[pyspark][doc] add more doc for pyspark (dmlc#8271)

Co-authored-by: fis <jm.yuan@outlook.com>

Author: wbo4958

doc/tutorials/spark_estimator.rst

@@ -1,12 +1,23 @@
-###############################
-Using XGBoost PySpark Estimator
-###############################
-Starting from version 1.7.0, xgboost supports pyspark estimator APIs.  The feature is
-still experimental and not yet ready for production use.
+################################
+Distributed XGBoost with PySpark
+################################
+
+Starting from version 1.7.0, xgboost supports pyspark estimator APIs.
+
+.. note::
+
+   The feature is still experimental and not yet ready for production use.
+
+.. contents::
+  :backlinks: none
+  :local:
+
+*************************
+XGBoost PySpark Estimator
+*************************
 
-*****************
 SparkXGBRegressor
-*****************
+=================
 
 SparkXGBRegressor is a PySpark ML estimator. It implements the XGBoost classification
 algorithm based on XGBoost python library, and it can be used in PySpark Pipeline
@@ -17,10 +28,14 @@ We can create a `SparkXGBRegressor` estimator like:
 .. code-block:: python
 
   from xgboost.spark import SparkXGBRegressor
-  spark_reg_estimator = SparkXGBRegressor(num_workers=2, max_depth=5)
+  spark_reg_estimator = SparkXGBRegressor(
+    features_col="features",
+    label_col="label",
+    num_workers=2,
+  )
 
 
-The above snippet create an spark estimator which can fit on a spark dataset,
+The above snippet creates a spark estimator which can fit on a spark dataset,
 and return a spark model that can transform a spark dataset and generate dataset
 with prediction column. We can set almost all of xgboost sklearn estimator parameters
 as `SparkXGBRegressor` parameters, but some parameter such as `nthread` is forbidden
@@ -30,8 +45,9 @@ such as `weight_col`, `validation_indicator_col`, `use_gpu`, for details please
 
 The following code snippet shows how to train a spark xgboost regressor model,
 first we need to prepare a training dataset as a spark dataframe contains
-"features" and "label" column, the "features" column must be `pyspark.ml.linalg.Vector`
-type or spark array type.
+"label" column and "features" column(s), the "features" column(s) must be `pyspark.ml.linalg.Vector`
+type or spark array type or a list of feature column names.
+
 
 .. code-block:: python
 
@@ -51,17 +67,156 @@ type or spark array type.
 The above snippet code returns a `transformed_test_spark_dataframe` that contains the input
 dataset columns and an appended column "prediction" representing the prediction results.
 
-
-******************
 SparkXGBClassifier
-******************
-
+==================
 
 `SparkXGBClassifier` estimator has similar API with `SparkXGBRegressor`, but it has some
 pyspark classifier specific params, e.g. `raw_prediction_col` and `probability_col` parameters.
 Correspondingly, by default, `SparkXGBClassifierModel` transforming test dataset will
 generate result dataset with 3 new columns:
-
 - "prediction": represents the predicted label.
 - "raw_prediction": represents the output margin values.
 - "probability": represents the prediction probability on each label.
+
+
+***************************
+XGBoost PySpark GPU support
+***************************
+
+XGBoost PySpark supports GPU training and prediction. To enable GPU support, you first need
+to install the xgboost and cudf packages. Then you can set `use_gpu` parameter to `True`.
+
+Below tutorial will show you how to train a model with XGBoost PySpark GPU on Spark
+standalone cluster.
+
+
+Write your PySpark application
+==============================
+
+.. code-block:: python
+
+  from xgboost.spark import SparkXGBRegressor
+  spark = SparkSession.builder.getOrCreate()
+
+  # read data into spark dataframe
+  train_data_path = "xxxx/train"
+  train_df = spark.read.parquet(data_path)
+
+  test_data_path = "xxxx/test"
+  test_df = spark.read.parquet(test_data_path)
+
+  # assume the label column is named "class"
+  label_name = "class"
+
+  # get a list with feature column names
+  feature_names = [x.name for x in train_df.schema if x.name != label]
+
+  # create a xgboost pyspark regressor estimator and set use_gpu=True
+  regressor = SparkXGBRegressor(
+    features_col=feature_names,
+    label_col=label_name,
+    num_workers=2,
+    use_gpu=True,
+  )
+
+  # train and return the model
+  model = regressor.fit(train_df)
+
+  # predict on test data
+  predict_df = model.transform(test_df)
+  predict_df.show()
+
+Prepare the necessary packages
+==============================
+
+We recommend using Conda or Virtualenv to manage python dependencies
+in PySpark. Please refer to
+`How to Manage Python Dependencies in PySpark <https://www.databricks.com/blog/2020/12/22/how-to-manage-python-dependencies-in-pyspark.html>`_.
+
+.. code-block:: bash
+
+  conda create -y -n xgboost-env -c conda-forge conda-pack python=3.9
+  conda activate xgboost-env
+  pip install xgboost
+  pip install cudf
+  conda pack -f -o xgboost-env.tar.gz
+
+
+Submit the PySpark application
+==============================
+
+Assuming you have configured your Spark cluster with GPU support, if not yet, please
+refer to `spark standalone configuration with GPU support <https://nvidia.github.io/spark-rapids/docs/get-started/getting-started-on-prem.html#spark-standalone-cluster>`_.
+
+.. code-block:: bash
+
+  export PYSPARK_DRIVER_PYTHON=python
+  export PYSPARK_PYTHON=./environment/bin/python
+
+  spark-submit \
+    --master spark://<master-ip>:7077 \
+    --conf spark.executor.resource.gpu.amount=1 \
+    --conf spark.task.resource.gpu.amount=1 \
+    --archives xgboost-env.tar.gz#environment \
+    xgboost_app.py
+
+
+Model Persistence
+=================
+
+Similar to standard PySpark ml estimators, one can persist and reuse the model with `save`
+and `load` methods:
+
+.. code-block:: python
+
+  regressor = SparkXGBRegressor()
+  model = regressor.fit(train_df)
+  # save the model
+  model.save("/tmp/xgboost-pyspark-model")
+  # load the model
+  model2 = SparkXGBRankerModel.load("/tmp/xgboost-pyspark-model")
+
+To export the underlying booster model used by XGBoost:
+
+.. code-block:: python
+
+  regressor = SparkXGBRegressor()
+  model = regressor.fit(train_df)
+  # the same booster object returned by xgboost.train
+  booster: xgb.Booster = model.get_booster()
+  booster.predict(...)
+  booster.save_model("model.json")
+
+This booster is shared by other Python interfaces and can be used by other language
+bindings like the C and R packages. Lastly, one can extract a booster file directly from
+saved spark estimator without going through the getter:
+
+.. code-block:: python
+
+  import xgboost as xgb
+  bst = xgb.Booster()
+  bst.load_model("/tmp/xgboost-pyspark-model/model/part-00000")
+
+Accelerate the whole pipeline of xgboost pyspark
+================================================
+
+With `RAPIDS Accelerator for Apache Spark <https://nvidia.github.io/spark-rapids/>`_,
+you can accelerate the whole pipeline (ETL, Train, Transform) for xgboost pyspark
+without any code change by leveraging GPU.
+
+Below is a simple example submit command for enabling GPU acceleration:
+
+.. code-block:: bash
+
+  export PYSPARK_DRIVER_PYTHON=python
+  export PYSPARK_PYTHON=./environment/bin/python
+
+  spark-submit \
+    --master spark://<master-ip>:7077 \
+    --conf spark.executor.resource.gpu.amount=1 \
+    --conf spark.task.resource.gpu.amount=1 \
+    --packages com.nvidia:rapids-4-spark_2.12:22.08.0 \
+    --conf spark.plugins=com.nvidia.spark.SQLPlugin \
+    --conf spark.sql.execution.arrow.maxRecordsPerBatch=1000000 \
+    --archives xgboost-env.tar.gz#environment \
+    xgboost_app.py