v23.04 release links updates (#287)

* v2304 release update links

Signed-off-by: liyuan <[email protected]>

* update recommended plugin version for databricks11.3 from v2302 to v2304

Signed-off-by: liyuan <[email protected]>

---------

Signed-off-by: liyuan <[email protected]>

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

@@ -6,9 +6,9 @@ This is a getting started guide to XGBoost4J-Spark on Databricks. At the end of
 Prerequisites
 -------------
 
-    * Apache Spark 3.x running in Databricks Runtime 9.1 ML, 10.4 ML or 11.3 ML with GPU
-    * AWS: 9.1 LTS ML (GPU, Scala 2.12, Spark 3.1.2), 10.4 LTS ML (GPU, Scala 2.12, Spark 3.2.1) or 11.3 LTS ML (GPU, Scala 2.12, Spark 3.3.0)
-    * Azure: 9.1 LTS ML (GPU, Scala 2.12, Spark 3.1.2) or 10.4 LTS ML (GPU, Scala 2.12, Spark 3.2.1) or 11.3 LTS ML (GPU, Scala 2.12, Spark 3.3.0)
+    * Apache Spark 3.x running in Databricks Runtime 10.4 ML or 11.3 ML with GPU
+    * AWS: 10.4 LTS ML (GPU, Scala 2.12, Spark 3.2.1) or 11.3 LTS ML (GPU, Scala 2.12, Spark 3.3.0)
+    * Azure: 10.4 LTS ML (GPU, Scala 2.12, Spark 3.2.1) or 11.3 LTS ML (GPU, Scala 2.12, Spark 3.3.0)
 
 The number of GPUs per node dictates the number of Spark executors that can run in that node. Each executor should only be allowed to run 1 task at any given time.
 
@@ -37,26 +37,18 @@ cluster.
    your workspace.  See [Managing
    Notebooks](https://docs.databricks.com/notebooks/notebooks-manage.html#id2) for instructions on
    how to import a notebook.  
-   Select the initialization script based on the Databricks runtime
+   Select the version of the RAPIDS Accelerator for Apache Spark based on the Databricks runtime
    version:
-
-    - [Databricks 9.1 LTS
-    ML](https://docs.databricks.com/release-notes/runtime/9.1ml.html#system-environment) has CUDA 11
-    installed.  Users will need to use 21.12.0 or later on Databricks 9.1 LTS ML. In this case use
-    [generate-init-script.ipynb](generate-init-script.ipynb) which will install
-    the RAPIDS Spark plugin.
-
-    - [Databricks 10.4 LTS
-    ML](https://docs.databricks.com/release-notes/runtime/9.1ml.html#system-environment) has CUDA 11
-    installed.  Users will need to use 22.04.0 or later on Databricks 10.4 LTS ML. In this case use
-    [generate-init-script-10.4.ipynb](generate-init-script-10.4.ipynb) which will install
-    the RAPIDS Spark plugin.
-
-    - [Databricks 11.3 LTS
-    ML](https://docs.databricks.com/release-notes/runtime/11.3ml.html#system-environment) has CUDA 11
-    installed.  Users will need to use 23.02.0 or later on Databricks 11.3 LTS ML. In this case use
-    [generate-init-script-11.3.ipynb](generate-init-script-11.3.ipynb) which will install
-    the RAPIDS Spark plugin.
+   - [Databricks 10.4 LTS
+     ML](https://docs.databricks.com/release-notes/runtime/10.4ml.html#system-environment) has CUDA 11
+     installed.  Users will need to use 22.04.0 or later on Databricks 10.4 LTS ML.
+   - [Databricks 11.3 LTS
+     ML](https://docs.databricks.com/release-notes/runtime/11.3ml.html#system-environment) has CUDA 11
+     installed.  Users will need to use 23.04.0 or later on Databricks 11.3 LTS ML.
+
+     In both cases use
+     [generate-init-script.ipynb](./generate-init-script.ipynb) which will install
+     the RAPIDS Spark plugin.
 
 2. Once you are in the notebook, click the “Run All” button.
 3. Ensure that the newly created init.sh script is present in the output from cell 2 and that the
@@ -77,21 +69,17 @@ cluster.
     The
     [`spark.task.resource.gpu.amount`](https://spark.apache.org/docs/latest/configuration.html#scheduling)
     configuration is defaulted to 1 by Databricks. That means that only 1 task can run on an
-    executor with 1 GPU, which is limiting, especially on the reads and writes from Parquet.  Set
+    executor with 1 GPU, which is limiting, especially on the reads and writes from Parquet. Set
     this to 1/(number of cores per executor) which will allow multiple tasks to run in parallel just
-    like the CPU side.  Having the value smaller is fine as well.
-
-	There is an incompatibility between the Databricks specific implementation of adaptive query
-    execution (AQE) and the spark-rapids plugin.  In order to mitigate this,
-    `spark.sql.adaptive.enabled` should be set to false.  In addition, the plugin does not work with
-    the Databricks `spark.databricks.delta.optimizeWrite` option.
+    like the CPU side. Having the value smaller is fine as well.
+    Note: Please remove the `spark.task.resource.gpu.amount` config for a single-node Databricks 
+    cluster because Spark local mode does not support GPU scheduling.
 
     ```bash
      spark.plugins com.nvidia.spark.SQLPlugin
      spark.task.resource.gpu.amount 0.1
      spark.rapids.memory.pinnedPool.size 2G
      spark.rapids.sql.concurrentGpuTasks 2
-     spark.databricks.optimizer.dynamicFilePruning false
     ```
 
     ![Spark Config](../../../../img/databricks/sparkconfig.png)
@@ -196,8 +184,4 @@ Limitations
 5. In Databricks 11.3, an incorrect result is returned for window frames defined by a range in case 
    of DecimalTypes with precision greater than 38. There is a bug filed in Apache Spark for it 
    [here](https://issues.apache.org/jira/browse/SPARK-41793), whereas when using the plugin the 
-   correct result will be returned.
-
-6. A query may fail when Dynamic File Pruning is enabled. As a workaround, please
-   disable the feature by setting `spark.databricks.optimizer.dynamicFilePruning false`. More details
-   are in [issue-7648](https://github.com/NVIDIA/spark-rapids/issues/7648).
+   correct result will be returned.

docs/get-started/xgboost-examples/csp/databricks/generate-init-script-10.4.ipynb

@@ -24,7 +24,7 @@
    "source": [
     "%sh\n",
     "cd ../../dbfs/FileStore/jars/\n",
-    "sudo wget -O rapids-4-spark_2.12-23.02.0.jar https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/23.02.0/rapids-4-spark_2.12-23.02.0.jar\n",
+    "sudo wget -O rapids-4-spark_2.12-23.04.0.jar https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/23.04.0/rapids-4-spark_2.12-23.04.0.jar\n",
     "sudo wget -O 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\n",
     "sudo wget -O 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\n",
     "ls -ltr\n",
@@ -60,7 +60,7 @@
     "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\n",
     "\n",
     "sudo cp /dbfs/FileStore/jars/xgboost4j-gpu_2.12-1.7.1.jar /databricks/jars/\n",
-    "sudo cp /dbfs/FileStore/jars/rapids-4-spark_2.12-23.02.0.jar /databricks/jars/\n",
+    "sudo cp /dbfs/FileStore/jars/rapids-4-spark_2.12-23.04.0.jar /databricks/jars/\n",
     "sudo cp /dbfs/FileStore/jars/xgboost4j-spark-gpu_2.12-1.7.1.jar /databricks/jars/\"\"\", True)"
    ]
   },
@@ -133,7 +133,7 @@
     "1. Edit your cluster, adding an initialization script from `dbfs:/databricks/init_scripts/init.sh` in the \"Advanced Options\" under \"Init Scripts\" tab\n",
     "2. Reboot the cluster\n",
     "3. Go to \"Libraries\" tab under your cluster and install `dbfs:/FileStore/jars/xgboost4j-spark-gpu_2.12-1.7.1.jar` in your cluster by selecting the \"DBFS\" option for installing jars\n",
-    "4. Import the mortgage example notebook from `https://github.com/NVIDIA/spark-rapids-examples/blob/branch-23.02/examples/XGBoost-Examples/mortgage/notebooks/python/mortgage-gpu.ipynb`\n",
+    "4. Import the mortgage example notebook from `https://github.com/NVIDIA/spark-rapids-examples/blob/branch-23.04/examples/XGBoost-Examples/mortgage/notebooks/python/mortgage-gpu.ipynb`\n",
     "5. Inside the mortgage example notebook, update the data paths\n",
     "  `train_data = reader.schema(schema).option('header', True).csv('/data/mortgage/csv/small-train.csv')`\n",
     "  `trans_data = reader.schema(schema).option('header', True).csv('/data/mortgage/csv/small-trans.csv')`"

docs/get-started/xgboost-examples/csp/databricks/generate-init-script-11.3.ipynb

@@ -24,7 +24,7 @@
    "source": [
     "%sh\n",
     "cd ../../dbfs/FileStore/jars/\n",
-    "sudo wget -O rapids-4-spark_2.12-23.02.0.jar https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/23.02.0/rapids-4-spark_2.12-23.02.0.jar\n",
+    "sudo wget -O rapids-4-spark_2.12-23.04.0.jar https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/23.04.0/rapids-4-spark_2.12-23.04.0.jar\n",
     "sudo wget -O xgboost4j-gpu_2.12-1.7.3.jar https://repo1.maven.org/maven2/ml/dmlc/xgboost4j-gpu_2.12/1.7.3/xgboost4j-gpu_2.12-1.7.3.jar\n",
     "sudo wget -O xgboost4j-spark-gpu_2.12-1.7.3.jar https://repo1.maven.org/maven2/ml/dmlc/xgboost4j-spark-gpu_2.12/1.7.3/xgboost4j-spark-gpu_2.12-1.7.3.jar\n",
     "ls -ltr\n",
@@ -60,7 +60,7 @@
     "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\n",
     "\n",
     "sudo cp /dbfs/FileStore/jars/xgboost4j-gpu_2.12-1.7.3.jar /databricks/jars/\n",
-    "sudo cp /dbfs/FileStore/jars/rapids-4-spark_2.12-23.02.0.jar /databricks/jars/\n",
+    "sudo cp /dbfs/FileStore/jars/rapids-4-spark_2.12-23.04.0.jar /databricks/jars/\n",
     "sudo cp /dbfs/FileStore/jars/xgboost4j-spark-gpu_2.12-1.7.3.jar /databricks/jars/\"\"\", True)"
    ]
   },
@@ -133,7 +133,7 @@
     "1. Edit your cluster, adding an initialization script from `dbfs:/databricks/init_scripts/init.sh` in the \"Advanced Options\" under \"Init Scripts\" tab\n",
     "2. Reboot the cluster\n",
     "3. Go to \"Libraries\" tab under your cluster and install `dbfs:/FileStore/jars/xgboost4j-spark-gpu_2.12-1.7.3.jar` in your cluster by selecting the \"DBFS\" option for installing jars\n",
-    "4. Import the mortgage example notebook from `https://github.com/NVIDIA/spark-rapids-examples/blob/branch-23.02/examples/XGBoost-Examples/mortgage/notebooks/python/mortgage-gpu.ipynb`\n",
+    "4. Import the mortgage example notebook from `https://github.com/NVIDIA/spark-rapids-examples/blob/branch-23.04/examples/XGBoost-Examples/mortgage/notebooks/python/mortgage-gpu.ipynb`\n",
     "5. Inside the mortgage example notebook, update the data paths\n",
     "  `train_data = reader.schema(schema).option('header', True).csv('/data/mortgage/csv/small-train.csv')`\n",
     "  `trans_data = reader.schema(schema).option('header', True).csv('/data/mortgage/csv/small-trans.csv')`"

docs/get-started/xgboost-examples/csp/databricks/generate-init-script.ipynb

@@ -24,7 +24,7 @@
    "source": [
     "%sh\n",
     "cd ../../dbfs/FileStore/jars/\n",
-    "sudo wget -O rapids-4-spark_2.12-23.02.0.jar https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/23.02.0/rapids-4-spark_2.12-23.02.0.jar\n",
+    "sudo wget -O rapids-4-spark_2.12-23.04.0.jar https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/23.04.0/rapids-4-spark_2.12-23.04.0.jar\n",
     "sudo wget -O 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\n",
     "sudo wget -O 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\n",
     "ls -ltr\n",
@@ -60,7 +60,7 @@
     "sudo rm -f /databricks/jars/spark--maven-trees--ml--9.x--xgboost-gpu--ml.dmlc--xgboost4j-spark-gpu_2.12--ml.dmlc__xgboost4j-spark-gpu_2.12__1.4.1.jar\n",
     "\n",
     "sudo cp /dbfs/FileStore/jars/xgboost4j-gpu_2.12-1.7.1.jar /databricks/jars/\n",
-    "sudo cp /dbfs/FileStore/jars/rapids-4-spark_2.12-23.02.0.jar /databricks/jars/\n",
+    "sudo cp /dbfs/FileStore/jars/rapids-4-spark_2.12-23.04.0.jar /databricks/jars/\n",
     "sudo cp /dbfs/FileStore/jars/xgboost4j-spark-gpu_2.12-1.7.1.jar /databricks/jars/\"\"\", True)"
    ]
   },
@@ -133,7 +133,7 @@
     "1. Edit your cluster, adding an initialization script from `dbfs:/databricks/init_scripts/init.sh` in the \"Advanced Options\" under \"Init Scripts\" tab\n",
     "2. Reboot the cluster\n",
     "3. Go to \"Libraries\" tab under your cluster and install `dbfs:/FileStore/jars/xgboost4j-spark-gpu_2.12-1.7.1.jar` in your cluster by selecting the \"DBFS\" option for installing jars\n",
-    "4. Import the mortgage example notebook from `https://github.com/NVIDIA/spark-rapids-examples/blob/branch-23.02/examples/XGBoost-Examples/mortgage/notebooks/python/mortgage-gpu.ipynb`\n",
+    "4. Import the mortgage example notebook from `https://github.com/NVIDIA/spark-rapids-examples/blob/branch-23.04/examples/XGBoost-Examples/mortgage/notebooks/python/mortgage-gpu.ipynb`\n",
     "5. Inside the mortgage example notebook, update the data paths\n",
     "  `train_data = reader.schema(schema).option('header', True).csv('/data/mortgage/csv/small-train.csv')`\n",
     "  `trans_data = reader.schema(schema).option('header', True).csv('/data/mortgage/csv/small-trans.csv')`"

docs/get-started/xgboost-examples/csp/dataproc/gcp.md

@@ -17,7 +17,7 @@
 gcloud dataproc clusters create $CLUSTER_NAME  \
     --region=$REGION \
     --image-version=2.0-ubuntu18 \
-    --master-machine-type=n1-standard-16 \
+    --master-machine-type=n2-standard-16 \
     --num-workers=$NUM_WORKERS \
     --worker-accelerator=type=nvidia-tesla-t4,count=$NUM_GPUS \
     --worker-machine-type=n1-highmem-32\

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-23.02/dockerfile/Dockerfile
+wget https://github.com/NVIDIA/spark-rapids-examples/raw/branch-23.04/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_2.12/23.02.0/rapids-4-spark_2.12-23.02.0.jar)
+  * [RAPIDS Spark Package](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/23.04.0/rapids-4-spark_2.12-23.04.0.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_2.12/23.02.0/rapids-4-spark_2.12-23.02.0.jar)
+   * [RAPIDS Spark Package](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/23.04.0/rapids-4-spark_2.12-23.04.0.jar)
 
 ### Build XGBoost Scala Examples
 

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

@@ -12,7 +12,7 @@ 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_2.12-23.02.0.jar](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/23.02.0/rapids-4-spark_2.12-23.02.0.jar)
+[rapids-4-spark_2.12-23.04.0.jar](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/23.04.0/rapids-4-spark_2.12-23.04.0.jar)
 
 
 ## Sample code

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_2.12-23.02.0.jar\")\n"
+    "RAPIDS_JAR = os.getenv(\"RAPIDS_JAR\", \"/your-path/rapids-4-spark_2.12-23.04.0.jar\")\n"
    ]
   },
   {

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

@@ -108,7 +108,7 @@ See above Prerequisites section
 First finish the steps in "Building with Native Code Examples and run test cases" section, then do the following in the docker.
 
 ### Get jars from Maven Central
-[rapids-4-spark_2.12-23.02.0.jar](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/23.02.0/rapids-4-spark_2.12-23.02.0.jar)
+[rapids-4-spark_2.12-23.04.0.jar](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/23.04.0/rapids-4-spark_2.12-23.04.0.jar)
 
 ### Launch a local mode Spark
 

examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/cpp/CMakeLists.txt

@@ -32,7 +32,7 @@ if(DEFINED GPU_ARCHS)
 endif()
 rapids_cuda_init_architectures(UDFEXAMPLESJNI)
 
-project(UDFEXAMPLESJNI VERSION 23.02.0 LANGUAGES C CXX CUDA)
+project(UDFEXAMPLESJNI VERSION 23.04.0 LANGUAGES C CXX CUDA)
 
 option(PER_THREAD_DEFAULT_STREAM "Build with per-thread default stream" OFF)
 option(BUILD_UDF_BENCHMARKS "Build the benchmarks" OFF)

examples/UDF-Examples/Spark-cuSpatial/Dockerfile.awsdb

@@ -48,7 +48,7 @@ RUN wget -q https://repo.continuum.io/miniconda/Miniconda3-py38_4.9.2-Linux-x86_
     conda config --system --set always_yes True && \
     conda clean --all
 
-RUN conda install -c rapidsai-nightly -c nvidia -c conda-forge  -c defaults libcuspatial=23.02
+RUN conda install -c rapidsai-nightly -c nvidia -c conda-forge  -c defaults libcuspatial=23.04
 RUN conda install -c conda-forge libgdal==3.3.1
 RUN pip install jupyter
 ENV JAVA_HOME /usr/lib/jvm/java-1.8.0-openjdk-amd64

examples/UDF-Examples/Spark-cuSpatial/README.md

@@ -47,7 +47,7 @@ or [in local machine](#build-in-local-machine) after prerequisites.
      ```
 2. Bash into the Docker and install libcuspatial
      ```Bash
-     conda install -c rapidsai-nightly -c nvidia -c conda-forge -c defaults libcuspatial=23.02
+     conda install -c rapidsai-nightly -c nvidia -c conda-forge -c defaults libcuspatial=23.04
      ```
 3. Get the code, then run `mvn package`.
      ```Bash
@@ -69,7 +69,7 @@ Note: The docker env is just for building the jar, not for running the applicati
 4. [cuspatial](https://github.com/rapidsai/cuspatial): install libcuspatial
     ```Bash
     # Install libcuspatial from conda
-    conda install -c rapidsai-nightly -c nvidia -c conda-forge -c defaults libcuspatial=23.02
+    conda install -c rapidsai-nightly -c nvidia -c conda-forge -c defaults libcuspatial=23.04
     ```
 5. Build the JAR using `mvn package`.
      ```Bash
@@ -84,7 +84,7 @@ Note: The docker env is just for building the jar, not for running the applicati
 1. Set up [a standalone cluster](/docs/get-started/xgboost-examples/on-prem-cluster/standalone-scala.md) of Spark. Make sure the conda/lib is included in LD_LIBRARY_PATH, so that spark executors can load libcuspatial.so.
 
 2. Download Spark RAPIDS JAR
-   * [Spark RAPIDS JAR v23.02.0](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/23.02.0/rapids-4-spark_2.12-23.02.0.jar) or above
+   * [Spark RAPIDS JAR v23.04.0](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/23.04.0/rapids-4-spark_2.12-23.04.0.jar) or above
 3. Prepare sample dataset and JARs. Copy the [sample dataset](../../../datasets/cuspatial_data.tar.gz) to `/data/cuspatial_data/`.
     Copy Spark RAPIDS JAR and `spark-cuspatial-<version>.jar` to `/data/cuspatial_data/jars/`.
     If you build the `spark-cuspatial-<version>.jar` in docker, please copy the jar from docker to local:

examples/XGBoost-Examples/agaricus/notebooks/python/agaricus-gpu.ipynb

@@ -73,7 +73,7 @@
       "Setting default log level to \"WARN\".\n",
       "To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n",
       "2022-11-30 06:57:40,550 WARN resource.ResourceUtils: The configuration of cores (exec = 2 task = 1, runnable tasks = 2) will result in wasted resources due to resource gpu limiting the number of runnable tasks per executor to: 1. Please adjust your configuration.\n",
-      "2022-11-30 06:57:54,195 WARN rapids.RapidsPluginUtils: RAPIDS Accelerator 23.02.0 using cudf 23.02.0.\n",
+      "2022-11-30 06:57:54,195 WARN rapids.RapidsPluginUtils: RAPIDS Accelerator 23.04.0 using cudf 23.04.0.\n",
       "2022-11-30 06:57:54,210 WARN rapids.RapidsPluginUtils: spark.rapids.sql.multiThreadedRead.numThreads is set to 20.\n",
       "2022-11-30 06:57:54,214 WARN rapids.RapidsPluginUtils: RAPIDS Accelerator is enabled, to disable GPU support set `spark.rapids.sql.enabled` to false.\n",
       "2022-11-30 06:57:54,214 WARN rapids.RapidsPluginUtils: spark.rapids.sql.explain is set to `NOT_ON_GPU`. Set it to 'NONE' to suppress the diagnostics logging about the query placement on the GPU.\n",