost natively on jvm frameworks such as Apache Flink and Apache
+Spark.
 
-You can find more about XGBoost on [Documentation](https://xgboost.readthedocs.org/en/latest/jvm/index.html) and [Resource Page](../demo/README.md).
-
-## Add Maven Dependency
-
-XGBoost4J, XGBoost4J-Spark, etc. in maven repository is compiled with g++-4.8.5.
-
-### Access release version
-
-<b>Maven</b>
-
-```
-<dependency>
-    <groupId>ml.dmlc</groupId>
-    <artifactId>xgboost4j_2.12</artifactId>
-    <version>latest_version_num</version>
-</dependency>
-<dependency>
-    <groupId>ml.dmlc</groupId>
-    <artifactId>xgboost4j-spark_2.12</artifactId>
-    <version>latest_version_num</version>
-</dependency>
-```
-or 
-```
-<dependency>
-    <groupId>ml.dmlc</groupId>
-    <artifactId>xgboost4j_2.13</artifactId>
-    <version>latest_version_num</version>
-</dependency>
-<dependency>
-    <groupId>ml.dmlc</groupId>
-    <artifactId>xgboost4j-spark_2.13</artifactId>
-    <version>latest_version_num</version>
-</dependency>
-```
-
-<b>sbt</b>
-```sbt
-libraryDependencies ++= Seq(
-  "ml.dmlc" %% "xgboost4j" % "latest_version_num",
-  "ml.dmlc" %% "xgboost4j-spark" % "latest_version_num"
-)
-```
-
-For the latest release version number, please check [here](https://github.com/dmlc/xgboost/releases).
-
-
-### Access SNAPSHOT version
-
-First add the following Maven repository hosted by the XGBoost project:
-
-<b>Maven</b>:
-
-```xml
-<repository>
-  <id>XGBoost4J Snapshot Repo</id>
-  <name>XGBoost4J Snapshot Repo</name>
-  <url>https://s3-us-west-2.amazonaws.com/xgboost-maven-repo/snapshot/</url>
-</repository>
-```
-
-<b>sbt</b>:
-
-```sbt
-resolvers += "XGBoost4J Snapshot Repo" at "https://s3-us-west-2.amazonaws.com/xgboost-maven-repo/snapshot/"
-```
-
-Then add XGBoost4J as a dependency:
-
-<b>Maven</b>
-
-```
-<dependency>
-    <groupId>ml.dmlc</groupId>
-    <artifactId>xgboost4j_2.12</artifactId>
-    <version>latest_version_num-SNAPSHOT</version>
-</dependency>
-<dependency>
-    <groupId>ml.dmlc</groupId>
-    <artifactId>xgboost4j-spark_2.12</artifactId>
-    <version>latest_version_num-SNAPSHOT</version>
-</dependency>
-```
-or with scala 2.13 
-```
-<dependency>
-    <groupId>ml.dmlc</groupId>
-    <artifactId>xgboost4j_2.13</artifactId>
-    <version>latest_version_num-SNAPSHOT</version>
-</dependency>
-<dependency>
-    <groupId>ml.dmlc</groupId>
-    <artifactId>xgboost4j-spark_2.13</artifactId>
-    <version>latest_version_num-SNAPSHOT</version>
-</dependency>
-```
-
-<b>sbt</b>
-```sbt
-libraryDependencies ++= Seq(
-  "ml.dmlc" %% "xgboost4j" % "latest_version_num-SNAPSHOT",
-  "ml.dmlc" %% "xgboost4j-spark" % "latest_version_num-SNAPSHOT"
-)
-```
-
-For the latest release version number, please check [the repository listing](https://s3-us-west-2.amazonaws.com/xgboost-maven-repo/list.html).
-
-### GPU algorithm
-To enable the GPU algorithm (`tree_method='gpu_hist'`), use artifacts `xgboost4j-gpu_2.12` and `xgboost4j-spark-gpu_2.12` instead.
-Note that scala 2.13 is not supported by the [NVIDIA/spark-rapids#1525](https://github.com/NVIDIA/spark-rapids/issues/1525) yet, so the GPU algorithm can only be used with scala 2.12.
-
-## Examples
-
-Full code examples for Scala, Java, Apache Spark, and Apache Flink can
-be found in the [examples package](https://github.com/dmlc/xgboost/tree/master/jvm-packages/xgboost4j-example).
-
-**NOTE on LIBSVM Format**:
-
-There is an inconsistent issue between XGBoost4J-Spark and other language bindings of XGBoost.
-
-When users use Spark to load trainingset/testset in LIBSVM format with the following code snippet:
-
-```scala
-spark.read.format("libsvm").load("trainingset_libsvm")
-```
-
-Spark assumes that the dataset is 1-based indexed. However, when you do prediction with other bindings of XGBoost (e.g. Python API of XGBoost), XGBoost assumes that the dataset is 0-based indexed. It creates a pitfall for the users who train model with Spark but predict with the dataset in the same format in other bindings of XGBoost.
-
-## Development
-
-You can build/package xgboost4j locally with the following steps:
-
-**Linux:**
-1. Ensure [Docker for Linux](https://docs.docker.com/install/) is installed.
-2. Clone this repo: `git clone --recursive https://github.com/dmlc/xgboost.git`
-3. Run the following command:
-  - With Tests: `./xgboost/jvm-packages/dev/build-linux.sh`
-  - Skip Tests: `./xgboost/jvm-packages/dev/build-linux.sh --skip-tests`
-
-**Windows:**
-1. Ensure [Docker for Windows](https://docs.docker.com/docker-for-windows/install/) is installed.
-2. Clone this repo: `git clone --recursive https://github.com/dmlc/xgboost.git`
-3. Run the following command:
-  - With Tests: `.\xgboost\jvm-packages\dev\build-linux.cmd`
-  - Skip Tests: `.\xgboost\jvm-packages\dev\build-linux.cmd --skip-tests`
-
-*Note: this will create jars for deployment on Linux machines.*
+You can find more about XGBoost on [Documentation](https://xgboost.readthedocs.org/en/stable/jvm/index.html) and [Resource Page](../demo/README.md).