[Doc] Update readme, ipynb files for 24.02 version [skip ci]

.github/workflows/auto-merge.yml

@@ -1,4 +1,4 @@
-# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+# Copyright (c) 2022-2024, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -18,7 +18,7 @@ name: auto-merge HEAD to BASE
 on:
   pull_request_target:
     branches:
-    - branch-23.12
+    - branch-24.02
     types: [closed]
 
 jobs:
@@ -29,14 +29,14 @@ jobs:
     steps:
       - uses: actions/checkout@v3
         with:
-          ref: branch-23.12 # force to fetch from latest upstream instead of PR ref
+          ref: branch-24.02 # force to fetch from latest upstream instead of PR ref
 
       - name: auto-merge job
         uses: ./.github/workflows/auto-merge
         env:
           OWNER: NVIDIA
           REPO_NAME: spark-rapids-examples
-          HEAD: branch-23.12
-          BASE: branch-24.02
+          HEAD: branch-24.02
+          BASE: branch-24.04
           AUTOMERGE_TOKEN: ${{ secrets.AUTOMERGE_TOKEN }} # use to merge PR
 

.github/workflows/markdown-links-check.yml

@@ -1,4 +1,4 @@
-# Copyright (c) 2022, NVIDIA CORPORATION.
+# Copyright (c) 2022-2024, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -30,6 +30,5 @@ jobs:
       with:
         max-depth: -1
         use-verbose-mode: 'yes'
-        check-modified-files-only: 'yes'
         config-file: '.github/workflows/markdown-links-check/markdown-links-check-config.json'
         base-branch: 'main'

.github/workflows/markdown-links-check/markdown-links-check-config.json

@@ -1,4 +1,18 @@
 {
+  "ignorePatterns": [
+    {
+      "pattern": "/docs"
+    },
+    {
+      "pattern": "/datasets"
+    },
+    {
+      "pattern": "/dockerfile"
+    },
+    {
+      "pattern": "/examples"
+    }
+  ],
   "timeout": "15s",
   "retryOn429": true,
   "retryCount":30,

README.md

@@ -37,7 +37,7 @@ can be built for running on GPU with RAPIDS Accelerator in this repo:
 | 3 | XGBoost | Taxi (Scala) | End-to-end ETL + XGBoost example to predict taxi trip fare amount with [NYC taxi trips data set](https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page)
 | 4 | ML/DL | PCA End-to-End | Spark MLlib based PCA example to train and transform with a synthetic dataset
 | 5 | UDF | cuSpatial - Point in Polygon | Spark cuSpatial example for Point in Polygon function using NYC Taxi pickup location dataset
-| 6 | UDF | URL Decode | Decodes URL-encoded strings using the [Java APIs of RAPIDS cudf](https://docs.rapids.ai/api/cudf-java/stable/)
-| 7 | UDF | URL Encode | URL-encodes strings using the [Java APIs of RAPIDS cudf](https://docs.rapids.ai/api/cudf-java/stable/)
+| 6 | UDF | URL Decode | Decodes URL-encoded strings using the [Java APIs of RAPIDS cudf](https://docs.rapids.ai/api/cudf-java/legacy/)
+| 7 | UDF | URL Encode | URL-encodes strings using the [Java APIs of RAPIDS cudf](https://docs.rapids.ai/api/cudf-java/legacy/)
 | 8 | UDF | [CosineSimilarity](./examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/java/com/nvidia/spark/rapids/udf/java/CosineSimilarity.java) | Computes the cosine similarity between two float vectors using [native code](./examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/cpp/src)
 | 9 | UDF | [StringWordCount](./examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/java/com/nvidia/spark/rapids/udf/hive/StringWordCount.java)  | Implements a Hive simple UDF using [native code](./examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/cpp/src) to count words in strings

docs/get-started/xgboost-examples/csp/databricks/databricks.md

@@ -21,7 +21,7 @@ Navigate to your home directory in the UI and select **Create** > **File** from
 create an `init.sh` scripts with contents:   
    ```bash
    #!/bin/bash
-   sudo wget -O /databricks/jars/rapids-4-spark_2.12-23.12.1.jar https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/23.12.1/rapids-4-spark_2.12-23.12.1.jar
+   sudo wget -O /databricks/jars/rapids-4-spark_2.12-24.02.0.jar https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/24.02.0/rapids-4-spark_2.12-24.02.0.jar
    ```
 1. Select the Databricks Runtime Version from one of the supported runtimes specified in the
    Prerequisites section.
@@ -68,7 +68,7 @@ create an `init.sh` scripts with contents:
     ```bash
     spark.rapids.sql.python.gpu.enabled true
     spark.python.daemon.module rapids.daemon_databricks
-    spark.executorEnv.PYTHONPATH /databricks/jars/rapids-4-spark_2.12-23.12.1.jar:/databricks/spark/python
+    spark.executorEnv.PYTHONPATH /databricks/jars/rapids-4-spark_2.12-24.02.0.jar:/databricks/spark/python
     ```
    Note that since python memory pool require installing the cudf library, so you need to install cudf library in 
    each worker nodes `pip install cudf-cu11 --extra-index-url=https://pypi.nvidia.com` or disable python memory pool

docs/get-started/xgboost-examples/csp/databricks/init.sh

@@ -1,7 +1,7 @@
 sudo rm -f /databricks/jars/spark--maven-trees--ml--10.x--xgboost-gpu--ml.dmlc--xgboost4j-gpu_2.12--ml.dmlc__xgboost4j-gpu_2.12__1.5.2.jar
 sudo rm -f /databricks/jars/spark--maven-trees--ml--10.x--xgboost-gpu--ml.dmlc--xgboost4j-spark-gpu_2.12--ml.dmlc__xgboost4j-spark-gpu_2.12__1.5.2.jar
 
-sudo wget -O /databricks/jars/rapids-4-spark_2.12-23.12.1.jar https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/23.12.1/rapids-4-spark_2.12-23.12.1.jar
+sudo wget -O /databricks/jars/rapids-4-spark_2.12-24.02.0.jar https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/24.02.0/rapids-4-spark_2.12-24.02.0.jar
 sudo wget -O /databricks/jars/xgboost4j-gpu_2.12-1.7.1.jar https://repo1.maven.org/maven2/ml/dmlc/xgboost4j-gpu_2.12/1.7.1/xgboost4j-gpu_2.12-1.7.1.jar
 sudo wget -O /databricks/jars/xgboost4j-spark-gpu_2.12-1.7.1.jar https://repo1.maven.org/maven2/ml/dmlc/xgboost4j-spark-gpu_2.12/1.7.1/xgboost4j-spark-gpu_2.12-1.7.1.jar
 ls -ltr

docs/get-started/xgboost-examples/on-prem-cluster/kubernetes-scala.md

@@ -40,7 +40,7 @@ export SPARK_DOCKER_IMAGE=<gpu spark docker image repo and name>
 export SPARK_DOCKER_TAG=<spark docker image tag>
 
 pushd ${SPARK_HOME}
-wget https://github.com/NVIDIA/spark-rapids-examples/raw/branch-23.12/dockerfile/Dockerfile
+wget https://github.com/NVIDIA/spark-rapids-examples/raw/branch-24.02/dockerfile/Dockerfile
 
 # Optionally install additional jars into ${SPARK_HOME}/jars/
 

docs/get-started/xgboost-examples/prepare-package-data/preparation-python.md

@@ -5,7 +5,7 @@ For simplicity export the location to these jars. All examples assume the packag
 ### Download the jars
 
 Download the RAPIDS Accelerator for Apache Spark plugin jar
-  * [RAPIDS Spark Package](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/23.12.1/rapids-4-spark_2.12-23.12.1.jar)
+  * [RAPIDS Spark Package](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/24.02.0/rapids-4-spark_2.12-24.02.0.jar)
 
 ### Build XGBoost Python Examples
 

docs/get-started/xgboost-examples/prepare-package-data/preparation-scala.md

@@ -5,7 +5,7 @@ For simplicity export the location to these jars. All examples assume the packag
 ### Download the jars
 
 1. Download the RAPIDS Accelerator for Apache Spark plugin jar
-   * [RAPIDS Spark Package](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/23.12.1/rapids-4-spark_2.12-23.12.1.jar)
+   * [RAPIDS Spark Package](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/24.02.0/rapids-4-spark_2.12-24.02.0.jar)
 
 ### Build XGBoost Scala Examples
 

examples/ML+DL-Examples/Spark-DL/criteo_train/README.md

@@ -7,7 +7,7 @@ _Please note: The following demo is dedicated for DGX-2 machine(with V100 GPUs).
 ## Dataset
 
 The dataset used here is from Criteo clicklog dataset. 
-It's preprocessed by [DLRM](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow2/Recommendation/DLRM/preproc) 
+It's preprocessed by [DLRM](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow2/Recommendation/DLRM_and_DCNv2/preproc) 
 ETL job on Spark. We also provide a small size sample data in sample_data folder.
 All 40 columns(1 label + 39 features) are already numeric.
 

examples/ML+DL-Examples/Spark-DL/criteo_train/notebooks/Criteo-Training.ipynb

@@ -9,7 +9,7 @@
     "\n",
     "This notebook contains the same content as \"criteo_keras.py\" but in a notebook(interactive) form.\n",
     "\n",
-    "The dataset used here is from Criteo clicklog dataset. It's preprocessed by DLRM(https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow2/Recommendation/DLRM/preproc) ETL job on Spark.\n",
+    "The dataset used here is from Criteo clicklog dataset. It's preprocessed by DLRM(https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow2/Recommendation/DLRM_and_DCNv2/preproc) ETL job on Spark.\n",
     "\n",
     "We provide a small size sample data in `sample_data` folder.\n",
     "\n",

examples/ML+DL-Examples/Spark-cuML/pca/Dockerfile

@@ -18,7 +18,7 @@
 ARG CUDA_VER=11.8.0
 FROM nvidia/cuda:${CUDA_VER}-devel-ubuntu20.04
 # Please do not update the BRANCH_VER version
-ARG BRANCH_VER=23.12
+ARG BRANCH_VER=24.02
 
 RUN apt-get update
 RUN apt-get install -y wget ninja-build git

examples/ML+DL-Examples/Spark-cuML/pca/README.md

@@ -12,9 +12,9 @@ User can also download the release jar from Maven central:
 
 [rapids-4-spark-ml_2.12-22.02.0-cuda11.jar](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark-ml_2.12/22.02.0/rapids-4-spark-ml_2.12-22.02.0-cuda11.jar)
 
-[rapids-4-spark_2.12-23.12.1.jar](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/23.12.1/rapids-4-spark_2.12-23.12.1.jar)
+[rapids-4-spark_2.12-24.02.0.jar](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/24.02.0/rapids-4-spark_2.12-24.02.0.jar)
 
-Note: This demo could only work with v22.02.0 spark-ml version, and only compatible with spark-rapids versions prior to 23.12.1 . Please do not update the version in release.
+Note: This demo could only work with v22.02.0 spark-ml version, and only compatible with spark-rapids versions prior to 24.02.0 . Please do not update the version in release.
 
 ## Sample code
 
@@ -49,7 +49,7 @@ It is assumed that a Standalone Spark cluster has been set up, the `SPARK_MASTER
 
     ``` bash
     RAPIDS_ML_JAR=PATH_TO_rapids-4-spark-ml_2.12-22.02.0-cuda11.jar
-    PLUGIN_JAR=PATH_TO_rapids-4-spark_2.12-23.12.1.jar
+    PLUGIN_JAR=PATH_TO_rapids-4-spark_2.12-24.02.0.jar
 
     jupyter toree install                                \
     --spark_home=${SPARK_HOME}                             \

examples/ML+DL-Examples/Spark-cuML/pca/pom.xml

@@ -21,7 +21,7 @@
     <groupId>com.nvidia</groupId>
     <artifactId>PCAExample</artifactId>
     <packaging>jar</packaging>
-    <version>23.12.0-SNAPSHOT</version>
+    <version>24.02.0-SNAPSHOT</version>
 
     <properties>
         <maven.compiler.source>8</maven.compiler.source>

examples/ML+DL-Examples/Spark-cuML/pca/spark-submit.sh

@@ -17,7 +17,7 @@
 
 # Note that the last rapids-4-spark-ml release version is 22.02.0, snapshot version is 23.04.0-SNPASHOT, please do not update the version in release
 ML_JAR=/root/.m2/repository/com/nvidia/rapids-4-spark-ml_2.12/22.02.0/rapids-4-spark-ml_2.12-22.02.0.jar
-PLUGIN_JAR=/root/.m2/repository/com/nvidia/rapids-4-spark_2.12/23.12.1/rapids-4-spark_2.12-23.12.1.jar
+PLUGIN_JAR=/root/.m2/repository/com/nvidia/rapids-4-spark_2.12/24.02.0/rapids-4-spark_2.12-24.02.0.jar
 Note: The last rapids-4-spark-ml release version is 22.02.0, snapshot version is 23.04.0-SNPASHOT.
 
 $SPARK_HOME/bin/spark-submit \
@@ -40,4 +40,4 @@ $SPARK_HOME/bin/spark-submit \
 --conf spark.network.timeout=1000s \
 --jars $ML_JAR,$PLUGIN_JAR \
 --class com.nvidia.spark.examples.pca.Main \
-/workspace/target/PCAExample-23.12.1-SNAPSHOT.jar
+/workspace/target/PCAExample-24.02.0-SNAPSHOT.jar

examples/SQL+DF-Examples/micro-benchmarks/notebooks/micro-benchmarks-gpu.ipynb

@@ -22,7 +22,7 @@
     "import os\n",
     "# Change to your cluster ip:port and directories\n",
     "SPARK_MASTER_URL = os.getenv(\"SPARK_MASTER_URL\", \"spark:your-ip:port\")\n",
-    "RAPIDS_JAR = os.getenv(\"RAPIDS_JAR\", \"/your-path/rapids-4-spark_2.12-23.12.1.jar\")\n"
+    "RAPIDS_JAR = os.getenv(\"RAPIDS_JAR\", \"/your-path/rapids-4-spark_2.12-24.02.0.jar\")\n"
    ]
   },
   {

examples/UDF-Examples/RAPIDS-accelerated-UDFs/Dockerfile

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2021-2023, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2021-2024, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -70,3 +70,18 @@ RUN cd /tmp \
  && make install -j${PARALLEL_LEVEL} \
  && cd /tmp && rm -rf /tmp/cmake-$CMAKE_VERSION*
 
+# Install ccache
+ARG CCACHE_VERSION=4.6
+RUN cd /tmp && wget --quiet https://github.com/ccache/ccache/releases/download/v${CCACHE_VERSION}/ccache-${CCACHE_VERSION}.tar.gz && \
+  tar zxf ccache-${CCACHE_VERSION}.tar.gz && \
+  rm ccache-${CCACHE_VERSION}.tar.gz && \
+  cd ccache-${CCACHE_VERSION} && \
+  mkdir build && \
+  cd build && \
+  cmake .. \
+    -DCMAKE_BUILD_TYPE=Release \
+    -DZSTD_FROM_INTERNET=ON \
+    -DREDIS_STORAGE_BACKEND=OFF && \
+  cmake --build . --parallel ${PARALLEL_LEVEL} --target install && \
+  cd ../.. && \
+  rm -rf ccache-${CCACHE_VERSION}

examples/UDF-Examples/RAPIDS-accelerated-UDFs/README.md

@@ -18,7 +18,7 @@ which provides a single method we need to override called
 evaluateColumnar returns a cudf ColumnVector, because the GPU get its speed by performing operations
 on many rows at a time. In the `evaluateColumnar` function, there is a cudf implementation of URL
 decode that we're leveraging, so we don't need to write any native C++ code. This is all done
-through the [Java APIs of RAPIDS cudf](https://docs.rapids.ai/api/cudf-java/stable). The benefit to
+through the [Java APIs of RAPIDS cudf](https://docs.rapids.ai/api/cudf-java/legacy). The benefit to
 implement via the Java API is ease of development, but the memory model is not friendly for doing
 GPU operations because the JVM makes the assumption that everything we're trying to do is in heap
 memory. We need to free the GPU resources in a timely manner with try-finally blocks. Note that we
@@ -27,10 +27,10 @@ involving the RAPIDS accelerated UDF falls back to the CPU.
 
 - [URLDecode](src/main/scala/com/nvidia/spark/rapids/udf/scala/URLDecode.scala)
   decodes URL-encoded strings using the
-  [Java APIs of RAPIDS cudf](https://docs.rapids.ai/api/cudf-java/stable)
+  [Java APIs of RAPIDS cudf](https://docs.rapids.ai/api/cudf-java/legacy)
 - [URLEncode](src/main/scala/com/nvidia/spark/rapids/udf/scala/URLEncode.scala)
   URL-encodes strings using the
-  [Java APIs of RAPIDS cudf](https://docs.rapids.ai/api/cudf-java/stable)
+  [Java APIs of RAPIDS cudf](https://docs.rapids.ai/api/cudf-java/legacy)
 
 ## Spark Java UDF Examples
 
@@ -53,10 +53,10 @@ significant effort.
 
 - [URLDecode](src/main/java/com/nvidia/spark/rapids/udf/java/URLDecode.java)
   decodes URL-encoded strings using the
-  [Java APIs of RAPIDS cudf](https://docs.rapids.ai/api/cudf-java/stable)
+  [Java APIs of RAPIDS cudf](https://docs.rapids.ai/api/cudf-java/legacy)
 - [URLEncode](src/main/java/com/nvidia/spark/rapids/udf/java/URLEncode.java)
   URL-encodes strings using the
-  [Java APIs of RAPIDS cudf](https://docs.rapids.ai/api/cudf-java/stable)
+  [Java APIs of RAPIDS cudf](https://docs.rapids.ai/api/cudf-java/legacy)
 - [CosineSimilarity](src/main/java/com/nvidia/spark/rapids/udf/java/CosineSimilarity.java)
   computes the [cosine similarity](https://en.wikipedia.org/wiki/Cosine_similarity)
   between two float vectors using [native code](src/main/cpp/src)
@@ -67,11 +67,11 @@ Below are some examples for implementing RAPIDS accelerated Hive UDF via JNI and
 
 - [URLDecode](src/main/java/com/nvidia/spark/rapids/udf/hive/URLDecode.java)
   implements a Hive simple UDF using the
-  [Java APIs of RAPIDS cudf](https://docs.rapids.ai/api/cudf-java/stable)
+  [Java APIs of RAPIDS cudf](https://docs.rapids.ai/api/cudf-java/legacy)
   to decode URL-encoded strings
 - [URLEncode](src/main/java/com/nvidia/spark/rapids/udf/hive/URLEncode.java)
   implements a Hive generic UDF using the
-  [Java APIs of RAPIDS cudf](https://docs.rapids.ai/api/cudf-java/stable)
+  [Java APIs of RAPIDS cudf](https://docs.rapids.ai/api/cudf-java/legacy)
   to URL-encode strings
 - [StringWordCount](src/main/java/com/nvidia/spark/rapids/udf/hive/StringWordCount.java)
   implements a Hive simple UDF using
@@ -118,8 +118,6 @@ and other settings. See the top of the `Dockerfile` for details.
 
 First install docker and [nvidia-docker](https://github.com/NVIDIA/nvidia-docker)
 
-Run the following commands to build and start a docker
-
 ```bash
 cd spark-rapids-examples/examples/UDF-Examples/RAPIDS-accelerated-UDFs
 docker build -t my-local:my-udf-example-ubuntu .
@@ -133,11 +131,34 @@ In the Docker container, clone the code and compile.
 ```bash
 git clone https://github.com/NVIDIA/spark-rapids-examples.git
 cd spark-rapids-examples/examples/UDF-Examples/RAPIDS-accelerated-UDFs
+export LOCAL_CCACHE_DIR="$HOME/.ccache"
+mkdir -p $LOCAL_CCACHE_DIR
+export CCACHE_DIR="$LOCAL_CCACHE_DIR"
+export CMAKE_C_COMPILER_LAUNCHER="ccache"
+export CMAKE_CXX_COMPILER_LAUNCHER="ccache"
+export CMAKE_CUDA_COMPILER_LAUNCHER="ccache"
+export CMAKE_CXX_LINKER_LAUNCHER="ccache
 mvn clean package -Pudf-native-examples
 ```
 
-The build could take a long time (e.g.: 1.5 hours). Then the rapids-4-spark-udf-examples*.jar is
+The Docker container has installed ccache 4.6 to accelerate the incremental building.
+You can change the LOCAL_CCACHE_DIR to a mounted folder so that the cache can persist.
+If you don't want to use ccache, you can remove or unset the ccache environment variables.
+
+```bash
+unset CCACHE_DIR
+unset CMAKE_C_COMPILER_LAUNCHER
+unset CMAKE_CXX_COMPILER_LAUNCHER
+unset CMAKE_CUDA_COMPILER_LAUNCHER
+unset CMAKE_CXX_LINKER_LAUNCHER
+```
+
+The first build could take a long time (e.g.: 1.5 hours). Then the rapids-4-spark-udf-examples*.jar is
 generated under RAPIDS-accelerated-UDFs/target directory.
+The following build can benefit from ccache if you enable it.
+
+If you want to enable building with ccache on your own system,
+please refer to the commands which build ccache from the source code in the Dockerfile.
 
 ### Run all the examples including native examples in the docker
 
@@ -163,7 +184,7 @@ then do the following inside the Docker container.
 
 ### Get jars from Maven Central
 
-[rapids-4-spark_2.12-23.12.1.jar](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/23.12.1/rapids-4-spark_2.12-23.12.1.jar)
+[rapids-4-spark_2.12-24.02.0.jar](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/24.02.0/rapids-4-spark_2.12-24.02.0.jar)
 
 
 ### Launch a local mode Spark
@@ -192,18 +213,17 @@ schema = StructType([
     StructField("c2", IntegerType()),
 ])
 data = [
-    ("s1",1),
-    ("s2",2),
-    ("s1",3),
-    ("s2",3),
-    ("s1",3),
+    ("a b c d",1),
+    ("",2),
+    (None,3),
+    ("the quick brown fox jumped over the lazy dog",3),
 ]
 df = spark.createDataFrame(
         SparkContext.getOrCreate().parallelize(data, numSlices=2),
         schema)
 df.createOrReplaceTempView("tab")
 
 spark.sql("CREATE TEMPORARY FUNCTION {} AS '{}'".format("wordcount", "com.nvidia.spark.rapids.udf.hive.StringWordCount"))
-spark.sql("select wordcount(c1) from tab group by c1").show()
-spark.sql("select wordcount(c1) from tab group by c1").explain()
+spark.sql("select c1, wordcount(c1) from tab").show()
+spark.sql("select c1, wordcount(c1) from tab").explain()
 ```

examples/UDF-Examples/RAPIDS-accelerated-UDFs/pom.xml

@@ -25,7 +25,7 @@
         user defined functions for use with the RAPIDS Accelerator
         for Apache Spark
     </description>
-    <version>23.12.0-SNAPSHOT</version>
+    <version>24.02.0-SNAPSHOT</version>
 
     <properties>
         <maven.compiler.source>1.8</maven.compiler.source>
@@ -37,7 +37,7 @@
         <cuda.version>cuda11</cuda.version>
         <scala.binary.version>2.12</scala.binary.version>
         <!-- Depends on release version, Snapshot version is not published to the Maven Central -->
-        <rapids4spark.version>23.12.1</rapids4spark.version>
+        <rapids4spark.version>24.02.0</rapids4spark.version>
         <spark.version>3.1.1</spark.version>
         <scala.version>2.12.15</scala.version>
         <udf.native.build.path>${project.build.directory}/cpp-build</udf.native.build.path>