Update plugin version to v26.08.0 and cuDF Spark branding

CONTRIBUTING.md

@@ -71,4 +71,4 @@ By making a contribution to this project, I certify that:
     this project or the open source license(s) involved.
 ```
 
-Note: This section `Sign your work` is derived from [https://github.com/NVIDIA/spark-rapids](https://github.com/NVIDIA/spark-rapids)
+Note: This section `Sign your work` is derived from [https://github.com/NVIDIA/cudf-spark](https://github.com/NVIDIA/cudf-spark)

README.md

@@ -1,15 +1,15 @@
 # spark-rapids-examples
 
-This is the [RAPIDS Accelerator for Apache Spark](https://nvidia.github.io/spark-rapids/) examples repo.
-RAPIDS Accelerator for Apache Spark accelerates Spark applications with no code changes.
-You can download the latest version of RAPIDS Accelerator [here](https://nvidia.github.io/spark-rapids/docs/download.html).
+This is the [NVIDIA cuDF plugin for Apache Spark](https://nvidia.github.io/cudf-spark/) examples repo.
+The cuDF plugin accelerates Spark applications with no code changes.
+You can download the latest version of the cuDF plugin [here](https://nvidia.github.io/cudf-spark/docs/download.html).
 This repo contains examples and applications that showcases the performance and benefits of using 
-RAPIDS Accelerator in data processing and machine learning pipelines. 
+the cuDF plugin in data processing and machine learning pipelines.
 There are broadly five categories of examples in this repo: 
 1. [SQL/Dataframe](./examples/SQL+DF-Examples) 
 2. [Spark XGBoost](./examples/XGBoost-Examples) 
 3. [Machine Learning/Deep Learning](./examples/ML+DL-Examples) 
-4. [RAPIDS UDF](./examples/UDF-Examples)
+4. [cuDF plugin UDF](./examples/UDF-Examples)
 5. [Databricks Tools demo notebooks](./tools/databricks)
 
 For more information on each of the examples please look into respective categories.
@@ -29,7 +29,7 @@ Here is the list of notebooks in this repo:
 | 9 | SQL/DF | [TPC-DS](https://www.tpc.org/tpcds/) Scale Factor 10 | Comparison of Spark SQL CPU vs GPU. Easy to run locally and on Google Colab
 
 Here is the list of Apache Spark applications (Scala and PySpark) that 
-can be built for running on GPU with RAPIDS Accelerator in this repo:
+can be built for running on GPU with the cuDF plugin in this repo:
 
 |   | Category  | Notebook Name | Description
 | ------------- | ------------- | ------------- | -------------

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

@@ -15,13 +15,13 @@ The number of GPUs per node dictates the number of Spark executors that can run
 Start A Databricks Cluster
 --------------------------
 Before creating the cluster, we will need to create an [initialization script](https://docs.databricks.com/clusters/init-scripts.html) for the 
-cluster to install the RAPIDS jars. Databricks recommends storing all cluster-scoped init scripts using workspace files. 
+cluster to install the NVIDIA cuDF plugin for Apache Spark jar. Databricks recommends storing all cluster-scoped init scripts using workspace files.
 Each user has a Home directory configured under the /Users directory in the workspace. 
 Navigate to your home directory in the UI and select **Create** > **File** from the menu, 
 create an `init.sh` scripts with contents:   
    ```bash
    #!/bin/bash
-   sudo wget -O /databricks/jars/rapids-4-spark_2.12-26.06.0.jar https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/26.06.0/rapids-4-spark_2.12-26.06.0.jar
+   sudo wget -O /databricks/jars/rapids-4-spark_2.12-26.08.0.jar https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/26.08.0/rapids-4-spark_2.12-26.08.0.jar
    ```
 1. Select the Databricks Runtime Version from one of the supported runtimes specified in the
    Prerequisites section.
@@ -34,8 +34,7 @@ create an `init.sh` scripts with contents:
    ![Init Script](../../../../img/databricks/initscript.png)
 6. Now select the “Spark” tab, and paste the following config options into the Spark Config section.
    Change the config values based on the workers you choose. See Apache Spark
-   [configuration](https://spark.apache.org/docs/latest/configuration.html) and RAPIDS Accelerator
-   for Apache Spark [descriptions](https://nvidia.github.io/spark-rapids/docs/configs.html) for each config.
+   [configuration](https://spark.apache.org/docs/latest/configuration.html) and the cuDF plugin [configuration descriptions](https://nvidia.github.io/cudf-spark/docs/configs.html) for each config.
 
     The
     [`spark.task.resource.gpu.amount`](https://spark.apache.org/docs/latest/configuration.html#scheduling)
@@ -60,15 +59,15 @@ create an `init.sh` scripts with contents:
     of python for Databricks. On Databricks, the python runtime requires different parameters than the
     Spark one, so a dedicated python demon module `rapids.daemon_databricks` is created and should
     be specified here. Set the config
-    [`spark.rapids.sql.python.gpu.enabled`](https://nvidia.github.io/spark-rapids/docs/configs.html#sql.python.gpu.enabled) to `true` to
+    [`spark.rapids.sql.python.gpu.enabled`](https://nvidia.github.io/cudf-spark/docs/configs.html#sql.python.gpu.enabled) to `true` to
     enable GPU support for python. Add the path of the plugin jar (supposing it is placed under
     `/databricks/jars/`) to the `spark.executorEnv.PYTHONPATH` option. For more details please go to
-    [GPU Scheduling For Pandas UDF](https://nvidia.github.io/spark-rapids/docs/additional-functionality/rapids-udfs.html#gpu-support-for-pandas-udf)
+    [GPU Scheduling For Pandas UDF](https://nvidia.github.io/cudf-spark/docs/additional-functionality/rapids-udfs.html#gpu-support-for-pandas-udf)
 
     ```bash
     spark.rapids.sql.python.gpu.enabled true
     spark.python.daemon.module rapids.daemon_databricks
-    spark.executorEnv.PYTHONPATH /databricks/jars/rapids-4-spark_2.12-26.06.0.jar:/databricks/spark/python
+    spark.executorEnv.PYTHONPATH /databricks/jars/rapids-4-spark_2.12-26.08.0.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
@@ -166,6 +165,6 @@ Limitations
 4. Databricks makes changes to the runtime without notification.
 
     Databricks makes changes to existing runtimes, applying patches, without notification.
-    [Issue-3098](https://github.com/NVIDIA/spark-rapids/issues/3098) is one example of this.  We run
+    [Issue-3098](https://github.com/NVIDIA/cudf-spark/issues/3098) is one example of this.  We run
     regular integration tests on the Databricks environment to catch these issues and fix them once
     detected.

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

@@ -17,7 +17,7 @@
 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_2.12-26.06.0.jar https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/26.06.0/rapids-4-spark_2.12-26.06.0.jar
+sudo wget -O /databricks/jars/rapids-4-spark_2.12-26.08.0.jar https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/26.08.0/rapids-4-spark_2.12-26.08.0.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/csp/dataproc/gcp.md

@@ -1,4 +1,4 @@
-# Getting started pyspark+xgboost with RAPIDS Accelerator on GCP Dataproc
+# Getting started with PySpark, XGBoost, and the NVIDIA cuDF plugin for Apache Spark on GCP Dataproc
  [Google Cloud Dataproc](https://cloud.google.com/dataproc) is Google Cloud's fully managed Apache
  Spark and Hadoop service. Please make sure to install gcloud CLI by following 
  this [guide](https://cloud.google.com/sdk/docs/install) before getting started.

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

@@ -4,8 +4,8 @@ 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_2.12/26.06.0/rapids-4-spark_2.12-26.06.0.jar)
+Download the NVIDIA cuDF plugin for Apache Spark jar
+  * [cuDF plugin jar](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/26.08.0/rapids-4-spark_2.12-26.08.0.jar)
 
 ### Build XGBoost Python Examples
 

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

@@ -4,8 +4,8 @@ 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_2.12/26.06.0/rapids-4-spark_2.12-26.06.0.jar)
+1. Download the NVIDIA cuDF plugin for Apache Spark jar
+   * [cuDF plugin jar](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/26.08.0/rapids-4-spark_2.12-26.08.0.jar)
 
 ### Build XGBoost Scala Examples
 

examples/MIG-Support/device-plugins/gpu-mig/README.md

@@ -103,5 +103,5 @@ Note the getMIGGpus discovery script would is in the `scripts` directory in this
 `nvidia/miggpu`.
 
 ## Testing
-Run a Spark application using the [Rapids Accelerator for Apache Spark](https://nvidia.github.io/spark-rapids/) and request GPUs
+Run a Spark application using the [NVIDIA cuDF plugin for Apache Spark](https://nvidia.github.io/cudf-spark/) and request GPUs
 from YARN and verify they use the MIG enabled GPUs.

examples/MIG-Support/resource-types/gpu-mig/README.md

@@ -98,5 +98,5 @@ Environment variable for Spark application:
 ```
 
 ## Testing
-Run a Spark application using the [Rapids Accelerator for Apache Spark](https://nvidia.github.io/spark-rapids/) and request GPUs
+Run a Spark application using the [NVIDIA cuDF plugin for Apache Spark](https://nvidia.github.io/cudf-spark/) and request GPUs
 from YARN and verify they use the MIG enabled GPUs.

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

@@ -74,7 +74,7 @@ We provide **2 notebooks**, with differences in the backend/implementation. See
 - `optuna-dataframe.ipynb`: 
   - Uses Spark dataframes to distribute tasks on the cluster. 
   - Implements *Spark-I/O*, where Spark reads the dataset from a specified filepath, then duplicates and repartitions it so that each worker task is mapped onto a copy of the dataset.
-  - Dataframe operations are accelerated on GPU with the [Spark-RAPIDS Accelerator](https://nvidia.github.io/spark-rapids/).
+  - Dataframe operations are accelerated on GPU with the [NVIDIA cuDF plugin for Apache Spark](https://nvidia.github.io/cudf-spark/).
 
 ## Running Optuna on Spark Standalone
 
@@ -147,8 +147,8 @@ We use [RAPIDS](https://docs.rapids.ai/install/#get-rapids) for GPU-accelerated
 ``` shell
 sudo apt install libmysqlclient-dev
 
-conda create -n rapids-26.06 -c rapidsai -c conda-forge -c nvidia  \
-    cudf=26.06 cuml=26.06 python=3.10 'cuda-version>=12.0,<=12.5'
+conda create -n rapids-26.08 -c rapidsai -c conda-forge -c nvidia  \
+    cudf=26.08 cuml=26.08 python=3.10 'cuda-version>=12.0,<=12.5'
 conda activate optuna-spark
 pip install mysqlclient
 pip install optuna joblib joblibspark ipywidgets
@@ -201,7 +201,7 @@ Or, create a cluster via the web UI:
 - Under `Advanced Options > Spark > Environment variables`, set `LIBCUDF_CUFILE_POLICY=OFF`.
 - Make sure to use a GPU cluster and include task GPU resources.
 
-The init script will install the required libraries on all nodes, including RAPIDS and the Spark-RAPIDS plugin for GPU-accelerated ETL. On the driver, it will setup the MySQL server backend. 
+The init script will install the required libraries on all nodes, including RAPIDS and the cuDF plugin for GPU-accelerated ETL. On the driver, it will setup the MySQL server backend.
 
 ### 3. Run Notebook
 

examples/ML+DL-Examples/Optuna-Spark/optuna-examples/databricks/init_optuna.sh

@@ -55,7 +55,7 @@ fi
 
 
 # rapids import
-SPARK_RAPIDS_VERSION=26.06.0
+SPARK_RAPIDS_VERSION=26.08.0
 curl -L https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/${SPARK_RAPIDS_VERSION}/rapids-4-spark_2.12-${SPARK_RAPIDS_VERSION}.jar -o \
     /databricks/jars/rapids-4-spark_2.12-${SPARK_RAPIDS_VERSION}.jar
 

examples/ML+DL-Examples/Optuna-Spark/optuna-examples/databricks/start_cluster.sh

@@ -26,7 +26,7 @@ json_config=$(cat <<EOF
     "spark_version": "13.3.x-gpu-ml-scala2.12",
     "spark_conf": {
         "spark.task.resource.gpu.amount": "1",
-        "spark.executorEnv.PYTHONPATH": "/databricks/jars/rapids-4-spark_2.12-26.06.0.jar:/databricks/spark/python:/databricks/python3",
+        "spark.executorEnv.PYTHONPATH": "/databricks/jars/rapids-4-spark_2.12-26.08.0.jar:/databricks/spark/python:/databricks/python3",
         "spark.executor.cores": "8",
         "spark.rapids.memory.gpu.minAllocFraction": "0.0001",
         "spark.plugins": "com.nvidia.spark.SQLPlugin",

examples/ML+DL-Examples/Optuna-Spark/optuna-examples/optuna-dataframe.ipynb

@@ -420,7 +420,7 @@
    "source": [
     "### PySpark\n",
     "\n",
-    "For standalone users, we need to create the Spark session with the Spark-Rapids plugin. For Databricks users, the Spark session will be preconfigured and this cell can be skipped."
+    "For standalone users, we need to create the Spark session with the NVIDIA cuDF plugin for Apache Spark. For Databricks users, the Spark session will be preconfigured and this cell can be skipped."
    ]
   },
   {
@@ -451,10 +451,10 @@
    ],
    "source": [
     "def get_rapids_jar():\n",
-    "    SPARK_RAPIDS_VERSION = \"26.06.0\"\n",
+    "    SPARK_RAPIDS_VERSION = \"26.08.0\"\n",
     "    rapids_jar = f\"rapids-4-spark_2.12-{SPARK_RAPIDS_VERSION}.jar\"\n",
     "    if not os.path.exists(rapids_jar):\n",
-    "        print(\"Downloading Spark Rapids jar\")\n",
+    "        print(\"Downloading the cuDF plugin jar\")\n",
     "        url = f\"https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/{SPARK_RAPIDS_VERSION}/{rapids_jar}\"\n",
     "        response = requests.get(url)\n",
     "        if response.status_code == 200:\n",

examples/ML+DL-Examples/Spark-DL/dl_inference/databricks/README.md

@@ -48,4 +48,4 @@ Make sure you are in [this](./) directory.
 
 7. Navigate to the notebook in your workspace and attach it to the cluster. The default cluster name is `spark-dl-inference-$FRAMEWORK`.  
 
-*Note that the RAPIDS Accelerator for Apache Spark is not compatible with this case, since [multiple GPUs per executor are not yet supported](https://docs.nvidia.com/spark-rapids/user-guide/latest/faq.html#why-are-multiple-gpus-per-executor-not-supported).
+*Note that the NVIDIA cuDF plugin for Apache Spark is not compatible with this case, since [multiple GPUs per executor are not yet supported](https://docs.nvidia.com/spark-rapids/user-guide/latest/faq.html#why-are-multiple-gpus-per-executor-not-supported).

examples/ML+DL-Examples/Spark-DL/dl_inference/dataproc/README.md

@@ -74,4 +74,4 @@ Make sure you are in [this](./) directory.
 8. Open and run the notebook interactively with the **Python 3 kernel**.  
 The notebooks can be found under `Local Disk/spark-dl-notebooks` on the master node (folder icon on the top left > Local Disk).
 
-*Note that the RAPIDS Accelerator for Apache Spark is not applicable in this case, since [multiple GPUs per executor are not yet supported](https://docs.nvidia.com/spark-rapids/user-guide/latest/faq.html#why-are-multiple-gpus-per-executor-not-supported).
+*Note that the NVIDIA cuDF plugin for Apache Spark is not applicable in this case, since [multiple GPUs per executor are not yet supported](https://docs.nvidia.com/spark-rapids/user-guide/latest/faq.html#why-are-multiple-gpus-per-executor-not-supported).

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

@@ -7,10 +7,10 @@ The notebook uses PCA to reduce a random dataset with 2048 feature dimensions to
 
 Please refer to the Spark-Rapids-ML [README](https://github.com/NVIDIA/spark-rapids-ml/blob/HEAD/python) to setup the RAPIDS conda environment and install Spark-Rapids-ML dependencies.
 
-## Download RAPIDS Jar from Maven Central
+## Download the NVIDIA cuDF plugin for Apache Spark jar from Maven Central
 
-Download the [Spark-Rapids plugin](https://nvidia.github.io/spark-rapids/docs/download.html#download-rapids-accelerator-for-apache-spark-v26060).
-For Spark-RAPIDS-ML version 26.06.0, download the RAPIDS jar from Maven Central: [rapids-4-spark_2.12-26.06.0.jar](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/26.06.0/rapids-4-spark_2.12-26.06.0.jar).
+Download the [cuDF plugin](https://nvidia.github.io/cudf-spark/docs/download.html#download-rapids-accelerator-for-apache-spark-v26080).
+For Spark-RAPIDS-ML version 26.08.0, download the cuDF plugin jar from Maven Central: [rapids-4-spark_2.12-26.08.0.jar](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/26.08.0/rapids-4-spark_2.12-26.08.0.jar).
 
 ## Running the Notebooks
 

examples/ML+DL-Examples/Spark-Rapids-ML/pca/notebooks/pca.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "## Principal Component Analysis (PCA)\n",
     "\n",
-    "In this notebook, we will demonstrate the end-to-end workflow of Spark RAPIDS accelerated PCA."
+    "In this notebook, we will demonstrate the end-to-end workflow of PCA accelerated by the NVIDIA cuDF plugin for Apache Spark."
    ]
   },
   {
@@ -57,7 +57,7 @@
     "    import os\n",
     "    import requests\n",
     "\n",
-    "    SPARK_RAPIDS_VERSION = \"26.06.0\"\n",
+    "    SPARK_RAPIDS_VERSION = \"26.08.0\"\n",
     "    rapids_jar = f\"rapids-4-spark_2.12-{SPARK_RAPIDS_VERSION}.jar\"\n",
     "    if not os.path.exists(rapids_jar):\n",
     "        print(\"Downloading spark rapids jar\")\n",

examples/SQL+DF-Examples/demo/Spark_get_json_object.ipynb

@@ -7,7 +7,7 @@
         "id": "Td_alkbOv3Aj"
       },
       "source": [
-        "# Spark RAPIDS get_json_object acceleration\n",
+        "# NVIDIA cuDF plugin for Apache Spark: get_json_object acceleration\n",
         "\n"
       ]
     },
@@ -41,7 +41,7 @@
         "id": "ZfNDlz0SM0DB"
       },
       "source": [
-        "# Let's get started using the RAPIDS Accelerator for Apache Spark"
+        "# Let's get started using the cuDF plugin"
       ]
     },
     {

examples/SQL+DF-Examples/demo/Spark_parquet_microkernels.ipynb

@@ -16,7 +16,7 @@
     "        \"id\": \"Td_alkbOv3Aj\"\n",
     "      },\n",
     "      \"source\": [\n",
-    "        \"# Spark RAPIDS Parquet acceleration\\n\",\n",
+    "        \"# NVIDIA cuDF plugin for Apache Spark: Parquet acceleration\\n\",\n",
     "        \"\\n\"\n",
     "      ]\n",
     "    },\n",
@@ -50,7 +50,7 @@
     "        \"id\": \"ZfNDlz0SM0DB\"\n",
     "      },\n",
     "      \"source\": [\n",
-    "        \"# Let's get started using the RAPIDS Accelerator for Apache Spark\"\n",
+    "        \"# Let's get started using the cuDF plugin\"\n",
     "      ]\n",
     "    },\n",
     "    {\n",

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_2.12-26.06.0.jar\")\n"
+    "RAPIDS_JAR = os.getenv(\"RAPIDS_JAR\", \"/your-path/rapids-4-spark_2.12-26.08.0.jar\")\n"
    ]
   },
   {

examples/SQL+DF-Examples/retail-analytics/README.md

@@ -4,4 +4,4 @@ This repository contains two Jupyter notebooks:
 
 Data Generation: This notebook generates sample data that can be used for analysis. It demonstrates how to use various Python libraries to create synthetic data sets that can be used for testing and experimentation. This notebook can be run in GCP n1-standard-32 instance type
 
-Data Cleaning and Analysis: This notebook takes the generated data and performs a series of cleaning and analysis tasks. It demonstrates how to use Spark RAPIDS library to manipulate and analyze data sets.
+Data Cleaning and Analysis: This notebook takes the generated data and performs a series of cleaning and analysis tasks. It demonstrates how to use the NVIDIA cuDF plugin for Apache Spark to manipulate and analyze data sets.