Merge pull request #263 from nvliyuan/main-2212-release

merge branch-22.12 to main branch

.github/workflows/auto-merge.yml

@@ -18,7 +18,7 @@ name: auto-merge HEAD to BASE
 on:
   pull_request_target:
     branches:
-    - branch-22.10
+    - branch-22.12
     types: [closed]
 
 jobs:
@@ -27,15 +27,15 @@ jobs:
     runs-on: ubuntu-latest
 
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v3
         with:
-          ref: branch-22.10 # force to fetch from latest upstream instead of PR ref
+          ref: branch-22.12 # 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-22.10
-          BASE: branch-22.12
+          HEAD: branch-22.12
+          BASE: branch-23.02
           AUTOMERGE_TOKEN: ${{ secrets.AUTOMERGE_TOKEN }} # use to merge PR

README.md

@@ -10,6 +10,7 @@ There are broadly four categories of examples in this repo:
 2. [Spark XGBoost](./examples/XGBoost-Examples) 
 3. [Deep Learning/Machine Learning](./examples/ML+DL-Examples) 
 4. [RAPIDS UDF](./examples/UDF-Examples)
+5. [Databricks Tools demo notebooks](./tools/databricks)
 
 For more information on each of the examples please look into respective categories.
 

docs/get-started/xgboost-examples/csp/aws/ec2.md

@@ -177,8 +177,8 @@ spark-submit --master spark://$HOSTNAME:7077 \
         ${SAMPLE_JAR} \
         -num_workers=${NUM_EXECUTORS} \
         -format=csv \
-        -dataPath="train::s3a://spark-xgboost-mortgage-dataset/csv/train/2000Q1" \
-        -dataPath="trans::s3a://spark-xgboost-mortgage-dataset/csv/eval/2000Q1" \
+        -dataPath="train::your-train-data-path" \
+        -dataPath="trans::your-eval-data-path" \
         -numRound=100 -max_depth=8 -nthread=$NUM_EXECUTOR_CORES -showFeatures=0 \
         -tree_method=gpu_hist
 ```

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

@@ -24,9 +24,9 @@
    "source": [
     "%sh\n",
     "cd ../../dbfs/FileStore/jars/\n",
-    "sudo wget -O rapids-4-spark_2.12-22.10.0.jar https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/22.10.0/rapids-4-spark_2.12-22.10.0.jar\n",
-    "sudo wget -O xgboost4j-gpu_2.12-1.6.1.jar https://repo1.maven.org/maven2/ml/dmlc/xgboost4j-gpu_2.12/1.6.1/xgboost4j-gpu_2.12-1.6.1.jar\n",
-    "sudo wget -O xgboost4j-spark-gpu_2.12-1.6.1.jar https://repo1.maven.org/maven2/ml/dmlc/xgboost4j-spark-gpu_2.12/1.6.1/xgboost4j-spark-gpu_2.12-1.6.1.jar\n",
+    "sudo wget -O rapids-4-spark_2.12-22.12.0.jar https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/22.12.0/rapids-4-spark_2.12-22.12.0.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",
     "\n",
     "# Your Jars are downloaded in dbfs:/FileStore/jars directory"
@@ -59,9 +59,9 @@
     "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\n",
     "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_2.12-1.6.1.jar /databricks/jars/\n",
-    "sudo cp /dbfs/FileStore/jars/rapids-4-spark_2.12-22.10.0.jar /databricks/jars/\n",
-    "sudo cp /dbfs/FileStore/jars/xgboost4j-spark-gpu_2.12-1.6.1.jar /databricks/jars/\"\"\", True)"
+    "sudo cp /dbfs/FileStore/jars/xgboost4j-gpu_2.12-1.7.1.jar /databricks/jars/\n",
+    "sudo cp /dbfs/FileStore/jars/rapids-4-spark_2.12-22.12.0.jar /databricks/jars/\n",
+    "sudo cp /dbfs/FileStore/jars/xgboost4j-spark-gpu_2.12-1.7.1.jar /databricks/jars/\"\"\", True)"
    ]
   },
   {
@@ -132,8 +132,8 @@
     "\n",
     "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_2.12-1.6.1.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-22.10/examples/XGBoost-Examples/mortgage/notebooks/python/mortgage-gpu.ipynb`\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-22.12/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

@@ -24,9 +24,9 @@
    "source": [
     "%sh\n",
     "cd ../../dbfs/FileStore/jars/\n",
-    "sudo wget -O rapids-4-spark_2.12-22.10.0.jar https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/22.10.0/rapids-4-spark_2.12-22.10.0.jar\n",
-    "sudo wget -O xgboost4j-gpu_2.12-1.6.1.jar https://repo1.maven.org/maven2/ml/dmlc/xgboost4j-gpu_2.12/1.6.1/xgboost4j-gpu_2.12-1.6.1.jar\n",
-    "sudo wget -O xgboost4j-spark-gpu_2.12-1.6.1.jar https://repo1.maven.org/maven2/ml/dmlc/xgboost4j-spark-gpu_2.12/1.6.1/xgboost4j-spark-gpu_2.12-1.6.1.jar\n",
+    "sudo wget -O rapids-4-spark_2.12-22.12.0.jar https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/22.12.0/rapids-4-spark_2.12-22.12.0.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",
     "\n",
     "# Your Jars are downloaded in dbfs:/FileStore/jars directory"
@@ -59,9 +59,9 @@
     "sudo rm -f /databricks/jars/spark--maven-trees--ml--9.x--xgboost-gpu--ml.dmlc--xgboost4j-gpu_2.12--ml.dmlc__xgboost4j-gpu_2.12__1.4.1.jar\n",
     "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_2.12-1.6.1.jar /databricks/jars/\n",
-    "sudo cp /dbfs/FileStore/jars/rapids-4-spark_2.12-22.10.0.jar /databricks/jars/\n",
-    "sudo cp /dbfs/FileStore/jars/xgboost4j-spark-gpu_2.12-1.6.1.jar /databricks/jars/\"\"\", True)"
+    "sudo cp /dbfs/FileStore/jars/xgboost4j-gpu_2.12-1.7.1.jar /databricks/jars/\n",
+    "sudo cp /dbfs/FileStore/jars/rapids-4-spark_2.12-22.12.0.jar /databricks/jars/\n",
+    "sudo cp /dbfs/FileStore/jars/xgboost4j-spark-gpu_2.12-1.7.1.jar /databricks/jars/\"\"\", True)"
    ]
   },
   {
@@ -132,8 +132,8 @@
     "\n",
     "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_2.12-1.6.1.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-22.10/examples/XGBoost-Examples/mortgage/notebooks/python/mortgage-gpu.ipynb`\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-22.12/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/notebook/python-notebook.md

@@ -67,7 +67,3 @@ and the home directory for Apache Spark respectively.
 - Mortgage ETL Notebook: [Python](../../../../examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL.ipynb)
 - Taxi ETL Notebook: [Python](../../../../examples/XGBoost-Examples/taxi/notebooks/python/taxi-ETL.ipynb)
 - Note: Agaricus does not have ETL part.
-
-For PySpark based XGBoost, please refer to the
-[Spark-RAPIDS-examples 22.04 branch](https://github.com/NVIDIA/spark-rapids-examples/blob/branch-22.04/docs/get-started/xgboost-examples/notebook/python-notebook.md) 
-that uses [NVIDIA’s Spark XGBoost version](https://repo1.maven.org/maven2/com/nvidia/xgboost4j-spark_3.0/). 

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-22.10/dockerfile/Dockerfile
+wget https://github.com/NVIDIA/spark-rapids-examples/raw/branch-22.12/dockerfile/Dockerfile
 
 # Optionally install additional jars into ${SPARK_HOME}/jars/
 

docs/get-started/xgboost-examples/on-prem-cluster/standalone-python.md

@@ -12,11 +12,13 @@ Prerequisites
   * Multi-node clusters with homogenous GPU configuration
 * Software Requirements
   * Ubuntu 18.04, 20.04/CentOS7, CentOS8
-  * CUDA 11.0+
+  * CUDA 11.5+
   * NVIDIA driver compatible with your CUDA
   * NCCL 2.7.8+
-  * Python 3.6+
+  * Python 3.8 or 3.9
   * NumPy
+  * XGBoost 1.7.0+
+  * cudf-cu11  
 
 The number of GPUs in each host dictates the number of Spark executors that can run there.
 Additionally, cores per Spark executor and cores per Spark task must match, such that each executor can run 1 task at any given time.
@@ -47,6 +49,14 @@ And here are the steps to enable the GPU resources discovery for Spark 3.1+.
     spark.worker.resource.gpu.amount 1
     spark.worker.resource.gpu.discoveryScript ${SPARK_HOME}/examples/src/main/scripts/getGpusResources.sh
     ```
+3. Install the XGBoost, cudf-cu11, numpy libraries on all nodes before running XGBoost application.
+
+``` bash
+pip install xgboost
+pip install cudf-cu11 --extra-index-url=https://pypi.ngc.nvidia.com
+pip install numpy
+pip install scikit-learn
+```
 
 Get Application Files, Jar and Dataset
 -------------------------------
@@ -182,6 +192,10 @@ export EXAMPLE_CLASS=com.nvidia.spark.examples.mortgage.gpu_main
 
 # tree construction algorithm
 export TREE_METHOD=gpu_hist
+
+# if you enable archive python environment
+export PYSPARK_DRIVER_PYTHON=python
+export PYSPARK_PYTHON=./environment/bin/python
 ```
 
 Run spark-submit:
@@ -197,8 +211,9 @@ ${SPARK_HOME}/bin/spark-submit
  --driver-memory ${SPARK_DRIVER_MEMORY}                                         \
  --executor-memory ${SPARK_EXECUTOR_MEMORY}                                     \
  --conf spark.cores.max=${TOTAL_CORES}                                          \
- --jars ${RAPIDS_JAR},${XGBOOST4J_JAR},${XGBOOST4J_SPARK_JAR}     \
- --py-files ${XGBOOST4J_SPARK_JAR},${SAMPLE_ZIP}                   \
+ --archives your_pyspark_venv.tar.gz#environment     #if you enabled archive python environment \
+ --jars ${RAPIDS_JAR}    \
+ --py-files ${SAMPLE_ZIP}                   \
  ${MAIN_PY}                                                     \
  --mainClass=${EXAMPLE_CLASS}                                                   \
  --dataPath=train::${SPARK_XGBOOST_DIR}/mortgage/output/train/      \
@@ -261,6 +276,10 @@ export EXAMPLE_CLASS=com.nvidia.spark.examples.mortgage.cpu_main
 
 # tree construction algorithm
 export TREE_METHOD=hist
+
+# if you enable archive python environment
+export PYSPARK_DRIVER_PYTHON=python
+export PYSPARK_PYTHON=./environment/bin/python
 ```
 
 This is the same command as for the GPU example, repeated for convenience:
@@ -271,8 +290,9 @@ ${SPARK_HOME}/bin/spark-submit
  --driver-memory ${SPARK_DRIVER_MEMORY}                                         \
  --executor-memory ${SPARK_EXECUTOR_MEMORY}                                     \
  --conf spark.cores.max=${TOTAL_CORES}                                          \
- --jars ${XGBOOST4J_JAR},${XGBOOST4J_SPARK_JAR}       \
- --py-files ${XGBOOST4J_SPARK_JAR},${SAMPLE_ZIP}                       \
+ --archives your_pyspark_venv.tar.gz#environment     #if you enabled archive python environment \
+ --jars ${RAPIDS_JAR}     \
+ --py-files ${SAMPLE_ZIP}                       \
  ${SPARK_PYTHON_ENTRYPOINT}                                                     \
  --mainClass=${EXAMPLE_CLASS}                                                   \
  --dataPath=train::${DATA_PATH}/mortgage/output/train/      \

docs/get-started/xgboost-examples/on-prem-cluster/yarn-python.md

@@ -12,12 +12,14 @@ Prerequisites
   * Multi-node clusters with homogenous GPU configuration
 * Software Requirements
   * Ubuntu 18.04, 20.04/CentOS7, CentOS8
-  * CUDA 11.0+
+  * CUDA 11.5+
   * NVIDIA driver compatible with your CUDA
   * NCCL 2.7.8+
-  * Python 3.6+
+  * Python 3.8 or 3.9
   * NumPy
-
+  * XGBoost 1.7.0+
+  * cudf-cu11  
+
 The number of GPUs per NodeManager dictates the number of Spark executors that can run in that NodeManager. 
 Additionally, cores per Spark executor and cores per Spark task must match, such that each executor can run 1 task at any given time.
 
@@ -32,6 +34,32 @@ We use `SPARK_HOME` environment variable to point to the Apache Spark cluster.
 And as to how to enable GPU scheduling and isolation for Yarn,
 please refer to [here](https://hadoop.apache.org/docs/r3.1.0/hadoop-yarn/hadoop-yarn-site/UsingGpus.html).
 
+Please make sure to install the XGBoost, cudf-cu11, numpy libraries on all nodes before running XGBoost application.
+``` bash
+pip install xgboost
+pip install cudf-cu11 --extra-index-url=https://pypi.ngc.nvidia.com
+pip install numpy
+pip install scikit-learn
+```
+You can also create an isolated python environment by using (Virtualenv)[https://virtualenv.pypa.io/en/latest/],
+and then directly pass/unpack the archive file and enable the environment on executors
+by leveraging the --archives option or spark.archives configuration.
+``` bash
+# create an isolated python environment and install libraries
+python -m venv pyspark_venv
+source pyspark_venv/bin/activate
+pip install xgboost
+pip install cudf-cu11 --extra-index-url=https://pypi.ngc.nvidia.com
+pip install numpy
+pip install scikit-learn
+venv-pack -o pyspark_venv.tar.gz
+
+# enable archive python environment on executors
+export PYSPARK_DRIVER_PYTHON=python # Do not set in cluster modes.
+export PYSPARK_PYTHON=./environment/bin/python
+spark-submit --archives pyspark_venv.tar.gz#environment app.py
+```
+
 Get Application Files, Jar and Dataset
 -------------------------------
 
@@ -114,6 +142,10 @@ export EXAMPLE_CLASS=com.nvidia.spark.examples.mortgage.gpu_main
 
 # tree construction algorithm
 export TREE_METHOD=gpu_hist
+
+# if you enable archive python environment
+export PYSPARK_DRIVER_PYTHON=python
+export PYSPARK_PYTHON=./environment/bin/python
 ```
 
 Run spark-submit:
@@ -129,11 +161,12 @@ ${SPARK_HOME}/bin/spark-submit
  --files ${SPARK_HOME}/examples/src/main/scripts/getGpusResources.sh            \
  --master yarn                                                                  \
  --deploy-mode ${SPARK_DEPLOY_MODE}                                             \
+ --archives your_pyspark_venv.tar.gz#environment     #if you enabled archive python environment \
  --num-executors ${SPARK_NUM_EXECUTORS}                                         \
  --driver-memory ${SPARK_DRIVER_MEMORY}                                         \
  --executor-memory ${SPARK_EXECUTOR_MEMORY}                                     \
- --jars ${RAPIDS_JAR},${XGBOOST4J_JAR}        \
- --py-files ${XGBOOST4J_SPARK_JAR},${SAMPLE_ZIP}                   \
+ --jars ${RAPIDS_JAR}        \
+ --py-files ${SAMPLE_ZIP}                   \
  ${MAIN_PY}                                                     \
  --mainClass=${EXAMPLE_CLASS}                                                   \
  --dataPath=train::${DATA_PATH}/mortgage/out/train/      \
@@ -190,19 +223,24 @@ export EXAMPLE_CLASS=com.nvidia.spark.examples.mortgage.cpu_main
 
 # tree construction algorithm
 export TREE_METHOD=hist
+
+# if you enable archive python environment
+export PYSPARK_DRIVER_PYTHON=python
+export PYSPARK_PYTHON=./environment/bin/python
 ```
 
 This is the same command as for the GPU example, repeated for convenience:
 
 ``` bash
 ${SPARK_HOME}/bin/spark-submit                                                  \
  --master yarn                                                                  \
+ --archives your_pyspark_venv.tar.gz#environment     #if you enabled archive python environment \
  --deploy-mode ${SPARK_DEPLOY_MODE}                                             \
  --num-executors ${SPARK_NUM_EXECUTORS}                                         \
  --driver-memory ${SPARK_DRIVER_MEMORY}                                         \
  --executor-memory ${SPARK_EXECUTOR_MEMORY}                                     \
- --jars ${XGBOOST4J_JAR},${XGBOOST4J_SPARK_JAR}                                 \
- --py-files ${XGBOOST4J_SPARK_JAR},${SAMPLE_ZIP}                                  \
+ --jars ${RAPIDS_JAR}        \
+ --py-files ${SAMPLE_ZIP}                                  \
  ${MAIN_PY}                                                     \
  --mainClass=${EXAMPLE_CLASS}                                                   \
  --dataPath=train::${DATA_PATH}/mortgage/output/train/       \

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

@@ -9,7 +9,7 @@ For simplicity export the location to these jars. All examples assume the packag
    * [XGBoost4j-Spark Package](https://repo1.maven.org/maven2/com/nvidia/xgboost4j-spark_3.0/1.4.2-0.3.0/)
 
 2. Download the RAPIDS Accelerator for Apache Spark plugin jar
-   * [RAPIDS Spark Package](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/22.10.0/rapids-4-spark_2.12-22.10.0.jar)
+   * [RAPIDS Spark Package](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/22.12.0/rapids-4-spark_2.12-22.12.0.jar)
 
 ### Build XGBoost Python Examples
 
@@ -21,14 +21,3 @@ You need to copy the dataset to `/opt/xgboost`. Use the following links to downl
 1. [Mortgage dataset](/docs/get-started/xgboost-examples/dataset/mortgage.md)
 2. [Taxi dataset](https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page)
 3. [Agaricus dataset](https://gust.dev/r/xgboost-agaricus)
-
-### Setup environments
-
-``` bash
-export SPARK_XGBOOST_DIR=/opt/xgboost
-export RAPIDS_JAR=${SPARK_XGBOOST_DIR}/rapids-4-spark_2.12-22.10.0.jar
-export XGBOOST4J_JAR=${SPARK_XGBOOST_DIR}/xgboost4j_3.0-1.4.2-0.3.0.jar
-export XGBOOST4J_SPARK_JAR=${SPARK_XGBOOST_DIR}/xgboost4j-spark_3.0-1.4.2-0.3.0.jar
-export SAMPLE_ZIP=${SPARK_XGBOOST_DIR}/samples.zip
-export MAIN_PY=${SPARK_XGBOOST_DIR}/main.py
-```

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_2.12/22.10.0/rapids-4-spark_2.12-22.10.0.jar)
+   * [RAPIDS Spark Package](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/22.12.0/rapids-4-spark_2.12-22.12.0.jar)
 
 ### Build XGBoost Scala Examples
 
@@ -17,11 +17,3 @@ You need to copy the dataset to `/opt/xgboost`. Use the following links to downl
 1. [Mortgage dataset](/docs/get-started/xgboost-examples/dataset/mortgage.md)
 2. [Taxi dataset](https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page)
 3. [Agaricus dataset](https://gust.dev/r/xgboost-agaricus)
-
-### Setup environments
-
-``` bash
-export SPARK_XGBOOST_DIR=/opt/xgboost
-export RAPIDS_JAR=${SPARK_XGBOOST_DIR}/rapids-4-spark_2.12-22.10.0.jar
-export SAMPLE_JAR=${SPARK_XGBOOST_DIR}/sample_xgboost_apps-0.2.3-jar-with-dependencies.jar
-```

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

@@ -17,7 +17,7 @@
 
 ARG CUDA_VER=11.5.1
 FROM nvidia/cuda:${CUDA_VER}-devel-ubuntu20.04
-ARG BRANCH_VER=22.10
+ARG BRANCH_VER=22.12
 
 RUN apt-get update
 RUN apt-get install -y wget ninja-build git