[jvm-packages] add doc for xgboost4j-spark-gpu (#7779)
Co-authored-by: Jiaming Yuan <jm.yuan@outlook.com>
This commit is contained in:
Bobby Wang
GitHub
parent
2454407f3a
commit
89d6419fd5
@ -101,7 +101,7 @@ R
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JVM
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JVM
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---
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---
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You can use XGBoost4J in your Java/Scala application by adding XGBoost4J as a dependency:
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* XGBoost4j/XGBoost4j-Spark
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.. code-block:: xml
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.. code-block:: xml
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:caption: Maven
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:caption: Maven
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@ -134,6 +134,39 @@ You can use XGBoost4J in your Java/Scala application by adding XGBoost4J as a de
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"ml.dmlc" %% "xgboost4j-spark" % "latest_version_num"
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"ml.dmlc" %% "xgboost4j-spark" % "latest_version_num"
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)
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)
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* XGBoost4j-GPU/XGBoost4j-Spark-GPU
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.. code-block:: xml
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:caption: Maven
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<properties>
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...
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<!-- Specify Scala version in package name -->
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<scala.binary.version>2.12</scala.binary.version>
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</properties>
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<dependencies>
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...
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<dependency>
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<groupId>ml.dmlc</groupId>
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<artifactId>xgboost4j-gpu_${scala.binary.version}</artifactId>
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<version>latest_version_num</version>
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</dependency>
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<dependency>
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<groupId>ml.dmlc</groupId>
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<artifactId>xgboost4j-spark-gpu_${scala.binary.version}</artifactId>
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<version>latest_version_num</version>
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</dependency>
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</dependencies>
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.. code-block:: scala
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:caption: sbt
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libraryDependencies ++= Seq(
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"ml.dmlc" %% "xgboost4j-gpu" % "latest_version_num",
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"ml.dmlc" %% "xgboost4j-spark-gpu" % "latest_version_num"
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)
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This will check out the latest stable version from the Maven Central.
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This will check out the latest stable version from the Maven Central.
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For the latest release version number, please check `release page <https://github.com/dmlc/xgboost/releases>`_.
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For the latest release version number, please check `release page <https://github.com/dmlc/xgboost/releases>`_.
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@ -185,7 +218,7 @@ and Windows.) Download it and run the following commands:
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JVM
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JVM
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---
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---
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First add the following Maven repository hosted by the XGBoost project:
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* XGBoost4j/XGBoost4j-Spark
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.. code-block:: xml
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.. code-block:: xml
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:caption: Maven
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:caption: Maven
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@ -234,6 +267,40 @@ Then add XGBoost4J as a dependency:
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"ml.dmlc" %% "xgboost4j-spark" % "latest_version_num-SNAPSHOT"
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"ml.dmlc" %% "xgboost4j-spark" % "latest_version_num-SNAPSHOT"
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)
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)
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* XGBoost4j-GPU/XGBoost4j-Spark-GPU
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.. code-block:: xml
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:caption: maven
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<properties>
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...
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<!-- Specify Scala version in package name -->
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<scala.binary.version>2.12</scala.binary.version>
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</properties>
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<dependencies>
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...
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<dependency>
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<groupId>ml.dmlc</groupId>
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<artifactId>xgboost4j-gpu_${scala.binary.version}</artifactId>
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<version>latest_version_num-SNAPSHOT</version>
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</dependency>
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<dependency>
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<groupId>ml.dmlc</groupId>
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<artifactId>xgboost4j-spark-gpu_${scala.binary.version}</artifactId>
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<version>latest_version_num-SNAPSHOT</version>
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</dependency>
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</dependencies>
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.. code-block:: scala
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:caption: sbt
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libraryDependencies ++= Seq(
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"ml.dmlc" %% "xgboost4j-gpu" % "latest_version_num-SNAPSHOT",
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"ml.dmlc" %% "xgboost4j-spark-gpu" % "latest_version_num-SNAPSHOT"
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)
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Look up the ``version`` field in `pom.xml <https://github.com/dmlc/xgboost/blob/master/jvm-packages/pom.xml>`_ to get the correct version number.
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Look up the ``version`` field in `pom.xml <https://github.com/dmlc/xgboost/blob/master/jvm-packages/pom.xml>`_ to get the correct version number.
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The SNAPSHOT JARs are hosted by the XGBoost project. Every commit in the ``master`` branch will automatically trigger generation of a new SNAPSHOT JAR. You can control how often Maven should upgrade your SNAPSHOT installation by specifying ``updatePolicy``. See `here <http://maven.apache.org/pom.html#Repositories>`_ for details.
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The SNAPSHOT JARs are hosted by the XGBoost project. Every commit in the ``master`` branch will automatically trigger generation of a new SNAPSHOT JAR. You can control how often Maven should upgrade your SNAPSHOT installation by specifying ``updatePolicy``. See `here <http://maven.apache.org/pom.html#Repositories>`_ for details.
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@ -35,6 +35,7 @@ Contents
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java_intro
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java_intro
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XGBoost4J-Spark Tutorial <xgboost4j_spark_tutorial>
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XGBoost4J-Spark Tutorial <xgboost4j_spark_tutorial>
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XGBoost4J-Spark-GPU Turorial <xgboost4j_spark_gpu_tutorial>
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Code Examples <https://github.com/dmlc/xgboost/tree/master/jvm-packages/xgboost4j-example>
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Code Examples <https://github.com/dmlc/xgboost/tree/master/jvm-packages/xgboost4j-example>
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XGBoost4J Java API <javadocs/index>
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XGBoost4J Java API <javadocs/index>
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XGBoost4J Scala API <scaladocs/xgboost4j/index>
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XGBoost4J Scala API <scaladocs/xgboost4j/index>
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246
doc/jvm/xgboost4j_spark_gpu_tutorial.rst
Normal file
246
doc/jvm/xgboost4j_spark_gpu_tutorial.rst
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@ -0,0 +1,246 @@
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#############################################
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XGBoost4J-Spark-GPU Tutorial (version 1.6.0+)
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#############################################
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**XGBoost4J-Spark-GPU** is a project aiming to accelerate XGBoost distributed training on Spark from
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end to end with GPUs by leveraging the `Spark-Rapids <https://nvidia.github.io/spark-rapids/>`_ project.
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This tutorial will show you how to use **XGBoost4J-Spark-GPU**.
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.. contents::
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:backlinks: none
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:local:
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************************************************
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Build an ML Application with XGBoost4J-Spark-GPU
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************************************************
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Adding XGBoost to Your Project
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==============================
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Before we go into the tour of how to use XGBoost4J-Spark-GPU, you should first consult
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:ref:`Installation from Maven repository <install_jvm_packages>` in order to add XGBoost4J-Spark-GPU as
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a dependency for your project. We provide both stable releases and snapshots.
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Data Preparation
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================
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In this section, we use `Iris <https://archive.ics.uci.edu/ml/datasets/iris>`_ dataset as an example to
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showcase how we use Spark to transform raw dataset and make it fit to the data interface of XGBoost.
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Iris dataset is shipped in CSV format. Each instance contains 4 features, "sepal length", "sepal width",
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"petal length" and "petal width". In addition, it contains the "class" column, which is essentially the
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label with three possible values: "Iris Setosa", "Iris Versicolour" and "Iris Virginica".
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Read Dataset with Spark's Built-In Reader
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-----------------------------------------
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.. code-block:: scala
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import org.apache.spark.sql.SparkSession
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import org.apache.spark.sql.types.{DoubleType, StringType, StructField, StructType}
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val spark = SparkSession.builder().getOrCreate()
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val labelName = "class"
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val schema = new StructType(Array(
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StructField("sepal length", DoubleType, true),
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StructField("sepal width", DoubleType, true),
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StructField("petal length", DoubleType, true),
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StructField("petal width", DoubleType, true),
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StructField(labelName, StringType, true)))
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val xgbInput = spark.read.option("header", "false")
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.schema(schema)
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.csv(dataPath)
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At the first line, we create an instance of `SparkSession <https://spark.apache.org/docs/latest/sql-getting-started.html#starting-point-sparksession>`_
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which is the entry of any Spark program working with DataFrame. The ``schema`` variable
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defines the schema of DataFrame wrapping Iris data. With this explicitly set schema, we
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can define the columns' name as well as their types; otherwise the column name would be
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the default ones derived by Spark, such as ``_col0``, etc. Finally, we can use Spark's
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built-in csv reader to load Iris csv file as a DataFrame named ``xgbInput``.
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Spark also contains many built-in readers for other format. eg ORC, Parquet, Avro, Json.
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Transform Raw Iris Dataset
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--------------------------
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To make Iris dataset be recognizable to XGBoost, we need to encode String-typed
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label, i.e. "class", to Double-typed label.
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One way to convert the String-typed label to Double is to use Spark's built-in feature transformer
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`StringIndexer <https://spark.apache.org/docs/2.3.1/api/scala/index.html#org.apache.spark.ml.feature.StringIndexer>`_.
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but it has not been accelerated by Spark-Rapids yet, which means it will fall back
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to CPU to run and cause performance issue. Instead, we use an alternative way to acheive
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the same goal by the following code
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.. code-block:: scala
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import org.apache.spark.sql.expressions.Window
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import org.apache.spark.sql.functions._
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val spec = Window.orderBy(labelName)
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val Array(train, test) = xgbInput
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.withColumn("tmpClassName", dense_rank().over(spec) - 1)
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.drop(labelName)
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.withColumnRenamed("tmpClassName", labelName)
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.randomSplit(Array(0.7, 0.3), seed = 1)
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train.show(5)
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.. code-block:: none
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+------------+-----------+------------+-----------+-----+
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|sepal length|sepal width|petal length|petal width|class|
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+------------+-----------+------------+-----------+-----+
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| 4.3| 3.0| 1.1| 0.1| 0|
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| 4.4| 2.9| 1.4| 0.2| 0|
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| 4.4| 3.0| 1.3| 0.2| 0|
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| 4.4| 3.2| 1.3| 0.2| 0|
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| 4.6| 3.2| 1.4| 0.2| 0|
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+------------+-----------+------------+-----------+-----+
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With window operations, we have mapped string column of labels to label indices.
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Training
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========
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The GPU version of XGBoost-Spark supports both regression and classification
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models. Although we use the Iris dataset in this tutorial to show how we use
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``XGBoost/XGBoost4J-Spark-GPU`` to resolve a multi-classes classification problem, the
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usage in Regression is very similar to classification.
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To train a XGBoost model for classification, we need to claim a XGBoostClassifier first:
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.. code-block:: scala
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import ml.dmlc.xgboost4j.scala.spark.XGBoostClassifier
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val xgbParam = Map(
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"objective" -> "multi:softprob",
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"num_class" -> 3,
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"num_round" -> 100,
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"tree_method" -> "gpu_hist",
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"num_workers" -> 1)
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val featuresNames = schema.fieldNames.filter(name => name != labelName)
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val xgbClassifier = new XGBoostClassifier(xgbParam)
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.setFeaturesCol(featuresNames)
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.setLabelCol(labelName)
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The available parameters for training a XGBoost model can be found in :doc:`here </parameter>`.
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Similar to the XGBoost4J-Spark package, in addition to the default set of parameters,
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XGBoost4J-Spark-GPU also supports the camel-case variant of these parameters to be
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consistent with Spark's MLLIB naming convention.
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Specifically, each parameter in :doc:`this page </parameter>` has its equivalent form in
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XGBoost4J-Spark-GPU with camel case. For example, to set ``max_depth`` for each tree, you can pass
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parameter just like what we did in the above code snippet (as ``max_depth`` wrapped in a Map), or
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you can do it through setters in XGBoostClassifer:
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.. code-block:: scala
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val xgbClassifier = new XGBoostClassifier(xgbParam)
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.setFeaturesCol(featuresNames)
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.setLabelCol(labelName)
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xgbClassifier.setMaxDepth(2)
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.. note::
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In contrast to the XGBoost4J-Spark package, which needs to first assemble the numeric
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feature columns into one column with VectorUDF type by VectorAssembler, the
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XGBoost4J-Spark-GPU does not require such transformation, it accepts an array of feature
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column names by ``setFeaturesCol(value: Array[String])``.
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After we set XGBoostClassifier parameters and feature/label columns, we can build a
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transformer, XGBoostClassificationModel by fitting XGBoostClassifier with the input
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DataFrame. This ``fit`` operation is essentially the training process and the generated
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model can then be used in other tasks like prediction.
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.. code-block:: scala
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val xgbClassificationModel = xgbClassifier.fit(train)
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Prediction
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==========
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When we get a model, either XGBoostClassificationModel or XGBoostRegressionModel, it takes a DataFrame,
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read the column containing feature vectors, predict for each feature vector, and output a new DataFrame
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with the following columns by default:
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* XGBoostClassificationModel will output margins (``rawPredictionCol``), probabilities(``probabilityCol``) and the eventual prediction labels (``predictionCol``) for each possible label.
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* XGBoostRegressionModel will output prediction label(``predictionCol``).
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.. code-block:: scala
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val xgbClassificationModel = xgbClassifier.fit(train)
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val results = xgbClassificationModel.transform(test)
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results.show()
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With the above code snippet, we get a DataFrame as result, which contains the margin, probability for each class,
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and the prediction for each instance
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.. code-block:: none
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+------------+-----------+------------------+-------------------+-----+--------------------+--------------------+----------+
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|sepal length|sepal width| petal length| petal width|class| rawPrediction| probability|prediction|
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+------------+-----------+------------------+-------------------+-----+--------------------+--------------------+----------+
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| 4.5| 2.3| 1.3|0.30000000000000004| 0|[3.16666603088378...|[0.98853939771652...| 0.0|
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| 4.6| 3.1| 1.5| 0.2| 0|[3.25857257843017...|[0.98969423770904...| 0.0|
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| 4.8| 3.1| 1.6| 0.2| 0|[3.25857257843017...|[0.98969423770904...| 0.0|
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| 4.8| 3.4| 1.6| 0.2| 0|[3.25857257843017...|[0.98969423770904...| 0.0|
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| 4.8| 3.4|1.9000000000000001| 0.2| 0|[3.25857257843017...|[0.98969423770904...| 0.0|
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| 4.9| 2.4| 3.3| 1.0| 1|[-2.1498908996582...|[0.00596602633595...| 1.0|
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| 4.9| 2.5| 4.5| 1.7| 2|[-2.1498908996582...|[0.00596602633595...| 1.0|
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| 5.0| 3.5| 1.3|0.30000000000000004| 0|[3.25857257843017...|[0.98969423770904...| 0.0|
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| 5.1| 2.5| 3.0| 1.1| 1|[3.16666603088378...|[0.98853939771652...| 0.0|
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| 5.1| 3.3| 1.7| 0.5| 0|[3.25857257843017...|[0.98969423770904...| 0.0|
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| 5.1| 3.5| 1.4| 0.2| 0|[3.25857257843017...|[0.98969423770904...| 0.0|
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| 5.1| 3.8| 1.6| 0.2| 0|[3.25857257843017...|[0.98969423770904...| 0.0|
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| 5.2| 3.4| 1.4| 0.2| 0|[3.25857257843017...|[0.98969423770904...| 0.0|
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| 5.2| 3.5| 1.5| 0.2| 0|[3.25857257843017...|[0.98969423770904...| 0.0|
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| 5.2| 4.1| 1.5| 0.1| 0|[3.25857257843017...|[0.98969423770904...| 0.0|
|
||||||
|
| 5.4| 3.9| 1.7| 0.4| 0|[3.25857257843017...|[0.98969423770904...| 0.0|
|
||||||
|
| 5.5| 2.4| 3.8| 1.1| 1|[-2.1498908996582...|[0.00596602633595...| 1.0|
|
||||||
|
| 5.5| 4.2| 1.4| 0.2| 0|[3.25857257843017...|[0.98969423770904...| 0.0|
|
||||||
|
| 5.7| 2.5| 5.0| 2.0| 2|[-2.1498908996582...|[0.00280966912396...| 2.0|
|
||||||
|
| 5.7| 3.0| 4.2| 1.2| 1|[-2.1498908996582...|[0.00643939292058...| 1.0|
|
||||||
|
+------------+-----------+------------------+-------------------+-----+--------------------+--------------------+----------+
|
||||||
|
|
||||||
|
**********************
|
||||||
|
Submit the application
|
||||||
|
**********************
|
||||||
|
|
||||||
|
Take submitting the spark job to Spark Standalone cluster as an example, and assuming your application main class
|
||||||
|
is ``Iris`` and the application jar is ``iris-1.0.0.jar``
|
||||||
|
|
||||||
|
.. code-block:: bash
|
||||||
|
|
||||||
|
cudf_version=22.02.0
|
||||||
|
rapids_version=22.02.0
|
||||||
|
xgboost_version=1.6.0
|
||||||
|
main_class=Iris
|
||||||
|
app_jar=iris-1.0.0.jar
|
||||||
|
|
||||||
|
spark-submit \
|
||||||
|
--master $master \
|
||||||
|
--packages ai.rapids:cudf:${cudf_version},com.nvidia:rapids-4-spark_2.12:${rapids_version},ml.dmlc:xgboost4j-gpu_2.12:${xgboost_version},ml.dmlc:xgboost4j-spark-gpu_2.12:${xgboost_version} \
|
||||||
|
--conf spark.executor.cores=12 \
|
||||||
|
--conf spark.task.cpus=1 \
|
||||||
|
--conf spark.executor.resource.gpu.amount=1 \
|
||||||
|
--conf spark.task.resource.gpu.amount=0.08 \
|
||||||
|
--conf spark.rapids.sql.csv.read.double.enabled=true \
|
||||||
|
--conf spark.rapids.sql.hasNans=false \
|
||||||
|
--conf spark.plugins=com.nvidia.spark.SQLPlugin \
|
||||||
|
--class ${main_class} \
|
||||||
|
${app_jar}
|
||||||
|
|
||||||
|
* First, we need to specify the ``spark-rapids, cudf, xgboost4j-gpu, xgboost4j-spark-gpu`` packages by ``--packages``
|
||||||
|
* Second, ``spark-rapids`` is a Spark plugin, so we need to configure it by specifying ``spark.plugins=com.nvidia.spark.SQLPlugin``
|
||||||
|
|
||||||
|
For details about ``spark-rapids`` other configurations, please refer to `configuration <https://nvidia.github.io/spark-rapids/docs/configs.html>`_.
|
||||||
|
|
||||||
|
For ``spark-rapids Frequently Asked Questions``, please refer to
|
||||||
|
`frequently-asked-questions <https://nvidia.github.io/spark-rapids/docs/FAQ.html#frequently-asked-questions>`_.
|
||||||
@ -14,6 +14,7 @@ See `Awesome XGBoost <https://github.com/dmlc/xgboost/tree/master/demo>`_ for mo
|
|||||||
Distributed XGBoost with AWS YARN <aws_yarn>
|
Distributed XGBoost with AWS YARN <aws_yarn>
|
||||||
kubernetes
|
kubernetes
|
||||||
Distributed XGBoost with XGBoost4J-Spark <https://xgboost.readthedocs.io/en/latest/jvm/xgboost4j_spark_tutorial.html>
|
Distributed XGBoost with XGBoost4J-Spark <https://xgboost.readthedocs.io/en/latest/jvm/xgboost4j_spark_tutorial.html>
|
||||||
|
Distributed XGBoost with XGBoost4J-Spark-GPU <https://xgboost.readthedocs.io/en/latest/jvm/xgboost4j_spark_gpu_tutorial.html>
|
||||||
dask
|
dask
|
||||||
ray
|
ray
|
||||||
dart
|
dart
|
||||||
|
|||||||
Loading…
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Reference in New Issue
Block a user