Merge pull request #373 from SurajAralihalli/main-24.02-release

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

Author: SurajAralihalli

.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.