Update Spark-RAPIDS-ML PCA

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

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+# Spark-Rapids-ML PCA example
+
+This is an example of the GPU accelerated PCA algorithm from the [Spark-Rapids-ML](https://github.com/NVIDIA/spark-rapids-ml) library, which provides PySpark ML compatible algorithms powered by RAPIDS cuML. 
+The notebook uses PCA to reduce a random dataset with 2048 feature dimensions to 3 dimensions. We train both the GPU and CPU algorithms for comparison. 
+
+## Build
+
+Please refer to the Spark-Rapids-ML [README](https://github.com/NVIDIA/spark-rapids-ml/blob/HEAD/python) for Python build instructions and API usage.
+
+## Running the Notebooks
+
+Once you have built your environment, please follow these instructions to run the notebooks.
+
+**Note**: for demonstration purposes, these examples just use a local Spark Standalone cluster with a single executor, but you should be able to run them on any distributed Spark cluster.
+```
+# setup environment variables
+export SPARK_HOME=/path/to/spark
+export MASTER=spark://$(hostname):7077
+export SPARK_WORKER_INSTANCES=1
+export CORES_PER_WORKER=8
+export PYSPARK_DRIVER_PYTHON=jupyter
+export PYSPARK_DRIVER_PYTHON_OPTS='lab'
+
+# start spark standalone cluster
+${SPARK_HOME}/sbin/start-master.sh; ${SPARK_HOME}/sbin/start-worker.sh -c ${CORES_PER_WORKER} -m 16G ${MASTER}
+
+# start jupyter with pyspark
+${SPARK_HOME}/bin/pyspark --master ${MASTER} \
+--driver-memory 10G \
+--executor-memory 8G \
+--conf spark.python.worker.reuse=True
+
+# BROWSE to localhost:8888 to view/run notebooks
+
+# stop spark standalone cluster
+${SPARK_HOME}/sbin/stop-worker.sh; ${SPARK_HOME}/sbin/stop-master.sh
+```