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7.3 KiB
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229 lines
7.3 KiB
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##################
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Installation Guide
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##################
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XGBoost provides binary packages for some language bindings. The binary packages support
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the GPU algorithm (``gpu_hist``) on machines with NVIDIA GPUs. Please note that **training
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with multiple GPUs is only supported for Linux platform**. See :doc:`gpu/index`. Also we
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have both stable releases and nightly builds, see below for how to install them. For
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building from source, visit :doc:`this page </build>`.
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.. contents:: Contents
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Stable Release
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==============
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Python
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------
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Pre-built binary are uploaded to PyPI (Python Package Index) for each release. Supported platforms are Linux (x86_64, aarch64), Windows (x86_64) and MacOS (x86_64).
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.. code-block:: bash
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pip install xgboost
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You might need to run the command with ``--user`` flag or use ``virtualenv`` if you run
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into permission errors. Python pre-built binary capability for each platform:
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.. |tick| unicode:: U+2714
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.. |cross| unicode:: U+2718
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+-------------------+---------+----------------------+
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| Platform | GPU | Multi-Node-Multi-GPU |
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+===================+=========+======================+
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| Linux x86_64 | |tick| | |tick| |
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+-------------------+---------+----------------------+
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| Linux aarch64 | |cross| | |cross| |
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+-------------------+---------+----------------------+
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| MacOS | |cross| | |cross| |
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+-------------------+---------+----------------------+
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| Windows | |tick| | |cross| |
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+-------------------+---------+----------------------+
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If you are using **Apple Silicon**, please use the Conda packaging manager to install XGBoost:
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.. code-block:: bash
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conda install -c conda-forge xgboost
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Visit the `Miniconda website <https://docs.conda.io/en/latest/miniconda.html>`_ to obtain Conda.
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R
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-
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* From CRAN:
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.. code-block:: R
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install.packages("xgboost")
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.. note:: Using all CPU cores (threads) on Mac OSX
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If you are using Mac OSX, you should first install OpenMP library (``libomp``) by running
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.. code-block:: bash
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brew install libomp
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and then run ``install.packages("xgboost")``. Without OpenMP, XGBoost will only use a
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single CPU core, leading to suboptimal training speed.
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* We also provide **experimental** pre-built binary with GPU support. With this binary,
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you will be able to use the GPU algorithm without building XGBoost from the source.
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Download the binary package from the Releases page. The file name will be of the form
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``xgboost_r_gpu_[os]_[version].tar.gz``, where ``[os]`` is either ``linux`` or ``win64``.
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(We build the binaries for 64-bit Linux and Windows.)
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Then install XGBoost by running:
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.. code-block:: bash
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# Install dependencies
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R -q -e "install.packages(c('data.table', 'jsonlite'))"
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# Install XGBoost
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R CMD INSTALL ./xgboost_r_gpu_linux.tar.gz
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JVM
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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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.. 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_${scala.binary.version}</artifactId>
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<version>latest_version_num</version>
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</dependency>
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<dependency>
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<groupId>ml.dmlc</groupId>
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<artifactId>xgboost4j-spark_${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" % "latest_version_num",
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"ml.dmlc" %% "xgboost4j-spark" % "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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For the latest release version number, please check `release page <https://github.com/dmlc/xgboost/releases>`_.
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To enable the GPU algorithm (``tree_method='gpu_hist'``), use artifacts ``xgboost4j-gpu_2.12`` and ``xgboost4j-spark-gpu_2.12`` instead (note the ``gpu`` suffix).
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.. note:: Windows not supported in the JVM package
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Currently, XGBoost4J-Spark does not support Windows platform, as the distributed training algorithm is inoperational for Windows. Please use Linux or MacOS.
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Nightly Build
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=============
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Python
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------
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Nightly builds are available. You can go to `this page <https://s3-us-west-2.amazonaws.com/xgboost-nightly-builds/list.html>`_,
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find the wheel with the commit ID you want and install it with pip:
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.. code-block:: bash
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pip install <url to the wheel>
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The capability of Python pre-built wheel is the same as stable release.
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R
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-
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Other than standard CRAN installation, we also provide *experimental* pre-built binary on
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with GPU support. You can go to `this page
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<https://s3-us-west-2.amazonaws.com/xgboost-nightly-builds/list.html>`_, Find the commit
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ID you want to install and then locate the file ``xgboost_r_gpu_[os]_[commit].tar.gz``,
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where ``[os]`` is either ``linux`` or ``win64``. (We build the binaries for 64-bit Linux
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and Windows.) Download it and run the following commands:
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.. code-block:: bash
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# Install dependencies
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R -q -e "install.packages(c('data.table', 'jsonlite', 'remotes'))"
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# Install XGBoost
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R CMD INSTALL ./xgboost_r_gpu_linux.tar.gz
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JVM
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---
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First add the following Maven repository hosted by the XGBoost project:
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.. code-block:: xml
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:caption: Maven
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<repository>
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<id>XGBoost4J Snapshot Repo</id>
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<name>XGBoost4J Snapshot Repo</name>
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<url>https://s3-us-west-2.amazonaws.com/xgboost-maven-repo/snapshot/</url>
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</repository>
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.. code-block:: scala
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:caption: sbt
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resolvers += "XGBoost4J Snapshot Repo" at "https://s3-us-west-2.amazonaws.com/xgboost-maven-repo/snapshot/"
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Then add XGBoost4J as a dependency:
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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_${scala.binary.version}</artifactId>
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<version>latest_version_num-SNAPSHOT</version>
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</dependency>
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<dependency>
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<groupId>ml.dmlc</groupId>
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<artifactId>xgboost4j-spark_${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" % "latest_version_num-SNAPSHOT",
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"ml.dmlc" %% "xgboost4j-spark" % "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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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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You can browse the file listing of the Maven repository at https://s3-us-west-2.amazonaws.com/xgboost-maven-repo/list.html.
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To enable the GPU algorithm (``tree_method='gpu_hist'``), use artifacts ``xgboost4j-gpu_2.12`` and ``xgboost4j-spark-gpu_2.12`` instead (note the ``gpu`` suffix).
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