[jvm-packages] Add the new device parameter. (#9385)
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@@ -121,7 +121,7 @@ To train a XGBoost model for classification, we need to claim a XGBoostClassifie
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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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"device" -> "cuda",
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"num_workers" -> 1)
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val featuresNames = schema.fieldNames.filter(name => name != labelName)
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@@ -130,15 +130,14 @@ To train a XGBoost model for classification, we need to claim a XGBoostClassifie
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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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The ``device`` parameter is for informing XGBoost that CUDA devices should be used instead of CPU. Unlike the single-node mode, GPUs are managed by spark instead of by XGBoost. Therefore, explicitly specified device ordinal like ``cuda:1`` is not support.
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The available parameters for training a XGBoost model can be found in :doc:`here </parameter>`. Similar to the XGBoost4J-Spark package, in addition to the default set of parameters, XGBoost4J-Spark-GPU also supports the camel-case variant of these parameters to be 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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XGBoost4J-Spark-GPU with camel case. For example, to set ``max_depth`` for each tree, you
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can pass parameter just like what we did in the above code snippet (as ``max_depth``
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wrapped in a Map), or you can do it through setters in XGBoostClassifer:
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.. code-block:: scala
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