From 59f3a00ef62216197a581f35c76aed01dba321a2 Mon Sep 17 00:00:00 2001 From: Suraj Aralihalli Date: Thu, 23 Jun 2022 22:14:16 -0400 Subject: [PATCH 01/17] initial commit: fix print statement error in XGBoostArgs.scala (#185) Signed-off-by: Suraj Aralihalli Co-authored-by: Suraj Aralihalli --- .../com/nvidia/spark/examples/utility/XGBoostArgs.scala | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/examples/XGBoost-Examples/utility/scala/src/com/nvidia/spark/examples/utility/XGBoostArgs.scala b/examples/XGBoost-Examples/utility/scala/src/com/nvidia/spark/examples/utility/XGBoostArgs.scala index 75faaaa5a..d8cca3fcd 100644 --- a/examples/XGBoost-Examples/utility/scala/src/com/nvidia/spark/examples/utility/XGBoostArgs.scala +++ b/examples/XGBoost-Examples/utility/scala/src/com/nvidia/spark/examples/utility/XGBoostArgs.scala @@ -97,10 +97,10 @@ object XGBoostArgs { println(" -saveDict=value: Boolean\n" + " Whether to save the dictionary table for Mortgage ETL. It is saved under '/.dict'. Default is true.\n") println(" -rabitTrackerHost=value: String\n" + - " Specify rabit tracker host IP address. In some environments XGBoost might fail to resolve - the IP address of the rabit tracker, a symptom is user receiving ``OSError: [Errno 99] - Cannot assign requested address`` error during training. A quick workaround is to - specify the address explicitly.\n") + " Specify rabit tracker host IP address. In some environments XGBoost might fail to resolve\n" + + "the IP address of the rabit tracker, a symptom is user receiving ``OSError: [Errno 99]\n" + + "Cannot assign requested address`` error during training. A quick workaround is to\n" + + "specify the address explicitly.\n") println("For XGBoost arguments:") println(" Now we pass all XGBoost parameters transparently to XGBoost, no longer to verify them.") println(" Both of the formats are supported, such as 'numWorkers'. You can pass as either one below:") From b44cfaccd986ac0d571adc1ff42bf2015ff6d724 Mon Sep 17 00:00:00 2001 From: Tim Liu Date: Fri, 24 Jun 2022 10:16:29 +0800 Subject: [PATCH 02/17] Update version of udf-examples and pca-examples to 22.08.0-SNAPSHOT (#183) Signed-off-by: Tim Liu --- examples/ML+DL-Examples/Spark-cuML/pca/pom.xml | 2 +- examples/ML+DL-Examples/Spark-cuML/pca/spark-submit.sh | 4 ++-- examples/UDF-Examples/RAPIDS-accelerated-UDFs/pom.xml | 2 +- examples/UDF-Examples/Spark-cuSpatial/Dockerfile | 1 + examples/UDF-Examples/Spark-cuSpatial/README.md | 4 ++-- examples/UDF-Examples/Spark-cuSpatial/gpu-run.sh | 2 +- .../notebooks/cuspatial_sample_standalone.ipynb | 2 +- examples/UDF-Examples/Spark-cuSpatial/pom.xml | 2 +- 8 files changed, 10 insertions(+), 9 deletions(-) diff --git a/examples/ML+DL-Examples/Spark-cuML/pca/pom.xml b/examples/ML+DL-Examples/Spark-cuML/pca/pom.xml index 6a856d9f9..4524476d4 100644 --- a/examples/ML+DL-Examples/Spark-cuML/pca/pom.xml +++ b/examples/ML+DL-Examples/Spark-cuML/pca/pom.xml @@ -21,7 +21,7 @@ com.nvidia PCAExample jar - 22.06.0-SNAPSHOT + 22.08.0-SNAPSHOT 8 diff --git a/examples/ML+DL-Examples/Spark-cuML/pca/spark-submit.sh b/examples/ML+DL-Examples/Spark-cuML/pca/spark-submit.sh index 4e5f796d1..dca5264f0 100755 --- a/examples/ML+DL-Examples/Spark-cuML/pca/spark-submit.sh +++ b/examples/ML+DL-Examples/Spark-cuML/pca/spark-submit.sh @@ -16,7 +16,7 @@ # ML_JAR=/root/.m2/repository/com/nvidia/rapids-4-spark-ml_2.12/22.06.0-SNAPSHOT/rapids-4-spark-ml_2.12-22.06.0-SNAPSHOT.jar -PLUGIN_JAR=/root/.m2/repository/com/nvidia/rapids-4-spark_2.12/22.06.0-SNAPSHOT/rapids-4-spark_2.12-22.06.0-SNAPSHOT.jar +PLUGIN_JAR=/root/.m2/repository/com/nvidia/rapids-4-spark_2.12/22.08.0-SNAPSHOT/rapids-4-spark_2.12-22.08.0-SNAPSHOT.jar $SPARK_HOME/bin/spark-submit \ --master spark://127.0.0.1:7077 \ @@ -38,4 +38,4 @@ $SPARK_HOME/bin/spark-submit \ --conf spark.network.timeout=1000s \ --jars $ML_JAR,$PLUGIN_JAR \ --class com.nvidia.spark.examples.pca.Main \ -/workspace/target/PCAExample-22.06.0-SNAPSHOT.jar +/workspace/target/PCAExample-22.08.0-SNAPSHOT.jar diff --git a/examples/UDF-Examples/RAPIDS-accelerated-UDFs/pom.xml b/examples/UDF-Examples/RAPIDS-accelerated-UDFs/pom.xml index 237d2d2cb..d7b07f56b 100644 --- a/examples/UDF-Examples/RAPIDS-accelerated-UDFs/pom.xml +++ b/examples/UDF-Examples/RAPIDS-accelerated-UDFs/pom.xml @@ -25,7 +25,7 @@ user defined functions for use with the RAPIDS Accelerator for Apache Spark - 22.06.0-SNAPSHOT + 22.08.0-SNAPSHOT 1.8 diff --git a/examples/UDF-Examples/Spark-cuSpatial/Dockerfile b/examples/UDF-Examples/Spark-cuSpatial/Dockerfile index 253ecc290..bbf9655b0 100644 --- a/examples/UDF-Examples/Spark-cuSpatial/Dockerfile +++ b/examples/UDF-Examples/Spark-cuSpatial/Dockerfile @@ -18,6 +18,7 @@ ARG CUDA_VER=11.2.2 FROM nvidia/cuda:${CUDA_VER}-devel-ubuntu18.04 +RUN apt-key adv --fetch-keys https://developer.download.nvidia.cn/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub RUN apt-get update RUN apt-get install -y wget ninja-build git diff --git a/examples/UDF-Examples/Spark-cuSpatial/README.md b/examples/UDF-Examples/Spark-cuSpatial/README.md index 49598396e..39126ea8e 100644 --- a/examples/UDF-Examples/Spark-cuSpatial/README.md +++ b/examples/UDF-Examples/Spark-cuSpatial/README.md @@ -68,7 +68,7 @@ or you can build it [in local](#build-in-local) machine after some prerequisites 3. Download spark-rapids jars * [spark-rapids v22.06.0](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/22.06.0/rapids-4-spark_2.12-22.06.0.jar) or above 4. Prepare the dataset & jars. Copy the sample dataset from [cuspatial_data](../../../datasets/cuspatial_data.tar.gz) to "/data/cuspatial_data". - Copy spark-rapids & spark-cuspatial-22.06.0-SNAPSHOT.jar to "/data/cuspatial_data/jars". + Copy spark-rapids & spark-cuspatial-22.08.0-SNAPSHOT.jar to "/data/cuspatial_data/jars". You can use your own path, but remember to update the paths in "gpu-run.sh" accordingly. 5. Run "gpu-run.sh" ```Bash @@ -103,5 +103,5 @@ or you can build it [in local](#build-in-local) machine after some prerequisites points polygons ``` -4. Import the Library "spark-cuspatial-22.06.0-SNAPSHOT.jar" to the Databricks, then install it to your cluster. +4. Import the Library "spark-cuspatial-22.08.0-SNAPSHOT.jar" to the Databricks, then install it to your cluster. 5. Import [cuspatial_sample.ipynb](notebooks/cuspatial_sample_db.ipynb) to your workspace in Databricks. Attach to your cluster, then run it. diff --git a/examples/UDF-Examples/Spark-cuSpatial/gpu-run.sh b/examples/UDF-Examples/Spark-cuSpatial/gpu-run.sh index 27571388d..ce1168a29 100755 --- a/examples/UDF-Examples/Spark-cuSpatial/gpu-run.sh +++ b/examples/UDF-Examples/Spark-cuSpatial/gpu-run.sh @@ -31,7 +31,7 @@ rm -rf $DATA_OUT_PATH # the path to keep the jars of spark-rapids & spark-cuspatial JARS=$ROOT_PATH/jars -JARS_PATH=$JARS/rapids-4-spark_2.12-22.06.0.jar,$JARS/spark-cuspatial-22.06.0-SNAPSHOT.jar +JARS_PATH=$JARS/rapids-4-spark_2.12-22.06.0.jar,$JARS/spark-cuspatial-22.08.0-SNAPSHOT.jar $SPARK_HOME/bin/spark-submit --master spark://$HOSTNAME:7077 \ --name "Gpu Spatial Join UDF" \ diff --git a/examples/UDF-Examples/Spark-cuSpatial/notebooks/cuspatial_sample_standalone.ipynb b/examples/UDF-Examples/Spark-cuSpatial/notebooks/cuspatial_sample_standalone.ipynb index a53a6d813..62b48cc74 100644 --- a/examples/UDF-Examples/Spark-cuSpatial/notebooks/cuspatial_sample_standalone.ipynb +++ b/examples/UDF-Examples/Spark-cuSpatial/notebooks/cuspatial_sample_standalone.ipynb @@ -9,7 +9,7 @@ "source": [ "from pyspark.sql import SparkSession\n", "spark = SparkSession.builder \\\n", - " .config(\"spark.jars\", \"/data/cuspatial_data/jars/rapids-4-spark_2.12-22.06.0.jar,/data/cuspatial_data/jars/spark-cuspatial-22.06.0-SNAPSHOT.jar\") \\\n", + " .config(\"spark.jars\", \"/data/cuspatial_data/jars/rapids-4-spark_2.12-22.06.0.jar,/data/cuspatial_data/jars/spark-cuspatial-22.08.0-SNAPSHOT.jar\") \\\n", " .config(\"spark.sql.adaptive.enabled\", \"false\") \\\n", " .config(\"spark.executor.memory\", \"20GB\") \\\n", " .config(\"spark.executor.cores\", \"6\") \\\n", diff --git a/examples/UDF-Examples/Spark-cuSpatial/pom.xml b/examples/UDF-Examples/Spark-cuSpatial/pom.xml index 1196fd775..811e1d534 100644 --- a/examples/UDF-Examples/Spark-cuSpatial/pom.xml +++ b/examples/UDF-Examples/Spark-cuSpatial/pom.xml @@ -24,7 +24,7 @@ UDF of the cuSpatial case for the RAPIDS Accelerator The RAPIDS accelerated user defined function of the cuSpatial case for use with the RAPIDS Accelerator for Apache Spark - 22.06.0-SNAPSHOT + 22.08.0-SNAPSHOT 1.8 From eb3e303085e2e9d5cc9b5a6071f53a0c2f16dfd8 Mon Sep 17 00:00:00 2001 From: liyuan <84758614+nvliyuan@users.noreply.github.com> Date: Fri, 24 Jun 2022 15:13:21 +0800 Subject: [PATCH 03/17] fix cicd job issues due to wrong path (#188) * fix cicd errors Signed-off-by: liyuan * remove notebook temp files Signed-off-by: liyuan * fix cicd issue due to kernel name Signed-off-by: liyuan --- .../micro-benchmarks/notebooks/micro-benchmarks-cpu.ipynb | 2 +- .../micro-benchmarks/notebooks/micro-benchmarks-gpu.ipynb | 2 +- .../agaricus/notebooks/scala/agaricus-gpu.ipynb | 4 ++-- .../mortgage/notebooks/scala/mortgage-ETL.ipynb | 4 ++-- .../mortgage/notebooks/scala/mortgage-gpu.ipynb | 4 ++-- .../notebooks/scala/mortgage_gpu_crossvalidation.ipynb | 4 ++-- examples/XGBoost-Examples/taxi/notebooks/scala/taxi-ETL.ipynb | 4 ++-- examples/XGBoost-Examples/taxi/notebooks/scala/taxi-gpu.ipynb | 4 ++-- .../taxi/notebooks/scala/taxi_gpu_crossvalidation.ipynb | 4 ++-- 9 files changed, 16 insertions(+), 16 deletions(-) diff --git a/examples/SQL+DF-Examples/micro-benchmarks/notebooks/micro-benchmarks-cpu.ipynb b/examples/SQL+DF-Examples/micro-benchmarks/notebooks/micro-benchmarks-cpu.ipynb index a90f57aa6..ce5c5a797 100644 --- a/examples/SQL+DF-Examples/micro-benchmarks/notebooks/micro-benchmarks-cpu.ipynb +++ b/examples/SQL+DF-Examples/micro-benchmarks/notebooks/micro-benchmarks-cpu.ipynb @@ -120,7 +120,7 @@ "# By default, Spark will try to distribute the data among all the tasks in the cluster, \n", "# but on large clusters with large parquet files the splittable portions of the parquet files end up not being distributed evenly \n", "# and it is faster to re-partition the data to redistribute it than to deal with skew.\n", - "spark.read.parquet(\"hdfs:///data/tpcds_sf3000-parquet/useDecimal=true,useDate=true,filterNull=false/customer\").repartition(512).createOrReplaceTempView(\"customer\")\n", + "spark.read.parquet(dataRoot + \"/tpcds/customer\").repartition(512).createOrReplaceTempView(\"customer\")\n", "\n", "print(\"-\"*50)" ] diff --git a/examples/SQL+DF-Examples/micro-benchmarks/notebooks/micro-benchmarks-gpu.ipynb b/examples/SQL+DF-Examples/micro-benchmarks/notebooks/micro-benchmarks-gpu.ipynb index a72b24727..f37ae39fb 100644 --- a/examples/SQL+DF-Examples/micro-benchmarks/notebooks/micro-benchmarks-gpu.ipynb +++ b/examples/SQL+DF-Examples/micro-benchmarks/notebooks/micro-benchmarks-gpu.ipynb @@ -497,7 +497,7 @@ ], "source": [ "start = time() \n", - "spark.read.parquet(\"hdfs:///data/tpcds_sf3000-parquet/useDecimal=true,useDate=true,filterNull=false/customer\").limit(1000000).write.format(\"parquet\").mode(\"overwrite\").save(\"/data/tmp/customer1m\")\n", + "spark.read.parquet(dataRoot + \"/tpcds/customer\").limit(1000000).write.format(\"parquet\").mode(\"overwrite\").save(\"/data/tmp/customer1m\")\n", "end = time()\n", "# Parquet file scanning and writing will be about 3 times faster running on GPU\n", "print(\"scanning and writing parquet cost : {} seconds\".format(round(end - start, 2)))\n", diff --git a/examples/XGBoost-Examples/agaricus/notebooks/scala/agaricus-gpu.ipynb b/examples/XGBoost-Examples/agaricus/notebooks/scala/agaricus-gpu.ipynb index 738a5a0cb..06efa2ba9 100644 --- a/examples/XGBoost-Examples/agaricus/notebooks/scala/agaricus-gpu.ipynb +++ b/examples/XGBoost-Examples/agaricus/notebooks/scala/agaricus-gpu.ipynb @@ -604,9 +604,9 @@ ], "metadata": { "kernelspec": { - "display_name": "XGBoost4j-Spark-2206 - Scala", + "display_name": "XGBoost4j-Spark - Scala", "language": "scala", - "name": "xgboost4j-spark-2206_scala" + "name": "XGBoost4j-Spark_scala" }, "language_info": { "codemirror_mode": "text/x-scala", diff --git a/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-ETL.ipynb b/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-ETL.ipynb index 840c068b7..715187758 100644 --- a/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-ETL.ipynb +++ b/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-ETL.ipynb @@ -918,9 +918,9 @@ ], "metadata": { "kernelspec": { - "display_name": "XGBoost4j-Spark-2206 - Scala", + "display_name": "XGBoost4j-Spark - Scala", "language": "scala", - "name": "xgboost4j-spark-2206_scala" + "name": "XGBoost4j-Spark_scala" }, "language_info": { "codemirror_mode": "text/x-scala", diff --git a/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-gpu.ipynb b/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-gpu.ipynb index 6eaec8c72..9376f0de4 100644 --- a/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-gpu.ipynb +++ b/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-gpu.ipynb @@ -617,9 +617,9 @@ ], "metadata": { "kernelspec": { - "display_name": "XGBoost4j-Spark-2206 - Scala", + "display_name": "XGBoost4j-Spark - Scala", "language": "scala", - "name": "xgboost4j-spark-2206_scala" + "name": "XGBoost4j-Spark_scala" }, "language_info": { "codemirror_mode": "text/x-scala", diff --git a/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage_gpu_crossvalidation.ipynb b/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage_gpu_crossvalidation.ipynb index a83c2bcdb..74bb38f9d 100644 --- a/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage_gpu_crossvalidation.ipynb +++ b/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage_gpu_crossvalidation.ipynb @@ -491,9 +491,9 @@ ], "metadata": { "kernelspec": { - "display_name": "XGBoost4j-Spark-2206 - Scala", + "display_name": "XGBoost4j-Spark - Scala", "language": "scala", - "name": "xgboost4j-spark-2206_scala" + "name": "XGBoost4j-Spark_scala" }, "language_info": { "codemirror_mode": "text/x-scala", diff --git a/examples/XGBoost-Examples/taxi/notebooks/scala/taxi-ETL.ipynb b/examples/XGBoost-Examples/taxi/notebooks/scala/taxi-ETL.ipynb index 6a849b5b1..9532f1a1c 100644 --- a/examples/XGBoost-Examples/taxi/notebooks/scala/taxi-ETL.ipynb +++ b/examples/XGBoost-Examples/taxi/notebooks/scala/taxi-ETL.ipynb @@ -563,9 +563,9 @@ ], "metadata": { "kernelspec": { - "display_name": "XGBoost4j-Spark-2206 - Scala", + "display_name": "XGBoost4j-Spark - Scala", "language": "scala", - "name": "xgboost4j-spark-2206_scala" + "name": "XGBoost4j-Spark_scala" }, "language_info": { "codemirror_mode": "text/x-scala", diff --git a/examples/XGBoost-Examples/taxi/notebooks/scala/taxi-gpu.ipynb b/examples/XGBoost-Examples/taxi/notebooks/scala/taxi-gpu.ipynb index b59f74473..58dd84eb0 100644 --- a/examples/XGBoost-Examples/taxi/notebooks/scala/taxi-gpu.ipynb +++ b/examples/XGBoost-Examples/taxi/notebooks/scala/taxi-gpu.ipynb @@ -594,9 +594,9 @@ ], "metadata": { "kernelspec": { - "display_name": "XGBoost4j-Spark-2206 - Scala", + "display_name": "XGBoost4j-Spark - Scala", "language": "scala", - "name": "xgboost4j-spark-2206_scala" + "name": "XGBoost4j-Spark_scala" }, "language_info": { "codemirror_mode": "text/x-scala", diff --git a/examples/XGBoost-Examples/taxi/notebooks/scala/taxi_gpu_crossvalidation.ipynb b/examples/XGBoost-Examples/taxi/notebooks/scala/taxi_gpu_crossvalidation.ipynb index b8e524e4d..b5d1da710 100644 --- a/examples/XGBoost-Examples/taxi/notebooks/scala/taxi_gpu_crossvalidation.ipynb +++ b/examples/XGBoost-Examples/taxi/notebooks/scala/taxi_gpu_crossvalidation.ipynb @@ -490,9 +490,9 @@ ], "metadata": { "kernelspec": { - "display_name": "XGBoost4j-Spark-2206 - Scala", + "display_name": "XGBoost4j-Spark - Scala", "language": "scala", - "name": "xgboost4j-spark-2206_scala" + "name": "XGBoost4j-Spark_scala" }, "language_info": { "codemirror_mode": "text/x-scala", From 339123aef3e3e7cdf8b0572d6eb3abe57f0f73e9 Mon Sep 17 00:00:00 2001 From: Tim Liu Date: Fri, 24 Jun 2022 18:09:53 +0800 Subject: [PATCH 04/17] UDF example jar refer to rapids branch-22.08 (#190) Signed-off-by: Tim Liu --- .../RAPIDS-accelerated-UDFs/src/main/cpp/CMakeLists.txt | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/cpp/CMakeLists.txt b/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/cpp/CMakeLists.txt index 11b21ae15..b9b4929d5 100755 --- a/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/cpp/CMakeLists.txt +++ b/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/cpp/CMakeLists.txt @@ -16,7 +16,7 @@ cmake_minimum_required(VERSION 3.20.1 FATAL_ERROR) -file(DOWNLOAD https://raw.githubusercontent.com/rapidsai/rapids-cmake/branch-22.06/RAPIDS.cmake +file(DOWNLOAD https://raw.githubusercontent.com/rapidsai/rapids-cmake/branch-22.08/RAPIDS.cmake ${CMAKE_BINARY_DIR}/RAPIDS.cmake) include(${CMAKE_BINARY_DIR}/RAPIDS.cmake) @@ -32,7 +32,7 @@ if(DEFINED GPU_ARCHS) endif() rapids_cuda_init_architectures(UDFEXAMPLESJNI) -project(UDFEXAMPLESJNI VERSION 22.06.0 LANGUAGES C CXX CUDA) +project(UDFEXAMPLESJNI VERSION 22.08.0 LANGUAGES C CXX CUDA) option(PER_THREAD_DEFAULT_STREAM "Build with per-thread default stream" OFF) option(BUILD_UDF_BENCHMARKS "Build the benchmarks" OFF) @@ -84,10 +84,10 @@ set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -w --expt-extended-lambda --expt-relax set(CUDA_USE_STATIC_CUDA_RUNTIME OFF) rapids_cpm_init() -rapids_cpm_find(cudf 22.06.00 +rapids_cpm_find(cudf 22.08.00 CPM_ARGS GIT_REPOSITORY https://github.com/rapidsai/cudf.git - GIT_TAG branch-22.06 + GIT_TAG branch-22.08 GIT_SHALLOW TRUE SOURCE_SUBDIR cpp OPTIONS "BUILD_TESTS OFF" From 1f5cbbf3540b5d0032a241ab444b9f0febdbca9c Mon Sep 17 00:00:00 2001 From: Tim Liu Date: Mon, 27 Jun 2022 18:52:26 +0800 Subject: [PATCH 05/17] spark-rapids-ml version to 22.08.0-SNAPSHOT (#192) Signed-off-by: Tim Liu --- examples/ML+DL-Examples/Spark-cuML/pca/pom.xml | 2 +- examples/ML+DL-Examples/Spark-cuML/pca/spark-submit.sh | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/ML+DL-Examples/Spark-cuML/pca/pom.xml b/examples/ML+DL-Examples/Spark-cuML/pca/pom.xml index 4524476d4..7a7b399d5 100644 --- a/examples/ML+DL-Examples/Spark-cuML/pca/pom.xml +++ b/examples/ML+DL-Examples/Spark-cuML/pca/pom.xml @@ -51,7 +51,7 @@ com.nvidia rapids-4-spark-ml_2.12 - 22.06.0-SNAPSHOT + 22.08.0-SNAPSHOT diff --git a/examples/ML+DL-Examples/Spark-cuML/pca/spark-submit.sh b/examples/ML+DL-Examples/Spark-cuML/pca/spark-submit.sh index dca5264f0..03381d8e9 100755 --- a/examples/ML+DL-Examples/Spark-cuML/pca/spark-submit.sh +++ b/examples/ML+DL-Examples/Spark-cuML/pca/spark-submit.sh @@ -15,7 +15,7 @@ # limitations under the License. # -ML_JAR=/root/.m2/repository/com/nvidia/rapids-4-spark-ml_2.12/22.06.0-SNAPSHOT/rapids-4-spark-ml_2.12-22.06.0-SNAPSHOT.jar +ML_JAR=/root/.m2/repository/com/nvidia/rapids-4-spark-ml_2.12/22.08.0-SNAPSHOT/rapids-4-spark-ml_2.12-22.08.0-SNAPSHOT.jar PLUGIN_JAR=/root/.m2/repository/com/nvidia/rapids-4-spark_2.12/22.08.0-SNAPSHOT/rapids-4-spark_2.12-22.08.0-SNAPSHOT.jar $SPARK_HOME/bin/spark-submit \ From 5346c03c0d8d38e810047d2f8c0778cc6da8786c Mon Sep 17 00:00:00 2001 From: eordentlich <36281329+eordentlich@users.noreply.github.com> Date: Wed, 29 Jun 2022 08:46:25 -0700 Subject: [PATCH 06/17] update databricks custom docker image for criteo dl+nvtabular example (#193) * update databricks dockerfile * clean up and disable cufile/gds in image * signature commit Signed-off-by: Erik Ordentlich --- .../Spark-DL/criteo_train/Dockerfile.conda_db | 24 +++++++++---------- 1 file changed, 11 insertions(+), 13 deletions(-) diff --git a/examples/ML+DL-Examples/Spark-DL/criteo_train/Dockerfile.conda_db b/examples/ML+DL-Examples/Spark-DL/criteo_train/Dockerfile.conda_db index 135a3328b..475b99149 100644 --- a/examples/ML+DL-Examples/Spark-DL/criteo_train/Dockerfile.conda_db +++ b/examples/ML+DL-Examples/Spark-DL/criteo_train/Dockerfile.conda_db @@ -13,15 +13,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # - -FROM nvidia/cuda:11.2.1-cudnn8-devel-ubuntu20.04 - +FROM nvidia/cuda:11.4.3-cudnn8-devel-ubuntu20.04 ENV DEBIAN_FRONTEND=noninteractive # Disable NVIDIA repos to prevent accidental upgrades. RUN cd /etc/apt/sources.list.d && \ - mv cuda.list cuda.list.disabled && \ - mv nvidia-ml.list nvidia-ml.list.disabled + mv cuda.list cuda.list.disabled # See https://github.com/databricks/containers/blob/master/ubuntu/minimal/Dockerfile RUN apt-get update && \ @@ -52,21 +49,20 @@ RUN wget -q https://repo.continuum.io/miniconda/Miniconda3-py38_4.9.2-Linux-x86_ conda clean --all # install openjdk8, cmake, openmpi openmpi-mpicc -RUN conda install cmake openmpi openmpi-mpicc -y -RUN pip install jupyter +RUN conda install cmake openmpi openmpi-mpicc -y ENV JAVA_HOME /usr/lib/jvm/java-1.8.0-openjdk-amd64 ENV PATH $PATH:/usr/lib/jvm/java-1.8.0-openjdk-amd64/jre/bin:/usr/lib/jvm/java-1.8.0-openjdk-amd64/bin -RUN pip uninstall tensorflow -y; pip install tensorflow +RUN conda install -y -c nvidia -c rapidsai -c numba -c conda-forge nvtabular=1.2.2 python=3.8 cudatoolkit=11.4 scikit-learn -RUN HOROVOD_WITH_MPI=1 HOROVOD_GPU_OPERATIONS=NCCL HOROVOD_WITH_TENSORFLOW=1 \ +RUN pip uninstall tensorflow -y; pip install tensorflow-gpu==2.8 +RUN pip install torch==1.11.0+cu115 torchvision==0.12.0+cu115 torchaudio===0.11.0+cu115 -f https://download.pytorch.org/whl/cu115/torch_stable.html +RUN rm -rf /databricks/conda/include/google +RUN HOROVOD_WITH_MPI=1 HOROVOD_GPU_OPERATIONS=NCCL HOROVOD_WITH_TENSORFLOW=1 HOROVOD_WITH_PYTORCH=1 \ pip install horovod[spark] --no-cache-dir +RUN pip install pynvml jupyter matplotlib -RUN conda install -c nvidia -c rapidsai -c numba -c conda-forge nvtabular=0.9.0 python=3.8 cudatoolkit=11.2 -RUN pip install pynvml -RUN conda install -c conda-forge ipython==7.19.0 matplotlib==3.4.2 jinja2==2.11.3 -RUN pip uninstall pandas -y; pip install pandas==1.1.5 RUN apt-get update && apt-get install wget openssh-client openssh-server \ -y --allow-downgrades --allow-change-held-packages --no-install-recommends RUN useradd --create-home --shell /bin/bash --groups sudo ubuntu @@ -75,6 +71,8 @@ ENV PYSPARK_PYTHON=/databricks/conda/bin/python ENV USER root ENV DEFAULT_DATABRICKS_ROOT_CONDA_ENV=base ENV DATABRICKS_ROOT_CONDA_ENV=base +# disable gds due to errors +ENV LIBCUDF_CUFILE_POLICY=OFF # required by DB RUN pip install virtualenv RUN pip install adlfs From 8c4809cbe36f3c16823c86e28517ba47231c0f60 Mon Sep 17 00:00:00 2001 From: liyuan <84758614+nvliyuan@users.noreply.github.com> Date: Tue, 19 Jul 2022 10:07:53 +0800 Subject: [PATCH 07/17] refine cupsatial demo to make it more clear for customers (#187) * update cuda pub key to avoid GPG error Signed-off-by: liyuan * address review comments Signed-off-by: liyuan * address review comments Signed-off-by: liyuan * fit img size Signed-off-by: liyuan * fit img size Signed-off-by: liyuan * fit some types Signed-off-by: liyuan * try resize img Signed-off-by: liyuan * try resize img Signed-off-by: liyuan * try resize img Signed-off-by: liyuan * try resize img Signed-off-by: liyuan * try resize img Signed-off-by: liyuan * fix dockerbuild error Signed-off-by: liyuan * update cuspatial version in build-in-local step Signed-off-by: liyuan * add cpu notebook Signed-off-by: liyuan * Update examples/UDF-Examples/Spark-cuSpatial/README.md Co-authored-by: Liangcai Li * Update the path to make it consistent Signed-off-by: liyuan * Update examples/UDF-Examples/Spark-cuSpatial/README.md Co-authored-by: Hao Zhu <9665750+viadea@users.noreply.github.com> * Update examples/UDF-Examples/Spark-cuSpatial/README.md Co-authored-by: Hao Zhu <9665750+viadea@users.noreply.github.com> * Update examples/UDF-Examples/Spark-cuSpatial/README.md Co-authored-by: Hao Zhu <9665750+viadea@users.noreply.github.com> * Update examples/UDF-Examples/Spark-cuSpatial/README.md Co-authored-by: Hao Zhu <9665750+viadea@users.noreply.github.com> * Update examples/UDF-Examples/Spark-cuSpatial/README.md Co-authored-by: Hao Zhu <9665750+viadea@users.noreply.github.com> * Update examples/UDF-Examples/Spark-cuSpatial/README.md Co-authored-by: Hao Zhu <9665750+viadea@users.noreply.github.com> * Update examples/UDF-Examples/Spark-cuSpatial/README.md Co-authored-by: Hao Zhu <9665750+viadea@users.noreply.github.com> * Update examples/UDF-Examples/Spark-cuSpatial/README.md Co-authored-by: Hao Zhu <9665750+viadea@users.noreply.github.com> * Update examples/UDF-Examples/Spark-cuSpatial/README.md Co-authored-by: Hao Zhu <9665750+viadea@users.noreply.github.com> * Update examples/UDF-Examples/Spark-cuSpatial/README.md Co-authored-by: Hao Zhu <9665750+viadea@users.noreply.github.com> * Update examples/UDF-Examples/Spark-cuSpatial/README.md Co-authored-by: Hao Zhu <9665750+viadea@users.noreply.github.com> * Update examples/UDF-Examples/Spark-cuSpatial/README.md Co-authored-by: Hao Zhu <9665750+viadea@users.noreply.github.com> * Update examples/UDF-Examples/Spark-cuSpatial/README.md Co-authored-by: Hao Zhu <9665750+viadea@users.noreply.github.com> * remove Run CPU Demo step4 Signed-off-by: liyuan * break the long sql to multi lines Signed-off-by: liyuan * Update examples/UDF-Examples/Spark-cuSpatial/README.md Co-authored-by: Hao Zhu <9665750+viadea@users.noreply.github.com> * Update examples/UDF-Examples/Spark-cuSpatial/README.md Co-authored-by: Hao Zhu <9665750+viadea@users.noreply.github.com> * Update examples/UDF-Examples/Spark-cuSpatial/README.md Co-authored-by: Hao Zhu <9665750+viadea@users.noreply.github.com> * Update examples/UDF-Examples/Spark-cuSpatial/README.md Co-authored-by: Hao Zhu <9665750+viadea@users.noreply.github.com> * Update examples/UDF-Examples/Spark-cuSpatial/README.md Co-authored-by: Hao Zhu <9665750+viadea@users.noreply.github.com> * Update examples/UDF-Examples/Spark-cuSpatial/README.md Co-authored-by: Hao Zhu <9665750+viadea@users.noreply.github.com> * Update examples/UDF-Examples/Spark-cuSpatial/README.md Co-authored-by: Hao Zhu <9665750+viadea@users.noreply.github.com> * Update examples/UDF-Examples/Spark-cuSpatial/README.md Co-authored-by: Hao Zhu <9665750+viadea@users.noreply.github.com> * Update examples/UDF-Examples/Spark-cuSpatial/README.md Co-authored-by: Hao Zhu <9665750+viadea@users.noreply.github.com> * Update examples/UDF-Examples/Spark-cuSpatial/README.md Co-authored-by: Hao Zhu <9665750+viadea@users.noreply.github.com> * Update examples/UDF-Examples/Spark-cuSpatial/README.md Co-authored-by: Hao Zhu <9665750+viadea@users.noreply.github.com> * Update examples/UDF-Examples/Spark-cuSpatial/README.md Co-authored-by: Hao Zhu <9665750+viadea@users.noreply.github.com> * Update examples/UDF-Examples/Spark-cuSpatial/README.md Co-authored-by: Hao Zhu <9665750+viadea@users.noreply.github.com> * Update examples/UDF-Examples/Spark-cuSpatial/README.md Co-authored-by: Hao Zhu <9665750+viadea@users.noreply.github.com> * Update examples/UDF-Examples/Spark-cuSpatial/README.md Co-authored-by: Hao Zhu <9665750+viadea@users.noreply.github.com> * Update examples/UDF-Examples/Spark-cuSpatial/README.md Co-authored-by: Hao Zhu <9665750+viadea@users.noreply.github.com> * Update examples/UDF-Examples/Spark-cuSpatial/README.md Co-authored-by: Hao Zhu <9665750+viadea@users.noreply.github.com> * Update examples/UDF-Examples/Spark-cuSpatial/README.md Co-authored-by: Hao Zhu <9665750+viadea@users.noreply.github.com> * Update examples/UDF-Examples/Spark-cuSpatial/README.md Co-authored-by: Hao Zhu <9665750+viadea@users.noreply.github.com> * Update examples/UDF-Examples/Spark-cuSpatial/README.md Co-authored-by: Hao Zhu <9665750+viadea@users.noreply.github.com> * Update examples/UDF-Examples/Spark-cuSpatial/README.md Co-authored-by: Hao Zhu <9665750+viadea@users.noreply.github.com> * Update examples/UDF-Examples/Spark-cuSpatial/README.md Co-authored-by: Hao Zhu <9665750+viadea@users.noreply.github.com> * Update examples/UDF-Examples/Spark-cuSpatial/README.md Co-authored-by: Hao Zhu <9665750+viadea@users.noreply.github.com> * Update examples/UDF-Examples/Spark-cuSpatial/README.md Co-authored-by: Hao Zhu <9665750+viadea@users.noreply.github.com> * Update examples/UDF-Examples/Spark-cuSpatial/README.md Co-authored-by: Hao Zhu <9665750+viadea@users.noreply.github.com> * Update examples/UDF-Examples/Spark-cuSpatial/README.md Co-authored-by: Hao Zhu <9665750+viadea@users.noreply.github.com> * Update examples/UDF-Examples/Spark-cuSpatial/README.md Co-authored-by: Hao Zhu <9665750+viadea@users.noreply.github.com> * try to workarround broken link error Signed-off-by: liyuan * try to workarround broken link error Signed-off-by: liyuan * try to workarround broken link error Signed-off-by: liyuan * try to workarround broken link error Signed-off-by: liyuan * try to workarround broken link error Signed-off-by: liyuan * try to workarround broken link error Signed-off-by: liyuan * revert markdown links checker conf Signed-off-by: liyuan Co-authored-by: Liangcai Li Co-authored-by: Hao Zhu <9665750+viadea@users.noreply.github.com> --- .../cuspatial/Nycd-Community-Districts.png | Bin 0 -> 151848 bytes docs/img/guides/cuspatial/Nyct2000.png | Bin 0 -> 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zQ0Dls%c`739-c->iMRWe7;PjFYu$e;Y!I9gSa>~2L!R$QCC;$ + +nyct2000 map: + + + +nyct-community-districts map: + + ## Build -You can build the jar file [in Docker](#build-in-docker) with the provided [Dockerfile](Dockerfile) -or you can build it [in local](#build-in-local) machine after some prerequisites. +Firstly build the UDF JAR from source code before running this demo. +You can build the JAR [in Docker](#build-in-docker) with the provided [Dockerfile](Dockerfile), +or [in local machine](#build-in-local-machine) after prerequisites. ### Build in Docker 1. Build the docker image [Dockerfile](Dockerfile), then run the container. @@ -25,16 +45,18 @@ or you can build it [in local](#build-in-local) machine after some prerequisites docker build -f Dockerfile . -t build-spark-cuspatial docker run -it build-spark-cuspatial bash ``` -2. Get the code, then run "mvn package". +2. Get the code, then run `mvn package`. ```Bash git clone https://github.com/NVIDIA/spark-rapids-examples.git cd spark-rapids-examples/examples/UDF-Examples/Spark-cuSpatial/ mvn package ``` -3. You'll get the jar named like "spark-cuspatial-.jar" in the target folder. +3. You'll get the jar named `spark-cuspatial-.jar` in the target folder. + +Note: The docker env is just for building the jar, not for running the application. -### Build in Local: -1. essential build tools: +### Build in local machine: +1. Essential build tools: - [cmake(>=3.20)](https://cmake.org/download/), - [ninja(>=1.10)](https://github.com/ninja-build/ninja/releases), - [gcc(>=9.3)](https://gcc.gnu.org/releases.html) @@ -42,40 +64,42 @@ or you can build it [in local](#build-in-local) machine after some prerequisites 3. conda: use [miniconda](https://docs.conda.io/en/latest/miniconda.html) to maintain header files and cmake dependecies 4. [cuspatial](https://github.com/rapidsai/cuspatial): install libcuspatial ```Bash - # get libcuspatial from conda - conda install -c rapidsai -c nvidia -c conda-forge -c defaults libcuspatial=22.04 + # Install libcuspatial from conda + conda install -c rapidsai -c nvidia -c conda-forge -c defaults libcuspatial=22.06 # or below command for the nightly (aka SNAPSHOT) version. - conda install -c rapidsai-nightly -c nvidia -c conda-forge -c defaults libcuspatial=22.06 + conda install -c rapidsai-nightly -c nvidia -c conda-forge -c defaults libcuspatial=22.08 ``` -5. Get the code, then run "mvn package". +5. Build the JAR using `mvn package`. ```Bash git clone https://github.com/NVIDIA/spark-rapids-examples.git cd spark-rapids-examples/examples/Spark-cuSpatial/ mvn package ``` -6. You'll get "spark-cuspatial-.jar" in the target folder. - +6. `spark-cuspatial-.jar` will be generated in the target folder. ## Run -### Run on-premises clusters: standalone +### GPU Demo on Spark Standalone on-premises cluster 1. Install necessary libraries. Besides `cudf` and `cuspatial`, the `gdal` library that is compatible with the installed `cuspatial` may also be needed. - Install it by running the command below. ``` conda install -c conda-forge libgdal=3.3.1 ``` 2. 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. -3. Download spark-rapids jars - * [spark-rapids v22.06.0](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/22.06.0/rapids-4-spark_2.12-22.06.0.jar) or above -4. Prepare the dataset & jars. Copy the sample dataset from [cuspatial_data](../../../datasets/cuspatial_data.tar.gz) to "/data/cuspatial_data". - Copy spark-rapids & spark-cuspatial-22.08.0-SNAPSHOT.jar to "/data/cuspatial_data/jars". - You can use your own path, but remember to update the paths in "gpu-run.sh" accordingly. -5. Run "gpu-run.sh" +3. Download Spark RAPIDS JAR + * [Spark RAPIDS JAR v22.06.0](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/22.06.0/rapids-4-spark_2.12-22.06.0.jar) or above +4. 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-.jar` to `/data/cuspatial_data/jars/`. + If you build the `spark-cuspatial-.jar` in docker, please copy the jar from docker to local: + ``` + docker cp YOUR_DOCKER_CONTAINER:/PATH/TO/spark-cuspatial-.jar ./YOUR_LOCAL_PATH + ``` + Note: update the paths in `gpu-run.sh` accordingly. +5. Run `gpu-run.sh` ```Bash ./gpu-run.sh ``` -### Run on AWS Databricks -1. Build the customized docker image [Dockerfile.awsdb](Dockerfile.awsdb) and push to dockerhub so that it can be accessible by AWS Databricks. +### GPU Demo on AWS Databricks +1. Build a customized docker image using [Dockerfile.awsdb](Dockerfile.awsdb) and push to a Docker registry such as [Docker Hub](https://hub.docker.com/) which can be accessible by AWS Databricks. ```Bash # replace your dockerhub repo, your tag or any other repo AWS DB can access docker build -f Dockerfile.awsdb . -t : @@ -83,15 +107,15 @@ or you can build it [in local](#build-in-local) machine after some prerequisites ``` 2. Follow the [Spark-rapids get-started document](https://nvidia.github.io/spark-rapids/docs/get-started/getting-started-databricks.html#start-a-databricks-cluster) to create a GPU cluster on AWS Databricks. - Something different from the document. + Below are some different steps since a custom docker image is used with Databricks: * Databricks Runtime Version - You should choose a Standard version of the Runtime version like "Runtime: 9.1 LTS(Scala 2.12, Spark 3.1.2)" and - choose GPU instance type like "g4dn.xlarge". Note that ML runtime does not support customized docker container. - If you choose a ML version, it says "Support for Databricks container services requires runtime version 5.3+" - and the "Confirm" button is disabled. + Choose a non-ML Databricks Runtime such as `Runtime: 9.1 LTS(Scala 2.12, Spark 3.1.2)` and + choose GPU AWS instance type such as `g4dn.xlarge`. Note that ML runtime does not support customized docker container with below messages: +`Support for Databricks container services requires runtime version 5.3+` + and the `Confirm` button is disabled. * Use your own Docker container - Input "Docker Image URL" as "your-dockerhub-repo:your-tag" - * For the other configurations, you can follow the get-started document. + Input `Docker Image URL` as `your-dockerhub-repo:your-tag` + * Follow the Databricks get-started document for other steps. 3. Copy the sample [cuspatial_data.tar.gz](../../../datasets/cuspatial_data.tar.gz) or your data to DBFS by using Databricks CLI. ```Bash @@ -103,5 +127,38 @@ or you can build it [in local](#build-in-local) machine after some prerequisites points polygons ``` -4. Import the Library "spark-cuspatial-22.08.0-SNAPSHOT.jar" to the Databricks, then install it to your cluster. -5. Import [cuspatial_sample.ipynb](notebooks/cuspatial_sample_db.ipynb) to your workspace in Databricks. Attach to your cluster, then run it. + The sample points and polygons are randomly generated. + + Sample polygons: + + + +4. Upload `spark-cuspatial-.jar` on dbfs and then install it in Databricks cluster. + + + +5. Import [cuspatial_sample.ipynb](notebooks/cuspatial_sample_db.ipynb) to Databricks workspace, attach it to Databricks cluster and run it. + +### CPU Demo on AWS Databricks +1. Create a Databricks cluster. For example, Databricks Runtime 10.3. + +2. Install the Sedona jars and Sedona Python libs on Databricks using web UI. + The Sedona version should be 1.1.1-incubating or higher. + * Install below jars from Maven Coordinates in Libraries tab: + ```Bash + org.apache.sedona:sedona-python-adapter-3.0_2.12:1.2.0-incubating + org.datasyslab:geotools-wrapper:1.1.0-25.2 + ``` + * To enable python support, install below python lib from PyPI in Libraries tab + ```Bash + apache-sedona + ``` +3. From your cluster configuration (Cluster -> Edit -> Configuration -> Advanced options -> Spark) activate the + Sedona functions and the kryo serializer by adding below to the Spark Config + ```Bash + spark.sql.extensions org.apache.sedona.viz.sql.SedonaVizExtensions,org.apache.sedona.sql.SedonaSqlExtensions + spark.serializer org.apache.spark.serializer.KryoSerializer + spark.kryo.registrator org.apache.sedona.core.serde.SedonaKryoRegistrator + ``` + +4. Upload the sample data files to DBFS, start the cluster, attach the [notebook](notebooks/spacial-cpu-apache-sedona_db.ipynb) to the cluster, and run it. diff --git a/examples/UDF-Examples/Spark-cuSpatial/notebooks/cuspatial_sample_db.ipynb b/examples/UDF-Examples/Spark-cuSpatial/notebooks/cuspatial_sample_db.ipynb index ba535f63d..4797ada9c 100644 --- a/examples/UDF-Examples/Spark-cuSpatial/notebooks/cuspatial_sample_db.ipynb +++ b/examples/UDF-Examples/Spark-cuSpatial/notebooks/cuspatial_sample_db.ipynb @@ -1 +1,328 @@ -{"cells":[{"cell_type":"code","source":["#define the input & output path\ninputPath='dbfs:/data/cuspatial_data/points'\noutputPath='dbfs:/data/output'\n\n# add the shapefile(.shp & .shx)\nspark.sparkContext.addFile(\"dbfs:/data/cuspatial_data/polygons/polygons.shp\")\nspark.sparkContext.addFile(\"dbfs:/data/cuspatial_data/polygons/polygons.shx\")\nspark.conf.set(\"spark.cuspatial.sql.udf.shapeFileName\", \"polygons.shp\")"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"4f12a9eb-6580-4575-a6a8-4c08820fe8e0"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"

","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["\n
"]}}],"execution_count":0},{"cell_type":"code","source":["# register the UDF\nspark.udf.registerJavaFunction(\"point_in_polygon\", \"com.nvidia.spark.rapids.udf.PointInPolygon\", None)\n\ndf = spark.read.parquet(inputPath)\ndf = df.selectExpr('x', 'y', 'point_in_polygon(x, y) as ret')\ndf.write.mode(\"overwrite\").parquet(outputPath)"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"741761ee-d5cf-4c43-ae73-30703d246901"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"
","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["\n
"]}}],"execution_count":0},{"cell_type":"code","source":["print(\"Input rows: \", df.count())"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"592bfcde-ca6f-4b94-aac1-e3b298f25fcb"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"
Input rows: 71\n
","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["\n
Input rows: 71\n
"]}}],"execution_count":0},{"cell_type":"code","source":["# show the result\nresult = spark.read.parquet(outputPath)\nprint(\"Output rows: \", result.count())\nresult.show(71)"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"f1b744a4-13a3-4260-b8f0-efcbbf1c0337"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"
Output rows: 71\n+-------------------+--------------------+---+\n| x| y|ret|\n+-------------------+--------------------+---+\n|0.48171647380517046| 1.9022922214961997| []|\n| 1.2591725716781235| 0.1448705855995005| []|\n| 0.1895259128530169| 0.5431061133894604| []|\n| 3.028362149164369|0.027638405909631958| []|\n| 1.3890664414691907| 1.5177694304735412| []|\n| 3.1907684812039956| 0.2621847215928189| []|\n| 3.7080407833612004| 0.09804238103130436| []|\n| 3.0706987088385853| 0.9376313558467103| []|\n| 2.0697434332621234| 1.1809465376402173| []|\n| 2.175448214220591| 1.2372448404986038| []|\n| 2.113652420701984| 1.2774712415624014| []|\n| 2.9909779614491687| 1.2420487904041893| []|\n| 4.07037627210835| 1.9486902798139454| []|\n| 4.822583857757069| 0.3234041700489503| []|\n| 4.849847745942472| 1.9531893897409585| []|\n| 4.732546857961497| 0.5659923375279095| []|\n| 4.529792124514895| 1.942673409259531| []|\n| 3.2648444465931474| 2.693039435509084| []|\n| 2.1807636574967466| 3.2296461832828114|[3]|\n| 3.7164018490892348| 2.4612194182614333| []|\n| 2.2006520196663066| 3.7672478678985257|[3]|\n| 6.291790729917634| 2.983311357415729|[1]|\n| 2.5104987015171574| 3.0668114607133137|[3]|\n| 2.3007438625108882| 3.6045900851589048|[3]|\n| 6.101327777646798| 2.5239201807166616|[1]|\n| 6.109985464455084| 2.2235950639628523|[1]|\n| 6.4274219368674315| 2.9754616970668213|[1]|\n| 7.886010001346151| 3.538128217886674| []|\n| 7.5085184104988| 3.623862886287816| []|\n| 7.430677191305505| 3.380489849365283| []|\n| 1.7015273093278767| 7.478882372510933| []|\n| 7.769497359206111| 3.253257011908445| []|\n| 3.86008672302403| 7.513564222799629| []|\n| 0.059011873032214| 5.823535317960799| []|\n| 3.154282922203257| 5.788316610960881| []|\n| 2.4264509160270813| 5.188939408363776|[0]|\n| 1.9804558865545805| 1.3472225743317712| []|\n| 0.8178039499335275| 0.8138440641113271| []|\n| 0.2536015260915061| 1.8762161698642947| []|\n| 3.710910700915217| 0.9937713340192049| []|\n| 3.918090468102582| 0.3338651960183463| []|\n| 3.572744183805594| 0.33184908855075124| []|\n| 3.70669993057843| 0.7485845679979923| []|\n| 3.3588457228653024| 0.2346381514128677| []|\n| 2.520755151373394| 1.902015274420646| []|\n| 2.5322042870739683| 1.419555755682142| []|\n| 2.4613232527836137| 1.0484414482621331| []|\n| 4.975578758530645| 0.9606291981013242| []|\n| 4.5584381091040616| 1.8996548860019926| []|\n| 4.300706849071861|0.021365525588281198| []|\n| 3.01954722322135| 2.57810040095543| []|\n| 3.7622247877537456| 2.8709552313924487| []|\n| 4.75489831780737| 0.7800065259479418| []|\n| 2.566986568683904| 3.6607732238530897|[3]|\n| 3.7002781846945347| 2.3345952955903906| []|\n| 2.493975723955388| 3.3999020934055837|[3]|\n| 2.8222482218882474| 3.8159308233351266|[3]|\n| 6.0821276168848994| 2.5470532680258002|[1]|\n| 2.241538022180476| 3.8812819070357545|[3]|\n| 6.325158445513714| 2.8765450351723674|[1]|\n| 6.444584786789386| 2.174562817047202|[1]|\n| 6.6793884701899| 2.5605928243991434|[1]|\n| 7.250745898479374| 3.4154469467473447| []|\n| 7.079453687660189| 3.063690547962938|[1]|\n| 7.897735998643542| 3.380784914178574| []|\n| 2.2065031771469| 6.896038613284851| []|\n| 1.8703303641352362| 4.209727933188015|[3]|\n| 2.7456295127617385| 7.474216636277054| []|\n| 1.9143371250907073| 6.885401350515916| []|\n| 3.7176098065039747| 6.194330707468438| []|\n| 3.1162712022943757| 6.789029097334483| []|\n+-------------------+--------------------+---+\n\n
","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["\n
Output rows: 71\n+-------------------+--------------------+---+\n x| y|ret|\n+-------------------+--------------------+---+\n0.48171647380517046| 1.9022922214961997| []|\n 1.2591725716781235| 0.1448705855995005| []|\n 0.1895259128530169| 0.5431061133894604| []|\n 3.028362149164369|0.027638405909631958| []|\n 1.3890664414691907| 1.5177694304735412| []|\n 3.1907684812039956| 0.2621847215928189| []|\n 3.7080407833612004| 0.09804238103130436| []|\n 3.0706987088385853| 0.9376313558467103| []|\n 2.0697434332621234| 1.1809465376402173| []|\n 2.175448214220591| 1.2372448404986038| []|\n 2.113652420701984| 1.2774712415624014| []|\n 2.9909779614491687| 1.2420487904041893| []|\n 4.07037627210835| 1.9486902798139454| []|\n 4.822583857757069| 0.3234041700489503| []|\n 4.849847745942472| 1.9531893897409585| []|\n 4.732546857961497| 0.5659923375279095| []|\n 4.529792124514895| 1.942673409259531| []|\n 3.2648444465931474| 2.693039435509084| []|\n 2.1807636574967466| 3.2296461832828114|[3]|\n 3.7164018490892348| 2.4612194182614333| []|\n 2.2006520196663066| 3.7672478678985257|[3]|\n 6.291790729917634| 2.983311357415729|[1]|\n 2.5104987015171574| 3.0668114607133137|[3]|\n 2.3007438625108882| 3.6045900851589048|[3]|\n 6.101327777646798| 2.5239201807166616|[1]|\n 6.109985464455084| 2.2235950639628523|[1]|\n 6.4274219368674315| 2.9754616970668213|[1]|\n 7.886010001346151| 3.538128217886674| []|\n 7.5085184104988| 3.623862886287816| []|\n 7.430677191305505| 3.380489849365283| []|\n 1.7015273093278767| 7.478882372510933| []|\n 7.769497359206111| 3.253257011908445| []|\n 3.86008672302403| 7.513564222799629| []|\n 0.059011873032214| 5.823535317960799| []|\n 3.154282922203257| 5.788316610960881| []|\n 2.4264509160270813| 5.188939408363776|[0]|\n 1.9804558865545805| 1.3472225743317712| []|\n 0.8178039499335275| 0.8138440641113271| []|\n 0.2536015260915061| 1.8762161698642947| []|\n 3.710910700915217| 0.9937713340192049| []|\n 3.918090468102582| 0.3338651960183463| []|\n 3.572744183805594| 0.33184908855075124| []|\n 3.70669993057843| 0.7485845679979923| []|\n 3.3588457228653024| 0.2346381514128677| []|\n 2.520755151373394| 1.902015274420646| []|\n 2.5322042870739683| 1.419555755682142| []|\n 2.4613232527836137| 1.0484414482621331| []|\n 4.975578758530645| 0.9606291981013242| []|\n 4.5584381091040616| 1.8996548860019926| []|\n 4.300706849071861|0.021365525588281198| []|\n 3.01954722322135| 2.57810040095543| []|\n 3.7622247877537456| 2.8709552313924487| []|\n 4.75489831780737| 0.7800065259479418| []|\n 2.566986568683904| 3.6607732238530897|[3]|\n 3.7002781846945347| 2.3345952955903906| []|\n 2.493975723955388| 3.3999020934055837|[3]|\n 2.8222482218882474| 3.8159308233351266|[3]|\n 6.0821276168848994| 2.5470532680258002|[1]|\n 2.241538022180476| 3.8812819070357545|[3]|\n 6.325158445513714| 2.8765450351723674|[1]|\n 6.444584786789386| 2.174562817047202|[1]|\n 6.6793884701899| 2.5605928243991434|[1]|\n 7.250745898479374| 3.4154469467473447| []|\n 7.079453687660189| 3.063690547962938|[1]|\n 7.897735998643542| 3.380784914178574| []|\n 2.2065031771469| 6.896038613284851| []|\n 1.8703303641352362| 4.209727933188015|[3]|\n 2.7456295127617385| 7.474216636277054| []|\n 1.9143371250907073| 6.885401350515916| []|\n 3.7176098065039747| 6.194330707468438| []|\n 3.1162712022943757| 6.789029097334483| []|\n+-------------------+--------------------+---+\n\n
"]}}],"execution_count":0}],"metadata":{"application/vnd.databricks.v1+notebook":{"notebookName":"cuspatial_sample","dashboards":[],"notebookMetadata":{"pythonIndentUnit":2},"language":"python","widgets":{},"notebookOrigID":3352849421916703}},"nbformat":4,"nbformat_minor":0} +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "inputWidgets": {}, + "nuid": "4f12a9eb-6580-4575-a6a8-4c08820fe8e0", + "showTitle": false, + "title": "" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
" + ] + }, + "metadata": { + "application/vnd.databricks.v1+output": { + "addedWidgets": {}, + "arguments": {}, + "data": "
", + "datasetInfos": [], + "metadata": {}, + "removedWidgets": [], + "type": "html" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "#define the input & output path\n", + "inputPath='dbfs:/data/cuspatial_data/points'\n", + "outputPath='dbfs:/data/output'\n", + "\n", + "# add the shapefile(.shp & .shx)\n", + "spark.sparkContext.addFile(\"dbfs:/data/cuspatial_data/polygons/polygons.shp\")\n", + "spark.sparkContext.addFile(\"dbfs:/data/cuspatial_data/polygons/polygons.shx\")\n", + "spark.conf.set(\"spark.cuspatial.sql.udf.shapeFileName\", \"polygons.shp\")" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "inputWidgets": {}, + "nuid": "741761ee-d5cf-4c43-ae73-30703d246901", + "showTitle": false, + "title": "" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
" + ] + }, + "metadata": { + "application/vnd.databricks.v1+output": { + "addedWidgets": {}, + "arguments": {}, + "data": "
", + "datasetInfos": [], + "metadata": {}, + "removedWidgets": [], + "type": "html" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# register the UDF\n", + "spark.udf.registerJavaFunction(\"point_in_polygon\", \"com.nvidia.spark.rapids.udf.PointInPolygon\", None)\n", + "\n", + "df = spark.read.parquet(inputPath)\n", + "df = df.selectExpr('x', 'y', 'point_in_polygon(x, y) as ret')\n", + "df.write.mode(\"overwrite\").parquet(outputPath)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "inputWidgets": {}, + "nuid": "592bfcde-ca6f-4b94-aac1-e3b298f25fcb", + "showTitle": false, + "title": "" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
Input rows: 71\n", + "
" + ] + }, + "metadata": { + "application/vnd.databricks.v1+output": { + "addedWidgets": {}, + "arguments": {}, + "data": "
Input rows: 71\n
", + "datasetInfos": [], + "metadata": {}, + "removedWidgets": [], + "type": "html" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "print(\"Input rows: \", df.count())" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "inputWidgets": {}, + "nuid": "f1b744a4-13a3-4260-b8f0-efcbbf1c0337", + "showTitle": false, + "title": "" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
Output rows: 71\n", + "+-------------------+--------------------+---+\n", + " x| y|ret|\n", + "+-------------------+--------------------+---+\n", + "0.48171647380517046| 1.9022922214961997| []|\n", + " 1.2591725716781235| 0.1448705855995005| []|\n", + " 0.1895259128530169| 0.5431061133894604| []|\n", + " 3.028362149164369|0.027638405909631958| []|\n", + " 1.3890664414691907| 1.5177694304735412| []|\n", + " 3.1907684812039956| 0.2621847215928189| []|\n", + " 3.7080407833612004| 0.09804238103130436| []|\n", + " 3.0706987088385853| 0.9376313558467103| []|\n", + " 2.0697434332621234| 1.1809465376402173| []|\n", + " 2.175448214220591| 1.2372448404986038| []|\n", + " 2.113652420701984| 1.2774712415624014| []|\n", + " 2.9909779614491687| 1.2420487904041893| []|\n", + " 4.07037627210835| 1.9486902798139454| []|\n", + " 4.822583857757069| 0.3234041700489503| []|\n", + " 4.849847745942472| 1.9531893897409585| []|\n", + " 4.732546857961497| 0.5659923375279095| []|\n", + " 4.529792124514895| 1.942673409259531| []|\n", + " 3.2648444465931474| 2.693039435509084| []|\n", + " 2.1807636574967466| 3.2296461832828114|[3]|\n", + " 3.7164018490892348| 2.4612194182614333| []|\n", + " 2.2006520196663066| 3.7672478678985257|[3]|\n", + " 6.291790729917634| 2.983311357415729|[1]|\n", + " 2.5104987015171574| 3.0668114607133137|[3]|\n", + " 2.3007438625108882| 3.6045900851589048|[3]|\n", + " 6.101327777646798| 2.5239201807166616|[1]|\n", + " 6.109985464455084| 2.2235950639628523|[1]|\n", + " 6.4274219368674315| 2.9754616970668213|[1]|\n", + " 7.886010001346151| 3.538128217886674| []|\n", + " 7.5085184104988| 3.623862886287816| []|\n", + " 7.430677191305505| 3.380489849365283| []|\n", + " 1.7015273093278767| 7.478882372510933| []|\n", + " 7.769497359206111| 3.253257011908445| []|\n", + " 3.86008672302403| 7.513564222799629| []|\n", + " 0.059011873032214| 5.823535317960799| []|\n", + " 3.154282922203257| 5.788316610960881| []|\n", + " 2.4264509160270813| 5.188939408363776|[0]|\n", + " 1.9804558865545805| 1.3472225743317712| []|\n", + " 0.8178039499335275| 0.8138440641113271| []|\n", + " 0.2536015260915061| 1.8762161698642947| []|\n", + " 3.710910700915217| 0.9937713340192049| []|\n", + " 3.918090468102582| 0.3338651960183463| []|\n", + " 3.572744183805594| 0.33184908855075124| []|\n", + " 3.70669993057843| 0.7485845679979923| []|\n", + " 3.3588457228653024| 0.2346381514128677| []|\n", + " 2.520755151373394| 1.902015274420646| []|\n", + " 2.5322042870739683| 1.419555755682142| []|\n", + " 2.4613232527836137| 1.0484414482621331| []|\n", + " 4.975578758530645| 0.9606291981013242| []|\n", + " 4.5584381091040616| 1.8996548860019926| []|\n", + " 4.300706849071861|0.021365525588281198| []|\n", + " 3.01954722322135| 2.57810040095543| []|\n", + " 3.7622247877537456| 2.8709552313924487| []|\n", + " 4.75489831780737| 0.7800065259479418| []|\n", + " 2.566986568683904| 3.6607732238530897|[3]|\n", + " 3.7002781846945347| 2.3345952955903906| []|\n", + " 2.493975723955388| 3.3999020934055837|[3]|\n", + " 2.8222482218882474| 3.8159308233351266|[3]|\n", + " 6.0821276168848994| 2.5470532680258002|[1]|\n", + " 2.241538022180476| 3.8812819070357545|[3]|\n", + " 6.325158445513714| 2.8765450351723674|[1]|\n", + " 6.444584786789386| 2.174562817047202|[1]|\n", + " 6.6793884701899| 2.5605928243991434|[1]|\n", + " 7.250745898479374| 3.4154469467473447| []|\n", + " 7.079453687660189| 3.063690547962938|[1]|\n", + " 7.897735998643542| 3.380784914178574| []|\n", + " 2.2065031771469| 6.896038613284851| []|\n", + " 1.8703303641352362| 4.209727933188015|[3]|\n", + " 2.7456295127617385| 7.474216636277054| []|\n", + " 1.9143371250907073| 6.885401350515916| []|\n", + " 3.7176098065039747| 6.194330707468438| []|\n", + " 3.1162712022943757| 6.789029097334483| []|\n", + "+-------------------+--------------------+---+\n", + "\n", + "
" + ] + }, + "metadata": { + "application/vnd.databricks.v1+output": { + "addedWidgets": {}, + "arguments": {}, + "data": "
Output rows: 71\n+-------------------+--------------------+---+\n| x| y|ret|\n+-------------------+--------------------+---+\n|0.48171647380517046| 1.9022922214961997| []|\n| 1.2591725716781235| 0.1448705855995005| []|\n| 0.1895259128530169| 0.5431061133894604| []|\n| 3.028362149164369|0.027638405909631958| []|\n| 1.3890664414691907| 1.5177694304735412| []|\n| 3.1907684812039956| 0.2621847215928189| []|\n| 3.7080407833612004| 0.09804238103130436| []|\n| 3.0706987088385853| 0.9376313558467103| []|\n| 2.0697434332621234| 1.1809465376402173| []|\n| 2.175448214220591| 1.2372448404986038| []|\n| 2.113652420701984| 1.2774712415624014| []|\n| 2.9909779614491687| 1.2420487904041893| []|\n| 4.07037627210835| 1.9486902798139454| []|\n| 4.822583857757069| 0.3234041700489503| []|\n| 4.849847745942472| 1.9531893897409585| []|\n| 4.732546857961497| 0.5659923375279095| []|\n| 4.529792124514895| 1.942673409259531| []|\n| 3.2648444465931474| 2.693039435509084| []|\n| 2.1807636574967466| 3.2296461832828114|[3]|\n| 3.7164018490892348| 2.4612194182614333| []|\n| 2.2006520196663066| 3.7672478678985257|[3]|\n| 6.291790729917634| 2.983311357415729|[1]|\n| 2.5104987015171574| 3.0668114607133137|[3]|\n| 2.3007438625108882| 3.6045900851589048|[3]|\n| 6.101327777646798| 2.5239201807166616|[1]|\n| 6.109985464455084| 2.2235950639628523|[1]|\n| 6.4274219368674315| 2.9754616970668213|[1]|\n| 7.886010001346151| 3.538128217886674| []|\n| 7.5085184104988| 3.623862886287816| []|\n| 7.430677191305505| 3.380489849365283| []|\n| 1.7015273093278767| 7.478882372510933| []|\n| 7.769497359206111| 3.253257011908445| []|\n| 3.86008672302403| 7.513564222799629| []|\n| 0.059011873032214| 5.823535317960799| []|\n| 3.154282922203257| 5.788316610960881| []|\n| 2.4264509160270813| 5.188939408363776|[0]|\n| 1.9804558865545805| 1.3472225743317712| []|\n| 0.8178039499335275| 0.8138440641113271| []|\n| 0.2536015260915061| 1.8762161698642947| []|\n| 3.710910700915217| 0.9937713340192049| []|\n| 3.918090468102582| 0.3338651960183463| []|\n| 3.572744183805594| 0.33184908855075124| []|\n| 3.70669993057843| 0.7485845679979923| []|\n| 3.3588457228653024| 0.2346381514128677| []|\n| 2.520755151373394| 1.902015274420646| []|\n| 2.5322042870739683| 1.419555755682142| []|\n| 2.4613232527836137| 1.0484414482621331| []|\n| 4.975578758530645| 0.9606291981013242| []|\n| 4.5584381091040616| 1.8996548860019926| []|\n| 4.300706849071861|0.021365525588281198| []|\n| 3.01954722322135| 2.57810040095543| []|\n| 3.7622247877537456| 2.8709552313924487| []|\n| 4.75489831780737| 0.7800065259479418| []|\n| 2.566986568683904| 3.6607732238530897|[3]|\n| 3.7002781846945347| 2.3345952955903906| []|\n| 2.493975723955388| 3.3999020934055837|[3]|\n| 2.8222482218882474| 3.8159308233351266|[3]|\n| 6.0821276168848994| 2.5470532680258002|[1]|\n| 2.241538022180476| 3.8812819070357545|[3]|\n| 6.325158445513714| 2.8765450351723674|[1]|\n| 6.444584786789386| 2.174562817047202|[1]|\n| 6.6793884701899| 2.5605928243991434|[1]|\n| 7.250745898479374| 3.4154469467473447| []|\n| 7.079453687660189| 3.063690547962938|[1]|\n| 7.897735998643542| 3.380784914178574| []|\n| 2.2065031771469| 6.896038613284851| []|\n| 1.8703303641352362| 4.209727933188015|[3]|\n| 2.7456295127617385| 7.474216636277054| []|\n| 1.9143371250907073| 6.885401350515916| []|\n| 3.7176098065039747| 6.194330707468438| []|\n| 3.1162712022943757| 6.789029097334483| []|\n+-------------------+--------------------+---+\n\n
", + "datasetInfos": [], + "metadata": {}, + "removedWidgets": [], + "type": "html" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# show the result\n", + "result = spark.read.parquet(outputPath)\n", + "print(\"Output rows: \", result.count())\n", + "result.show(71)" + ] + } + ], + "metadata": { + "application/vnd.databricks.v1+notebook": { + "dashboards": [], + "language": "python", + "notebookMetadata": { + "pythonIndentUnit": 2 + }, + "notebookName": "cuspatial_sample", + "notebookOrigID": 3352849421916703, + "widgets": {} + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.2" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/examples/UDF-Examples/Spark-cuSpatial/notebooks/spacial-cpu-apache-sedona_db.ipynb b/examples/UDF-Examples/Spark-cuSpatial/notebooks/spacial-cpu-apache-sedona_db.ipynb new file mode 100644 index 000000000..47cf5e872 --- /dev/null +++ b/examples/UDF-Examples/Spark-cuSpatial/notebooks/spacial-cpu-apache-sedona_db.ipynb @@ -0,0 +1,364 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "inputWidgets": {}, + "nuid": "61a74001-716b-4411-aecb-77d07058d200", + "showTitle": false, + "title": "" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
Requirement already satisfied: geopandas in /databricks/python3/lib/python3.8/site-packages (0.11.0)\n", + "Requirement already satisfied: pyproj>=2.6.1.post1 in /databricks/python3/lib/python3.8/site-packages (from geopandas) (3.3.1)\n", + "Requirement already satisfied: shapely<2,>=1.7 in /databricks/python3/lib/python3.8/site-packages (from geopandas) (1.8.2)\n", + "Requirement already satisfied: pandas>=1.0.0 in /databricks/python3/lib/python3.8/site-packages (from geopandas) (1.2.4)\n", + "Requirement already satisfied: packaging in /databricks/python3/lib/python3.8/site-packages (from geopandas) (20.9)\n", + "Requirement already satisfied: fiona>=1.8 in /databricks/python3/lib/python3.8/site-packages (from geopandas) (1.8.21)\n", + "Requirement already satisfied: six>=1.7 in /databricks/python3/lib/python3.8/site-packages (from fiona>=1.8->geopandas) (1.15.0)\n", + "Requirement already satisfied: setuptools in /usr/local/lib/python3.8/dist-packages (from fiona>=1.8->geopandas) (52.0.0)\n", + "Requirement already satisfied: click>=4.0 in /databricks/python3/lib/python3.8/site-packages (from fiona>=1.8->geopandas) (8.1.3)\n", + "Requirement already satisfied: certifi in /databricks/python3/lib/python3.8/site-packages (from fiona>=1.8->geopandas) (2020.12.5)\n", + "Requirement already satisfied: munch in /databricks/python3/lib/python3.8/site-packages (from fiona>=1.8->geopandas) (2.5.0)\n", + "Requirement already satisfied: cligj>=0.5 in /databricks/python3/lib/python3.8/site-packages (from fiona>=1.8->geopandas) (0.7.2)\n", + "Requirement already satisfied: click-plugins>=1.0 in /databricks/python3/lib/python3.8/site-packages (from fiona>=1.8->geopandas) (1.1.1)\n", + "Requirement already satisfied: attrs>=17 in /databricks/python3/lib/python3.8/site-packages (from fiona>=1.8->geopandas) (20.3.0)\n", + "Requirement already satisfied: pytz>=2017.3 in /databricks/python3/lib/python3.8/site-packages (from pandas>=1.0.0->geopandas) (2020.5)\n", + "Requirement already satisfied: numpy>=1.16.5 in /databricks/python3/lib/python3.8/site-packages (from pandas>=1.0.0->geopandas) (1.20.1)\n", + "Requirement already satisfied: python-dateutil>=2.7.3 in /databricks/python3/lib/python3.8/site-packages (from pandas>=1.0.0->geopandas) (2.8.1)\n", + "Requirement already satisfied: pyparsing>=2.0.2 in /databricks/python3/lib/python3.8/site-packages (from packaging->geopandas) (2.4.7)\n", + "WARNING: You are using pip version 21.0.1; however, version 22.1.2 is available.\n", + "You should consider upgrading via the '/databricks/python3/bin/python -m pip install --upgrade pip' command.\n", + "
" + ] + }, + "metadata": { + "application/vnd.databricks.v1+output": { + "addedWidgets": {}, + "arguments": {}, + "data": "
Requirement already satisfied: geopandas in /databricks/python3/lib/python3.8/site-packages (0.11.0)\nRequirement already satisfied: pyproj>=2.6.1.post1 in /databricks/python3/lib/python3.8/site-packages (from geopandas) (3.3.1)\nRequirement already satisfied: shapely<2,>=1.7 in /databricks/python3/lib/python3.8/site-packages (from geopandas) (1.8.2)\nRequirement already satisfied: pandas>=1.0.0 in /databricks/python3/lib/python3.8/site-packages (from geopandas) (1.2.4)\nRequirement already satisfied: packaging in /databricks/python3/lib/python3.8/site-packages (from geopandas) (20.9)\nRequirement already satisfied: fiona>=1.8 in /databricks/python3/lib/python3.8/site-packages (from geopandas) (1.8.21)\nRequirement already satisfied: six>=1.7 in /databricks/python3/lib/python3.8/site-packages (from fiona>=1.8->geopandas) (1.15.0)\nRequirement already satisfied: setuptools in /usr/local/lib/python3.8/dist-packages (from fiona>=1.8->geopandas) (52.0.0)\nRequirement already satisfied: click>=4.0 in /databricks/python3/lib/python3.8/site-packages (from fiona>=1.8->geopandas) (8.1.3)\nRequirement already satisfied: certifi in /databricks/python3/lib/python3.8/site-packages (from fiona>=1.8->geopandas) (2020.12.5)\nRequirement already satisfied: munch in /databricks/python3/lib/python3.8/site-packages (from fiona>=1.8->geopandas) (2.5.0)\nRequirement already satisfied: cligj>=0.5 in /databricks/python3/lib/python3.8/site-packages (from fiona>=1.8->geopandas) (0.7.2)\nRequirement already satisfied: click-plugins>=1.0 in /databricks/python3/lib/python3.8/site-packages (from fiona>=1.8->geopandas) (1.1.1)\nRequirement already satisfied: attrs>=17 in /databricks/python3/lib/python3.8/site-packages (from fiona>=1.8->geopandas) (20.3.0)\nRequirement already satisfied: pytz>=2017.3 in /databricks/python3/lib/python3.8/site-packages (from pandas>=1.0.0->geopandas) (2020.5)\nRequirement already satisfied: numpy>=1.16.5 in /databricks/python3/lib/python3.8/site-packages (from pandas>=1.0.0->geopandas) (1.20.1)\nRequirement already satisfied: python-dateutil>=2.7.3 in /databricks/python3/lib/python3.8/site-packages (from pandas>=1.0.0->geopandas) (2.8.1)\nRequirement already satisfied: pyparsing>=2.0.2 in /databricks/python3/lib/python3.8/site-packages (from packaging->geopandas) (2.4.7)\nWARNING: You are using pip version 21.0.1; however, version 22.1.2 is available.\nYou should consider upgrading via the '/databricks/python3/bin/python -m pip install --upgrade pip' command.\n
", + "datasetInfos": [], + "metadata": {}, + "removedWidgets": [], + "type": "html" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "%sh \n", + "pip install geopandas" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "inputWidgets": {}, + "nuid": "4fd97bf3-dc08-495f-9cfe-e9e551f40e16", + "showTitle": false, + "title": "" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
" + ] + }, + "metadata": { + "application/vnd.databricks.v1+output": { + "addedWidgets": {}, + "arguments": {}, + "data": "
", + "datasetInfos": [], + "metadata": {}, + "removedWidgets": [], + "type": "html" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "import os\n", + "import geopandas as gpd\n", + "from pyspark.sql.functions import col, expr, when\n", + "from sedona.register import SedonaRegistrator\n", + "from sedona.utils import SedonaKryoRegistrator, KryoSerializer" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "inputWidgets": {}, + "nuid": "6b15de34-d411-457b-89fb-7232587ae949", + "showTitle": false, + "title": "" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
/databricks/spark/python/pyspark/sql/pandas/conversion.py:340: UserWarning: createDataFrame attempted Arrow optimization because 'spark.sql.execution.arrow.pyspark.enabled' is set to true; however, failed by the reason below:\n", + " Did not pass numpy.dtype object\n", + "Attempting non-optimization as 'spark.sql.execution.arrow.pyspark.fallback.enabled' is set to true.\n", + " warnings.warn(msg)\n", + "Out[9]: 4
" + ] + }, + "metadata": { + "application/vnd.databricks.v1+output": { + "addedWidgets": {}, + "arguments": {}, + "data": "
/databricks/spark/python/pyspark/sql/pandas/conversion.py:340: UserWarning: createDataFrame attempted Arrow optimization because 'spark.sql.execution.arrow.pyspark.enabled' is set to true; however, failed by the reason below:\n Did not pass numpy.dtype object\nAttempting non-optimization as 'spark.sql.execution.arrow.pyspark.fallback.enabled' is set to true.\n warnings.warn(msg)\nOut[9]: 4
", + "datasetInfos": [], + "metadata": {}, + "removedWidgets": [], + "type": "html" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# read the shapefile\n", + "polygons = gpd.read_file(\"/dbfs/data/cuspatial_data/polygons/polygons.shp\")\n", + "polygons_df = spark.createDataFrame(\n", + " polygons\n", + ")\n", + "polygons_df.createOrReplaceTempView(\"polygons\")\n", + "polygons_df.count()" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "inputWidgets": {}, + "nuid": "3a4d1a80-72e4-490d-8152-f6f231cac37f", + "showTitle": false, + "title": "" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
+--------------------+\n", + " mypoint|\n", + "+--------------------+\n", + "POINT (0.48171647...|\n", + "POINT (1.25917257...|\n", + "POINT (0.18952591...|\n", + "POINT (3.02836214...|\n", + "POINT (1.38906644...|\n", + "+--------------------+\n", + "only showing top 5 rows\n", + "\n", + "
" + ] + }, + "metadata": { + "application/vnd.databricks.v1+output": { + "addedWidgets": {}, + "arguments": {}, + "data": "
+--------------------+\n| mypoint|\n+--------------------+\n|POINT (0.48171647...|\n|POINT (1.25917257...|\n|POINT (0.18952591...|\n|POINT (3.02836214...|\n|POINT (1.38906644...|\n+--------------------+\nonly showing top 5 rows\n\n
", + "datasetInfos": [], + "metadata": {}, + "removedWidgets": [], + "type": "html" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "point_parquet_df = spark.read.format(\"parquet\").\\\n", + " load(\"dbfs:/data/cuspatial_data/points\")\n", + "\n", + "point_parquet_df.createOrReplaceTempView(\"pointtable\")\n", + "point_df = spark.sql(\"select ST_Point(x, y) as mypoint from pointtable\")\n", + "point_df.show(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "inputWidgets": {}, + "nuid": "8ec977f5-937e-45ce-89d6-46fa3b48cc39", + "showTitle": false, + "title": "" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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+--------------------+------------------+------------------+\n| geometry| x| y|\n+--------------------+------------------+------------------+\n|POLYGON ((2.08811...|2.1807636574967466|3.2296461832828114|\n|POLYGON ((2.08811...|2.2006520196663066|3.7672478678985257|\n|POLYGON ((2.08811...|2.5104987015171574|3.0668114607133137|\n|POLYGON ((2.08811...|2.3007438625108882|3.6045900851589048|\n|POLYGON ((2.08811...| 2.566986568683904|3.6607732238530897|\n|POLYGON ((2.08811...| 2.493975723955388|3.3999020934055837|\n|POLYGON ((2.08811...|2.8222482218882474|3.8159308233351266|\n|POLYGON ((2.08811...| 2.241538022180476|3.8812819070357545|\n|POLYGON ((2.08811...|1.8703303641352362| 4.209727933188015|\n|POLYGON ((2.48845...|2.4264509160270813| 5.188939408363776|\n|POLYGON ((5.03982...| 6.291790729917634| 2.983311357415729|\n|POLYGON ((5.03982...| 6.101327777646798|2.5239201807166616|\n|POLYGON ((5.03982...| 6.109985464455084|2.2235950639628523|\n|POLYGON ((5.03982...|6.4274219368674315|2.9754616970668213|\n|POLYGON ((5.03982...|6.0821276168848994|2.5470532680258002|\n|POLYGON ((5.03982...| 6.325158445513714|2.8765450351723674|\n|POLYGON ((5.03982...| 6.444584786789386| 2.174562817047202|\n|POLYGON ((5.03982...| 6.6793884701899|2.5605928243991434|\n|POLYGON ((5.03982...| 7.079453687660189| 3.063690547962938|\n+--------------------+------------------+------------------+\n\n
", + "datasetInfos": [], + "metadata": {}, + "removedWidgets": [], + "type": "html" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "result = spark.sql(\"\\\n", + "SELECT polygons.*, pointtable.* FROM polygons ,pointtable \\\n", + "WHERE ST_Contains(polygons.geometry, ST_Point(pointtable.x, pointtable.y)) \\\n", + "\")\n", + "result.show()" + ] + } + ], + "metadata": { + "application/vnd.databricks.v1+notebook": { + "dashboards": [], + "language": "python", + "notebookMetadata": { + "pythonIndentUnit": 2 + }, + "notebookName": "spacial-cpu-apache-sedona", + "notebookOrigID": 1618423020047086, + "widgets": {} + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.2" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} From ac355c08175359094147536765d64c544406a7f7 Mon Sep 17 00:00:00 2001 From: Suraj Aralihalli Date: Mon, 25 Jul 2022 01:45:34 -0400 Subject: [PATCH 08/17] initial commit: modified scala etls to accept Fannie Mae data (#191) * initial commit: modified scala etls to accept Fannie Mae data Signed-off-by: Suraj Aralihalli * updated pyspark etls to consume raw mortgage data Signed-off-by: Suraj Aralihalli * updated pyspark application docs Signed-off-by: Suraj Aralihalli * updated scala spark application docs Signed-off-by: Suraj Aralihalli * updated MortgageETL.ipynb notebook Signed-off-by: Suraj Aralihalli * fixed accuracy issue in scala and python ETLs Signed-off-by: Suraj Aralihalli * tested MortgageETL Signed-off-by: Suraj Aralihalli * added scala notebook etl Signed-off-by: Suraj Aralihalli * updated MortgageETL+XGBoost.ipynb Signed-off-by: Suraj Aralihalli * fix bugs in docs Signed-off-by: Suraj Aralihalli * update docs to reflect fannie mae data Signed-off-by: Suraj Aralihalli * updated ipynb files with future links Signed-off-by: Suraj Aralihalli * link updated Signed-off-by: Suraj Aralihalli * remove maxPartitionBytes Signed-off-by: Suraj Aralihalli * added code to save train and test datasets Signed-off-by: Suraj Aralihalli * removed incompatibleOps Signed-off-by: Suraj Aralihalli * resolved spark/rapids configs Signed-off-by: Suraj Aralihalli * added instructions to download dataset Signed-off-by: Suraj Aralihalli * modified readme files to reflect config changes Signed-off-by: Suraj Aralihalli * fixed a bug in utility Mortgage.scala Signed-off-by: Suraj Aralihalli * fixed scala application bus Signed-off-by: Suraj Aralihalli * fixed python spark application bugs Signed-off-by: Suraj Aralihalli * added cpu etl section in readMe Signed-off-by: Suraj Aralihalli * fixed scala notebooks Signed-off-by: Suraj Aralihalli * fixed python notebooks Signed-off-by: Suraj Aralihalli * added step to run on CPU in scala notebook etl Signed-off-by: Suraj Aralihalli * fixed cv scala notebook bug Signed-off-by: Suraj Aralihalli * improve documentation Signed-off-by: Suraj Aralihalli * read data from disk before random split Signed-off-by: Suraj Aralihalli --- datasets/mortgage-small.tar.gz | Bin 929957 -> 0 bytes .../building-sample-apps/python.md | 3 +- .../xgboost-examples/dataset/mortgage.md | 22 + .../notebook/python-notebook.md | 2 + .../on-prem-cluster/kubernetes-scala.md | 40 +- .../on-prem-cluster/standalone-python.md | 57 +- .../on-prem-cluster/standalone-scala.md | 70 +- .../on-prem-cluster/yarn-python.md | 25 +- .../on-prem-cluster/yarn-scala.md | 49 +- .../preparation-python.md | 7 +- .../prepare-package-data/preparation-scala.md | 7 +- .../python/MortgageETL+XGBoost.ipynb | 364 +-- .../notebooks/python/MortgageETL.ipynb | 2045 +++++++++-------- .../notebooks/python/cv-mortgage-gpu.ipynb | 13 +- .../notebooks/python/mortgage-gpu.ipynb | 17 +- .../notebooks/scala/mortgage-ETL.ipynb | 373 ++- .../notebooks/scala/mortgage-gpu.ipynb | 62 +- .../scala/mortgage_gpu_crossvalidation.ipynb | 70 +- .../nvidia/spark/examples/mortgage/consts.py | 172 +- .../com/nvidia/spark/examples/mortgage/etl.py | 96 +- .../spark/examples/mortgage/etl_main.py | 1 - .../spark/examples/mortgage/ETLMain.scala | 34 +- .../spark/examples/mortgage/Mortgage.scala | 4 +- .../spark/examples/mortgage/XGBoostETL.scala | 226 +- 24 files changed, 2228 insertions(+), 1531 deletions(-) delete mode 100644 datasets/mortgage-small.tar.gz create mode 100644 docs/get-started/xgboost-examples/dataset/mortgage.md diff --git a/datasets/mortgage-small.tar.gz b/datasets/mortgage-small.tar.gz deleted file mode 100644 index 2f7c6a0164a9ebde023b0f45a142a6d55a41f0ce..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 929957 zcmYIuby!?Y%x?{s|oxvN#!*uINsz&>un|j48#G!n`l}&s6Ad2^6^S(9L zcX%=C##QtFT!K>%H0ULMQ)Ny3JIPG!dJ@r{zrZgStnE){Ny5($?a!AFUbT;?)%QGs 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z_#!lZ7f&Awc*?g8R!{NPp+(%c^eS3?7f&AwNVs1k=NIwgDc&1X0ilCC*z=2+@{9i{ zk315tDkbr_)WnN+8~kMbOv3mX##bL&#zYGX^G+@U0T>uyX$3q`h^6O1FP!+H0x)tw z@O%9z+D(;lfAV}h0Y&yBzATNO!Yr0^5m`twi!F~LW&yZbE@dn_&B0|j@oEI#YB(J8 zEs+(-y~)uO1@frT1DnCDh70foWL_gbiqZw(YPirw{%;OwgUJ^ls^pjD0%mUmtCRZ^ z59$dh>g4fdq5KqPwVZ>yxG96@W`L{Z;NALwmun89gUKf#ishH(95Vj1ZA|XOwRqx* zVtIU7EkA|fhAX|pfecm7x|;zmhN~Ss7SF==VDbTE-EO%-{9GE_#N9>%a4zBxU&dQfo})r= z~W7Deb^NJ-ov6N zfFeiI9%)HZp|2(fMC3?FkvYSofm=A!^DO#eazI3mWKraF78N+&;s3IH_Cyp(i}uJ9 z`Z`WXH8~+7X+fGy4K$e=fvSu{EuzM2?J#PpY39zqE054_KYFTny( zguLyMWFku$$t5CPk_((Mk4`;BDupf*ZviO6zyatT&J>jXd7BKpZcDtKfbD5ABLPKN zb_s|vN+w2rU+KFu#ajgQ4esH`VlfiU-(*q;fHF!Vz+Ny-NRd3*&Q}X}K?X^L2PdX| n#A~>~Q(-Z``KLaunch Mortgage or Taxi ETL Part --------------------------- +Use the ETL app to process raw Mortgage data. You can either use this ETLed data to split into training and evaluation data or run the ETL on different subsets of the dataset to produce training and evaluation datasets. Run spark-submit ``` bash ${SPARK_HOME}/bin/spark-submit \ --conf spark.plugins=com.nvidia.spark.SQLPlugin \ - --conf spark.rapids.memory.gpu.pooling.enabled=false \ --conf spark.executor.resource.gpu.amount=1 \ --conf spark.task.resource.gpu.amount=1 \ + --conf spark.rapids.sql.incompatibleDateFormats.enabled=true \ + --conf spark.rapids.sql.csv.read.double.enabled=true \ --conf spark.executor.resource.gpu.discoveryScript=./getGpusResources.sh \ + --conf spark.sql.cache.serializer=com.nvidia.spark.ParquetCachedBatchSerializer \ + --conf spark.rapids.sql.hasNans=false \ --files $SPARK_HOME/examples/src/main/scripts/getGpusResources.sh \ --jars ${RAPIDS_JAR} \ --master \ @@ -106,18 +111,15 @@ ${SPARK_HOME}/bin/spark-submit \ --num-executors ${SPARK_NUM_EXECUTORS} \ --driver-memory ${SPARK_DRIVER_MEMORY} \ --executor-memory ${SPARK_EXECUTOR_MEMORY} \ - --class ${EXAMPLE_CLASS} \ --class com.nvidia.spark.examples.mortgage.ETLMain \ $SAMPLE_JAR \ -format=csv \ - -dataPath="perf::${SPARK_XGBOOST_DIR}/mortgage/perf-train/" \ - -dataPath="acq::${SPARK_XGBOOST_DIR}/mortgage/acq-train/" \ - -dataPath="out::${SPARK_XGBOOST_DIR}/mortgage/out/train/" - -# if generating eval data, change the data path to eval as well as the corresponding perf-eval and acq-eval data -# -dataPath="perf::${SPARK_XGBOOST_DIR}/mortgage/perf-eval" -# -dataPath="acq::${SPARK_XGBOOST_DIR}/mortgage/acq-eval" -# -dataPath="out::${SPARK_XGBOOST_DIR}/mortgage/out/eval/" + -dataPath="data::${SPARK_XGBOOST_DIR}/mortgage/input/" \ + -dataPath="out::${SPARK_XGBOOST_DIR}/mortgage/output/train/" + +# if generating eval data, change the data path to eval +# -dataPath="data::${SPARK_XGBOOST_DIR}/mortgage/input/" +# -dataPath="out::${SPARK_XGBOOST_DIR}/mortgage/output/eval/" # if running Taxi ETL benchmark, change the class and data path params to # -class com.nvidia.spark.examples.taxi.ETLMain # -dataPath="raw::${SPARK_XGBOOST_DIR}/taxi/your-path" @@ -163,9 +165,9 @@ export SPARK_DRIVER_MEMORY=4g export SPARK_EXECUTOR_MEMORY=8g # example class to use -export EXAMPLE_CLASS=com.nvidia.spark.examples.mortgage.GPUMain -# or change to com.nvidia.spark.examples.taxi.GPUMain to run Taxi Xgboost benchmark -# or change to com.nvidia.spark.examples.agaricus.GPUMain to run Agaricus Xgboost benchmark +export EXAMPLE_CLASS=com.nvidia.spark.examples.mortgage.Main +# or change to com.nvidia.spark.examples.taxi.Main to run Taxi Xgboost benchmark +# or change to com.nvidia.spark.examples.agaricus.Main to run Agaricus Xgboost benchmark # tree construction algorithm export TREE_METHOD=gpu_hist @@ -192,9 +194,9 @@ ${SPARK_HOME}/bin/spark-submit --conf spark.kubernetes.executor.podTemplateFile=${TEMPLATE_PATH} \ --conf spark.kubernetes.authenticate.driver.serviceAccountName=spark \ ${SAMPLE_JAR} \ - -dataPath=train::${DATA_PATH}/mortgage/csv/train/mortgage_train_merged.csv \ - -dataPath=trans::${DATA_PATH}/mortgage/csv/test/mortgage_eval_merged.csv \ - -format=csv \ + -dataPath=train::${SPARK_XGBOOST_DIR}/mortgage/output/train/ \ + -dataPath=trans::${SPARK_XGBOOST_DIR}/mortgage/output/eval/ \ + -format=parquet \ -numWorkers=${SPARK_NUM_EXECUTORS} \ -treeMethod=${TREE_METHOD} \ -numRound=100 \ diff --git a/docs/get-started/xgboost-examples/on-prem-cluster/standalone-python.md b/docs/get-started/xgboost-examples/on-prem-cluster/standalone-python.md index 55ac2a1c4..ae06e201c 100644 --- a/docs/get-started/xgboost-examples/on-prem-cluster/standalone-python.md +++ b/docs/get-started/xgboost-examples/on-prem-cluster/standalone-python.md @@ -53,6 +53,13 @@ Get Application Files, Jar and Dataset Make sure you have prepared the necessary packages and dataset by following this [guide](/docs/get-started/xgboost-examples/prepare-package-data/preparation-python.md) + +#### Note: +1. Mortgage and Taxi jobs have ETLs to generate the processed data. +2. For convenience, a subset of [Taxi](/datasets/) dataset is made available in this repo that can be readily used for launching XGBoost job. Use [ETL](#etl) to generate larger datasets for trainig and testing. +3. Agaricus does not have an ETL process, it is combined with XGBoost as there is just a filter operation. + + Launch a Standalone Spark Cluster --------------------------------- @@ -83,9 +90,8 @@ Launch a Standalone Spark Cluster Launch Mortgage or Taxi ETL Part --------------------------- - -Run spark-submit - +Use the ETL app to process raw Mortgage data. You can either use this ETLed data to split into training and evaluation data or run the ETL on different subsets of the dataset to produce training and evaluation datasets. +### ETL on GPU ``` bash ${SPARK_HOME}/bin/spark-submit \ --master spark://$HOSTNAME:7077 \ @@ -95,18 +101,39 @@ ${SPARK_HOME}/bin/spark-submit \ --conf spark.plugins=com.nvidia.spark.SQLPlugin \ --conf spark.rapids.sql.incompatibleDateFormats.enabled=true \ --conf spark.rapids.sql.csv.read.double.enabled=true \ + --conf spark.sql.cache.serializer=com.nvidia.spark.ParquetCachedBatchSerializer \ + --conf spark.rapids.sql.hasNans=false \ --py-files ${SAMPLE_ZIP} \ main.py \ --mainClass='com.nvidia.spark.examples.mortgage.etl_main' \ --format=csv \ - --dataPath="perf::${SPARK_XGBOOST_DIR}/mortgage/perf-train/" \ - --dataPath="acq::${SPARK_XGBOOST_DIR}/mortgage/acq-train/" \ - --dataPath="out::${SPARK_XGBOOST_DIR}/mortgage/out/train/" - -# if generating eval data, change the data path to eval as well as the corresponding perf-eval and acq-eval data -# --dataPath="perf::${SPARK_XGBOOST_DIR}/mortgage/perf-eval" -# --dataPath="acq::${SPARK_XGBOOST_DIR}/mortgage/acq-eval" -# --dataPath="out::${SPARK_XGBOOST_DIR}/mortgage/out/eval/" + --dataPath="data::${SPARK_XGBOOST_DIR}/mortgage/input/" \ + --dataPath="out::${SPARK_XGBOOST_DIR}/mortgage/output/train/" + +# if generating eval data, change the data path to eval +# --dataPath="data::${SPARK_XGBOOST_DIR}/mortgage/input/" +# --dataPath="out::${SPARK_XGBOOST_DIR}/mortgage/output/eval/" +# if running Taxi ETL benchmark, change the class and data path params to +# -class com.nvidia.spark.examples.taxi.ETLMain +# -dataPath="raw::${SPARK_XGBOOST_DIR}/taxi/your-path" +# -dataPath="out::${SPARK_XGBOOST_DIR}/taxi/your-path" +``` +### ETL on CPU +```bash +${SPARK_HOME}/bin/spark-submit \ + --master spark://$HOSTNAME:7077 \ + --executor-memory 32G \ + --conf spark.executor.instances=1 \ + --py-files ${SAMPLE_ZIP} \ + main.py \ + --mainClass='com.nvidia.spark.examples.mortgage.etl_main' \ + --format=csv \ + --dataPath="data::${SPARK_XGBOOST_DIR}/mortgage/input/" \ + --dataPath="out::${SPARK_XGBOOST_DIR}/mortgage/output/train/" + +# if generating eval data, change the data path to eval +# --dataPath="data::${SPARK_XGBOOST_DIR}/mortgage/input/" +# --dataPath="out::${SPARK_XGBOOST_DIR}/mortgage/output/eval/" # if running Taxi ETL benchmark, change the class and data path params to # -class com.nvidia.spark.examples.taxi.ETLMain # -dataPath="raw::${SPARK_XGBOOST_DIR}/taxi/your-path" @@ -166,8 +193,8 @@ ${SPARK_HOME}/bin/spark-submit --py-files ${XGBOOST4J_SPARK_JAR},${SAMPLE_ZIP} \ ${MAIN_PY} \ --mainClass=${EXAMPLE_CLASS} \ - --dataPath=train::${SPARK_XGBOOST_DIR}/mortgage/out/train/ \ - --dataPath=trans::${SPARK_XGBOOST_DIR}/mortgage/out/eval/ \ + --dataPath=train::${SPARK_XGBOOST_DIR}/mortgage/output/train/ \ + --dataPath=trans::${SPARK_XGBOOST_DIR}/mortgage/output/eval/ \ --format=parquet \ --numWorkers=${SPARK_NUM_EXECUTORS} \ --treeMethod=${TREE_METHOD} \ @@ -240,8 +267,8 @@ ${SPARK_HOME}/bin/spark-submit --py-files ${XGBOOST4J_SPARK_JAR},${SAMPLE_ZIP} \ ${SPARK_PYTHON_ENTRYPOINT} \ --mainClass=${EXAMPLE_CLASS} \ - --dataPath=train::${DATA_PATH}/mortgage/out/train/ \ - --dataPath=trans::${DATA_PATH}/mortgage/out/eval/ \ + --dataPath=train::${DATA_PATH}/mortgage/output/train/ \ + --dataPath=trans::${DATA_PATH}/mortgage/output/eval/ \ --format=parquet \ --numWorkers=${SPARK_NUM_EXECUTORS} \ --treeMethod=${TREE_METHOD} \ diff --git a/docs/get-started/xgboost-examples/on-prem-cluster/standalone-scala.md b/docs/get-started/xgboost-examples/on-prem-cluster/standalone-scala.md index 5493340c2..35a2ed238 100644 --- a/docs/get-started/xgboost-examples/on-prem-cluster/standalone-scala.md +++ b/docs/get-started/xgboost-examples/on-prem-cluster/standalone-scala.md @@ -53,9 +53,11 @@ Get Jars and Dataset Make sure you have prepared the necessary packages and dataset by following this [guide](/docs/get-started/xgboost-examples/prepare-package-data/preparation-scala.md) -Note: the `mortgage_eval_merged.csv` and `mortgage_train_merged.csv` are not Mortgage raw data, -they are the data produced by Mortgage ETL job. If user wants to use a larger size Mortgage data, please refer to [Launch ETL job](#etl). -Taxi ETL job is the same. But Agaricus does not have ETL process, it is combined with XGBoost as there is just a filter operation. +#### Note: +1. Mortgage and Taxi jobs have ETLs to generate the processed data. +2. For convenience, a subset of [Taxi](/datasets/) dataset is made available in this repo that can be readily used for launching XGBoost job. Use [ETL](#etl) to generate larger datasets for trainig and testing. +3. Agaricus does not have an ETL process, it is combined with XGBoost as there is just a filter operation. + Launch a Standalone Spark Cluster --------------------------------- @@ -90,31 +92,53 @@ Launch a Standalone Spark Cluster Launch Mortgage or Taxi ETL Part --------------------------- -If user wants to use a larger size dataset other than the default one, we provide an ETL app to process raw Mortgage data. - +Use the ETL app to process raw Mortgage data. You can either use this ETLed data to split into training and evaluation data or run the ETL on different subsets of the dataset to produce training and evaluation datasets. Run spark-submit +### ETL on GPU ``` bash ${SPARK_HOME}/bin/spark-submit \ --master spark://$HOSTNAME:7077 \ --executor-memory 32G \ - --conf spark.rapids.memory.gpu.pooling.enabled=false \ --conf spark.executor.resource.gpu.amount=1 \ --conf spark.task.resource.gpu.amount=1 \ --conf spark.plugins=com.nvidia.spark.SQLPlugin \ --conf spark.rapids.sql.incompatibleDateFormats.enabled=true \ --conf spark.rapids.sql.csv.read.double.enabled=true \ + --conf spark.sql.cache.serializer=com.nvidia.spark.ParquetCachedBatchSerializer \ + --conf spark.rapids.sql.hasNans=false \ --class com.nvidia.spark.examples.mortgage.ETLMain \ $SAMPLE_JAR \ -format=csv \ - -dataPath="perf::${SPARK_XGBOOST_DIR}/mortgage/perf-train/" \ - -dataPath="acq::${SPARK_XGBOOST_DIR}/mortgage/acq-train/" \ - -dataPath="out::${SPARK_XGBOOST_DIR}/mortgage/out/train/" - -# if generating eval data, change the data path to eval as well as the corresponding perf-eval and acq-eval data -# -dataPath="perf::${SPARK_XGBOOST_DIR}/mortgage/perf-eval" -# -dataPath="acq::${SPARK_XGBOOST_DIR}/mortgage/acq-eval" -# -dataPath="out::${SPARK_XGBOOST_DIR}/mortgage/out/eval/" + -dataPath="data::${SPARK_XGBOOST_DIR}/mortgage/input/" \ + -dataPath="out::${SPARK_XGBOOST_DIR}/mortgage/output/train/" + +# if generating eval data, change the data path to eval +# -dataPath="data::${SPARK_XGBOOST_DIR}/mortgage/input/" +# -dataPath="out::${SPARK_XGBOOST_DIR}/mortgage/output/eval/" +# if running Taxi ETL benchmark, change the class and data path params to +# -class com.nvidia.spark.examples.taxi.ETLMain +# -dataPath="raw::${SPARK_XGBOOST_DIR}/taxi/your-path" +# -dataPath="out::${SPARK_XGBOOST_DIR}/taxi/your-path" +``` + +### ETL on CPU + +```bash +${SPARK_HOME}/bin/spark-submit \ +--master spark://$HOSTNAME:7077 \ +--executor-memory 32G \ +--conf spark.executor.instances=1 \ +--conf spark.sql.broadcastTimeout=700 \ +--class com.nvidia.spark.examples.mortgage.ETLMain \ +$SAMPLE_JAR \ +-format=csv \ +-dataPath="data::${SPARK_XGBOOST_DIR}/mortgage/input/" \ +-dataPath="out::${SPARK_XGBOOST_DIR}/mortgage/output/train/" + +# if generating eval data, change the data path to eval +# -dataPath="data::${SPARK_XGBOOST_DIR}/mortgage/input/" +# -dataPath="out::${SPARK_XGBOOST_DIR}/mortgage/output/eval/" # if running Taxi ETL benchmark, change the class and data path params to # -class com.nvidia.spark.examples.taxi.ETLMain # -dataPath="raw::${SPARK_XGBOOST_DIR}/taxi/your-path" @@ -150,7 +174,7 @@ export SPARK_DRIVER_MEMORY=4g export SPARK_EXECUTOR_MEMORY=8g # example class to use -export EXAMPLE_CLASS=com.nvidia.spark.examples.mortgage.GPUMain +export EXAMPLE_CLASS=com.nvidia.spark.examples.mortgage.Main # or change to com.nvidia.spark.examples.taxi.GPUMain to run Taxi Xgboost benchmark # or change to com.nvidia.spark.examples.agaricus.GPUMain to run Agaricus Xgboost benchmark @@ -172,9 +196,9 @@ ${SPARK_HOME}/bin/spark-submit --conf spark.cores.max=${TOTAL_CORES} \ --class ${EXAMPLE_CLASS} \ ${SAMPLE_JAR} \ - -dataPath=train::${SPARK_XGBOOST_DIR}/mortgage/csv/train/mortgage_train_merged.csv \ - -dataPath=trans::${SPARK_XGBOOST_DIR}/mortgage/csv/test/mortgage_eval_merged.csv \ - -format=csv \ + -dataPath=train::${SPARK_XGBOOST_DIR}/mortgage/output/train/ \ + -dataPath=trans::${SPARK_XGBOOST_DIR}/mortgage/output/eval/ \ + -format=parquet \ -numWorkers=${SPARK_NUM_EXECUTORS} \ -treeMethod=${TREE_METHOD} \ -numRound=100 \ @@ -229,7 +253,7 @@ export SPARK_DRIVER_MEMORY=4g export SPARK_EXECUTOR_MEMORY=8g # example class to use -export EXAMPLE_CLASS=com.nvidia.spark.examples.mortgage.CPUMain +export EXAMPLE_CLASS=com.nvidia.spark.examples.mortgage.Main # Please make sure to change the class while running Taxi or Agaricus benchmark # tree construction algorithm @@ -238,7 +262,7 @@ export TREE_METHOD=hist This is the same command as for the GPU example, repeated for convenience: -``` bash +```bash ${SPARK_HOME}/bin/spark-submit \ --master ${SPARK_MASTER} \ --driver-memory ${SPARK_DRIVER_MEMORY} \ @@ -246,9 +270,9 @@ ${SPARK_HOME}/bin/spark-submit --conf spark.cores.max=${TOTAL_CORES} \ --class ${EXAMPLE_CLASS} \ ${SAMPLE_JAR} \ - -dataPath=train::${SPARK_XGBOOST_DIR}/mortgage/csv/train/mortgage_train_merged.csv \ - -dataPath=trans::${SPARK_XGBOOST_DIR}/mortgage/csv/test/mortgage_eval_merged.csv \ - -format=csv \ + -dataPath=train::${SPARK_XGBOOST_DIR}/mortgage/output/train/ \ + -dataPath=trans::${SPARK_XGBOOST_DIR}/mortgage/output/eval/ \ + -format=parquet \ -numWorkers=${SPARK_NUM_EXECUTORS} \ -treeMethod=${TREE_METHOD} \ -numRound=100 \ diff --git a/docs/get-started/xgboost-examples/on-prem-cluster/yarn-python.md b/docs/get-started/xgboost-examples/on-prem-cluster/yarn-python.md index 7966791a2..fe0605aef 100644 --- a/docs/get-started/xgboost-examples/on-prem-cluster/yarn-python.md +++ b/docs/get-started/xgboost-examples/on-prem-cluster/yarn-python.md @@ -47,25 +47,28 @@ Then create a directory in HDFS, and run below commands, Launch Mortgage or Taxi ETL Part --------------------------- -Run spark-submit: - +Use the ETL app to process raw Mortgage data. You can either use this ETLed data to split into training and evaluation data or run the ETL on different subsets of the dataset to produce training and evaluation datasets. ``` bash # location where data was downloaded export DATA_PATH=hdfs:/tmp/xgboost4j_spark_python/ ${SPARK_HOME}/bin/spark-submit \ - --master yarn - --deploy-mode cluster + --master yarn \ + --deploy-mode cluster \ + --conf spark.rapids.sql.incompatibleDateFormats.enabled=true \ + --conf spark.rapids.sql.csv.read.double.enabled=true \ + --conf spark.sql.cache.serializer=com.nvidia.spark.ParquetCachedBatchSerializer \ + --conf spark.rapids.sql.hasNans=false \ --jars ${RAPIDS_JAR}\ ${MAIN_PY} \ --mainClass='com.nvidia.spark.examples.mortgage.etl_main' \ --format=csv \ - --dataPath="perf::${DATA_PATH}/mortgage/data/mortgage/perf/" \ - --dataPath="acq::${DATA_PATH}/mortgage/data/mortgage/acq/" \ - --dataPath="out::${DATA_PATH}/mortgage/data/mortgage/out/train/" + --dataPath="data::${DATA_PATH}/mortgage/data/mortgage/input/" \ + --dataPath="out::${DATA_PATH}/mortgage/data/mortgage/output/train/" -# if generate eval data, change the data path to eval -# --dataPath="out::${DATA_PATH}/mortgage/data/mortgage/out/eval/ +# if generating eval data, change the data path to eval +# --dataPath="data::${SPARK_XGBOOST_DIR}/mortgage/input/" +# --dataPath="out::${SPARK_XGBOOST_DIR}/mortgage/output/eval/" # if running Taxi ETL benchmark, change the class and data path params to # -class com.nvidia.spark.examples.taxi.ETLMain # -dataPath="raw::${SPARK_XGBOOST_DIR}/taxi/your-path" @@ -194,8 +197,8 @@ ${SPARK_HOME}/bin/spark-submit --py-files ${XGBOOST4J_SPARK_JAR},${SAMPLE_ZIP} \ ${MAIN_PY} \ --mainClass=${EXAMPLE_CLASS} \ - --dataPath=train::${DATA_PATH}/mortgage/out/train/ \ - --dataPath=trans::${DATA_PATH}/mortgage/out/eval/ \ + --dataPath=train::${DATA_PATH}/mortgage/output/train/ \ + --dataPath=trans::${DATA_PATH}/mortgage/output/eval/ \ --format=parquet \ --numWorkers=${SPARK_NUM_EXECUTORS} \ --treeMethod=${TREE_METHOD} \ diff --git a/docs/get-started/xgboost-examples/on-prem-cluster/yarn-scala.md b/docs/get-started/xgboost-examples/on-prem-cluster/yarn-scala.md index 9e6e4367b..052d62ec2 100644 --- a/docs/get-started/xgboost-examples/on-prem-cluster/yarn-scala.md +++ b/docs/get-started/xgboost-examples/on-prem-cluster/yarn-scala.md @@ -35,6 +35,11 @@ Get Jars and Dataset Make sure you have prepared the necessary packages and dataset by following this [guide](/docs/get-started/xgboost-examples/prepare-package-data/preparation-scala.md) +#### Note: +1. Mortgage and Taxi jobs have ETLs to generate the processed data. +2. For convenience, a subset of [Taxi](/datasets/) dataset is made available in this repo that can be readily used for launching XGBoost job. Use [ETL](#etl) to generate larger datasets for trainig and testing. +3. Agaricus does not have an ETL process, it is combined with XGBoost as there is just a filter operation. + Create a directory in HDFS, and copy: ``` bash @@ -45,19 +50,20 @@ Create a directory in HDFS, and copy: Launch Mortgage or Taxi ETL Part --------------------------- -Note: the `mortgage_eval_merged.csv` and `mortgage_train_merged.csv` are not Mortgage raw data, -they are the data produced by Mortgage ETL job. If user wants to use a larger size Mortgage data, please refer to [Launch ETL job](#etl). -Taxi ETL job is the same. But Agaricus does not have ETL process, it is combined with XGBoost as there is just a filter operation. +Use the ETL app to process raw Mortgage data. You can either use this ETLed data to split into training and evaluation data or run the ETL on different subsets of the dataset to produce training and evaluation datasets. Run spark-submit ``` bash ${SPARK_HOME}/bin/spark-submit \ --conf spark.plugins=com.nvidia.spark.SQLPlugin \ - --conf spark.rapids.memory.gpu.pooling.enabled=false \ --conf spark.executor.resource.gpu.amount=1 \ --conf spark.task.resource.gpu.amount=1 \ + --conf spark.rapids.sql.incompatibleDateFormats.enabled=true \ + --conf spark.rapids.sql.csv.read.double.enabled=true \ --conf spark.executor.resource.gpu.discoveryScript=./getGpusResources.sh \ + --conf spark.sql.cache.serializer=com.nvidia.spark.ParquetCachedBatchSerializer \ + --conf spark.rapids.sql.hasNans=false \ --files $SPARK_HOME/examples/src/main/scripts/getGpusResources.sh \ --jars ${RAPIDS_JAR} \ --master yarn \ @@ -65,18 +71,15 @@ ${SPARK_HOME}/bin/spark-submit \ --num-executors ${SPARK_NUM_EXECUTORS} \ --driver-memory ${SPARK_DRIVER_MEMORY} \ --executor-memory ${SPARK_EXECUTOR_MEMORY} \ - --class ${EXAMPLE_CLASS} \ --class com.nvidia.spark.examples.mortgage.ETLMain \ $SAMPLE_JAR \ -format=csv \ - -dataPath="perf::${SPARK_XGBOOST_DIR}/mortgage/perf-train/" \ - -dataPath="acq::${SPARK_XGBOOST_DIR}/mortgage/acq-train/" \ - -dataPath="out::${SPARK_XGBOOST_DIR}/mortgage/out/train/" - -# if generating eval data, change the data path to eval as well as the corresponding perf-eval and acq-eval data -# -dataPath="perf::${SPARK_XGBOOST_DIR}/mortgage/perf-eval" -# -dataPath="acq::${SPARK_XGBOOST_DIR}/mortgage/acq-eval" -# -dataPath="out::${SPARK_XGBOOST_DIR}/mortgage/out/eval/" + -dataPath="data::${SPARK_XGBOOST_DIR}/mortgage/input/" \ + -dataPath="out::${SPARK_XGBOOST_DIR}/mortgage/output/train/" + +# if generating eval data, change the data path to eval +# -dataPath="data::${SPARK_XGBOOST_DIR}/mortgage/input/" +# -dataPath="out::${SPARK_XGBOOST_DIR}/mortgage/output/eval/" # if running Taxi ETL benchmark, change the class and data path params to # -class com.nvidia.spark.examples.taxi.ETLMain # -dataPath="raw::${SPARK_XGBOOST_DIR}/taxi/your-path" @@ -106,9 +109,9 @@ export SPARK_DRIVER_MEMORY=4g export SPARK_EXECUTOR_MEMORY=8g # example class to use -export EXAMPLE_CLASS=com.nvidia.spark.examples.mortgage.GPUMain -# or change to com.nvidia.spark.examples.taxi.GPUMain to run Taxi Xgboost benchmark -# or change to com.nvidia.spark.examples.agaricus.GPUMain to run Agaricus Xgboost benchmark +export EXAMPLE_CLASS=com.nvidia.spark.examples.mortgage.Main +# or change to com.nvidia.spark.examples.taxi.Main to run Taxi Xgboost benchmark +# or change to com.nvidia.spark.examples.agaricus.Main to run Agaricus Xgboost benchmark # tree construction algorithm export TREE_METHOD=gpu_hist @@ -132,9 +135,9 @@ ${SPARK_HOME}/bin/spark-submit --executor-memory ${SPARK_EXECUTOR_MEMORY} \ --class ${EXAMPLE_CLASS} \ ${SAMPLE_JAR} \ - -dataPath=train::${DATA_PATH}/mortgage/csv/train/mortgage_train_merged.csv \ - -dataPath=trans::${DATA_PATH}/mortgage/csv/test/mortgage_eval_merged.csv \ - -format=csv \ + -dataPath=train::${SPARK_XGBOOST_DIR}/mortgage/output/train/ \ + -dataPath=trans::${SPARK_XGBOOST_DIR}/mortgage/output/eval/ \ + -format=parquet \ -numWorkers=${SPARK_NUM_EXECUTORS} \ -treeMethod=${TREE_METHOD} \ -numRound=100 \ @@ -181,7 +184,7 @@ export SPARK_DRIVER_MEMORY=4g export SPARK_EXECUTOR_MEMORY=8g # example class to use -export EXAMPLE_CLASS=com.nvidia.spark.examples.mortgage.CPUMain +export EXAMPLE_CLASS=com.nvidia.spark.examples.mortgage.Main # Please make sure to change the class while running Taxi or Agaricus benchmark # tree construction algorithm @@ -199,9 +202,9 @@ ${SPARK_HOME}/bin/spark-submit --executor-memory ${SPARK_EXECUTOR_MEMORY} \ --class ${EXAMPLE_CLASS} \ ${SAMPLE_JAR} \ - -dataPath=train::${DATA_PATH}/mortgage/csv/train/mortgage_train_merged.csv \ - -dataPath=trans::${DATA_PATH}/mortgage/csv/test/mortgage_eval_merged.csv \ - -format=csv \ + -dataPath=train::${SPARK_XGBOOST_DIR}/mortgage/output/train/ \ + -dataPath=trans::${SPARK_XGBOOST_DIR}/mortgage/output/eval/ \ + -format=parquet \ -numWorkers=${SPARK_NUM_EXECUTORS} \ -treeMethod=${TREE_METHOD} \ -numRound=100 \ diff --git a/docs/get-started/xgboost-examples/prepare-package-data/preparation-python.md b/docs/get-started/xgboost-examples/prepare-package-data/preparation-python.md index cbeeccdbb..48558b409 100644 --- a/docs/get-started/xgboost-examples/prepare-package-data/preparation-python.md +++ b/docs/get-started/xgboost-examples/prepare-package-data/preparation-python.md @@ -17,9 +17,10 @@ Following this [guide](/docs/get-started/xgboost-examples/building-sample-apps/p ### Download dataset -You need to download Mortgage dataset to `/opt/xgboost` from this [site](https://docs.rapids.ai/datasets/mortgage-data) -, download Taxi dataset from this [site](https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page) -, download Agaricus dataset from this [site](https://gust.dev/r/xgboost-agaricus). +You need to copy the dataset to `/opt/xgboost`. Use the following links to download the data. +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 diff --git a/docs/get-started/xgboost-examples/prepare-package-data/preparation-scala.md b/docs/get-started/xgboost-examples/prepare-package-data/preparation-scala.md index a5f451778..e2cd6daa5 100644 --- a/docs/get-started/xgboost-examples/prepare-package-data/preparation-scala.md +++ b/docs/get-started/xgboost-examples/prepare-package-data/preparation-scala.md @@ -13,9 +13,10 @@ Following this [guide](/docs/get-started/xgboost-examples/building-sample-apps/s ### Download dataset -You need to download mortgage dataset to `/opt/xgboost` from this [site](https://docs.rapids.ai/datasets/mortgage-data) -, download Taxi dataset from this [site](https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page) -, download Agaricus dataset from this [site](https://gust.dev/r/xgboost-agaricus). +You need to copy the dataset to `/opt/xgboost`. Use the following links to download the data. +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 diff --git a/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL+XGBoost.ipynb b/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL+XGBoost.ipynb index a544f5795..7b29bed7b 100644 --- a/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL+XGBoost.ipynb +++ b/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL+XGBoost.ipynb @@ -49,9 +49,9 @@ "outputs": [], "source": [ "# The input path of dataset\n", - "dataRoot = os.getenv(\"DATA_ROOT\", \"/data\")\n", - "orig_perf_path=dataRoot + \"/mortgage/Performance/\"\n", - "orig_acq_path=dataRoot + \"/mortgage/Acquisition/\"" + "# dataRoot = os.getenv(\"DATA_ROOT\", \"/data\")\n", + "dataRoot = os.getenv(\"DATA_ROOT\", \"/mortgage\")\n", + "orig_raw_path = dataRoot + \"/input/\"" ] }, { @@ -72,15 +72,13 @@ "metadata": {}, "outputs": [], "source": [ - "# Set True to save the dataset after ETL\n", + "# Set True to save processed dataset after ETL\n", "# Set False, the dataset after ETL will be directly used in XGBoost train and transform\n", + "\n", "is_save_dataset=True\n", - "# the path to save the train dataset\n", - "output_path_train=dataRoot + \"/mortgage/output/train/\"\n", - "# the path to save the test dataset\n", - "output_path_test=dataRoot + \"/mortgage/output/test/\"\n", + "output_path_data=dataRoot + \"/output/data/\"\n", "# the path to save the xgboost model\n", - "output_path_model=dataRoot + \"/mortgage/new-model-path\"" + "output_path_model=dataRoot + \"/output/model/\"" ] }, { @@ -97,65 +95,117 @@ "outputs": [], "source": [ "# File schema\n", - "_csv_perf_schema = StructType([\n", - " StructField(\"loan_id\", LongType()),\n", - " StructField(\"monthly_reporting_period\", StringType()),\n", - " StructField(\"servicer\", StringType()),\n", - " StructField(\"interest_rate\", DoubleType()),\n", - " StructField(\"current_actual_upb\", DoubleType()),\n", - " StructField(\"loan_age\", DoubleType()),\n", - " StructField(\"remaining_months_to_legal_maturity\", DoubleType()),\n", - " StructField(\"adj_remaining_months_to_maturity\", DoubleType()),\n", - " StructField(\"maturity_date\", StringType()),\n", - " StructField(\"msa\", DoubleType()),\n", - " StructField(\"current_loan_delinquency_status\", IntegerType()),\n", - " StructField(\"mod_flag\", StringType()),\n", - " StructField(\"zero_balance_code\", StringType()),\n", - " StructField(\"zero_balance_effective_date\", StringType()),\n", - " StructField(\"last_paid_installment_date\", StringType()),\n", - " StructField(\"foreclosed_after\", StringType()),\n", - " StructField(\"disposition_date\", StringType()),\n", - " StructField(\"foreclosure_costs\", DoubleType()),\n", - " StructField(\"prop_preservation_and_repair_costs\", DoubleType()),\n", - " StructField(\"asset_recovery_costs\", DoubleType()),\n", - " StructField(\"misc_holding_expenses\", DoubleType()),\n", - " StructField(\"holding_taxes\", DoubleType()),\n", - " StructField(\"net_sale_proceeds\", DoubleType()),\n", - " StructField(\"credit_enhancement_proceeds\", DoubleType()),\n", - " StructField(\"repurchase_make_whole_proceeds\", StringType()),\n", - " StructField(\"other_foreclosure_proceeds\", DoubleType()),\n", - " StructField(\"non_interest_bearing_upb\", DoubleType()),\n", - " StructField(\"principal_forgiveness_upb\", StringType()),\n", - " StructField(\"repurchase_make_whole_proceeds_flag\", StringType()),\n", - " StructField(\"foreclosure_principal_write_off_amount\", StringType()),\n", - " StructField(\"servicing_activity_indicator\", StringType())])\n", "\n", - "_csv_acq_schema = StructType([\n", - " StructField(\"loan_id\", LongType()),\n", - " StructField(\"orig_channel\", StringType()),\n", - " StructField(\"seller_name\", StringType()),\n", - " StructField(\"orig_interest_rate\", DoubleType()),\n", - " StructField(\"orig_upb\", IntegerType()),\n", - " StructField(\"orig_loan_term\", IntegerType()),\n", - " StructField(\"orig_date\", StringType()),\n", - " StructField(\"first_pay_date\", StringType()),\n", - " StructField(\"orig_ltv\", DoubleType()),\n", - " StructField(\"orig_cltv\", DoubleType()),\n", - " StructField(\"num_borrowers\", DoubleType()),\n", - " StructField(\"dti\", DoubleType()),\n", - " StructField(\"borrower_credit_score\", DoubleType()),\n", - " StructField(\"first_home_buyer\", StringType()),\n", - " StructField(\"loan_purpose\", StringType()),\n", - " StructField(\"property_type\", StringType()),\n", - " StructField(\"num_units\", IntegerType()),\n", - " StructField(\"occupancy_status\", StringType()),\n", - " StructField(\"property_state\", StringType()),\n", - " StructField(\"zip\", IntegerType()),\n", - " StructField(\"mortgage_insurance_percent\", DoubleType()),\n", - " StructField(\"product_type\", StringType()),\n", - " StructField(\"coborrow_credit_score\", DoubleType()),\n", - " StructField(\"mortgage_insurance_type\", DoubleType()),\n", - " StructField(\"relocation_mortgage_indicator\", StringType())])" + "_csv_raw_schema = StructType([\n", + " StructField(\"reference_pool_id\", StringType()),\n", + " StructField(\"loan_id\", LongType()),\n", + " StructField(\"monthly_reporting_period\", StringType()),\n", + " StructField(\"orig_channel\", StringType()),\n", + " StructField(\"seller_name\", StringType()),\n", + " StructField(\"servicer\", StringType()),\n", + " StructField(\"master_servicer\", StringType()),\n", + " StructField(\"orig_interest_rate\", DoubleType()),\n", + " StructField(\"interest_rate\", DoubleType()),\n", + " StructField(\"orig_upb\", DoubleType()),\n", + " StructField(\"upb_at_issuance\", StringType()),\n", + " StructField(\"current_actual_upb\", DoubleType()),\n", + " StructField(\"orig_loan_term\", IntegerType()),\n", + " StructField(\"orig_date\", StringType()),\n", + " StructField(\"first_pay_date\", StringType()), \n", + " StructField(\"loan_age\", DoubleType()),\n", + " StructField(\"remaining_months_to_legal_maturity\", DoubleType()),\n", + " StructField(\"adj_remaining_months_to_maturity\", DoubleType()),\n", + " StructField(\"maturity_date\", StringType()),\n", + " StructField(\"orig_ltv\", DoubleType()),\n", + " StructField(\"orig_cltv\", DoubleType()),\n", + " StructField(\"num_borrowers\", DoubleType()),\n", + " StructField(\"dti\", DoubleType()),\n", + " StructField(\"borrower_credit_score\", DoubleType()),\n", + " StructField(\"coborrow_credit_score\", DoubleType()),\n", + " StructField(\"first_home_buyer\", StringType()),\n", + " StructField(\"loan_purpose\", StringType()),\n", + " StructField(\"property_type\", StringType()),\n", + " StructField(\"num_units\", IntegerType()),\n", + " StructField(\"occupancy_status\", StringType()),\n", + " StructField(\"property_state\", StringType()),\n", + " StructField(\"msa\", DoubleType()),\n", + " StructField(\"zip\", IntegerType()),\n", + " StructField(\"mortgage_insurance_percent\", DoubleType()),\n", + " StructField(\"product_type\", StringType()),\n", + " StructField(\"prepayment_penalty_indicator\", StringType()),\n", + " StructField(\"interest_only_loan_indicator\", StringType()),\n", + " StructField(\"interest_only_first_principal_and_interest_payment_date\", StringType()),\n", + " StructField(\"months_to_amortization\", StringType()),\n", + " StructField(\"current_loan_delinquency_status\", IntegerType()),\n", + " StructField(\"loan_payment_history\", StringType()),\n", + " StructField(\"mod_flag\", StringType()),\n", + " StructField(\"mortgage_insurance_cancellation_indicator\", StringType()),\n", + " StructField(\"zero_balance_code\", StringType()),\n", + " StructField(\"zero_balance_effective_date\", StringType()),\n", + " StructField(\"upb_at_the_time_of_removal\", StringType()),\n", + " StructField(\"repurchase_date\", StringType()),\n", + " StructField(\"scheduled_principal_current\", StringType()),\n", + " StructField(\"total_principal_current\", StringType()),\n", + " StructField(\"unscheduled_principal_current\", StringType()),\n", + " StructField(\"last_paid_installment_date\", StringType()),\n", + " StructField(\"foreclosed_after\", StringType()),\n", + " StructField(\"disposition_date\", StringType()),\n", + " StructField(\"foreclosure_costs\", DoubleType()),\n", + " StructField(\"prop_preservation_and_repair_costs\", DoubleType()),\n", + " StructField(\"asset_recovery_costs\", DoubleType()),\n", + " StructField(\"misc_holding_expenses\", DoubleType()),\n", + " StructField(\"holding_taxes\", DoubleType()),\n", + " StructField(\"net_sale_proceeds\", DoubleType()),\n", + " StructField(\"credit_enhancement_proceeds\", DoubleType()),\n", + " StructField(\"repurchase_make_whole_proceeds\", StringType()),\n", + " StructField(\"other_foreclosure_proceeds\", DoubleType()),\n", + " StructField(\"non_interest_bearing_upb\", DoubleType()),\n", + " StructField(\"principal_forgiveness_upb\", StringType()),\n", + " StructField(\"original_list_start_date\", StringType()),\n", + " StructField(\"original_list_price\", StringType()),\n", + " StructField(\"current_list_start_date\", StringType()),\n", + " StructField(\"current_list_price\", StringType()),\n", + " StructField(\"borrower_credit_score_at_issuance\", StringType()),\n", + " StructField(\"co-borrower_credit_score_at_issuance\", StringType()),\n", + " StructField(\"borrower_credit_score_current\", StringType()),\n", + " StructField(\"co-Borrower_credit_score_current\", StringType()),\n", + " StructField(\"mortgage_insurance_type\", DoubleType()),\n", + " StructField(\"servicing_activity_indicator\", StringType()),\n", + " StructField(\"current_period_modification_loss_amount\", StringType()),\n", + " StructField(\"cumulative_modification_loss_amount\", StringType()),\n", + " StructField(\"current_period_credit_event_net_gain_or_loss\", StringType()),\n", + " StructField(\"cumulative_credit_event_net_gain_or_loss\", StringType()),\n", + " StructField(\"homeready_program_indicator\", StringType()),\n", + " StructField(\"foreclosure_principal_write_off_amount\", StringType()),\n", + " StructField(\"relocation_mortgage_indicator\", StringType()),\n", + " StructField(\"zero_balance_code_change_date\", StringType()),\n", + " StructField(\"loan_holdback_indicator\", StringType()),\n", + " StructField(\"loan_holdback_effective_date\", StringType()),\n", + " StructField(\"delinquent_accrued_interest\", StringType()),\n", + " StructField(\"property_valuation_method\", StringType()),\n", + " StructField(\"high_balance_loan_indicator\", StringType()),\n", + " StructField(\"arm_initial_fixed-rate_period_lt_5_yr_indicator\", StringType()),\n", + " StructField(\"arm_product_type\", StringType()),\n", + " StructField(\"initial_fixed-rate_period\", StringType()),\n", + " StructField(\"interest_rate_adjustment_frequency\", StringType()),\n", + " StructField(\"next_interest_rate_adjustment_date\", StringType()),\n", + " StructField(\"next_payment_change_date\", StringType()),\n", + " StructField(\"index\", StringType()),\n", + " StructField(\"arm_cap_structure\", StringType()),\n", + " StructField(\"initial_interest_rate_cap_up_percent\", StringType()),\n", + " StructField(\"periodic_interest_rate_cap_up_percent\", StringType()),\n", + " StructField(\"lifetime_interest_rate_cap_up_percent\", StringType()),\n", + " StructField(\"mortgage_margin\", StringType()),\n", + " StructField(\"arm_balloon_indicator\", StringType()),\n", + " StructField(\"arm_plan_number\", StringType()),\n", + " StructField(\"borrower_assistance_plan\", StringType()),\n", + " StructField(\"hltv_refinance_option_indicator\", StringType()),\n", + " StructField(\"deal_name\", StringType()),\n", + " StructField(\"repurchase_make_whole_proceeds_flag\", StringType()),\n", + " StructField(\"alternative_delinquency_resolution\", StringType()),\n", + " StructField(\"alternative_delinquency_resolution_count\", StringType()),\n", + " StructField(\"total_deferral_amount\", StringType())\n", + " ])" ] }, { @@ -312,14 +362,14 @@ "outputs": [], "source": [ "def _get_quarter_from_csv_file_name():\n", - " return substring_index(substring_index(input_file_name(), \".\", 1), \"_\", -1)" + " return substring_index(substring_index(input_file_name(), \".\", 1), \"/\", -1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "* Define function to read Performance CSV data file" + "* Define function to read raw CSV data file" ] }, { @@ -328,37 +378,98 @@ "metadata": {}, "outputs": [], "source": [ - "def read_perf_csv(spark, path):\n", - " return spark.read.format(\"csv\") \\\n", - " .option(\"nullValue\", \"\") \\\n", - " .option(\"header\", \"false\") \\\n", - " .option(\"delimiter\", \"|\") \\\n", - " .schema(_csv_perf_schema) \\\n", + "def read_raw_csv(spark, path):\n", + " return spark.read.format('csv') \\\n", + " .option('nullValue', '') \\\n", + " .option('header', False) \\\n", + " .option('delimiter', '|') \\\n", + " .schema(_csv_raw_schema) \\\n", " .load(path) \\\n", - " .withColumn(\"quarter\", _get_quarter_from_csv_file_name())" + " .withColumn('quarter', _get_quarter_from_csv_file_name())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "* Define function to read Acquisition CSV file" + "* Functions to extract perf and acq columns from raw schema" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ - "def read_acq_csv(spark, path):\n", - " return spark.read.format(\"csv\") \\\n", - " .option(\"nullValue\", \"\") \\\n", - " .option(\"header\", \"false\") \\\n", - " .option(\"delimiter\", \"|\") \\\n", - " .schema(_csv_acq_schema) \\\n", - " .load(path) \\\n", - " .withColumn(\"quarter\", _get_quarter_from_csv_file_name())" + "def extract_perf_columns(rawDf):\n", + " perfDf = rawDf.select(\n", + " col(\"loan_id\"),\n", + " date_format(to_date(col(\"monthly_reporting_period\"),\"MMyyyy\"), \"MM/dd/yyyy\").alias(\"monthly_reporting_period\"),\n", + " upper(col(\"servicer\")).alias(\"servicer\"),\n", + " col(\"interest_rate\"),\n", + " col(\"current_actual_upb\"),\n", + " col(\"loan_age\"),\n", + " col(\"remaining_months_to_legal_maturity\"),\n", + " col(\"adj_remaining_months_to_maturity\"),\n", + " date_format(to_date(col(\"maturity_date\"),\"MMyyyy\"), \"MM/yyyy\").alias(\"maturity_date\"),\n", + " col(\"msa\"),\n", + " col(\"current_loan_delinquency_status\"),\n", + " col(\"mod_flag\"),\n", + " col(\"zero_balance_code\"),\n", + " date_format(to_date(col(\"zero_balance_effective_date\"),\"MMyyyy\"), \"MM/yyyy\").alias(\"zero_balance_effective_date\"),\n", + " date_format(to_date(col(\"last_paid_installment_date\"),\"MMyyyy\"), \"MM/dd/yyyy\").alias(\"last_paid_installment_date\"),\n", + " date_format(to_date(col(\"foreclosed_after\"),\"MMyyyy\"), \"MM/dd/yyyy\").alias(\"foreclosed_after\"),\n", + " date_format(to_date(col(\"disposition_date\"),\"MMyyyy\"), \"MM/dd/yyyy\").alias(\"disposition_date\"),\n", + " col(\"foreclosure_costs\"),\n", + " col(\"prop_preservation_and_repair_costs\"),\n", + " col(\"asset_recovery_costs\"),\n", + " col(\"misc_holding_expenses\"),\n", + " col(\"holding_taxes\"),\n", + " col(\"net_sale_proceeds\"),\n", + " col(\"credit_enhancement_proceeds\"),\n", + " col(\"repurchase_make_whole_proceeds\"),\n", + " col(\"other_foreclosure_proceeds\"),\n", + " col(\"non_interest_bearing_upb\"),\n", + " col(\"principal_forgiveness_upb\"),\n", + " col(\"repurchase_make_whole_proceeds_flag\"),\n", + " col(\"foreclosure_principal_write_off_amount\"),\n", + " col(\"servicing_activity_indicator\"),\n", + " col('quarter')\n", + " )\n", + " return perfDf.select(\"*\").filter(\"current_actual_upb != 0.0\")\n", + "\n", + "def extract_acq_columns(rawDf):\n", + " acqDf = rawDf.select(\n", + " col(\"loan_id\"),\n", + " col(\"orig_channel\"),\n", + " upper(col(\"seller_name\")).alias(\"seller_name\"),\n", + " col(\"orig_interest_rate\"),\n", + " col(\"orig_upb\"),\n", + " col(\"orig_loan_term\"),\n", + " date_format(to_date(col(\"orig_date\"),\"MMyyyy\"), \"MM/yyyy\").alias(\"orig_date\"),\n", + " date_format(to_date(col(\"first_pay_date\"),\"MMyyyy\"), \"MM/yyyy\").alias(\"first_pay_date\"),\n", + " col(\"orig_ltv\"),\n", + " col(\"orig_cltv\"),\n", + " col(\"num_borrowers\"),\n", + " col(\"dti\"),\n", + " col(\"borrower_credit_score\"),\n", + " col(\"first_home_buyer\"),\n", + " col(\"loan_purpose\"),\n", + " col(\"property_type\"),\n", + " col(\"num_units\"),\n", + " col(\"occupancy_status\"),\n", + " col(\"property_state\"),\n", + " col(\"zip\"),\n", + " col(\"mortgage_insurance_percent\"),\n", + " col(\"product_type\"),\n", + " col(\"coborrow_credit_score\"),\n", + " col(\"mortgage_insurance_type\"),\n", + " col(\"relocation_mortgage_indicator\"),\n", + " dense_rank().over(Window.partitionBy(\"loan_id\").orderBy(to_date(col(\"monthly_reporting_period\"),\"MMyyyy\"))).alias(\"rank\"),\n", + " col('quarter')\n", + " )\n", + "\n", + " return acqDf.select(\"*\").filter(col(\"rank\")==1)" ] }, { @@ -372,7 +483,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -398,7 +509,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -477,7 +588,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -524,7 +635,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -548,7 +659,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -583,7 +694,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -610,7 +721,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -619,12 +730,9 @@ "# CPU run, set to false, it can only make ETL run on CPU when is_save_dataset=True.\n", "# spark.conf.set(\"spark.rapids.sql.enabled\", \"false\")\n", "spark.conf.set(\"spark.sql.files.maxPartitionBytes\", \"1G\")\n", - "spark.conf.set(\"spark.sql.shuffle.partitions\", \"192\")\n", "spark.conf.set(\"spark.rapids.sql.explain\", \"ALL\")\n", - "spark.conf.set(\"spark.rapids.sql.incompatibleOps.enabled\", \"true\")\n", "spark.conf.set(\"spark.rapids.sql.batchSizeBytes\", \"512M\")\n", "spark.conf.set(\"spark.rapids.sql.reader.batchSizeBytes\", \"768M\")\n", - "spark.conf.set(\"spark.rapids.sql.incompatibleDateFormats.enabled\", \"true\")\n", "spark.conf.set(\"spark.rapids.sql.hasNans\", \"false\")\n", "# use GPU to read CSV\n", "spark.conf.set(\"spark.rapids.sql.csv.read.double.enabled\", \"true\")" @@ -639,7 +747,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 20, "metadata": { "scrolled": false }, @@ -648,27 +756,24 @@ "name": "stdout", "output_type": "stream", "text": [ - "ETL takes 41.10439682006836\n" + "ETL takes 135.9117729663849\n" ] } ], "source": [ "\n", "# read raw dataset\n", - "perf = read_perf_csv(spark, orig_perf_path)\n", - "acq = read_acq_csv(spark, orig_acq_path)\n", + "rawDf = read_raw_csv(spark, orig_raw_path)\n", + "acq = extract_acq_columns(rawDf)\n", + "perf = extract_perf_columns(rawDf)\n", "\n", "# run main function to process data\n", "out = run_mortgage(spark, perf, acq)\n", "\n", - "# split 80% for training, 20% for test\n", - "splits = out.randomSplit([0.8, 0.2])\n", - "\n", "# save processed data\n", "if is_save_dataset:\n", " start = time.time()\n", - " splits[0].write.parquet(output_path_train, mode=\"overwrite\")\n", - " splits[1].write.parquet(output_path_test, mode=\"overwrite\")\n", + " out.write.parquet(output_path_data, mode=\"overwrite\")\n", " end = time.time()\n", " print(\"ETL takes {}\".format(end - start))" ] @@ -689,7 +794,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -706,7 +811,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -725,7 +830,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -742,7 +847,7 @@ " StructField(\"seller_name\", FloatType()),\n", " StructField(\"mod_flag\", FloatType()),\n", " StructField(\"orig_interest_rate\", FloatType()),\n", - " StructField(\"orig_upb\", IntegerType()),\n", + " StructField(\"orig_upb\", DoubleType()),\n", " StructField(\"orig_loan_term\", IntegerType()),\n", " StructField(\"orig_ltv\", FloatType()),\n", " StructField(\"orig_cltv\", FloatType()),\n", @@ -764,17 +869,20 @@ "\n", "if is_save_dataset:\n", " # load dataset from file\n", - " train_data = reader.parquet(output_path_train)\n", - " test_data = reader.parquet(output_path_test)\n", + " etlDf = reader.parquet(output_path_data)\n", + " splits = etlDf.randomSplit([0.8, 0.2])\n", + " train_data = splits[0]\n", + " test_data = splits[1]\n", "else:\n", " # use Dataframe from ETL directly\n", + " splits = out.randomSplit([0.8, 0.2])\n", " train_data = splits[0]\n", " test_data = splits[1]" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -785,21 +893,21 @@ " \"growPolicy\": \"depthwise\",\n", " \"nthread\": 1,\n", " \"numRound\": 100,\n", - " \"numWorkers\": 2,\n", + " \"numWorkers\": 1,\n", "}\n", "classifier = XGBoostClassifier(**params).setLabelCol(label).setFeaturesCols(features)" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Training takes 23.666603565216064 seconds\n" + "Training takes 18.92583155632019 seconds\n" ] } ], @@ -815,7 +923,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -825,22 +933,22 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Transformation takes 10.464573383331299 seconds\n", + "Transformation takes 8.959877967834473 seconds\n", "+--------------+--------------------+--------------------+----------+\n", "|delinquency_12| rawPrediction| probability|prediction|\n", "+--------------+--------------------+--------------------+----------+\n", - "| 0|[11.3724613189697...|[0.99998849205439...| 0.0|\n", - "| 0|[8.75509834289550...|[0.99984236936143...| 0.0|\n", - "| 0|[8.56840324401855...|[0.99981002029380...| 0.0|\n", - "| 0|[8.45872020721435...|[0.99978800168901...| 0.0|\n", - "| 0|[8.45872020721435...|[0.99978800168901...| 0.0|\n", + "| 0|[7.92072248458862...|[0.99963699193904...| 0.0|\n", + "| 0|[7.92072248458862...|[0.99963699193904...| 0.0|\n", + "| 0|[8.43130302429199...|[0.99978211015695...| 0.0|\n", + "| 0|[8.20779895782470...|[0.99972755435737...| 0.0|\n", + "| 0|[8.885986328125,-...|[0.99986170543706...| 0.0|\n", "+--------------+--------------------+--------------------+----------+\n", "only showing top 5 rows\n", "\n" @@ -858,15 +966,15 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Evaluation takes 0.770418643951416 seconds\n", - "Accuracy is 0.9881320119084719\n" + "Evaluation takes 0.6158628463745117 seconds\n", + "Accuracy is 0.9861453808970397\n" ] } ], @@ -879,7 +987,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -903,7 +1011,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.2" + "version": "3.6.9" }, "name": "gpu-mortgage", "notebookId": 4440374682851873 diff --git a/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL.ipynb b/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL.ipynb index d36474176..c029770e1 100644 --- a/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL.ipynb +++ b/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL.ipynb @@ -6,7 +6,8 @@ "source": [ "## Prerequirement\n", "### 1. Download data\n", - "All data could be found at https://docs.rapids.ai/datasets/mortgage-data\n", + "\n", + "Download Single-Family Loan Performance Data from [Fannie Mae](https://datadynamics.fanniemae.com/data-dynamics/#/reportMenu;category=HP) website. \n", "\n", "### 2. Download needed jars\n", "* [rapids-4-spark_2.12-22.06.0.jar](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/22.06.0/rapids-4-spark_2.12-22.06.0.jar)\n", @@ -38,7 +39,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -60,7 +61,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -82,70 +83,121 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "# File schema\n", - "_csv_perf_schema = StructType([\n", - " StructField('loan_id', LongType()),\n", - " StructField('monthly_reporting_period', StringType()),\n", - " StructField('servicer', StringType()),\n", - " StructField('interest_rate', DoubleType()),\n", - " StructField('current_actual_upb', DoubleType()),\n", - " StructField('loan_age', DoubleType()),\n", - " StructField('remaining_months_to_legal_maturity', DoubleType()),\n", - " StructField('adj_remaining_months_to_maturity', DoubleType()),\n", - " StructField('maturity_date', StringType()),\n", - " StructField('msa', DoubleType()),\n", - " StructField('current_loan_delinquency_status', IntegerType()),\n", - " StructField('mod_flag', StringType()),\n", - " StructField('zero_balance_code', StringType()),\n", - " StructField('zero_balance_effective_date', StringType()),\n", - " StructField('last_paid_installment_date', StringType()),\n", - " StructField('foreclosed_after', StringType()),\n", - " StructField('disposition_date', StringType()),\n", - " StructField('foreclosure_costs', DoubleType()),\n", - " StructField('prop_preservation_and_repair_costs', DoubleType()),\n", - " StructField('asset_recovery_costs', DoubleType()),\n", - " StructField('misc_holding_expenses', DoubleType()),\n", - " StructField('holding_taxes', DoubleType()),\n", - " StructField('net_sale_proceeds', DoubleType()),\n", - " StructField('credit_enhancement_proceeds', DoubleType()),\n", - " StructField('repurchase_make_whole_proceeds', StringType()),\n", - " StructField('other_foreclosure_proceeds', DoubleType()),\n", - " StructField('non_interest_bearing_upb', DoubleType()),\n", - " StructField('principal_forgiveness_upb', StringType()),\n", - " StructField('repurchase_make_whole_proceeds_flag', StringType()),\n", - " StructField('foreclosure_principal_write_off_amount', StringType()),\n", - " StructField('servicing_activity_indicator', StringType())])\n", - "\n", - "_csv_acq_schema = StructType([\n", - " StructField('loan_id', LongType()),\n", - " StructField('orig_channel', StringType()),\n", - " StructField('seller_name', StringType()),\n", - " StructField('orig_interest_rate', DoubleType()),\n", - " StructField('orig_upb', IntegerType()),\n", - " StructField('orig_loan_term', IntegerType()),\n", - " StructField('orig_date', StringType()),\n", - " StructField('first_pay_date', StringType()),\n", - " StructField('orig_ltv', DoubleType()),\n", - " StructField('orig_cltv', DoubleType()),\n", - " StructField('num_borrowers', DoubleType()),\n", - " StructField('dti', DoubleType()),\n", - " StructField('borrower_credit_score', DoubleType()),\n", - " StructField('first_home_buyer', StringType()),\n", - " StructField('loan_purpose', StringType()),\n", - " StructField('property_type', StringType()),\n", - " StructField('num_units', IntegerType()),\n", - " StructField('occupancy_status', StringType()),\n", - " StructField('property_state', StringType()),\n", - " StructField('zip', IntegerType()),\n", - " StructField('mortgage_insurance_percent', DoubleType()),\n", - " StructField('product_type', StringType()),\n", - " StructField('coborrow_credit_score', DoubleType()),\n", - " StructField('mortgage_insurance_type', DoubleType()),\n", - " StructField('relocation_mortgage_indicator', StringType())])" + "_csv_raw_schema = StructType([\n", + " StructField(\"reference_pool_id\", StringType()),\n", + " StructField(\"loan_id\", LongType()),\n", + " StructField(\"monthly_reporting_period\", StringType()),\n", + " StructField(\"orig_channel\", StringType()),\n", + " StructField(\"seller_name\", StringType()),\n", + " StructField(\"servicer\", StringType()),\n", + " StructField(\"master_servicer\", StringType()),\n", + " StructField(\"orig_interest_rate\", DoubleType()),\n", + " StructField(\"interest_rate\", DoubleType()),\n", + " StructField(\"orig_upb\", DoubleType()),\n", + " StructField(\"upb_at_issuance\", StringType()),\n", + " StructField(\"current_actual_upb\", DoubleType()),\n", + " StructField(\"orig_loan_term\", IntegerType()),\n", + " StructField(\"orig_date\", StringType()),\n", + " StructField(\"first_pay_date\", StringType()), \n", + " StructField(\"loan_age\", DoubleType()),\n", + " StructField(\"remaining_months_to_legal_maturity\", DoubleType()),\n", + " StructField(\"adj_remaining_months_to_maturity\", DoubleType()),\n", + " StructField(\"maturity_date\", StringType()),\n", + " StructField(\"orig_ltv\", DoubleType()),\n", + " StructField(\"orig_cltv\", DoubleType()),\n", + " StructField(\"num_borrowers\", DoubleType()),\n", + " StructField(\"dti\", DoubleType()),\n", + " StructField(\"borrower_credit_score\", DoubleType()),\n", + " StructField(\"coborrow_credit_score\", DoubleType()),\n", + " StructField(\"first_home_buyer\", StringType()),\n", + " StructField(\"loan_purpose\", StringType()),\n", + " StructField(\"property_type\", StringType()),\n", + " StructField(\"num_units\", IntegerType()),\n", + " StructField(\"occupancy_status\", StringType()),\n", + " StructField(\"property_state\", StringType()),\n", + " StructField(\"msa\", DoubleType()),\n", + " StructField(\"zip\", IntegerType()),\n", + " StructField(\"mortgage_insurance_percent\", DoubleType()),\n", + " StructField(\"product_type\", StringType()),\n", + " StructField(\"prepayment_penalty_indicator\", StringType()),\n", + " StructField(\"interest_only_loan_indicator\", StringType()),\n", + " StructField(\"interest_only_first_principal_and_interest_payment_date\", StringType()),\n", + " StructField(\"months_to_amortization\", StringType()),\n", + " StructField(\"current_loan_delinquency_status\", IntegerType()),\n", + " StructField(\"loan_payment_history\", StringType()),\n", + " StructField(\"mod_flag\", StringType()),\n", + " StructField(\"mortgage_insurance_cancellation_indicator\", StringType()),\n", + " StructField(\"zero_balance_code\", StringType()),\n", + " StructField(\"zero_balance_effective_date\", StringType()),\n", + " StructField(\"upb_at_the_time_of_removal\", StringType()),\n", + " StructField(\"repurchase_date\", StringType()),\n", + " StructField(\"scheduled_principal_current\", StringType()),\n", + " StructField(\"total_principal_current\", StringType()),\n", + " StructField(\"unscheduled_principal_current\", StringType()),\n", + " StructField(\"last_paid_installment_date\", StringType()),\n", + " StructField(\"foreclosed_after\", StringType()),\n", + " StructField(\"disposition_date\", StringType()),\n", + " StructField(\"foreclosure_costs\", DoubleType()),\n", + " StructField(\"prop_preservation_and_repair_costs\", DoubleType()),\n", + " StructField(\"asset_recovery_costs\", DoubleType()),\n", + " StructField(\"misc_holding_expenses\", DoubleType()),\n", + " StructField(\"holding_taxes\", DoubleType()),\n", + " StructField(\"net_sale_proceeds\", DoubleType()),\n", + " StructField(\"credit_enhancement_proceeds\", DoubleType()),\n", + " StructField(\"repurchase_make_whole_proceeds\", StringType()),\n", + " StructField(\"other_foreclosure_proceeds\", DoubleType()),\n", + " StructField(\"non_interest_bearing_upb\", DoubleType()),\n", + " StructField(\"principal_forgiveness_upb\", StringType()),\n", + " StructField(\"original_list_start_date\", StringType()),\n", + " StructField(\"original_list_price\", StringType()),\n", + " StructField(\"current_list_start_date\", StringType()),\n", + " StructField(\"current_list_price\", StringType()),\n", + " StructField(\"borrower_credit_score_at_issuance\", StringType()),\n", + " StructField(\"co-borrower_credit_score_at_issuance\", StringType()),\n", + " StructField(\"borrower_credit_score_current\", StringType()),\n", + " StructField(\"co-Borrower_credit_score_current\", StringType()),\n", + " StructField(\"mortgage_insurance_type\", DoubleType()),\n", + " StructField(\"servicing_activity_indicator\", StringType()),\n", + " StructField(\"current_period_modification_loss_amount\", StringType()),\n", + " StructField(\"cumulative_modification_loss_amount\", StringType()),\n", + " StructField(\"current_period_credit_event_net_gain_or_loss\", StringType()),\n", + " StructField(\"cumulative_credit_event_net_gain_or_loss\", StringType()),\n", + " StructField(\"homeready_program_indicator\", StringType()),\n", + " StructField(\"foreclosure_principal_write_off_amount\", StringType()),\n", + " StructField(\"relocation_mortgage_indicator\", StringType()),\n", + " StructField(\"zero_balance_code_change_date\", StringType()),\n", + " StructField(\"loan_holdback_indicator\", StringType()),\n", + " StructField(\"loan_holdback_effective_date\", StringType()),\n", + " StructField(\"delinquent_accrued_interest\", StringType()),\n", + " StructField(\"property_valuation_method\", StringType()),\n", + " StructField(\"high_balance_loan_indicator\", StringType()),\n", + " StructField(\"arm_initial_fixed-rate_period_lt_5_yr_indicator\", StringType()),\n", + " StructField(\"arm_product_type\", StringType()),\n", + " StructField(\"initial_fixed-rate_period\", StringType()),\n", + " StructField(\"interest_rate_adjustment_frequency\", StringType()),\n", + " StructField(\"next_interest_rate_adjustment_date\", StringType()),\n", + " StructField(\"next_payment_change_date\", StringType()),\n", + " StructField(\"index\", StringType()),\n", + " StructField(\"arm_cap_structure\", StringType()),\n", + " StructField(\"initial_interest_rate_cap_up_percent\", StringType()),\n", + " StructField(\"periodic_interest_rate_cap_up_percent\", StringType()),\n", + " StructField(\"lifetime_interest_rate_cap_up_percent\", StringType()),\n", + " StructField(\"mortgage_margin\", StringType()),\n", + " StructField(\"arm_balloon_indicator\", StringType()),\n", + " StructField(\"arm_plan_number\", StringType()),\n", + " StructField(\"borrower_assistance_plan\", StringType()),\n", + " StructField(\"hltv_refinance_option_indicator\", StringType()),\n", + " StructField(\"deal_name\", StringType()),\n", + " StructField(\"repurchase_make_whole_proceeds_flag\", StringType()),\n", + " StructField(\"alternative_delinquency_resolution\", StringType()),\n", + " StructField(\"alternative_delinquency_resolution_count\", StringType()),\n", + " StructField(\"total_deferral_amount\", StringType())\n", + " ])" ] }, { @@ -157,7 +209,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -254,7 +306,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -300,67 +352,129 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 2. Define ETL Process\n", - "\n", - "Define the function to do the ETL process\n", - "\n", - "#### 2.1 Define Functions to Read Raw CSV File\n", - "\n", - "* Define function to get quarter from input CSV file name" + "* Functions to extract perf and acq columns from raw schema" ] }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ - "def _get_quarter_from_csv_file_name():\n", - " return substring_index(substring_index(input_file_name(), '.', 1), '_', -1)" + "def extract_perf_columns(rawDf):\n", + " perfDf = rawDf.select(\n", + " col(\"loan_id\"),\n", + " date_format(to_date(col(\"monthly_reporting_period\"),\"MMyyyy\"), \"MM/dd/yyyy\").alias(\"monthly_reporting_period\"),\n", + " upper(col(\"servicer\")).alias(\"servicer\"),\n", + " col(\"interest_rate\"),\n", + " col(\"current_actual_upb\"),\n", + " col(\"loan_age\"),\n", + " col(\"remaining_months_to_legal_maturity\"),\n", + " col(\"adj_remaining_months_to_maturity\"),\n", + " date_format(to_date(col(\"maturity_date\"),\"MMyyyy\"), \"MM/yyyy\").alias(\"maturity_date\"),\n", + " col(\"msa\"),\n", + " col(\"current_loan_delinquency_status\"),\n", + " col(\"mod_flag\"),\n", + " col(\"zero_balance_code\"),\n", + " date_format(to_date(col(\"zero_balance_effective_date\"),\"MMyyyy\"), \"MM/yyyy\").alias(\"zero_balance_effective_date\"),\n", + " date_format(to_date(col(\"last_paid_installment_date\"),\"MMyyyy\"), \"MM/dd/yyyy\").alias(\"last_paid_installment_date\"),\n", + " date_format(to_date(col(\"foreclosed_after\"),\"MMyyyy\"), \"MM/dd/yyyy\").alias(\"foreclosed_after\"),\n", + " date_format(to_date(col(\"disposition_date\"),\"MMyyyy\"), \"MM/dd/yyyy\").alias(\"disposition_date\"),\n", + " col(\"foreclosure_costs\"),\n", + " col(\"prop_preservation_and_repair_costs\"),\n", + " col(\"asset_recovery_costs\"),\n", + " col(\"misc_holding_expenses\"),\n", + " col(\"holding_taxes\"),\n", + " col(\"net_sale_proceeds\"),\n", + " col(\"credit_enhancement_proceeds\"),\n", + " col(\"repurchase_make_whole_proceeds\"),\n", + " col(\"other_foreclosure_proceeds\"),\n", + " col(\"non_interest_bearing_upb\"),\n", + " col(\"principal_forgiveness_upb\"),\n", + " col(\"repurchase_make_whole_proceeds_flag\"),\n", + " col(\"foreclosure_principal_write_off_amount\"),\n", + " col(\"servicing_activity_indicator\"),\n", + " col('quarter')\n", + " )\n", + "\n", + " return perfDf.select(\"*\").filter(\"current_actual_upb != 0.0\")\n", + "\n", + "def extract_acq_columns(rawDf):\n", + " acqDf = rawDf.select(\n", + " col(\"loan_id\"),\n", + " col(\"orig_channel\"),\n", + " upper(col(\"seller_name\")).alias(\"seller_name\"),\n", + " col(\"orig_interest_rate\"),\n", + " col(\"orig_upb\"),\n", + " col(\"orig_loan_term\"),\n", + " date_format(to_date(col(\"orig_date\"),\"MMyyyy\"), \"MM/yyyy\").alias(\"orig_date\"),\n", + " date_format(to_date(col(\"first_pay_date\"),\"MMyyyy\"), \"MM/yyyy\").alias(\"first_pay_date\"),\n", + " col(\"orig_ltv\"),\n", + " col(\"orig_cltv\"),\n", + " col(\"num_borrowers\"),\n", + " col(\"dti\"),\n", + " col(\"borrower_credit_score\"),\n", + " col(\"first_home_buyer\"),\n", + " col(\"loan_purpose\"),\n", + " col(\"property_type\"),\n", + " col(\"num_units\"),\n", + " col(\"occupancy_status\"),\n", + " col(\"property_state\"),\n", + " col(\"zip\"),\n", + " col(\"mortgage_insurance_percent\"),\n", + " col(\"product_type\"),\n", + " col(\"coborrow_credit_score\"),\n", + " col(\"mortgage_insurance_type\"),\n", + " col(\"relocation_mortgage_indicator\"),\n", + " dense_rank().over(Window.partitionBy(\"loan_id\").orderBy(to_date(col(\"monthly_reporting_period\"),\"MMyyyy\"))).alias(\"rank\"),\n", + " col('quarter')\n", + " )\n", + "\n", + " return acqDf.select(\"*\").filter(col(\"rank\")==1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "* Define function to read Performance CSV data file" + "### 2. Define ETL Process\n", + "\n", + "Define the function to do the ETL process\n", + "\n", + "#### 2.1 Define Functions to Read Raw CSV File\n", + "\n", + "* Define function to get quarter from input CSV file name" ] }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ - "def read_perf_csv(spark, path):\n", - " return spark.read.format('csv') \\\n", - " .option('nullValue', '') \\\n", - " .option('header', 'false') \\\n", - " .option('delimiter', '|') \\\n", - " .schema(_csv_perf_schema) \\\n", - " .load(path) \\\n", - " .withColumn('quarter', _get_quarter_from_csv_file_name())" + "def _get_quarter_from_csv_file_name():\n", + " return substring_index(substring_index(input_file_name(), '.', 1), '/', -1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "* Define function to read Acquisition CSV file" + "* Define function to read raw CSV data file" ] }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ - "def read_acq_csv(spark, path):\n", + "def read_raw_csv(spark, path):\n", " return spark.read.format('csv') \\\n", " .option('nullValue', '') \\\n", - " .option('header', 'false') \\\n", + " .option('header', False) \\\n", " .option('delimiter', '|') \\\n", - " .schema(_csv_acq_schema) \\\n", + " .schema(_csv_raw_schema) \\\n", " .load(path) \\\n", " .withColumn('quarter', _get_quarter_from_csv_file_name())" ] @@ -376,7 +490,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ @@ -402,7 +516,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 49, "metadata": {}, "outputs": [], "source": [ @@ -481,7 +595,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 50, "metadata": {}, "outputs": [], "source": [ @@ -528,7 +642,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -552,7 +666,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 52, "metadata": {}, "outputs": [], "source": [ @@ -587,7 +701,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 53, "metadata": {}, "outputs": [], "source": [ @@ -615,31 +729,13 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 54, "metadata": {}, "outputs": [], "source": [ "# You need to update them to your real paths!\n", - "dataRoot = os.getenv(\"DATA_ROOT\", \"/data\")\n", - "orig_perf_path=dataRoot + '/mortgage/Performance/'\n", - "orig_acq_path=dataRoot + '/mortgage/Acquisition/'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* Define temporary folder path " - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [], - "source": [ - "tmp_perf_path=dataRoot + '/mortgage/perf/'\n", - "tmp_acq_path=dataRoot + '/mortgage/acq/'" + "dataRoot = os.getenv(\"DATA_ROOT\", \"/mortgage\")\n", + "orig_raw_path = dataRoot + '/input/'" ] }, { @@ -651,11 +747,14 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 56, "metadata": {}, "outputs": [], "source": [ - "output_path=dataRoot + '/mortgage/output/'" + "output_path = dataRoot + '/output/data/'\n", + "output_path_train = dataRoot + '/output/train/'\n", + "output_path_eval = dataRoot + '/output/eval/'\n", + "save_train_eval_dataset = True" ] }, { @@ -667,12 +766,11 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 57, "metadata": {}, "outputs": [], "source": [ "spark.conf.set('spark.rapids.sql.explain', 'ALL')\n", - "spark.conf.set('spark.rapids.sql.incompatibleOps.enabled', 'true')\n", "spark.conf.set('spark.rapids.sql.batchSizeBytes', '512M')\n", "spark.conf.set('spark.rapids.sql.reader.batchSizeBytes', '768M')" ] @@ -681,50 +779,25 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Run Part\n", - "### Read Raw File and Transcode Data\n", - "#### 1. Add additional Spark settings" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [], - "source": [ - "# we want a few big files instead of lots of small files\n", - "spark.conf.set('spark.sql.files.maxPartitionBytes', '200G')" + "## Run Part" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### 2. Read Raw File and Transcode to Parquet" + "### Read Raw File" ] }, { "cell_type": "code", - "execution_count": 62, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "6.568682670593262\n" - ] - } - ], + "outputs": [], "source": [ - "start = time.time()\n", - "# read data and transcode to qarquet\n", - "acq = read_acq_csv(spark, orig_acq_path)\n", - "acq.repartition(12).write.parquet(tmp_acq_path, mode='overwrite')\n", - "perf = read_perf_csv(spark, orig_perf_path)\n", - "perf.coalesce(96).write.parquet(tmp_perf_path, mode='overwrite')\n", - "end = time.time()\n", - "print(end - start)" + "rawDf = read_raw_csv(spark, orig_raw_path)\n", + "acq = extract_acq_columns(rawDf)\n", + "perf = extract_perf_columns(rawDf)" ] }, { @@ -737,7 +810,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 60, "metadata": {}, "outputs": [], "source": [ @@ -746,7 +819,9 @@ "# CPU run, set to false\n", "# spark.conf.set('spark.rapids.sql.enabled', 'false')\n", "spark.conf.set('spark.sql.files.maxPartitionBytes', '1G')\n", - "spark.conf.set('spark.sql.shuffle.partitions', '192')" + "spark.conf.set(\"spark.rapids.sql.hasNans\", \"false\")\n", + "# use GPU to read CSV\n", + "spark.conf.set(\"spark.rapids.sql.csv.read.double.enabled\", \"true\")" ] }, { @@ -758,7 +833,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 61, "metadata": {}, "outputs": [ { @@ -766,786 +841,881 @@ "output_type": "stream", "text": [ "== Physical Plan ==\n", - "*(5) GpuColumnarToRow false\n", - "+- !GpuProject [gpucoalesce(orig_channel#27851, 0) AS orig_channel#29615, gpucoalesce(first_home_buyer#28053, 0) AS first_home_buyer#29616, gpucoalesce(loan_purpose#28255, 0) AS loan_purpose#29617, gpucoalesce(property_type#28457, 0) AS property_type#29618, gpucoalesce(occupancy_status#28659, 0) AS occupancy_status#29619, gpucoalesce(property_state#28861, 0) AS property_state#29620, gpucoalesce(relocation_mortgage_indicator#29063, 0) AS relocation_mortgage_indicator#29621, gpucoalesce(seller_name#29265, 0) AS seller_name#29622, gpucoalesce(id#27657, 0) AS mod_flag#29623, gpucoalesce(gpunanvl(orig_interest_rate#26291, null), 0.0) AS orig_interest_rate#29624, gpucoalesce(orig_upb#26292, 0) AS orig_upb#29625, gpucoalesce(orig_loan_term#26293, 0) AS orig_loan_term#29626, gpucoalesce(gpunanvl(orig_ltv#26296, null), 0.0) AS orig_ltv#29627, gpucoalesce(gpunanvl(orig_cltv#26297, null), 0.0) AS orig_cltv#29628, gpucoalesce(gpunanvl(num_borrowers#26298, null), 0.0) AS num_borrowers#29629, gpucoalesce(gpunanvl(dti#26299, null), 0.0) AS dti#29630, gpucoalesce(gpunanvl(borrower_credit_score#26300, null), 0.0) AS borrower_credit_score#29631, gpucoalesce(num_units#26304, 0) AS num_units#29632, gpucoalesce(zip#26307, 0) AS zip#29633, gpucoalesce(gpunanvl(mortgage_insurance_percent#26308, null), 0.0) AS mortgage_insurance_percent#29634, gpucoalesce(current_loan_delinquency_status#26234, 0) AS current_loan_delinquency_status#29635, gpucoalesce(gpunanvl(current_actual_upb#26228, null), 0.0) AS current_actual_upb#29636, gpucoalesce(gpunanvl(interest_rate#26227, null), 0.0) AS interest_rate#29637, gpucoalesce(gpunanvl(loan_age#26229, null), 0.0) AS loan_age#29638, ... 3 more fields]\n", - " +- !GpuBroadcastHashJoin [mod_flag#26235], [mod_flag#29333], LeftOuter, BuildRight\n", - " :- !GpuProject [interest_rate#26227, current_actual_upb#26228, loan_age#26229, msa#26233, current_loan_delinquency_status#26234, mod_flag#26235, non_interest_bearing_upb#26250, delinquency_12#27036, orig_interest_rate#26291, orig_upb#26292, orig_loan_term#26293, orig_ltv#26296, orig_cltv#26297, num_borrowers#26298, dti#26299, borrower_credit_score#26300, num_units#26304, zip#26307, mortgage_insurance_percent#26308, orig_channel#27851, first_home_buyer#28053, loan_purpose#28255, property_type#28457, occupancy_status#28659, ... 3 more fields]\n", - " : +- !GpuBroadcastHashJoin [seller_name#27396], [seller_name#29131], LeftOuter, BuildRight\n", - " : :- !GpuProject [interest_rate#26227, current_actual_upb#26228, loan_age#26229, msa#26233, current_loan_delinquency_status#26234, mod_flag#26235, non_interest_bearing_upb#26250, delinquency_12#27036, seller_name#27396, orig_interest_rate#26291, orig_upb#26292, orig_loan_term#26293, orig_ltv#26296, orig_cltv#26297, num_borrowers#26298, dti#26299, borrower_credit_score#26300, num_units#26304, zip#26307, mortgage_insurance_percent#26308, orig_channel#27851, first_home_buyer#28053, loan_purpose#28255, property_type#28457, ... 3 more fields]\n", - " : : +- !GpuBroadcastHashJoin [relocation_mortgage_indicator#26312], [relocation_mortgage_indicator#28929], LeftOuter, BuildRight\n", - " : : :- !GpuProject [interest_rate#26227, current_actual_upb#26228, loan_age#26229, msa#26233, current_loan_delinquency_status#26234, mod_flag#26235, non_interest_bearing_upb#26250, delinquency_12#27036, seller_name#27396, orig_interest_rate#26291, orig_upb#26292, orig_loan_term#26293, orig_ltv#26296, orig_cltv#26297, num_borrowers#26298, dti#26299, borrower_credit_score#26300, num_units#26304, zip#26307, mortgage_insurance_percent#26308, relocation_mortgage_indicator#26312, orig_channel#27851, first_home_buyer#28053, loan_purpose#28255, ... 3 more fields]\n", - " : : : +- !GpuBroadcastHashJoin [property_state#26306], [property_state#28727], LeftOuter, BuildRight\n", - " : : : :- !GpuProject [interest_rate#26227, current_actual_upb#26228, loan_age#26229, msa#26233, current_loan_delinquency_status#26234, mod_flag#26235, non_interest_bearing_upb#26250, delinquency_12#27036, seller_name#27396, orig_interest_rate#26291, orig_upb#26292, orig_loan_term#26293, orig_ltv#26296, orig_cltv#26297, num_borrowers#26298, dti#26299, borrower_credit_score#26300, num_units#26304, property_state#26306, zip#26307, mortgage_insurance_percent#26308, relocation_mortgage_indicator#26312, orig_channel#27851, first_home_buyer#28053, ... 3 more fields]\n", - " : : : : +- !GpuBroadcastHashJoin [occupancy_status#26305], [occupancy_status#28525], LeftOuter, BuildRight\n", - " : : : : :- !GpuProject [interest_rate#26227, current_actual_upb#26228, loan_age#26229, msa#26233, current_loan_delinquency_status#26234, mod_flag#26235, non_interest_bearing_upb#26250, delinquency_12#27036, seller_name#27396, orig_interest_rate#26291, orig_upb#26292, orig_loan_term#26293, orig_ltv#26296, orig_cltv#26297, num_borrowers#26298, dti#26299, borrower_credit_score#26300, num_units#26304, occupancy_status#26305, property_state#26306, zip#26307, mortgage_insurance_percent#26308, relocation_mortgage_indicator#26312, orig_channel#27851, ... 3 more fields]\n", - " : : : : : +- !GpuBroadcastHashJoin [property_type#26303], [property_type#28323], LeftOuter, BuildRight\n", - " : : : : : :- !GpuProject [interest_rate#26227, current_actual_upb#26228, loan_age#26229, msa#26233, current_loan_delinquency_status#26234, mod_flag#26235, non_interest_bearing_upb#26250, delinquency_12#27036, seller_name#27396, orig_interest_rate#26291, orig_upb#26292, orig_loan_term#26293, orig_ltv#26296, orig_cltv#26297, num_borrowers#26298, dti#26299, borrower_credit_score#26300, property_type#26303, num_units#26304, occupancy_status#26305, property_state#26306, zip#26307, mortgage_insurance_percent#26308, relocation_mortgage_indicator#26312, ... 3 more fields]\n", - " : : : : : : +- !GpuBroadcastHashJoin [loan_purpose#26302], [loan_purpose#28121], LeftOuter, BuildRight\n", - " : : : : : : :- !GpuProject [interest_rate#26227, current_actual_upb#26228, loan_age#26229, msa#26233, current_loan_delinquency_status#26234, mod_flag#26235, non_interest_bearing_upb#26250, delinquency_12#27036, seller_name#27396, orig_interest_rate#26291, orig_upb#26292, orig_loan_term#26293, orig_ltv#26296, orig_cltv#26297, num_borrowers#26298, dti#26299, borrower_credit_score#26300, loan_purpose#26302, property_type#26303, num_units#26304, occupancy_status#26305, property_state#26306, zip#26307, mortgage_insurance_percent#26308, ... 3 more fields]\n", - " : : : : : : : +- !GpuBroadcastHashJoin [first_home_buyer#26301], [first_home_buyer#27919], LeftOuter, BuildRight\n", - " : : : : : : : :- !GpuProject [interest_rate#26227, current_actual_upb#26228, loan_age#26229, msa#26233, current_loan_delinquency_status#26234, mod_flag#26235, non_interest_bearing_upb#26250, delinquency_12#27036, seller_name#27396, orig_interest_rate#26291, orig_upb#26292, orig_loan_term#26293, orig_ltv#26296, orig_cltv#26297, num_borrowers#26298, dti#26299, borrower_credit_score#26300, first_home_buyer#26301, loan_purpose#26302, property_type#26303, num_units#26304, occupancy_status#26305, property_state#26306, zip#26307, ... 3 more fields]\n", - " : : : : : : : : +- !GpuBroadcastHashJoin [orig_channel#26289], [orig_channel#27717], LeftOuter, BuildRight\n", - " : : : : : : : : :- !GpuProject [interest_rate#26227, current_actual_upb#26228, loan_age#26229, msa#26233, current_loan_delinquency_status#26234, mod_flag#26235, non_interest_bearing_upb#26250, delinquency_12#27036, orig_channel#26289, seller_name#27396, orig_interest_rate#26291, orig_upb#26292, orig_loan_term#26293, orig_ltv#26296, orig_cltv#26297, num_borrowers#26298, dti#26299, borrower_credit_score#26300, first_home_buyer#26301, loan_purpose#26302, property_type#26303, num_units#26304, occupancy_status#26305, property_state#26306, ... 3 more fields]\n", - " : : : : : : : : : +- !GpuShuffledHashJoin [loan_id#26224L, quarter#26255], [loan_id#26288L, quarter#26313], Inner, BuildRight\n", - " : : : : : : : : : :- GpuCoalesceBatches TargetSize(536870912)\n", - " : : : : : : : : : : +- !GpuColumnarExchange gpuhashpartitioning(loan_id#26224L, quarter#26255, 192), true, [id=#17112]\n", - " : : : : : : : : : : +- !GpuProject [quarter#26255, loan_id#26224L, interest_rate#26227, current_actual_upb#26228, loan_age#26229, msa#26233, current_loan_delinquency_status#26234, mod_flag#26235, non_interest_bearing_upb#26250, delinquency_12#27036]\n", - " : : : : : : : : : : +- !GpuShuffledHashJoin [quarter#26255, loan_id#26224L, cast(timestamp_year#27100 as bigint), cast(timestamp_month#27064 as bigint)], [quarter#27167, loan_id#27136L, timestamp_year#26990L, timestamp_month#27019L], LeftOuter, BuildRight\n", - " : : : : : : : : : : :- GpuCoalesceBatches TargetSize(536870912)\n", - " : : : : : : : : : : : +- !GpuColumnarExchange gpuhashpartitioning(quarter#26255, loan_id#26224L, cast(timestamp_year#27100 as bigint), cast(timestamp_month#27064 as bigint), 192), true, [id=#17081]\n", - " : : : : : : : : : : : +- GpuRowToColumnar TargetSize(536870912)\n", - " : : : : : : : : : : : +- *(1) Project [loan_id#26224L, interest_rate#26227, current_actual_upb#26228, loan_age#26229, msa#26233, current_loan_delinquency_status#26234, mod_flag#26235, non_interest_bearing_upb#26250, quarter#26255, month(cast(cast(unix_timestamp(monthly_reporting_period#26225, MM/dd/yyyy, Some(Asia/Shanghai)) as timestamp) as date)) AS timestamp_month#27064, year(cast(cast(unix_timestamp(monthly_reporting_period#26225, MM/dd/yyyy, Some(Asia/Shanghai)) as timestamp) as date)) AS timestamp_year#27100]\n", - " : : : : : : : : : : : +- *(1) GpuColumnarToRow false\n", - " : : : : : : : : : : : +- !GpuFilter (gpuisnotnull(loan_id#26224L) AND gpuisnotnull(quarter#26255))\n", - " : : : : : : : : : : : +- GpuFileScan parquet [loan_id#26224L,monthly_reporting_period#26225,interest_rate#26227,current_actual_upb#26228,loan_age#26229,msa#26233,current_loan_delinquency_status#26234,mod_flag#26235,non_interest_bearing_upb#26250,quarter#26255] Batched: true, DataFilters: [isnotnull(loan_id#26224L), isnotnull(quarter#26255)], Format: Parquet, Location: InMemoryFileIndex[file:/home/mengmengg/xgboost4j_spark/data/perf], PartitionFilters: [], PushedFilters: [IsNotNull(loan_id), IsNotNull(quarter)], ReadSchema: struct= 1) THEN cast(cast(unix_timestamp(monthly_reporting_period#26886, MM/dd/yyyy, Some(Asia/Shanghai)) as timestamp) as date) END AS delinquency_30#26658, CASE WHEN (current_loan_delinquency_status#26895 >= 3) THEN cast(cast(unix_timestamp(monthly_reporting_period#26886, MM/dd/yyyy, Some(Asia/Shanghai)) as timestamp) as date) END AS delinquency_90#26659, CASE WHEN (current_loan_delinquency_status#26895 >= 6) THEN cast(cast(unix_timestamp(monthly_reporting_period#26886, MM/dd/yyyy, Some(Asia/Shanghai)) as timestamp) as date) END AS delinquency_180#26660]\n", - " : : : : : : : : : : +- *(3) GpuColumnarToRow false\n", - " : : : : : : : : : : +- !GpuFilter (gpuisnotnull(loan_id#26885L) AND gpuisnotnull(quarter#26916))\n", - " : : : : : : : : : : +- GpuFileScan parquet [loan_id#26885L,monthly_reporting_period#26886,current_loan_delinquency_status#26895,quarter#26916] Batched: true, DataFilters: [isnotnull(loan_id#26885L), isnotnull(quarter#26916)], Format: Parquet, Location: InMemoryFileIndex[file:/home/mengmengg/xgboost4j_spark/data/perf], PartitionFilters: [], PushedFilters: [IsNotNull(loan_id), IsNotNull(quarter)], ReadSchema: struct\n", - " : : : : : : : : : +- GpuCoalesceBatches RequireSingleBatch\n", - " : : : : : : : : : +- !GpuColumnarExchange gpuhashpartitioning(quarter#27167, loan_id#27136L, timestamp_year#26990L, timestamp_month#27019L, 192), true, [id=#15140]\n", - " : : : : : : : : : +- GpuHashAggregate(keys=[quarter#27167, loan_id#27136L, josh_mody_n#26947L, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680, month_y#26931], functions=[]), filters=ArrayBuffer())\n", - " : : : : : : : : : +- GpuCoalesceBatches TargetSize(536870912)\n", - " : : : : : : : : : +- !GpuColumnarExchange gpuhashpartitioning(quarter#27167, loan_id#27136L, josh_mody_n#26947L, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680, month_y#26931, 192), true, [id=#15137]\n", - " : : : : : : : : : +- GpuHashAggregate(keys=[quarter#27167, loan_id#27136L, josh_mody_n#26947L, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680, month_y#26931], functions=[]), filters=ArrayBuffer())\n", - " : : : : : : : : : +- !GpuProject [quarter#27167, FLOOR((cast(((((timestamp_year#26770 * 12) + timestamp_month#26734) - 24000) - month_y#26931) as double) / 12.0)) AS josh_mody_n#26947L, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680, loan_id#27136L, month_y#26931]\n", - " : : : : : : : : : +- GpuCoalesceBatches TargetSize(536870912)\n", - " : : : : : : : : : +- !GpuFilter (gpuisnotnull(CASE WHEN ((((24000 + (FLOOR((cast(((((timestamp_year#26770 * 12) + timestamp_month#26734) - 24000) - month_y#26931) as double) / 12.0)) * 12)) + cast(month_y#26931 as bigint)) pmod 12) = 0) THEN 12 ELSE (((24000 + (FLOOR((cast(((((timestamp_year#26770 * 12) + timestamp_month#26734) - 24000) - month_y#26931) as double) / 12.0)) * 12)) + cast(month_y#26931 as bigint)) pmod 12) END) AND gpuisnotnull(FLOOR((cast(((24000 + (FLOOR((cast(((((timestamp_year#26770 * 12) + timestamp_month#26734) - 24000) - month_y#26931) as double) / 12.0)) * 12)) + cast((month_y#26931 - 1) as bigint)) as double) / 12.0))))\n", - " : : : : : : : : : +- GpuGenerate false, [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], [loan_id#27136L, quarter#27167, timestamp_month#26734, timestamp_year#26770, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680], [month_y#26931]\n", - " : : : : : : : : : +- !GpuProject [loan_id#27136L, quarter#27167, timestamp_month#26734, timestamp_year#26770, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680]\n", - " : : : : : : : : : +- !GpuBroadcastHashJoin [loan_id#27136L, quarter#27167], [loan_id#26885L, quarter#26916], LeftOuter, BuildRight\n", - " : : : : : : : : : :- GpuRowToColumnar TargetSize(536870912)\n", - " : : : : : : : : : : +- *(2) Project [quarter#27167, loan_id#27136L, month(cast(cast(unix_timestamp(monthly_reporting_period#27137, MM/dd/yyyy, Some(Asia/Shanghai)) as timestamp) as date)) AS timestamp_month#26734, year(cast(cast(unix_timestamp(monthly_reporting_period#27137, MM/dd/yyyy, Some(Asia/Shanghai)) as timestamp) as date)) AS timestamp_year#26770]\n", - " : : : : : : : : : : +- *(2) GpuColumnarToRow false\n", - " : : : : : : : : : : +- !GpuFilter (gpuisnotnull(loan_id#27136L) AND gpuisnotnull(quarter#27167))\n", - " : : : : : : : : : : +- GpuFileScan parquet [loan_id#27136L,monthly_reporting_period#27137,quarter#27167] Batched: true, DataFilters: [isnotnull(loan_id#27136L), isnotnull(quarter#27167)], Format: Parquet, Location: InMemoryFileIndex[file:/home/mengmengg/xgboost4j_spark/data/perf], PartitionFilters: [], PushedFilters: [IsNotNull(loan_id), IsNotNull(quarter)], ReadSchema: struct\n", - " : : : : : : : : : +- GpuBroadcastExchange HashedRelationBroadcastMode(List(input[1, bigint, true], input[0, string, true])), [id=#15129]\n", - " : : : : : : : : : +- GpuHashAggregate(keys=[quarter#26916, loan_id#26885L], functions=[gpumax(current_loan_delinquency_status#26895), gpumin(delinquency_30#26658), gpumin(delinquency_90#26659), gpumin(delinquency_180#26660)]), filters=ArrayBuffer(None, None, None, None))\n", - " : : : : : : : : : +- GpuCoalesceBatches TargetSize(536870912)\n", - " : : : : : : : : : +- !GpuColumnarExchange gpuhashpartitioning(quarter#26916, loan_id#26885L, 192), true, [id=#15126]\n", - " : : : : : : : : : +- GpuHashAggregate(keys=[quarter#26916, loan_id#26885L], functions=[partial_gpumax(current_loan_delinquency_status#26895), partial_gpumin(delinquency_30#26658), partial_gpumin(delinquency_90#26659), partial_gpumin(delinquency_180#26660)]), filters=ArrayBuffer(None, None, None, None))\n", - " : : : : : : : : : +- GpuRowToColumnar TargetSize(536870912)\n", - " : : : : : : : : : +- *(3) Project [quarter#26916, loan_id#26885L, current_loan_delinquency_status#26895, CASE WHEN (current_loan_delinquency_status#26895 >= 1) THEN cast(cast(unix_timestamp(monthly_reporting_period#26886, MM/dd/yyyy, Some(Asia/Shanghai)) as timestamp) as date) END AS delinquency_30#26658, CASE WHEN (current_loan_delinquency_status#26895 >= 3) THEN cast(cast(unix_timestamp(monthly_reporting_period#26886, MM/dd/yyyy, Some(Asia/Shanghai)) as timestamp) as date) END AS delinquency_90#26659, CASE WHEN (current_loan_delinquency_status#26895 >= 6) THEN cast(cast(unix_timestamp(monthly_reporting_period#26886, MM/dd/yyyy, Some(Asia/Shanghai)) as timestamp) as date) END AS delinquency_180#26660]\n", - " : : : : : : : : : +- *(3) GpuColumnarToRow false\n", - " : : : : : : : : : +- !GpuFilter (gpuisnotnull(loan_id#26885L) AND gpuisnotnull(quarter#26916))\n", - " : : : : : : : : : +- GpuFileScan parquet [loan_id#26885L,monthly_reporting_period#26886,current_loan_delinquency_status#26895,quarter#26916] Batched: true, DataFilters: [isnotnull(loan_id#26885L), isnotnull(quarter#26916)], Format: Parquet, Location: InMemoryFileIndex[file:/home/mengmengg/xgboost4j_spark/data/perf], PartitionFilters: [], PushedFilters: [IsNotNull(loan_id), IsNotNull(quarter)], ReadSchema: struct\n", - " : : : : : : : : +- GpuCoalesceBatches RequireSingleBatch\n", - " : : : : : : : : +- !GpuColumnarExchange gpuhashpartitioning(quarter#27167, loan_id#27136L, timestamp_year#26990L, timestamp_month#27019L, 192), true, [id=#15140]\n", - " : : : : : : : : +- GpuHashAggregate(keys=[quarter#27167, loan_id#27136L, josh_mody_n#26947L, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680, month_y#26931], functions=[]), filters=ArrayBuffer())\n", - " : : : : : : : : +- GpuCoalesceBatches TargetSize(536870912)\n", - " : : : : : : : : +- !GpuColumnarExchange gpuhashpartitioning(quarter#27167, loan_id#27136L, josh_mody_n#26947L, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680, month_y#26931, 192), true, [id=#15137]\n", - " : : : : : : : : +- GpuHashAggregate(keys=[quarter#27167, loan_id#27136L, josh_mody_n#26947L, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680, month_y#26931], functions=[]), filters=ArrayBuffer())\n", - " : : : : : : : : +- !GpuProject [quarter#27167, FLOOR((cast(((((timestamp_year#26770 * 12) + timestamp_month#26734) - 24000) - month_y#26931) as double) / 12.0)) AS josh_mody_n#26947L, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680, loan_id#27136L, month_y#26931]\n", - " : : : : : : : : +- GpuCoalesceBatches TargetSize(536870912)\n", - " : : : : : : : : +- !GpuFilter (gpuisnotnull(CASE WHEN ((((24000 + (FLOOR((cast(((((timestamp_year#26770 * 12) + timestamp_month#26734) - 24000) - month_y#26931) as double) / 12.0)) * 12)) + cast(month_y#26931 as bigint)) pmod 12) = 0) THEN 12 ELSE (((24000 + (FLOOR((cast(((((timestamp_year#26770 * 12) + timestamp_month#26734) - 24000) - month_y#26931) as double) / 12.0)) * 12)) + cast(month_y#26931 as bigint)) pmod 12) END) AND gpuisnotnull(FLOOR((cast(((24000 + (FLOOR((cast(((((timestamp_year#26770 * 12) + timestamp_month#26734) - 24000) - month_y#26931) as double) / 12.0)) * 12)) + cast((month_y#26931 - 1) as bigint)) as double) / 12.0))))\n", - " : : : : : : : : +- GpuGenerate false, [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], [loan_id#27136L, quarter#27167, timestamp_month#26734, timestamp_year#26770, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680], [month_y#26931]\n", - " : : : : : : : : +- !GpuProject [loan_id#27136L, quarter#27167, timestamp_month#26734, timestamp_year#26770, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680]\n", - " : : : : : : : : +- !GpuBroadcastHashJoin [loan_id#27136L, quarter#27167], [loan_id#26885L, quarter#26916], LeftOuter, BuildRight\n", - " : : : : : : : : :- GpuRowToColumnar TargetSize(536870912)\n", - " : : : : : : : : : +- *(2) Project [quarter#27167, loan_id#27136L, month(cast(cast(unix_timestamp(monthly_reporting_period#27137, MM/dd/yyyy, Some(Asia/Shanghai)) as timestamp) as date)) AS timestamp_month#26734, year(cast(cast(unix_timestamp(monthly_reporting_period#27137, MM/dd/yyyy, Some(Asia/Shanghai)) as timestamp) as date)) AS timestamp_year#26770]\n", - " : : : : : : : : : +- *(2) GpuColumnarToRow false\n", - " : : : : : : : : : +- !GpuFilter (gpuisnotnull(loan_id#27136L) AND gpuisnotnull(quarter#27167))\n", - " : : : : : : : : : +- GpuFileScan parquet [loan_id#27136L,monthly_reporting_period#27137,quarter#27167] Batched: true, DataFilters: [isnotnull(loan_id#27136L), isnotnull(quarter#27167)], Format: Parquet, Location: InMemoryFileIndex[file:/home/mengmengg/xgboost4j_spark/data/perf], PartitionFilters: [], PushedFilters: [IsNotNull(loan_id), IsNotNull(quarter)], ReadSchema: struct\n", - " : : : : : : : : +- GpuBroadcastExchange HashedRelationBroadcastMode(List(input[1, bigint, true], input[0, string, true])), [id=#15129]\n", - " : : : : : : : : +- GpuHashAggregate(keys=[quarter#26916, loan_id#26885L], functions=[gpumax(current_loan_delinquency_status#26895), gpumin(delinquency_30#26658), gpumin(delinquency_90#26659), gpumin(delinquency_180#26660)]), filters=ArrayBuffer(None, None, None, None))\n", - " : : : : : : : : +- GpuCoalesceBatches TargetSize(536870912)\n", - " : : : : : : : : +- !GpuColumnarExchange gpuhashpartitioning(quarter#26916, loan_id#26885L, 192), true, [id=#15126]\n", - " : : : : : : : : +- GpuHashAggregate(keys=[quarter#26916, loan_id#26885L], functions=[partial_gpumax(current_loan_delinquency_status#26895), partial_gpumin(delinquency_30#26658), partial_gpumin(delinquency_90#26659), partial_gpumin(delinquency_180#26660)]), filters=ArrayBuffer(None, None, None, None))\n", - " : : : : : : : : +- GpuRowToColumnar TargetSize(536870912)\n", - " : : : : : : : : +- *(3) Project [quarter#26916, loan_id#26885L, current_loan_delinquency_status#26895, CASE WHEN (current_loan_delinquency_status#26895 >= 1) THEN cast(cast(unix_timestamp(monthly_reporting_period#26886, MM/dd/yyyy, Some(Asia/Shanghai)) as timestamp) as date) END AS delinquency_30#26658, CASE WHEN (current_loan_delinquency_status#26895 >= 3) THEN cast(cast(unix_timestamp(monthly_reporting_period#26886, MM/dd/yyyy, Some(Asia/Shanghai)) as timestamp) as date) END AS delinquency_90#26659, CASE WHEN (current_loan_delinquency_status#26895 >= 6) THEN cast(cast(unix_timestamp(monthly_reporting_period#26886, MM/dd/yyyy, Some(Asia/Shanghai)) as timestamp) as date) END AS delinquency_180#26660]\n", - " : : : : : : : : +- *(3) GpuColumnarToRow false\n", - " : : : : : : : : +- !GpuFilter (gpuisnotnull(loan_id#26885L) AND gpuisnotnull(quarter#26916))\n", - " : : : : : : : : +- GpuFileScan parquet [loan_id#26885L,monthly_reporting_period#26886,current_loan_delinquency_status#26895,quarter#26916] Batched: true, DataFilters: [isnotnull(loan_id#26885L), isnotnull(quarter#26916)], Format: Parquet, Location: InMemoryFileIndex[file:/home/mengmengg/xgboost4j_spark/data/perf], PartitionFilters: [], PushedFilters: [IsNotNull(loan_id), IsNotNull(quarter)], ReadSchema: struct\n", - " : : : : : : : +- GpuCoalesceBatches RequireSingleBatch\n", - " : : : : : : : +- !GpuColumnarExchange gpuhashpartitioning(quarter#27167, loan_id#27136L, timestamp_year#26990L, timestamp_month#27019L, 192), true, [id=#15140]\n", - " : : : : : : : +- GpuHashAggregate(keys=[quarter#27167, loan_id#27136L, josh_mody_n#26947L, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680, month_y#26931], functions=[]), filters=ArrayBuffer())\n", - " : : : : : : : +- GpuCoalesceBatches TargetSize(536870912)\n", - " : : : : : : : +- !GpuColumnarExchange gpuhashpartitioning(quarter#27167, loan_id#27136L, josh_mody_n#26947L, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680, month_y#26931, 192), true, [id=#15137]\n", - " : : : : : : : +- GpuHashAggregate(keys=[quarter#27167, loan_id#27136L, josh_mody_n#26947L, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680, month_y#26931], functions=[]), filters=ArrayBuffer())\n", - " : : : : : : : +- !GpuProject [quarter#27167, FLOOR((cast(((((timestamp_year#26770 * 12) + timestamp_month#26734) - 24000) - month_y#26931) as double) / 12.0)) AS josh_mody_n#26947L, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680, loan_id#27136L, month_y#26931]\n", - " : : : : : : : +- GpuCoalesceBatches TargetSize(536870912)\n", - " : : : : : : : +- !GpuFilter (gpuisnotnull(CASE WHEN ((((24000 + (FLOOR((cast(((((timestamp_year#26770 * 12) + timestamp_month#26734) - 24000) - month_y#26931) as double) / 12.0)) * 12)) + cast(month_y#26931 as bigint)) pmod 12) = 0) THEN 12 ELSE (((24000 + (FLOOR((cast(((((timestamp_year#26770 * 12) + timestamp_month#26734) - 24000) - month_y#26931) as double) / 12.0)) * 12)) + cast(month_y#26931 as bigint)) pmod 12) END) AND gpuisnotnull(FLOOR((cast(((24000 + (FLOOR((cast(((((timestamp_year#26770 * 12) + timestamp_month#26734) - 24000) - month_y#26931) as double) / 12.0)) * 12)) + cast((month_y#26931 - 1) as bigint)) as double) / 12.0))))\n", - " : : : : : : : +- GpuGenerate false, [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], [loan_id#27136L, quarter#27167, timestamp_month#26734, timestamp_year#26770, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680], [month_y#26931]\n", - " : : : : : : : +- !GpuProject [loan_id#27136L, quarter#27167, timestamp_month#26734, timestamp_year#26770, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680]\n", - " : : : : : : : +- !GpuBroadcastHashJoin [loan_id#27136L, quarter#27167], [loan_id#26885L, quarter#26916], LeftOuter, BuildRight\n", - " : : : : : : : :- GpuRowToColumnar TargetSize(536870912)\n", - " : : : : : : : : +- *(2) Project [quarter#27167, loan_id#27136L, month(cast(cast(unix_timestamp(monthly_reporting_period#27137, MM/dd/yyyy, Some(Asia/Shanghai)) as timestamp) as date)) AS timestamp_month#26734, year(cast(cast(unix_timestamp(monthly_reporting_period#27137, MM/dd/yyyy, Some(Asia/Shanghai)) as timestamp) as date)) AS timestamp_year#26770]\n", - " : : : : : : : : +- *(2) GpuColumnarToRow false\n", - " : : : : : : : : +- !GpuFilter (gpuisnotnull(loan_id#27136L) AND gpuisnotnull(quarter#27167))\n", - " : : : : : : : : +- GpuFileScan parquet [loan_id#27136L,monthly_reporting_period#27137,quarter#27167] Batched: true, DataFilters: [isnotnull(loan_id#27136L), isnotnull(quarter#27167)], Format: Parquet, Location: InMemoryFileIndex[file:/home/mengmengg/xgboost4j_spark/data/perf], PartitionFilters: [], PushedFilters: [IsNotNull(loan_id), IsNotNull(quarter)], ReadSchema: struct\n", - " : : : : : : : +- GpuBroadcastExchange HashedRelationBroadcastMode(List(input[1, bigint, true], input[0, string, true])), [id=#15129]\n", - " : : : : : : : +- GpuHashAggregate(keys=[quarter#26916, loan_id#26885L], functions=[gpumax(current_loan_delinquency_status#26895), gpumin(delinquency_30#26658), gpumin(delinquency_90#26659), gpumin(delinquency_180#26660)]), filters=ArrayBuffer(None, None, None, None))\n", - " : : : : : : : +- GpuCoalesceBatches TargetSize(536870912)\n", - " : : : : : : : +- !GpuColumnarExchange gpuhashpartitioning(quarter#26916, loan_id#26885L, 192), true, [id=#15126]\n", - " : : : : : : : +- GpuHashAggregate(keys=[quarter#26916, loan_id#26885L], functions=[partial_gpumax(current_loan_delinquency_status#26895), partial_gpumin(delinquency_30#26658), partial_gpumin(delinquency_90#26659), partial_gpumin(delinquency_180#26660)]), filters=ArrayBuffer(None, None, None, None))\n", - " : : : : : : : +- GpuRowToColumnar TargetSize(536870912)\n", - " : : : : : : : +- *(3) Project [quarter#26916, loan_id#26885L, current_loan_delinquency_status#26895, CASE WHEN (current_loan_delinquency_status#26895 >= 1) THEN cast(cast(unix_timestamp(monthly_reporting_period#26886, MM/dd/yyyy, Some(Asia/Shanghai)) as timestamp) as date) END AS delinquency_30#26658, CASE WHEN (current_loan_delinquency_status#26895 >= 3) THEN cast(cast(unix_timestamp(monthly_reporting_period#26886, MM/dd/yyyy, Some(Asia/Shanghai)) as timestamp) as date) END AS delinquency_90#26659, CASE WHEN (current_loan_delinquency_status#26895 >= 6) THEN cast(cast(unix_timestamp(monthly_reporting_period#26886, MM/dd/yyyy, Some(Asia/Shanghai)) as timestamp) as date) END AS delinquency_180#26660]\n", - " : : : : : : : +- *(3) GpuColumnarToRow false\n", - " : : : : : : : +- !GpuFilter (gpuisnotnull(loan_id#26885L) AND gpuisnotnull(quarter#26916))\n", - " : : : : : : : +- GpuFileScan parquet [loan_id#26885L,monthly_reporting_period#26886,current_loan_delinquency_status#26895,quarter#26916] Batched: true, DataFilters: [isnotnull(loan_id#26885L), isnotnull(quarter#26916)], Format: Parquet, Location: InMemoryFileIndex[file:/home/mengmengg/xgboost4j_spark/data/perf], PartitionFilters: [], PushedFilters: [IsNotNull(loan_id), IsNotNull(quarter)], ReadSchema: struct\n", - " : : : : : : +- GpuCoalesceBatches RequireSingleBatch\n", - " : : : : : : +- !GpuColumnarExchange gpuhashpartitioning(quarter#27167, loan_id#27136L, timestamp_year#26990L, timestamp_month#27019L, 192), true, [id=#15140]\n", - " : : : : : : +- GpuHashAggregate(keys=[quarter#27167, loan_id#27136L, josh_mody_n#26947L, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680, month_y#26931], functions=[]), filters=ArrayBuffer())\n", - " : : : : : : +- GpuCoalesceBatches TargetSize(536870912)\n", - " : : : : : : +- !GpuColumnarExchange gpuhashpartitioning(quarter#27167, loan_id#27136L, josh_mody_n#26947L, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680, month_y#26931, 192), true, [id=#15137]\n", - " : : : : : : +- GpuHashAggregate(keys=[quarter#27167, loan_id#27136L, josh_mody_n#26947L, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680, month_y#26931], functions=[]), filters=ArrayBuffer())\n", - " : : : : : : +- !GpuProject [quarter#27167, FLOOR((cast(((((timestamp_year#26770 * 12) + timestamp_month#26734) - 24000) - month_y#26931) as double) / 12.0)) AS josh_mody_n#26947L, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680, loan_id#27136L, month_y#26931]\n", - " : : : : : : +- GpuCoalesceBatches TargetSize(536870912)\n", - " : : : : : : +- !GpuFilter (gpuisnotnull(CASE WHEN ((((24000 + (FLOOR((cast(((((timestamp_year#26770 * 12) + timestamp_month#26734) - 24000) - month_y#26931) as double) / 12.0)) * 12)) + cast(month_y#26931 as bigint)) pmod 12) = 0) THEN 12 ELSE (((24000 + (FLOOR((cast(((((timestamp_year#26770 * 12) + timestamp_month#26734) - 24000) - month_y#26931) as double) / 12.0)) * 12)) + cast(month_y#26931 as bigint)) pmod 12) END) AND gpuisnotnull(FLOOR((cast(((24000 + (FLOOR((cast(((((timestamp_year#26770 * 12) + timestamp_month#26734) - 24000) - month_y#26931) as double) / 12.0)) * 12)) + cast((month_y#26931 - 1) as bigint)) as double) / 12.0))))\n", - " : : : : : : +- GpuGenerate false, [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], [loan_id#27136L, quarter#27167, timestamp_month#26734, timestamp_year#26770, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680], [month_y#26931]\n", - " : : : : : : +- !GpuProject [loan_id#27136L, quarter#27167, timestamp_month#26734, timestamp_year#26770, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680]\n", - " : : : : : : +- !GpuBroadcastHashJoin [loan_id#27136L, quarter#27167], [loan_id#26885L, quarter#26916], LeftOuter, BuildRight\n", - " : : : : : : :- GpuRowToColumnar TargetSize(536870912)\n", - " : : : : : : : +- *(2) Project [quarter#27167, loan_id#27136L, month(cast(cast(unix_timestamp(monthly_reporting_period#27137, MM/dd/yyyy, Some(Asia/Shanghai)) as timestamp) as date)) AS timestamp_month#26734, year(cast(cast(unix_timestamp(monthly_reporting_period#27137, MM/dd/yyyy, Some(Asia/Shanghai)) as timestamp) as date)) AS timestamp_year#26770]\n", - " : : : : : : : +- *(2) GpuColumnarToRow false\n", - " : : : : : : : +- !GpuFilter (gpuisnotnull(loan_id#27136L) AND gpuisnotnull(quarter#27167))\n", - " : : : : : : : +- GpuFileScan parquet [loan_id#27136L,monthly_reporting_period#27137,quarter#27167] Batched: true, DataFilters: [isnotnull(loan_id#27136L), isnotnull(quarter#27167)], Format: Parquet, Location: InMemoryFileIndex[file:/home/mengmengg/xgboost4j_spark/data/perf], PartitionFilters: [], PushedFilters: [IsNotNull(loan_id), IsNotNull(quarter)], ReadSchema: struct\n", - " : : : : : : +- GpuBroadcastExchange HashedRelationBroadcastMode(List(input[1, bigint, true], input[0, string, true])), [id=#15129]\n", - " : : : : : : +- GpuHashAggregate(keys=[quarter#26916, loan_id#26885L], functions=[gpumax(current_loan_delinquency_status#26895), gpumin(delinquency_30#26658), gpumin(delinquency_90#26659), gpumin(delinquency_180#26660)]), filters=ArrayBuffer(None, None, None, None))\n", - " : : : : : : +- GpuCoalesceBatches TargetSize(536870912)\n", - " : : : : : : +- !GpuColumnarExchange gpuhashpartitioning(quarter#26916, loan_id#26885L, 192), true, [id=#15126]\n", - " : : : : : : +- GpuHashAggregate(keys=[quarter#26916, loan_id#26885L], functions=[partial_gpumax(current_loan_delinquency_status#26895), partial_gpumin(delinquency_30#26658), partial_gpumin(delinquency_90#26659), partial_gpumin(delinquency_180#26660)]), filters=ArrayBuffer(None, None, None, None))\n", - " : : : : : : +- GpuRowToColumnar TargetSize(536870912)\n", - " : : : : : : +- *(3) Project [quarter#26916, loan_id#26885L, current_loan_delinquency_status#26895, CASE WHEN (current_loan_delinquency_status#26895 >= 1) THEN cast(cast(unix_timestamp(monthly_reporting_period#26886, MM/dd/yyyy, Some(Asia/Shanghai)) as timestamp) as date) END AS delinquency_30#26658, CASE WHEN (current_loan_delinquency_status#26895 >= 3) THEN cast(cast(unix_timestamp(monthly_reporting_period#26886, MM/dd/yyyy, Some(Asia/Shanghai)) as timestamp) as date) END AS delinquency_90#26659, CASE WHEN (current_loan_delinquency_status#26895 >= 6) THEN cast(cast(unix_timestamp(monthly_reporting_period#26886, MM/dd/yyyy, Some(Asia/Shanghai)) as timestamp) as date) END AS delinquency_180#26660]\n", - " : : : : : : +- *(3) GpuColumnarToRow false\n", - " : : : : : : +- !GpuFilter (gpuisnotnull(loan_id#26885L) AND gpuisnotnull(quarter#26916))\n", - " : : : : : : +- GpuFileScan parquet [loan_id#26885L,monthly_reporting_period#26886,current_loan_delinquency_status#26895,quarter#26916] Batched: true, DataFilters: [isnotnull(loan_id#26885L), isnotnull(quarter#26916)], Format: Parquet, Location: InMemoryFileIndex[file:/home/mengmengg/xgboost4j_spark/data/perf], PartitionFilters: [], PushedFilters: [IsNotNull(loan_id), IsNotNull(quarter)], ReadSchema: struct\n", - " : : : : : +- GpuCoalesceBatches RequireSingleBatch\n", - " : : : : : +- !GpuColumnarExchange gpuhashpartitioning(quarter#27167, loan_id#27136L, timestamp_year#26990L, timestamp_month#27019L, 192), true, [id=#15140]\n", - " : : : : : +- GpuHashAggregate(keys=[quarter#27167, loan_id#27136L, josh_mody_n#26947L, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680, month_y#26931], functions=[]), filters=ArrayBuffer())\n", - " : : : : : +- GpuCoalesceBatches TargetSize(536870912)\n", - " : : : : : +- !GpuColumnarExchange gpuhashpartitioning(quarter#27167, loan_id#27136L, josh_mody_n#26947L, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680, month_y#26931, 192), true, [id=#15137]\n", - " : : : : : +- GpuHashAggregate(keys=[quarter#27167, loan_id#27136L, josh_mody_n#26947L, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680, month_y#26931], functions=[]), filters=ArrayBuffer())\n", - " : : : : : +- !GpuProject [quarter#27167, FLOOR((cast(((((timestamp_year#26770 * 12) + timestamp_month#26734) - 24000) - month_y#26931) as double) / 12.0)) AS josh_mody_n#26947L, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680, loan_id#27136L, month_y#26931]\n", - " : : : : : +- GpuCoalesceBatches TargetSize(536870912)\n", - " : : : : : +- !GpuFilter (gpuisnotnull(CASE WHEN ((((24000 + (FLOOR((cast(((((timestamp_year#26770 * 12) + timestamp_month#26734) - 24000) - month_y#26931) as double) / 12.0)) * 12)) + cast(month_y#26931 as bigint)) pmod 12) = 0) THEN 12 ELSE (((24000 + (FLOOR((cast(((((timestamp_year#26770 * 12) + timestamp_month#26734) - 24000) - month_y#26931) as double) / 12.0)) * 12)) + cast(month_y#26931 as bigint)) pmod 12) END) AND gpuisnotnull(FLOOR((cast(((24000 + (FLOOR((cast(((((timestamp_year#26770 * 12) + timestamp_month#26734) - 24000) - month_y#26931) as double) / 12.0)) * 12)) + cast((month_y#26931 - 1) as bigint)) as double) / 12.0))))\n", - " : : : : : +- GpuGenerate false, [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], [loan_id#27136L, quarter#27167, timestamp_month#26734, timestamp_year#26770, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680], [month_y#26931]\n", - " : : : : : +- !GpuProject [loan_id#27136L, quarter#27167, timestamp_month#26734, timestamp_year#26770, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680]\n", - " : : : : : +- !GpuBroadcastHashJoin [loan_id#27136L, quarter#27167], [loan_id#26885L, quarter#26916], LeftOuter, BuildRight\n", - " : : : : : :- GpuRowToColumnar TargetSize(536870912)\n", - " : : : : : : +- *(2) Project [quarter#27167, loan_id#27136L, month(cast(cast(unix_timestamp(monthly_reporting_period#27137, MM/dd/yyyy, Some(Asia/Shanghai)) as timestamp) as date)) AS timestamp_month#26734, year(cast(cast(unix_timestamp(monthly_reporting_period#27137, MM/dd/yyyy, Some(Asia/Shanghai)) as timestamp) as date)) AS timestamp_year#26770]\n", - " : : : : : : +- *(2) GpuColumnarToRow false\n", - " : : : : : : +- !GpuFilter (gpuisnotnull(loan_id#27136L) AND gpuisnotnull(quarter#27167))\n", - " : : : : : : +- GpuFileScan parquet [loan_id#27136L,monthly_reporting_period#27137,quarter#27167] Batched: true, DataFilters: [isnotnull(loan_id#27136L), isnotnull(quarter#27167)], Format: Parquet, Location: InMemoryFileIndex[file:/home/mengmengg/xgboost4j_spark/data/perf], PartitionFilters: [], PushedFilters: [IsNotNull(loan_id), IsNotNull(quarter)], ReadSchema: struct\n", - " : : : : : +- GpuBroadcastExchange HashedRelationBroadcastMode(List(input[1, bigint, true], input[0, string, true])), [id=#15129]\n", - " : : : : : +- GpuHashAggregate(keys=[quarter#26916, loan_id#26885L], functions=[gpumax(current_loan_delinquency_status#26895), gpumin(delinquency_30#26658), gpumin(delinquency_90#26659), gpumin(delinquency_180#26660)]), filters=ArrayBuffer(None, None, None, None))\n", - " : : : : : +- GpuCoalesceBatches TargetSize(536870912)\n", - " : : : : : +- !GpuColumnarExchange gpuhashpartitioning(quarter#26916, loan_id#26885L, 192), true, [id=#15126]\n", - " : : : : : +- GpuHashAggregate(keys=[quarter#26916, loan_id#26885L], functions=[partial_gpumax(current_loan_delinquency_status#26895), partial_gpumin(delinquency_30#26658), partial_gpumin(delinquency_90#26659), partial_gpumin(delinquency_180#26660)]), filters=ArrayBuffer(None, None, None, None))\n", - " : : : : : +- GpuRowToColumnar TargetSize(536870912)\n", - " : : : : : +- *(3) Project [quarter#26916, loan_id#26885L, current_loan_delinquency_status#26895, CASE WHEN (current_loan_delinquency_status#26895 >= 1) THEN cast(cast(unix_timestamp(monthly_reporting_period#26886, MM/dd/yyyy, Some(Asia/Shanghai)) as timestamp) as date) END AS delinquency_30#26658, CASE WHEN (current_loan_delinquency_status#26895 >= 3) THEN cast(cast(unix_timestamp(monthly_reporting_period#26886, MM/dd/yyyy, Some(Asia/Shanghai)) as timestamp) as date) END AS delinquency_90#26659, CASE WHEN (current_loan_delinquency_status#26895 >= 6) THEN cast(cast(unix_timestamp(monthly_reporting_period#26886, MM/dd/yyyy, Some(Asia/Shanghai)) as timestamp) as date) END AS delinquency_180#26660]\n", - " : : : : : +- *(3) GpuColumnarToRow false\n", - " : : : : : +- !GpuFilter (gpuisnotnull(loan_id#26885L) AND gpuisnotnull(quarter#26916))\n", - " : : : : : +- GpuFileScan parquet [loan_id#26885L,monthly_reporting_period#26886,current_loan_delinquency_status#26895,quarter#26916] Batched: true, DataFilters: [isnotnull(loan_id#26885L), isnotnull(quarter#26916)], Format: Parquet, Location: InMemoryFileIndex[file:/home/mengmengg/xgboost4j_spark/data/perf], PartitionFilters: [], PushedFilters: [IsNotNull(loan_id), IsNotNull(quarter)], ReadSchema: struct\n", - " : : : : +- GpuCoalesceBatches RequireSingleBatch\n", - " : : : : +- !GpuColumnarExchange gpuhashpartitioning(quarter#27167, loan_id#27136L, timestamp_year#26990L, timestamp_month#27019L, 192), true, [id=#15140]\n", - " : : : : +- GpuHashAggregate(keys=[quarter#27167, loan_id#27136L, josh_mody_n#26947L, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680, month_y#26931], functions=[]), filters=ArrayBuffer())\n", - " : : : : +- GpuCoalesceBatches TargetSize(536870912)\n", - " : : : : +- !GpuColumnarExchange gpuhashpartitioning(quarter#27167, loan_id#27136L, josh_mody_n#26947L, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680, month_y#26931, 192), true, [id=#15137]\n", - " : : : : +- GpuHashAggregate(keys=[quarter#27167, loan_id#27136L, josh_mody_n#26947L, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680, month_y#26931], functions=[]), filters=ArrayBuffer())\n", - " : : : : +- !GpuProject [quarter#27167, FLOOR((cast(((((timestamp_year#26770 * 12) + timestamp_month#26734) - 24000) - month_y#26931) as double) / 12.0)) AS josh_mody_n#26947L, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680, loan_id#27136L, month_y#26931]\n", - " : : : : +- GpuCoalesceBatches TargetSize(536870912)\n", - " : : : : +- !GpuFilter (gpuisnotnull(CASE WHEN ((((24000 + (FLOOR((cast(((((timestamp_year#26770 * 12) + timestamp_month#26734) - 24000) - month_y#26931) as double) / 12.0)) * 12)) + cast(month_y#26931 as bigint)) pmod 12) = 0) THEN 12 ELSE (((24000 + (FLOOR((cast(((((timestamp_year#26770 * 12) + timestamp_month#26734) - 24000) - month_y#26931) as double) / 12.0)) * 12)) + cast(month_y#26931 as bigint)) pmod 12) END) AND gpuisnotnull(FLOOR((cast(((24000 + (FLOOR((cast(((((timestamp_year#26770 * 12) + timestamp_month#26734) - 24000) - month_y#26931) as double) / 12.0)) * 12)) + cast((month_y#26931 - 1) as bigint)) as double) / 12.0))))\n", - " : : : : +- GpuGenerate false, [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], [loan_id#27136L, quarter#27167, timestamp_month#26734, timestamp_year#26770, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680], [month_y#26931]\n", - " : : : : +- !GpuProject [loan_id#27136L, quarter#27167, timestamp_month#26734, timestamp_year#26770, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680]\n", - " : : : : +- !GpuBroadcastHashJoin [loan_id#27136L, quarter#27167], [loan_id#26885L, quarter#26916], LeftOuter, BuildRight\n", - " : : : : :- GpuRowToColumnar TargetSize(536870912)\n", - " : : : : : +- *(2) Project [quarter#27167, loan_id#27136L, month(cast(cast(unix_timestamp(monthly_reporting_period#27137, MM/dd/yyyy, Some(Asia/Shanghai)) as timestamp) as date)) AS timestamp_month#26734, year(cast(cast(unix_timestamp(monthly_reporting_period#27137, MM/dd/yyyy, Some(Asia/Shanghai)) as timestamp) as date)) AS timestamp_year#26770]\n", - " : : : : : +- *(2) GpuColumnarToRow false\n", - " : : : : : +- !GpuFilter (gpuisnotnull(loan_id#27136L) AND gpuisnotnull(quarter#27167))\n", - " : : : : : +- GpuFileScan parquet [loan_id#27136L,monthly_reporting_period#27137,quarter#27167] Batched: true, DataFilters: [isnotnull(loan_id#27136L), isnotnull(quarter#27167)], Format: Parquet, Location: InMemoryFileIndex[file:/home/mengmengg/xgboost4j_spark/data/perf], PartitionFilters: [], PushedFilters: [IsNotNull(loan_id), IsNotNull(quarter)], ReadSchema: struct\n", - " : : : : +- GpuBroadcastExchange HashedRelationBroadcastMode(List(input[1, bigint, true], input[0, string, true])), [id=#15129]\n", - " : : : : +- GpuHashAggregate(keys=[quarter#26916, loan_id#26885L], functions=[gpumax(current_loan_delinquency_status#26895), gpumin(delinquency_30#26658), gpumin(delinquency_90#26659), gpumin(delinquency_180#26660)]), filters=ArrayBuffer(None, None, None, None))\n", - " : : : : +- GpuCoalesceBatches TargetSize(536870912)\n", - " : : : : +- !GpuColumnarExchange gpuhashpartitioning(quarter#26916, loan_id#26885L, 192), true, [id=#15126]\n", - " : : : : +- GpuHashAggregate(keys=[quarter#26916, loan_id#26885L], functions=[partial_gpumax(current_loan_delinquency_status#26895), partial_gpumin(delinquency_30#26658), partial_gpumin(delinquency_90#26659), partial_gpumin(delinquency_180#26660)]), filters=ArrayBuffer(None, None, None, None))\n", - " : : : : +- GpuRowToColumnar TargetSize(536870912)\n", - " : : : : +- *(3) Project [quarter#26916, loan_id#26885L, current_loan_delinquency_status#26895, CASE WHEN (current_loan_delinquency_status#26895 >= 1) THEN cast(cast(unix_timestamp(monthly_reporting_period#26886, MM/dd/yyyy, Some(Asia/Shanghai)) as timestamp) as date) END AS delinquency_30#26658, CASE WHEN (current_loan_delinquency_status#26895 >= 3) THEN cast(cast(unix_timestamp(monthly_reporting_period#26886, MM/dd/yyyy, Some(Asia/Shanghai)) as timestamp) as date) END AS delinquency_90#26659, CASE WHEN (current_loan_delinquency_status#26895 >= 6) THEN cast(cast(unix_timestamp(monthly_reporting_period#26886, MM/dd/yyyy, Some(Asia/Shanghai)) as timestamp) as date) END AS delinquency_180#26660]\n", - " : : : : +- *(3) GpuColumnarToRow false\n", - " : : : : +- !GpuFilter (gpuisnotnull(loan_id#26885L) AND gpuisnotnull(quarter#26916))\n", - " : : : : +- GpuFileScan parquet [loan_id#26885L,monthly_reporting_period#26886,current_loan_delinquency_status#26895,quarter#26916] Batched: true, DataFilters: [isnotnull(loan_id#26885L), isnotnull(quarter#26916)], Format: Parquet, Location: InMemoryFileIndex[file:/home/mengmengg/xgboost4j_spark/data/perf], PartitionFilters: [], PushedFilters: [IsNotNull(loan_id), IsNotNull(quarter)], ReadSchema: struct\n", - " : : : +- GpuCoalesceBatches RequireSingleBatch\n", - " : : : +- !GpuColumnarExchange gpuhashpartitioning(quarter#27167, loan_id#27136L, timestamp_year#26990L, timestamp_month#27019L, 192), true, [id=#15140]\n", - " : : : +- GpuHashAggregate(keys=[quarter#27167, loan_id#27136L, josh_mody_n#26947L, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680, month_y#26931], functions=[]), filters=ArrayBuffer())\n", - " : : : +- GpuCoalesceBatches TargetSize(536870912)\n", - " : : : +- !GpuColumnarExchange gpuhashpartitioning(quarter#27167, loan_id#27136L, josh_mody_n#26947L, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680, month_y#26931, 192), true, [id=#15137]\n", - " : : : +- GpuHashAggregate(keys=[quarter#27167, loan_id#27136L, josh_mody_n#26947L, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680, month_y#26931], functions=[]), filters=ArrayBuffer())\n", - " : : : +- !GpuProject [quarter#27167, FLOOR((cast(((((timestamp_year#26770 * 12) + timestamp_month#26734) - 24000) - month_y#26931) as double) / 12.0)) AS josh_mody_n#26947L, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680, loan_id#27136L, month_y#26931]\n", - " : : : +- GpuCoalesceBatches TargetSize(536870912)\n", - " : : : +- !GpuFilter (gpuisnotnull(CASE WHEN ((((24000 + (FLOOR((cast(((((timestamp_year#26770 * 12) + timestamp_month#26734) - 24000) - month_y#26931) as double) / 12.0)) * 12)) + cast(month_y#26931 as bigint)) pmod 12) = 0) THEN 12 ELSE (((24000 + (FLOOR((cast(((((timestamp_year#26770 * 12) + timestamp_month#26734) - 24000) - month_y#26931) as double) / 12.0)) * 12)) + cast(month_y#26931 as bigint)) pmod 12) END) AND gpuisnotnull(FLOOR((cast(((24000 + (FLOOR((cast(((((timestamp_year#26770 * 12) + timestamp_month#26734) - 24000) - month_y#26931) as double) / 12.0)) * 12)) + cast((month_y#26931 - 1) as bigint)) as double) / 12.0))))\n", - " : : : +- GpuGenerate false, [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], [loan_id#27136L, quarter#27167, timestamp_month#26734, timestamp_year#26770, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680], [month_y#26931]\n", - " : : : +- !GpuProject [loan_id#27136L, quarter#27167, timestamp_month#26734, timestamp_year#26770, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680]\n", - " : : : +- !GpuBroadcastHashJoin [loan_id#27136L, quarter#27167], [loan_id#26885L, quarter#26916], LeftOuter, BuildRight\n", - " : : : :- GpuRowToColumnar TargetSize(536870912)\n", - " : : : : +- *(2) Project [quarter#27167, loan_id#27136L, month(cast(cast(unix_timestamp(monthly_reporting_period#27137, MM/dd/yyyy, Some(Asia/Shanghai)) as timestamp) as date)) AS timestamp_month#26734, year(cast(cast(unix_timestamp(monthly_reporting_period#27137, MM/dd/yyyy, Some(Asia/Shanghai)) as timestamp) as date)) AS timestamp_year#26770]\n", - " : : : : +- *(2) GpuColumnarToRow false\n", - " : : : : +- !GpuFilter (gpuisnotnull(loan_id#27136L) AND gpuisnotnull(quarter#27167))\n", - " : : : : +- GpuFileScan parquet [loan_id#27136L,monthly_reporting_period#27137,quarter#27167] Batched: true, DataFilters: [isnotnull(loan_id#27136L), isnotnull(quarter#27167)], Format: Parquet, Location: InMemoryFileIndex[file:/home/mengmengg/xgboost4j_spark/data/perf], PartitionFilters: [], PushedFilters: [IsNotNull(loan_id), IsNotNull(quarter)], ReadSchema: struct\n", - " : : : +- GpuBroadcastExchange HashedRelationBroadcastMode(List(input[1, bigint, true], input[0, string, true])), [id=#15129]\n", - " : : : +- GpuHashAggregate(keys=[quarter#26916, loan_id#26885L], functions=[gpumax(current_loan_delinquency_status#26895), gpumin(delinquency_30#26658), gpumin(delinquency_90#26659), gpumin(delinquency_180#26660)]), filters=ArrayBuffer(None, None, None, None))\n", - " : : : +- GpuCoalesceBatches TargetSize(536870912)\n", - " : : : +- !GpuColumnarExchange gpuhashpartitioning(quarter#26916, loan_id#26885L, 192), true, [id=#15126]\n", - " : : : +- GpuHashAggregate(keys=[quarter#26916, loan_id#26885L], functions=[partial_gpumax(current_loan_delinquency_status#26895), partial_gpumin(delinquency_30#26658), partial_gpumin(delinquency_90#26659), partial_gpumin(delinquency_180#26660)]), filters=ArrayBuffer(None, None, None, None))\n", - " : : : +- GpuRowToColumnar TargetSize(536870912)\n", - " : : : +- *(3) Project [quarter#26916, loan_id#26885L, current_loan_delinquency_status#26895, CASE WHEN (current_loan_delinquency_status#26895 >= 1) THEN cast(cast(unix_timestamp(monthly_reporting_period#26886, MM/dd/yyyy, Some(Asia/Shanghai)) as timestamp) as date) END AS delinquency_30#26658, CASE WHEN (current_loan_delinquency_status#26895 >= 3) THEN cast(cast(unix_timestamp(monthly_reporting_period#26886, MM/dd/yyyy, Some(Asia/Shanghai)) as timestamp) as date) END AS delinquency_90#26659, CASE WHEN (current_loan_delinquency_status#26895 >= 6) THEN cast(cast(unix_timestamp(monthly_reporting_period#26886, MM/dd/yyyy, Some(Asia/Shanghai)) as timestamp) as date) END AS delinquency_180#26660]\n", - " : : : +- *(3) GpuColumnarToRow false\n", - " : : : +- !GpuFilter (gpuisnotnull(loan_id#26885L) AND gpuisnotnull(quarter#26916))\n", - " : : : +- GpuFileScan parquet [loan_id#26885L,monthly_reporting_period#26886,current_loan_delinquency_status#26895,quarter#26916] Batched: true, DataFilters: [isnotnull(loan_id#26885L), isnotnull(quarter#26916)], Format: Parquet, Location: InMemoryFileIndex[file:/home/mengmengg/xgboost4j_spark/data/perf], PartitionFilters: [], PushedFilters: [IsNotNull(loan_id), IsNotNull(quarter)], ReadSchema: struct\n", - " : : +- GpuCoalesceBatches RequireSingleBatch\n", - " : : +- !GpuColumnarExchange gpuhashpartitioning(quarter#27167, loan_id#27136L, timestamp_year#26990L, timestamp_month#27019L, 192), true, [id=#15140]\n", - " : : +- GpuHashAggregate(keys=[quarter#27167, loan_id#27136L, josh_mody_n#26947L, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680, month_y#26931], functions=[]), filters=ArrayBuffer())\n", - " : : +- GpuCoalesceBatches TargetSize(536870912)\n", - " : : +- !GpuColumnarExchange gpuhashpartitioning(quarter#27167, loan_id#27136L, josh_mody_n#26947L, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680, month_y#26931, 192), true, [id=#15137]\n", - " : : +- GpuHashAggregate(keys=[quarter#27167, loan_id#27136L, josh_mody_n#26947L, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680, month_y#26931], functions=[]), filters=ArrayBuffer())\n", - " : : +- !GpuProject [quarter#27167, FLOOR((cast(((((timestamp_year#26770 * 12) + timestamp_month#26734) - 24000) - month_y#26931) as double) / 12.0)) AS josh_mody_n#26947L, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680, loan_id#27136L, month_y#26931]\n", - " : : +- GpuCoalesceBatches TargetSize(536870912)\n", - " : : +- !GpuFilter (gpuisnotnull(CASE WHEN ((((24000 + (FLOOR((cast(((((timestamp_year#26770 * 12) + timestamp_month#26734) - 24000) - month_y#26931) as double) / 12.0)) * 12)) + cast(month_y#26931 as bigint)) pmod 12) = 0) THEN 12 ELSE (((24000 + (FLOOR((cast(((((timestamp_year#26770 * 12) + timestamp_month#26734) - 24000) - month_y#26931) as double) / 12.0)) * 12)) + cast(month_y#26931 as bigint)) pmod 12) END) AND gpuisnotnull(FLOOR((cast(((24000 + (FLOOR((cast(((((timestamp_year#26770 * 12) + timestamp_month#26734) - 24000) - month_y#26931) as double) / 12.0)) * 12)) + cast((month_y#26931 - 1) as bigint)) as double) / 12.0))))\n", - " : : +- GpuGenerate false, [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], [loan_id#27136L, quarter#27167, timestamp_month#26734, timestamp_year#26770, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680], [month_y#26931]\n", - " : : +- !GpuProject [loan_id#27136L, quarter#27167, timestamp_month#26734, timestamp_year#26770, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680]\n", - " : : +- !GpuBroadcastHashJoin [loan_id#27136L, quarter#27167], [loan_id#26885L, quarter#26916], LeftOuter, BuildRight\n", - " : : :- GpuRowToColumnar TargetSize(536870912)\n", - " : : : +- *(2) Project [quarter#27167, loan_id#27136L, month(cast(cast(unix_timestamp(monthly_reporting_period#27137, MM/dd/yyyy, Some(Asia/Shanghai)) as timestamp) as date)) AS timestamp_month#26734, year(cast(cast(unix_timestamp(monthly_reporting_period#27137, MM/dd/yyyy, Some(Asia/Shanghai)) as timestamp) as date)) AS timestamp_year#26770]\n", - " : : : +- *(2) GpuColumnarToRow false\n", - " : : : +- !GpuFilter (gpuisnotnull(loan_id#27136L) AND gpuisnotnull(quarter#27167))\n", - " : : : +- GpuFileScan parquet [loan_id#27136L,monthly_reporting_period#27137,quarter#27167] Batched: true, DataFilters: [isnotnull(loan_id#27136L), isnotnull(quarter#27167)], Format: Parquet, Location: InMemoryFileIndex[file:/home/mengmengg/xgboost4j_spark/data/perf], PartitionFilters: [], PushedFilters: [IsNotNull(loan_id), IsNotNull(quarter)], ReadSchema: struct\n", - " : : +- GpuBroadcastExchange HashedRelationBroadcastMode(List(input[1, bigint, true], input[0, string, true])), [id=#15129]\n", - " : : +- GpuHashAggregate(keys=[quarter#26916, loan_id#26885L], functions=[gpumax(current_loan_delinquency_status#26895), gpumin(delinquency_30#26658), gpumin(delinquency_90#26659), gpumin(delinquency_180#26660)]), filters=ArrayBuffer(None, None, None, None))\n", - " : : +- GpuCoalesceBatches TargetSize(536870912)\n", - " : : +- !GpuColumnarExchange gpuhashpartitioning(quarter#26916, loan_id#26885L, 192), true, [id=#15126]\n", - " : : +- GpuHashAggregate(keys=[quarter#26916, loan_id#26885L], functions=[partial_gpumax(current_loan_delinquency_status#26895), partial_gpumin(delinquency_30#26658), partial_gpumin(delinquency_90#26659), partial_gpumin(delinquency_180#26660)]), filters=ArrayBuffer(None, None, None, None))\n", - " : : +- GpuRowToColumnar TargetSize(536870912)\n", - " : : +- *(3) Project [quarter#26916, loan_id#26885L, current_loan_delinquency_status#26895, CASE WHEN (current_loan_delinquency_status#26895 >= 1) THEN cast(cast(unix_timestamp(monthly_reporting_period#26886, MM/dd/yyyy, Some(Asia/Shanghai)) as timestamp) as date) END AS delinquency_30#26658, CASE WHEN (current_loan_delinquency_status#26895 >= 3) THEN cast(cast(unix_timestamp(monthly_reporting_period#26886, MM/dd/yyyy, Some(Asia/Shanghai)) as timestamp) as date) END AS delinquency_90#26659, CASE WHEN (current_loan_delinquency_status#26895 >= 6) THEN cast(cast(unix_timestamp(monthly_reporting_period#26886, MM/dd/yyyy, Some(Asia/Shanghai)) as timestamp) as date) END AS delinquency_180#26660]\n", - " : : +- *(3) GpuColumnarToRow false\n", - " : : +- !GpuFilter (gpuisnotnull(loan_id#26885L) AND gpuisnotnull(quarter#26916))\n", - " : : +- GpuFileScan parquet [loan_id#26885L,monthly_reporting_period#26886,current_loan_delinquency_status#26895,quarter#26916] Batched: true, DataFilters: [isnotnull(loan_id#26885L), isnotnull(quarter#26916)], Format: Parquet, Location: InMemoryFileIndex[file:/home/mengmengg/xgboost4j_spark/data/perf], PartitionFilters: [], PushedFilters: [IsNotNull(loan_id), IsNotNull(quarter)], ReadSchema: struct\n", - " : +- GpuCoalesceBatches RequireSingleBatch\n", - " : +- !GpuColumnarExchange gpuhashpartitioning(quarter#27167, loan_id#27136L, timestamp_year#26990L, timestamp_month#27019L, 192), true, [id=#15140]\n", - " : +- GpuHashAggregate(keys=[quarter#27167, loan_id#27136L, josh_mody_n#26947L, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680, month_y#26931], functions=[]), filters=ArrayBuffer())\n", - " : +- GpuCoalesceBatches TargetSize(536870912)\n", - " : +- !GpuColumnarExchange gpuhashpartitioning(quarter#27167, loan_id#27136L, josh_mody_n#26947L, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680, month_y#26931, 192), true, [id=#15137]\n", - " : +- GpuHashAggregate(keys=[quarter#27167, loan_id#27136L, josh_mody_n#26947L, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680, month_y#26931], functions=[]), filters=ArrayBuffer())\n", - " : +- !GpuProject [quarter#27167, FLOOR((cast(((((timestamp_year#26770 * 12) + timestamp_month#26734) - 24000) - month_y#26931) as double) / 12.0)) AS josh_mody_n#26947L, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680, loan_id#27136L, month_y#26931]\n", - " : +- GpuCoalesceBatches TargetSize(536870912)\n", - " : +- !GpuFilter (gpuisnotnull(CASE WHEN ((((24000 + (FLOOR((cast(((((timestamp_year#26770 * 12) + timestamp_month#26734) - 24000) - month_y#26931) as double) / 12.0)) * 12)) + cast(month_y#26931 as bigint)) pmod 12) = 0) THEN 12 ELSE (((24000 + (FLOOR((cast(((((timestamp_year#26770 * 12) + timestamp_month#26734) - 24000) - month_y#26931) as double) / 12.0)) * 12)) + cast(month_y#26931 as bigint)) pmod 12) END) AND gpuisnotnull(FLOOR((cast(((24000 + (FLOOR((cast(((((timestamp_year#26770 * 12) + timestamp_month#26734) - 24000) - month_y#26931) as double) / 12.0)) * 12)) + cast((month_y#26931 - 1) as bigint)) as double) / 12.0))))\n", - " : +- GpuGenerate false, [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], [loan_id#27136L, quarter#27167, timestamp_month#26734, timestamp_year#26770, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680], [month_y#26931]\n", - " : +- !GpuProject [loan_id#27136L, quarter#27167, timestamp_month#26734, timestamp_year#26770, ever_30#26687, ever_90#26688, ever_180#26689, delinquency_30#26676, delinquency_90#26678, delinquency_180#26680]\n", - " : +- !GpuBroadcastHashJoin [loan_id#27136L, quarter#27167], [loan_id#26885L, quarter#26916], LeftOuter, BuildRight\n", - " : :- GpuRowToColumnar TargetSize(536870912)\n", - " : : +- *(2) Project [quarter#27167, loan_id#27136L, month(cast(cast(unix_timestamp(monthly_reporting_period#27137, MM/dd/yyyy, Some(Asia/Shanghai)) as timestamp) as date)) AS timestamp_month#26734, year(cast(cast(unix_timestamp(monthly_reporting_period#27137, MM/dd/yyyy, Some(Asia/Shanghai)) as timestamp) as date)) AS timestamp_year#26770]\n", - " : : +- *(2) GpuColumnarToRow false\n", - " : : +- !GpuFilter (gpuisnotnull(loan_id#27136L) AND gpuisnotnull(quarter#27167))\n", - " : : +- GpuFileScan parquet [loan_id#27136L,monthly_reporting_period#27137,quarter#27167] Batched: true, DataFilters: [isnotnull(loan_id#27136L), isnotnull(quarter#27167)], Format: Parquet, Location: InMemoryFileIndex[file:/home/mengmengg/xgboost4j_spark/data/perf], PartitionFilters: [], PushedFilters: [IsNotNull(loan_id), IsNotNull(quarter)], ReadSchema: struct\n", - " : +- GpuBroadcastExchange HashedRelationBroadcastMode(List(input[1, bigint, true], input[0, string, true])), [id=#15129]\n", - " : +- GpuHashAggregate(keys=[quarter#26916, loan_id#26885L], functions=[gpumax(current_loan_delinquency_status#26895), gpumin(delinquency_30#26658), gpumin(delinquency_90#26659), gpumin(delinquency_180#26660)]), filters=ArrayBuffer(None, None, None, None))\n", - " : +- GpuCoalesceBatches TargetSize(536870912)\n", - " : +- !GpuColumnarExchange gpuhashpartitioning(quarter#26916, loan_id#26885L, 192), true, [id=#15126]\n", - " : +- GpuHashAggregate(keys=[quarter#26916, loan_id#26885L], functions=[partial_gpumax(current_loan_delinquency_status#26895), partial_gpumin(delinquency_30#26658), partial_gpumin(delinquency_90#26659), partial_gpumin(delinquency_180#26660)]), filters=ArrayBuffer(None, None, None, None))\n", - " : +- GpuRowToColumnar TargetSize(536870912)\n", - " : +- *(3) Project [quarter#26916, loan_id#26885L, current_loan_delinquency_status#26895, CASE WHEN (current_loan_delinquency_status#26895 >= 1) THEN cast(cast(unix_timestamp(monthly_reporting_period#26886, MM/dd/yyyy, Some(Asia/Shanghai)) as timestamp) as date) END AS delinquency_30#26658, CASE WHEN (current_loan_delinquency_status#26895 >= 3) THEN cast(cast(unix_timestamp(monthly_reporting_period#26886, MM/dd/yyyy, Some(Asia/Shanghai)) as timestamp) as date) END AS delinquency_90#26659, CASE WHEN (current_loan_delinquency_status#26895 >= 6) THEN cast(cast(unix_timestamp(monthly_reporting_period#26886, MM/dd/yyyy, Some(Asia/Shanghai)) as timestamp) as date) END AS delinquency_180#26660]\n", - " : +- *(3) GpuColumnarToRow false\n", - " : +- !GpuFilter (gpuisnotnull(loan_id#26885L) AND gpuisnotnull(quarter#26916))\n", - " : +- GpuFileScan parquet [loan_id#26885L,monthly_reporting_period#26886,current_loan_delinquency_status#26895,quarter#26916] Batched: true, DataFilters: [isnotnull(loan_id#26885L), isnotnull(quarter#26916)], Format: Parquet, Location: InMemoryFileIndex[file:/home/mengmengg/xgboost4j_spark/data/perf], PartitionFilters: [], PushedFilters: [IsNotNull(loan_id), IsNotNull(quarter)], ReadSchema: struct= 1) THEN cast(gettimestamp(monthly_reporting_period#2203, MM/dd/yyyy, Some(America/Los_Angeles), false) as date) END AS delinquency_30#1975, CASE WHEN (current_loan_delinquency_status#2212 >= 3) THEN cast(gettimestamp(monthly_reporting_period#2203, MM/dd/yyyy, Some(America/Los_Angeles), false) as date) END AS delinquency_90#1976, CASE WHEN (current_loan_delinquency_status#2212 >= 6) THEN cast(gettimestamp(monthly_reporting_period#2203, MM/dd/yyyy, Some(America/Los_Angeles), false) as date) END AS delinquency_180#1977]\n", + " : : : : : : : : : : : +- GpuColumnarToRow false\n", + " : : : : : : : : : : : +- GpuFilter (gpuisnotnull(loan_id#2202L) AND gpuisnotnull(quarter#2233)), true\n", + " : : : : : : : : : : : +- GpuFileGpuScan parquet [loan_id#2202L,monthly_reporting_period#2203,current_loan_delinquency_status#2212,quarter#2233] Batched: true, DataFilters: [isnotnull(loan_id#2202L), isnotnull(quarter#2233)], Format: Parquet, Location: InMemoryFileIndex[file:/local/saralihalli/HOME/mortgage/perf], PartitionFilters: [], PushedFilters: [IsNotNull(loan_id), IsNotNull(quarter)], ReadSchema: struct\n", + " : : : : : : : : : : +- GpuColumnarToRow false\n", + " : : : : : : : : : : +- GpuSort [quarter#2484 ASC NULLS FIRST, loan_id#2453L ASC NULLS FIRST, timestamp_year#2307L ASC NULLS FIRST, timestamp_month#2336L ASC NULLS FIRST], false, com.nvidia.spark.rapids.OutOfCoreSort$@163d9f7d\n", + " : : : : : : : : : : +- GpuShuffleCoalesce 536870912\n", + " : : : : : : : : : : +- GpuColumnarExchange gpuhashpartitioning(quarter#2484, loan_id#2453L, timestamp_year#2307L, timestamp_month#2336L, 192), ENSURE_REQUIREMENTS, [id=#1114]\n", + " : : : : : : : : : : +- GpuRowToColumnar targetsize(536870912)\n", + " : : : : : : : : : : +- *(6) HashAggregate(keys=[quarter#2484, loan_id#2453L, josh_mody_n#2264L, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997, month_y#2248], functions=[])\n", + " : : : : : : : : : : +- GpuColumnarToRow false\n", + " : : : : : : : : : : +- GpuShuffleCoalesce 536870912\n", + " : : : : : : : : : : +- GpuColumnarExchange gpuhashpartitioning(quarter#2484, loan_id#2453L, josh_mody_n#2264L, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997, month_y#2248, 192), ENSURE_REQUIREMENTS, [id=#1107]\n", + " : : : : : : : : : : +- GpuHashAggregate(keys=[quarter#2484, loan_id#2453L, josh_mody_n#2264L, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997, month_y#2248], functions=[]), filters=ArrayBuffer())\n", + " : : : : : : : : : : +- GpuRowToColumnar targetsize(536870912)\n", + " : : : : : : : : : : +- *(5) Project [quarter#2484, FLOOR((cast(((((timestamp_year#2087 * 12) + timestamp_month#2051) - 24000) - month_y#2248) as double) / 12.0)) AS josh_mody_n#2264L, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997, loan_id#2453L, month_y#2248]\n", + " : : : : : : : : : : +- *(5) Filter (isnotnull(FLOOR((cast(((24000 + (FLOOR((cast(((((timestamp_year#2087 * 12) + timestamp_month#2051) - 24000) - month_y#2248) as double) / 12.0)) * 12)) + cast((month_y#2248 - 1) as bigint)) as double) / 12.0))) AND isnotnull(CASE WHEN (pmod(((24000 + (FLOOR((cast(((((timestamp_year#2087 * 12) + timestamp_month#2051) - 24000) - month_y#2248) as double) / 12.0)) * 12)) + cast(month_y#2248 as bigint)), 12) = 0) THEN 12 ELSE pmod(((24000 + (FLOOR((cast(((((timestamp_year#2087 * 12) + timestamp_month#2051) - 24000) - month_y#2248) as double) / 12.0)) * 12)) + cast(month_y#2248 as bigint)), 12) END))\n", + " : : : : : : : : : : +- GpuColumnarToRow false\n", + " : : : : : : : : : : +- GpuGenerate gpuexplode([0,1,2,3,4,5,6,7,8,9,10,11]), [loan_id#2453L, quarter#2484, timestamp_month#2051, timestamp_year#2087, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997], false, [month_y#2248]\n", + " : : : : : : : : : : +- GpuProject [loan_id#2453L, quarter#2484, timestamp_month#2051, timestamp_year#2087, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997]\n", + " : : : : : : : : : : +- GpuBroadcastHashJoin [loan_id#2453L, quarter#2484], [loan_id#2202L, quarter#2233], LeftOuter, GpuBuildRight\n", + " : : : : : : : : : : :- GpuRowToColumnar targetsize(536870912)\n", + " : : : : : : : : : : : +- *(3) Project [quarter#2484, loan_id#2453L, month(cast(gettimestamp(monthly_reporting_period#2454, MM/dd/yyyy, Some(America/Los_Angeles), false) as date)) AS timestamp_month#2051, year(cast(gettimestamp(monthly_reporting_period#2454, MM/dd/yyyy, Some(America/Los_Angeles), false) as date)) AS timestamp_year#2087]\n", + " : : : : : : : : : : : +- GpuColumnarToRow false\n", + " : : : : : : : : : : : +- GpuFilter (gpuisnotnull(quarter#2484) AND gpuisnotnull(loan_id#2453L)), true\n", + " : : : : : : : : : : : +- GpuFileGpuScan parquet [loan_id#2453L,monthly_reporting_period#2454,quarter#2484] Batched: true, DataFilters: [isnotnull(quarter#2484), isnotnull(loan_id#2453L)], Format: Parquet, Location: InMemoryFileIndex[file:/local/saralihalli/HOME/mortgage/perf], PartitionFilters: [], PushedFilters: [IsNotNull(quarter), IsNotNull(loan_id)], ReadSchema: struct\n", + " : : : : : : : : : : +- GpuBroadcastExchange HashedRelationBroadcastMode(List(input[1, bigint, true], input[0, string, true]),false), [id=#1096]\n", + " : : : : : : : : : : +- GpuHashAggregate(keys=[quarter#2233, loan_id#2202L], functions=[gpumax(current_loan_delinquency_status#2212), gpumin(delinquency_30#1975), gpumin(delinquency_90#1976), gpumin(delinquency_180#1977)]), filters=ArrayBuffer(None, None, None, None))\n", + " : : : : : : : : : : +- GpuShuffleCoalesce 536870912\n", + " : : : : : : : : : : +- GpuColumnarExchange gpuhashpartitioning(quarter#2233, loan_id#2202L, 192), ENSURE_REQUIREMENTS, [id=#1093]\n", + " : : : : : : : : : : +- GpuHashAggregate(keys=[quarter#2233, loan_id#2202L], functions=[partial_gpumax(current_loan_delinquency_status#2212), partial_gpumin(delinquency_30#1975), partial_gpumin(delinquency_90#1976), partial_gpumin(delinquency_180#1977)]), filters=ArrayBuffer(None, None, None, None))\n", + " : : : : : : : : : : +- GpuRowToColumnar targetsize(536870912)\n", + " : : : : : : : : : : +- *(4) Project [quarter#2233, loan_id#2202L, current_loan_delinquency_status#2212, CASE WHEN (current_loan_delinquency_status#2212 >= 1) THEN cast(gettimestamp(monthly_reporting_period#2203, MM/dd/yyyy, Some(America/Los_Angeles), false) as date) END AS delinquency_30#1975, CASE WHEN (current_loan_delinquency_status#2212 >= 3) THEN cast(gettimestamp(monthly_reporting_period#2203, MM/dd/yyyy, Some(America/Los_Angeles), false) as date) END AS delinquency_90#1976, CASE WHEN (current_loan_delinquency_status#2212 >= 6) THEN cast(gettimestamp(monthly_reporting_period#2203, MM/dd/yyyy, Some(America/Los_Angeles), false) as date) END AS delinquency_180#1977]\n", + " : : : : : : : : : : +- GpuColumnarToRow false\n", + " : : : : : : : : : : +- GpuFilter (gpuisnotnull(loan_id#2202L) AND gpuisnotnull(quarter#2233)), true\n", + " : : : : : : : : : : +- GpuFileGpuScan parquet [loan_id#2202L,monthly_reporting_period#2203,current_loan_delinquency_status#2212,quarter#2233] Batched: true, DataFilters: [isnotnull(loan_id#2202L), isnotnull(quarter#2233)], Format: Parquet, Location: InMemoryFileIndex[file:/local/saralihalli/HOME/mortgage/perf], PartitionFilters: [], PushedFilters: [IsNotNull(loan_id), IsNotNull(quarter)], ReadSchema: struct\n", + " : : : : : : : : : +- GpuColumnarToRow false\n", + " : : : : : : : : : +- GpuSort [quarter#2484 ASC NULLS FIRST, loan_id#2453L ASC NULLS FIRST, timestamp_year#2307L ASC NULLS FIRST, timestamp_month#2336L ASC NULLS FIRST], false, com.nvidia.spark.rapids.OutOfCoreSort$@163d9f7d\n", + " : : : : : : : : : +- GpuShuffleCoalesce 536870912\n", + " : : : : : : : : : +- GpuColumnarExchange gpuhashpartitioning(quarter#2484, loan_id#2453L, timestamp_year#2307L, timestamp_month#2336L, 192), ENSURE_REQUIREMENTS, [id=#1114]\n", + " : : : : : : : : : +- GpuRowToColumnar targetsize(536870912)\n", + " : : : : : : : : : +- *(6) HashAggregate(keys=[quarter#2484, loan_id#2453L, josh_mody_n#2264L, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997, month_y#2248], functions=[])\n", + " : : : : : : : : : +- GpuColumnarToRow false\n", + " : : : : : : : : : +- GpuShuffleCoalesce 536870912\n", + " : : : : : : : : : +- GpuColumnarExchange gpuhashpartitioning(quarter#2484, loan_id#2453L, josh_mody_n#2264L, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997, month_y#2248, 192), ENSURE_REQUIREMENTS, [id=#1107]\n", + " : : : : : : : : : +- GpuHashAggregate(keys=[quarter#2484, loan_id#2453L, josh_mody_n#2264L, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997, month_y#2248], functions=[]), filters=ArrayBuffer())\n", + " : : : : : : : : : +- GpuRowToColumnar targetsize(536870912)\n", + " : : : : : : : : : +- *(5) Project [quarter#2484, FLOOR((cast(((((timestamp_year#2087 * 12) + timestamp_month#2051) - 24000) - month_y#2248) as double) / 12.0)) AS josh_mody_n#2264L, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997, loan_id#2453L, month_y#2248]\n", + " : : : : : : : : : +- *(5) Filter (isnotnull(FLOOR((cast(((24000 + (FLOOR((cast(((((timestamp_year#2087 * 12) + timestamp_month#2051) - 24000) - month_y#2248) as double) / 12.0)) * 12)) + cast((month_y#2248 - 1) as bigint)) as double) / 12.0))) AND isnotnull(CASE WHEN (pmod(((24000 + (FLOOR((cast(((((timestamp_year#2087 * 12) + timestamp_month#2051) - 24000) - month_y#2248) as double) / 12.0)) * 12)) + cast(month_y#2248 as bigint)), 12) = 0) THEN 12 ELSE pmod(((24000 + (FLOOR((cast(((((timestamp_year#2087 * 12) + timestamp_month#2051) - 24000) - month_y#2248) as double) / 12.0)) * 12)) + cast(month_y#2248 as bigint)), 12) END))\n", + " : : : : : : : : : +- GpuColumnarToRow false\n", + " : : : : : : : : : +- GpuGenerate gpuexplode([0,1,2,3,4,5,6,7,8,9,10,11]), [loan_id#2453L, quarter#2484, timestamp_month#2051, timestamp_year#2087, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997], false, [month_y#2248]\n", + " : : : : : : : : : +- GpuProject [loan_id#2453L, quarter#2484, timestamp_month#2051, timestamp_year#2087, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997]\n", + " : : : : : : : : : +- GpuBroadcastHashJoin [loan_id#2453L, quarter#2484], [loan_id#2202L, quarter#2233], LeftOuter, GpuBuildRight\n", + " : : : : : : : : : :- GpuRowToColumnar targetsize(536870912)\n", + " : : : : : : : : : : +- *(3) Project [quarter#2484, loan_id#2453L, month(cast(gettimestamp(monthly_reporting_period#2454, MM/dd/yyyy, Some(America/Los_Angeles), false) as date)) AS timestamp_month#2051, year(cast(gettimestamp(monthly_reporting_period#2454, MM/dd/yyyy, Some(America/Los_Angeles), false) as date)) AS timestamp_year#2087]\n", + " : : : : : : : : : : +- GpuColumnarToRow false\n", + " : : : : : : : : : : +- GpuFilter (gpuisnotnull(quarter#2484) AND gpuisnotnull(loan_id#2453L)), true\n", + " : : : : : : : : : : +- GpuFileGpuScan parquet [loan_id#2453L,monthly_reporting_period#2454,quarter#2484] Batched: true, DataFilters: [isnotnull(quarter#2484), isnotnull(loan_id#2453L)], Format: Parquet, Location: InMemoryFileIndex[file:/local/saralihalli/HOME/mortgage/perf], PartitionFilters: [], PushedFilters: [IsNotNull(quarter), IsNotNull(loan_id)], ReadSchema: struct\n", + " : : : : : : : : : +- GpuBroadcastExchange HashedRelationBroadcastMode(List(input[1, bigint, true], input[0, string, true]),false), [id=#1096]\n", + " : : : : : : : : : +- GpuHashAggregate(keys=[quarter#2233, loan_id#2202L], functions=[gpumax(current_loan_delinquency_status#2212), gpumin(delinquency_30#1975), gpumin(delinquency_90#1976), gpumin(delinquency_180#1977)]), filters=ArrayBuffer(None, None, None, None))\n", + " : : : : : : : : : +- GpuShuffleCoalesce 536870912\n", + " : : : : : : : : : +- GpuColumnarExchange gpuhashpartitioning(quarter#2233, loan_id#2202L, 192), ENSURE_REQUIREMENTS, [id=#1093]\n", + " : : : : : : : : : +- GpuHashAggregate(keys=[quarter#2233, loan_id#2202L], functions=[partial_gpumax(current_loan_delinquency_status#2212), partial_gpumin(delinquency_30#1975), partial_gpumin(delinquency_90#1976), partial_gpumin(delinquency_180#1977)]), filters=ArrayBuffer(None, None, None, None))\n", + " : : : : : : : : : +- GpuRowToColumnar targetsize(536870912)\n", + " : : : : : : : : : +- *(4) Project [quarter#2233, loan_id#2202L, current_loan_delinquency_status#2212, CASE WHEN (current_loan_delinquency_status#2212 >= 1) THEN cast(gettimestamp(monthly_reporting_period#2203, MM/dd/yyyy, Some(America/Los_Angeles), false) as date) END AS delinquency_30#1975, CASE WHEN (current_loan_delinquency_status#2212 >= 3) THEN cast(gettimestamp(monthly_reporting_period#2203, MM/dd/yyyy, Some(America/Los_Angeles), false) as date) END AS delinquency_90#1976, CASE WHEN (current_loan_delinquency_status#2212 >= 6) THEN cast(gettimestamp(monthly_reporting_period#2203, MM/dd/yyyy, Some(America/Los_Angeles), false) as date) END AS delinquency_180#1977]\n", + " : : : : : : : : : +- GpuColumnarToRow false\n", + " : : : : : : : : : +- GpuFilter (gpuisnotnull(loan_id#2202L) AND gpuisnotnull(quarter#2233)), true\n", + " : : : : : : : : : +- GpuFileGpuScan parquet [loan_id#2202L,monthly_reporting_period#2203,current_loan_delinquency_status#2212,quarter#2233] Batched: true, DataFilters: [isnotnull(loan_id#2202L), isnotnull(quarter#2233)], Format: Parquet, Location: InMemoryFileIndex[file:/local/saralihalli/HOME/mortgage/perf], PartitionFilters: [], PushedFilters: [IsNotNull(loan_id), IsNotNull(quarter)], ReadSchema: struct\n", + " : : : : : : : : +- GpuColumnarToRow false\n", + " : : : : : : : : +- GpuSort [quarter#2484 ASC NULLS FIRST, loan_id#2453L ASC NULLS FIRST, timestamp_year#2307L ASC NULLS FIRST, timestamp_month#2336L ASC NULLS FIRST], false, com.nvidia.spark.rapids.OutOfCoreSort$@163d9f7d\n", + " : : : : : : : : +- GpuShuffleCoalesce 536870912\n", + " : : : : : : : : +- GpuColumnarExchange gpuhashpartitioning(quarter#2484, loan_id#2453L, timestamp_year#2307L, timestamp_month#2336L, 192), ENSURE_REQUIREMENTS, [id=#1114]\n", + " : : : : : : : : +- GpuRowToColumnar targetsize(536870912)\n", + " : : : : : : : : +- *(6) HashAggregate(keys=[quarter#2484, loan_id#2453L, josh_mody_n#2264L, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997, month_y#2248], functions=[])\n", + " : : : : : : : : +- GpuColumnarToRow false\n", + " : : : : : : : : +- GpuShuffleCoalesce 536870912\n", + " : : : : : : : : +- GpuColumnarExchange gpuhashpartitioning(quarter#2484, loan_id#2453L, josh_mody_n#2264L, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997, month_y#2248, 192), ENSURE_REQUIREMENTS, [id=#1107]\n", + " : : : : : : : : +- GpuHashAggregate(keys=[quarter#2484, loan_id#2453L, josh_mody_n#2264L, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997, month_y#2248], functions=[]), filters=ArrayBuffer())\n", + " : : : : : : : : +- GpuRowToColumnar targetsize(536870912)\n", + " : : : : : : : : +- *(5) Project [quarter#2484, FLOOR((cast(((((timestamp_year#2087 * 12) + timestamp_month#2051) - 24000) - month_y#2248) as double) / 12.0)) AS josh_mody_n#2264L, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997, loan_id#2453L, month_y#2248]\n", + " : : : : : : : : +- *(5) Filter (isnotnull(FLOOR((cast(((24000 + (FLOOR((cast(((((timestamp_year#2087 * 12) + timestamp_month#2051) - 24000) - month_y#2248) as double) / 12.0)) * 12)) + cast((month_y#2248 - 1) as bigint)) as double) / 12.0))) AND isnotnull(CASE WHEN (pmod(((24000 + (FLOOR((cast(((((timestamp_year#2087 * 12) + timestamp_month#2051) - 24000) - month_y#2248) as double) / 12.0)) * 12)) + cast(month_y#2248 as bigint)), 12) = 0) THEN 12 ELSE pmod(((24000 + (FLOOR((cast(((((timestamp_year#2087 * 12) + timestamp_month#2051) - 24000) - month_y#2248) as double) / 12.0)) * 12)) + cast(month_y#2248 as bigint)), 12) END))\n", + " : : : : : : : : +- GpuColumnarToRow false\n", + " : : : : : : : : +- GpuGenerate gpuexplode([0,1,2,3,4,5,6,7,8,9,10,11]), [loan_id#2453L, quarter#2484, timestamp_month#2051, timestamp_year#2087, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997], false, [month_y#2248]\n", + " : : : : : : : : +- GpuProject [loan_id#2453L, quarter#2484, timestamp_month#2051, timestamp_year#2087, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997]\n", + " : : : : : : : : +- GpuBroadcastHashJoin [loan_id#2453L, quarter#2484], [loan_id#2202L, quarter#2233], LeftOuter, GpuBuildRight\n", + " : : : : : : : : :- GpuRowToColumnar targetsize(536870912)\n", + " : : : : : : : : : +- *(3) Project [quarter#2484, loan_id#2453L, month(cast(gettimestamp(monthly_reporting_period#2454, MM/dd/yyyy, Some(America/Los_Angeles), false) as date)) AS timestamp_month#2051, year(cast(gettimestamp(monthly_reporting_period#2454, MM/dd/yyyy, Some(America/Los_Angeles), false) as date)) AS timestamp_year#2087]\n", + " : : : : : : : : : +- GpuColumnarToRow false\n", + " : : : : : : : : : +- GpuFilter (gpuisnotnull(quarter#2484) AND gpuisnotnull(loan_id#2453L)), true\n", + " : : : : : : : : : +- GpuFileGpuScan parquet [loan_id#2453L,monthly_reporting_period#2454,quarter#2484] Batched: true, DataFilters: [isnotnull(quarter#2484), isnotnull(loan_id#2453L)], Format: Parquet, Location: InMemoryFileIndex[file:/local/saralihalli/HOME/mortgage/perf], PartitionFilters: [], PushedFilters: [IsNotNull(quarter), IsNotNull(loan_id)], ReadSchema: struct\n", + " : : : : : : : : +- GpuBroadcastExchange HashedRelationBroadcastMode(List(input[1, bigint, true], input[0, string, true]),false), [id=#1096]\n", + " : : : : : : : : +- GpuHashAggregate(keys=[quarter#2233, loan_id#2202L], functions=[gpumax(current_loan_delinquency_status#2212), gpumin(delinquency_30#1975), gpumin(delinquency_90#1976), gpumin(delinquency_180#1977)]), filters=ArrayBuffer(None, None, None, None))\n", + " : : : : : : : : +- GpuShuffleCoalesce 536870912\n", + " : : : : : : : : +- GpuColumnarExchange gpuhashpartitioning(quarter#2233, loan_id#2202L, 192), ENSURE_REQUIREMENTS, [id=#1093]\n", + " : : : : : : : : +- GpuHashAggregate(keys=[quarter#2233, loan_id#2202L], functions=[partial_gpumax(current_loan_delinquency_status#2212), partial_gpumin(delinquency_30#1975), partial_gpumin(delinquency_90#1976), partial_gpumin(delinquency_180#1977)]), filters=ArrayBuffer(None, None, None, None))\n", + " : : : : : : : : +- GpuRowToColumnar targetsize(536870912)\n", + " : : : : : : : : +- *(4) Project [quarter#2233, loan_id#2202L, current_loan_delinquency_status#2212, CASE WHEN (current_loan_delinquency_status#2212 >= 1) THEN cast(gettimestamp(monthly_reporting_period#2203, MM/dd/yyyy, Some(America/Los_Angeles), false) as date) END AS delinquency_30#1975, CASE WHEN (current_loan_delinquency_status#2212 >= 3) THEN cast(gettimestamp(monthly_reporting_period#2203, MM/dd/yyyy, Some(America/Los_Angeles), false) as date) END AS delinquency_90#1976, CASE WHEN (current_loan_delinquency_status#2212 >= 6) THEN cast(gettimestamp(monthly_reporting_period#2203, MM/dd/yyyy, Some(America/Los_Angeles), false) as date) END AS delinquency_180#1977]\n", + " : : : : : : : : +- GpuColumnarToRow false\n", + " : : : : : : : : +- GpuFilter (gpuisnotnull(loan_id#2202L) AND gpuisnotnull(quarter#2233)), true\n", + " : : : : : : : : +- GpuFileGpuScan parquet [loan_id#2202L,monthly_reporting_period#2203,current_loan_delinquency_status#2212,quarter#2233] Batched: true, DataFilters: [isnotnull(loan_id#2202L), isnotnull(quarter#2233)], Format: Parquet, Location: InMemoryFileIndex[file:/local/saralihalli/HOME/mortgage/perf], PartitionFilters: [], PushedFilters: [IsNotNull(loan_id), IsNotNull(quarter)], ReadSchema: struct\n", + " : : : : : : : +- GpuColumnarToRow false\n", + " : : : : : : : +- GpuSort [quarter#2484 ASC NULLS FIRST, loan_id#2453L ASC NULLS FIRST, timestamp_year#2307L ASC NULLS FIRST, timestamp_month#2336L ASC NULLS FIRST], false, com.nvidia.spark.rapids.OutOfCoreSort$@163d9f7d\n", + " : : : : : : : +- GpuShuffleCoalesce 536870912\n", + " : : : : : : : +- GpuColumnarExchange gpuhashpartitioning(quarter#2484, loan_id#2453L, timestamp_year#2307L, timestamp_month#2336L, 192), ENSURE_REQUIREMENTS, [id=#1114]\n", + " : : : : : : : +- GpuRowToColumnar targetsize(536870912)\n", + " : : : : : : : +- *(6) HashAggregate(keys=[quarter#2484, loan_id#2453L, josh_mody_n#2264L, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997, month_y#2248], functions=[])\n", + " : : : : : : : +- GpuColumnarToRow false\n", + " : : : : : : : +- GpuShuffleCoalesce 536870912\n", + " : : : : : : : +- GpuColumnarExchange gpuhashpartitioning(quarter#2484, loan_id#2453L, josh_mody_n#2264L, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997, month_y#2248, 192), ENSURE_REQUIREMENTS, [id=#1107]\n", + " : : : : : : : +- GpuHashAggregate(keys=[quarter#2484, loan_id#2453L, josh_mody_n#2264L, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997, month_y#2248], functions=[]), filters=ArrayBuffer())\n", + " : : : : : : : +- GpuRowToColumnar targetsize(536870912)\n", + " : : : : : : : +- *(5) Project [quarter#2484, FLOOR((cast(((((timestamp_year#2087 * 12) + timestamp_month#2051) - 24000) - month_y#2248) as double) / 12.0)) AS josh_mody_n#2264L, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997, loan_id#2453L, month_y#2248]\n", + " : : : : : : : +- *(5) Filter (isnotnull(FLOOR((cast(((24000 + (FLOOR((cast(((((timestamp_year#2087 * 12) + timestamp_month#2051) - 24000) - month_y#2248) as double) / 12.0)) * 12)) + cast((month_y#2248 - 1) as bigint)) as double) / 12.0))) AND isnotnull(CASE WHEN (pmod(((24000 + (FLOOR((cast(((((timestamp_year#2087 * 12) + timestamp_month#2051) - 24000) - month_y#2248) as double) / 12.0)) * 12)) + cast(month_y#2248 as bigint)), 12) = 0) THEN 12 ELSE pmod(((24000 + (FLOOR((cast(((((timestamp_year#2087 * 12) + timestamp_month#2051) - 24000) - month_y#2248) as double) / 12.0)) * 12)) + cast(month_y#2248 as bigint)), 12) END))\n", + " : : : : : : : +- GpuColumnarToRow false\n", + " : : : : : : : +- GpuGenerate gpuexplode([0,1,2,3,4,5,6,7,8,9,10,11]), [loan_id#2453L, quarter#2484, timestamp_month#2051, timestamp_year#2087, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997], false, [month_y#2248]\n", + " : : : : : : : +- GpuProject [loan_id#2453L, quarter#2484, timestamp_month#2051, timestamp_year#2087, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997]\n", + " : : : : : : : +- GpuBroadcastHashJoin [loan_id#2453L, quarter#2484], [loan_id#2202L, quarter#2233], LeftOuter, GpuBuildRight\n", + " : : : : : : : :- GpuRowToColumnar targetsize(536870912)\n", + " : : : : : : : : +- *(3) Project [quarter#2484, loan_id#2453L, month(cast(gettimestamp(monthly_reporting_period#2454, MM/dd/yyyy, Some(America/Los_Angeles), false) as date)) AS timestamp_month#2051, year(cast(gettimestamp(monthly_reporting_period#2454, MM/dd/yyyy, Some(America/Los_Angeles), false) as date)) AS timestamp_year#2087]\n", + " : : : : : : : : +- GpuColumnarToRow false\n", + " : : : : : : : : +- GpuFilter (gpuisnotnull(quarter#2484) AND gpuisnotnull(loan_id#2453L)), true\n", + " : : : : : : : : +- GpuFileGpuScan parquet [loan_id#2453L,monthly_reporting_period#2454,quarter#2484] Batched: true, DataFilters: [isnotnull(quarter#2484), isnotnull(loan_id#2453L)], Format: Parquet, Location: InMemoryFileIndex[file:/local/saralihalli/HOME/mortgage/perf], PartitionFilters: [], PushedFilters: [IsNotNull(quarter), IsNotNull(loan_id)], ReadSchema: struct\n", + " : : : : : : : +- GpuBroadcastExchange HashedRelationBroadcastMode(List(input[1, bigint, true], input[0, string, true]),false), [id=#1096]\n", + " : : : : : : : +- GpuHashAggregate(keys=[quarter#2233, loan_id#2202L], functions=[gpumax(current_loan_delinquency_status#2212), gpumin(delinquency_30#1975), gpumin(delinquency_90#1976), gpumin(delinquency_180#1977)]), filters=ArrayBuffer(None, None, None, None))\n", + " : : : : : : : +- GpuShuffleCoalesce 536870912\n", + " : : : : : : : +- GpuColumnarExchange gpuhashpartitioning(quarter#2233, loan_id#2202L, 192), ENSURE_REQUIREMENTS, [id=#1093]\n", + " : : : : : : : +- GpuHashAggregate(keys=[quarter#2233, loan_id#2202L], functions=[partial_gpumax(current_loan_delinquency_status#2212), partial_gpumin(delinquency_30#1975), partial_gpumin(delinquency_90#1976), partial_gpumin(delinquency_180#1977)]), filters=ArrayBuffer(None, None, None, None))\n", + " : : : : : : : +- GpuRowToColumnar targetsize(536870912)\n", + " : : : : : : : +- *(4) Project [quarter#2233, loan_id#2202L, current_loan_delinquency_status#2212, CASE WHEN (current_loan_delinquency_status#2212 >= 1) THEN cast(gettimestamp(monthly_reporting_period#2203, MM/dd/yyyy, Some(America/Los_Angeles), false) as date) END AS delinquency_30#1975, CASE WHEN (current_loan_delinquency_status#2212 >= 3) THEN cast(gettimestamp(monthly_reporting_period#2203, MM/dd/yyyy, Some(America/Los_Angeles), false) as date) END AS delinquency_90#1976, CASE WHEN (current_loan_delinquency_status#2212 >= 6) THEN cast(gettimestamp(monthly_reporting_period#2203, MM/dd/yyyy, Some(America/Los_Angeles), false) as date) END AS delinquency_180#1977]\n", + " : : : : : : : +- GpuColumnarToRow false\n", + " : : : : : : : +- GpuFilter (gpuisnotnull(loan_id#2202L) AND gpuisnotnull(quarter#2233)), true\n", + " : : : : : : : +- GpuFileGpuScan parquet [loan_id#2202L,monthly_reporting_period#2203,current_loan_delinquency_status#2212,quarter#2233] Batched: true, DataFilters: [isnotnull(loan_id#2202L), isnotnull(quarter#2233)], Format: Parquet, Location: InMemoryFileIndex[file:/local/saralihalli/HOME/mortgage/perf], PartitionFilters: [], PushedFilters: [IsNotNull(loan_id), IsNotNull(quarter)], ReadSchema: struct\n", + " : : : : : : +- GpuColumnarToRow false\n", + " : : : : : : +- GpuSort [quarter#2484 ASC NULLS FIRST, loan_id#2453L ASC NULLS FIRST, timestamp_year#2307L ASC NULLS FIRST, timestamp_month#2336L ASC NULLS FIRST], false, com.nvidia.spark.rapids.OutOfCoreSort$@163d9f7d\n", + " : : : : : : +- GpuShuffleCoalesce 536870912\n", + " : : : : : : +- GpuColumnarExchange gpuhashpartitioning(quarter#2484, loan_id#2453L, timestamp_year#2307L, timestamp_month#2336L, 192), ENSURE_REQUIREMENTS, [id=#1114]\n", + " : : : : : : +- GpuRowToColumnar targetsize(536870912)\n", + " : : : : : : +- *(6) HashAggregate(keys=[quarter#2484, loan_id#2453L, josh_mody_n#2264L, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997, month_y#2248], functions=[])\n", + " : : : : : : +- GpuColumnarToRow false\n", + " : : : : : : +- GpuShuffleCoalesce 536870912\n", + " : : : : : : +- GpuColumnarExchange gpuhashpartitioning(quarter#2484, loan_id#2453L, josh_mody_n#2264L, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997, month_y#2248, 192), ENSURE_REQUIREMENTS, [id=#1107]\n", + " : : : : : : +- GpuHashAggregate(keys=[quarter#2484, loan_id#2453L, josh_mody_n#2264L, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997, month_y#2248], functions=[]), filters=ArrayBuffer())\n", + " : : : : : : +- GpuRowToColumnar targetsize(536870912)\n", + " : : : : : : +- *(5) Project [quarter#2484, FLOOR((cast(((((timestamp_year#2087 * 12) + timestamp_month#2051) - 24000) - month_y#2248) as double) / 12.0)) AS josh_mody_n#2264L, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997, loan_id#2453L, month_y#2248]\n", + " : : : : : : +- *(5) Filter (isnotnull(FLOOR((cast(((24000 + (FLOOR((cast(((((timestamp_year#2087 * 12) + timestamp_month#2051) - 24000) - month_y#2248) as double) / 12.0)) * 12)) + cast((month_y#2248 - 1) as bigint)) as double) / 12.0))) AND isnotnull(CASE WHEN (pmod(((24000 + (FLOOR((cast(((((timestamp_year#2087 * 12) + timestamp_month#2051) - 24000) - month_y#2248) as double) / 12.0)) * 12)) + cast(month_y#2248 as bigint)), 12) = 0) THEN 12 ELSE pmod(((24000 + (FLOOR((cast(((((timestamp_year#2087 * 12) + timestamp_month#2051) - 24000) - month_y#2248) as double) / 12.0)) * 12)) + cast(month_y#2248 as bigint)), 12) END))\n", + " : : : : : : +- GpuColumnarToRow false\n", + " : : : : : : +- GpuGenerate gpuexplode([0,1,2,3,4,5,6,7,8,9,10,11]), [loan_id#2453L, quarter#2484, timestamp_month#2051, timestamp_year#2087, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997], false, [month_y#2248]\n", + " : : : : : : +- GpuProject [loan_id#2453L, quarter#2484, timestamp_month#2051, timestamp_year#2087, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997]\n", + " : : : : : : +- GpuBroadcastHashJoin [loan_id#2453L, quarter#2484], [loan_id#2202L, quarter#2233], LeftOuter, GpuBuildRight\n", + " : : : : : : :- GpuRowToColumnar targetsize(536870912)\n", + " : : : : : : : +- *(3) Project [quarter#2484, loan_id#2453L, month(cast(gettimestamp(monthly_reporting_period#2454, MM/dd/yyyy, Some(America/Los_Angeles), false) as date)) AS timestamp_month#2051, year(cast(gettimestamp(monthly_reporting_period#2454, MM/dd/yyyy, Some(America/Los_Angeles), false) as date)) AS timestamp_year#2087]\n", + " : : : : : : : +- GpuColumnarToRow false\n", + " : : : : : : : +- GpuFilter (gpuisnotnull(quarter#2484) AND gpuisnotnull(loan_id#2453L)), true\n", + " : : : : : : : +- GpuFileGpuScan parquet [loan_id#2453L,monthly_reporting_period#2454,quarter#2484] Batched: true, DataFilters: [isnotnull(quarter#2484), isnotnull(loan_id#2453L)], Format: Parquet, Location: InMemoryFileIndex[file:/local/saralihalli/HOME/mortgage/perf], PartitionFilters: [], PushedFilters: [IsNotNull(quarter), IsNotNull(loan_id)], ReadSchema: struct\n", + " : : : : : : +- GpuBroadcastExchange HashedRelationBroadcastMode(List(input[1, bigint, true], input[0, string, true]),false), [id=#1096]\n", + " : : : : : : +- GpuHashAggregate(keys=[quarter#2233, loan_id#2202L], functions=[gpumax(current_loan_delinquency_status#2212), gpumin(delinquency_30#1975), gpumin(delinquency_90#1976), gpumin(delinquency_180#1977)]), filters=ArrayBuffer(None, None, None, None))\n", + " : : : : : : +- GpuShuffleCoalesce 536870912\n", + " : : : : : : +- GpuColumnarExchange gpuhashpartitioning(quarter#2233, loan_id#2202L, 192), ENSURE_REQUIREMENTS, [id=#1093]\n", + " : : : : : : +- GpuHashAggregate(keys=[quarter#2233, loan_id#2202L], functions=[partial_gpumax(current_loan_delinquency_status#2212), partial_gpumin(delinquency_30#1975), partial_gpumin(delinquency_90#1976), partial_gpumin(delinquency_180#1977)]), filters=ArrayBuffer(None, None, None, None))\n", + " : : : : : : +- GpuRowToColumnar targetsize(536870912)\n", + " : : : : : : +- *(4) Project [quarter#2233, loan_id#2202L, current_loan_delinquency_status#2212, CASE WHEN (current_loan_delinquency_status#2212 >= 1) THEN cast(gettimestamp(monthly_reporting_period#2203, MM/dd/yyyy, Some(America/Los_Angeles), false) as date) END AS delinquency_30#1975, CASE WHEN (current_loan_delinquency_status#2212 >= 3) THEN cast(gettimestamp(monthly_reporting_period#2203, MM/dd/yyyy, Some(America/Los_Angeles), false) as date) END AS delinquency_90#1976, CASE WHEN (current_loan_delinquency_status#2212 >= 6) THEN cast(gettimestamp(monthly_reporting_period#2203, MM/dd/yyyy, Some(America/Los_Angeles), false) as date) END AS delinquency_180#1977]\n", + " : : : : : : +- GpuColumnarToRow false\n", + " : : : : : : +- GpuFilter (gpuisnotnull(loan_id#2202L) AND gpuisnotnull(quarter#2233)), true\n", + " : : : : : : +- GpuFileGpuScan parquet [loan_id#2202L,monthly_reporting_period#2203,current_loan_delinquency_status#2212,quarter#2233] Batched: true, DataFilters: [isnotnull(loan_id#2202L), isnotnull(quarter#2233)], Format: Parquet, Location: InMemoryFileIndex[file:/local/saralihalli/HOME/mortgage/perf], PartitionFilters: [], PushedFilters: [IsNotNull(loan_id), IsNotNull(quarter)], ReadSchema: struct\n", + " : : : : : +- GpuColumnarToRow false\n", + " : : : : : +- GpuSort [quarter#2484 ASC NULLS FIRST, loan_id#2453L ASC NULLS FIRST, timestamp_year#2307L ASC NULLS FIRST, timestamp_month#2336L ASC NULLS FIRST], false, com.nvidia.spark.rapids.OutOfCoreSort$@163d9f7d\n", + " : : : : : +- GpuShuffleCoalesce 536870912\n", + " : : : : : +- GpuColumnarExchange gpuhashpartitioning(quarter#2484, loan_id#2453L, timestamp_year#2307L, timestamp_month#2336L, 192), ENSURE_REQUIREMENTS, [id=#1114]\n", + " : : : : : +- GpuRowToColumnar targetsize(536870912)\n", + " : : : : : +- *(6) HashAggregate(keys=[quarter#2484, loan_id#2453L, josh_mody_n#2264L, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997, month_y#2248], functions=[])\n", + " : : : : : +- GpuColumnarToRow false\n", + " : : : : : +- GpuShuffleCoalesce 536870912\n", + " : : : : : +- GpuColumnarExchange gpuhashpartitioning(quarter#2484, loan_id#2453L, josh_mody_n#2264L, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997, month_y#2248, 192), ENSURE_REQUIREMENTS, [id=#1107]\n", + " : : : : : +- GpuHashAggregate(keys=[quarter#2484, loan_id#2453L, josh_mody_n#2264L, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997, month_y#2248], functions=[]), filters=ArrayBuffer())\n", + " : : : : : +- GpuRowToColumnar targetsize(536870912)\n", + " : : : : : +- *(5) Project [quarter#2484, FLOOR((cast(((((timestamp_year#2087 * 12) + timestamp_month#2051) - 24000) - month_y#2248) as double) / 12.0)) AS josh_mody_n#2264L, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997, loan_id#2453L, month_y#2248]\n", + " : : : : : +- *(5) Filter (isnotnull(FLOOR((cast(((24000 + (FLOOR((cast(((((timestamp_year#2087 * 12) + timestamp_month#2051) - 24000) - month_y#2248) as double) / 12.0)) * 12)) + cast((month_y#2248 - 1) as bigint)) as double) / 12.0))) AND isnotnull(CASE WHEN (pmod(((24000 + (FLOOR((cast(((((timestamp_year#2087 * 12) + timestamp_month#2051) - 24000) - month_y#2248) as double) / 12.0)) * 12)) + cast(month_y#2248 as bigint)), 12) = 0) THEN 12 ELSE pmod(((24000 + (FLOOR((cast(((((timestamp_year#2087 * 12) + timestamp_month#2051) - 24000) - month_y#2248) as double) / 12.0)) * 12)) + cast(month_y#2248 as bigint)), 12) END))\n", + " : : : : : +- GpuColumnarToRow false\n", + " : : : : : +- GpuGenerate gpuexplode([0,1,2,3,4,5,6,7,8,9,10,11]), [loan_id#2453L, quarter#2484, timestamp_month#2051, timestamp_year#2087, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997], false, [month_y#2248]\n", + " : : : : : +- GpuProject [loan_id#2453L, quarter#2484, timestamp_month#2051, timestamp_year#2087, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997]\n", + " : : : : : +- GpuBroadcastHashJoin [loan_id#2453L, quarter#2484], [loan_id#2202L, quarter#2233], LeftOuter, GpuBuildRight\n", + " : : : : : :- GpuRowToColumnar targetsize(536870912)\n", + " : : : : : : +- *(3) Project [quarter#2484, loan_id#2453L, month(cast(gettimestamp(monthly_reporting_period#2454, MM/dd/yyyy, Some(America/Los_Angeles), false) as date)) AS timestamp_month#2051, year(cast(gettimestamp(monthly_reporting_period#2454, MM/dd/yyyy, Some(America/Los_Angeles), false) as date)) AS timestamp_year#2087]\n", + " : : : : : : +- GpuColumnarToRow false\n", + " : : : : : : +- GpuFilter (gpuisnotnull(quarter#2484) AND gpuisnotnull(loan_id#2453L)), true\n", + " : : : : : : +- GpuFileGpuScan parquet [loan_id#2453L,monthly_reporting_period#2454,quarter#2484] Batched: true, DataFilters: [isnotnull(quarter#2484), isnotnull(loan_id#2453L)], Format: Parquet, Location: InMemoryFileIndex[file:/local/saralihalli/HOME/mortgage/perf], PartitionFilters: [], PushedFilters: [IsNotNull(quarter), IsNotNull(loan_id)], ReadSchema: struct\n", + " : : : : : +- GpuBroadcastExchange HashedRelationBroadcastMode(List(input[1, bigint, true], input[0, string, true]),false), [id=#1096]\n", + " : : : : : +- GpuHashAggregate(keys=[quarter#2233, loan_id#2202L], functions=[gpumax(current_loan_delinquency_status#2212), gpumin(delinquency_30#1975), gpumin(delinquency_90#1976), gpumin(delinquency_180#1977)]), filters=ArrayBuffer(None, None, None, None))\n", + " : : : : : +- GpuShuffleCoalesce 536870912\n", + " : : : : : +- GpuColumnarExchange gpuhashpartitioning(quarter#2233, loan_id#2202L, 192), ENSURE_REQUIREMENTS, [id=#1093]\n", + " : : : : : +- GpuHashAggregate(keys=[quarter#2233, loan_id#2202L], functions=[partial_gpumax(current_loan_delinquency_status#2212), partial_gpumin(delinquency_30#1975), partial_gpumin(delinquency_90#1976), partial_gpumin(delinquency_180#1977)]), filters=ArrayBuffer(None, None, None, None))\n", + " : : : : : +- GpuRowToColumnar targetsize(536870912)\n", + " : : : : : +- *(4) Project [quarter#2233, loan_id#2202L, current_loan_delinquency_status#2212, CASE WHEN (current_loan_delinquency_status#2212 >= 1) THEN cast(gettimestamp(monthly_reporting_period#2203, MM/dd/yyyy, Some(America/Los_Angeles), false) as date) END AS delinquency_30#1975, CASE WHEN (current_loan_delinquency_status#2212 >= 3) THEN cast(gettimestamp(monthly_reporting_period#2203, MM/dd/yyyy, Some(America/Los_Angeles), false) as date) END AS delinquency_90#1976, CASE WHEN (current_loan_delinquency_status#2212 >= 6) THEN cast(gettimestamp(monthly_reporting_period#2203, MM/dd/yyyy, Some(America/Los_Angeles), false) as date) END AS delinquency_180#1977]\n", + " : : : : : +- GpuColumnarToRow false\n", + " : : : : : +- GpuFilter (gpuisnotnull(loan_id#2202L) AND gpuisnotnull(quarter#2233)), true\n", + " : : : : : +- GpuFileGpuScan parquet [loan_id#2202L,monthly_reporting_period#2203,current_loan_delinquency_status#2212,quarter#2233] Batched: true, DataFilters: [isnotnull(loan_id#2202L), isnotnull(quarter#2233)], Format: Parquet, Location: InMemoryFileIndex[file:/local/saralihalli/HOME/mortgage/perf], PartitionFilters: [], PushedFilters: [IsNotNull(loan_id), IsNotNull(quarter)], ReadSchema: struct\n", + " : : : : +- GpuColumnarToRow false\n", + " : : : : +- GpuSort [quarter#2484 ASC NULLS FIRST, loan_id#2453L ASC NULLS FIRST, timestamp_year#2307L ASC NULLS FIRST, timestamp_month#2336L ASC NULLS FIRST], false, com.nvidia.spark.rapids.OutOfCoreSort$@163d9f7d\n", + " : : : : +- GpuShuffleCoalesce 536870912\n", + " : : : : +- GpuColumnarExchange gpuhashpartitioning(quarter#2484, loan_id#2453L, timestamp_year#2307L, timestamp_month#2336L, 192), ENSURE_REQUIREMENTS, [id=#1114]\n", + " : : : : +- GpuRowToColumnar targetsize(536870912)\n", + " : : : : +- *(6) HashAggregate(keys=[quarter#2484, loan_id#2453L, josh_mody_n#2264L, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997, month_y#2248], functions=[])\n", + " : : : : +- GpuColumnarToRow false\n", + " : : : : +- GpuShuffleCoalesce 536870912\n", + " : : : : +- GpuColumnarExchange gpuhashpartitioning(quarter#2484, loan_id#2453L, josh_mody_n#2264L, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997, month_y#2248, 192), ENSURE_REQUIREMENTS, [id=#1107]\n", + " : : : : +- GpuHashAggregate(keys=[quarter#2484, loan_id#2453L, josh_mody_n#2264L, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997, month_y#2248], functions=[]), filters=ArrayBuffer())\n", + " : : : : +- GpuRowToColumnar targetsize(536870912)\n", + " : : : : +- *(5) Project [quarter#2484, FLOOR((cast(((((timestamp_year#2087 * 12) + timestamp_month#2051) - 24000) - month_y#2248) as double) / 12.0)) AS josh_mody_n#2264L, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997, loan_id#2453L, month_y#2248]\n", + " : : : : +- *(5) Filter (isnotnull(FLOOR((cast(((24000 + (FLOOR((cast(((((timestamp_year#2087 * 12) + timestamp_month#2051) - 24000) - month_y#2248) as double) / 12.0)) * 12)) + cast((month_y#2248 - 1) as bigint)) as double) / 12.0))) AND isnotnull(CASE WHEN (pmod(((24000 + (FLOOR((cast(((((timestamp_year#2087 * 12) + timestamp_month#2051) - 24000) - month_y#2248) as double) / 12.0)) * 12)) + cast(month_y#2248 as bigint)), 12) = 0) THEN 12 ELSE pmod(((24000 + (FLOOR((cast(((((timestamp_year#2087 * 12) + timestamp_month#2051) - 24000) - month_y#2248) as double) / 12.0)) * 12)) + cast(month_y#2248 as bigint)), 12) END))\n", + " : : : : +- GpuColumnarToRow false\n", + " : : : : +- GpuGenerate gpuexplode([0,1,2,3,4,5,6,7,8,9,10,11]), [loan_id#2453L, quarter#2484, timestamp_month#2051, timestamp_year#2087, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997], false, [month_y#2248]\n", + " : : : : +- GpuProject [loan_id#2453L, quarter#2484, timestamp_month#2051, timestamp_year#2087, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997]\n", + " : : : : +- GpuBroadcastHashJoin [loan_id#2453L, quarter#2484], [loan_id#2202L, quarter#2233], LeftOuter, GpuBuildRight\n", + " : : : : :- GpuRowToColumnar targetsize(536870912)\n", + " : : : : : +- *(3) Project [quarter#2484, loan_id#2453L, month(cast(gettimestamp(monthly_reporting_period#2454, MM/dd/yyyy, Some(America/Los_Angeles), false) as date)) AS timestamp_month#2051, year(cast(gettimestamp(monthly_reporting_period#2454, MM/dd/yyyy, Some(America/Los_Angeles), false) as date)) AS timestamp_year#2087]\n", + " : : : : : +- GpuColumnarToRow false\n", + " : : : : : +- GpuFilter (gpuisnotnull(quarter#2484) AND gpuisnotnull(loan_id#2453L)), true\n", + " : : : : : +- GpuFileGpuScan parquet [loan_id#2453L,monthly_reporting_period#2454,quarter#2484] Batched: true, DataFilters: [isnotnull(quarter#2484), isnotnull(loan_id#2453L)], Format: Parquet, Location: InMemoryFileIndex[file:/local/saralihalli/HOME/mortgage/perf], PartitionFilters: [], PushedFilters: [IsNotNull(quarter), IsNotNull(loan_id)], ReadSchema: struct\n", + " : : : : +- GpuBroadcastExchange HashedRelationBroadcastMode(List(input[1, bigint, true], input[0, string, true]),false), [id=#1096]\n", + " : : : : +- GpuHashAggregate(keys=[quarter#2233, loan_id#2202L], functions=[gpumax(current_loan_delinquency_status#2212), gpumin(delinquency_30#1975), gpumin(delinquency_90#1976), gpumin(delinquency_180#1977)]), filters=ArrayBuffer(None, None, None, None))\n", + " : : : : +- GpuShuffleCoalesce 536870912\n", + " : : : : +- GpuColumnarExchange gpuhashpartitioning(quarter#2233, loan_id#2202L, 192), ENSURE_REQUIREMENTS, [id=#1093]\n", + " : : : : +- GpuHashAggregate(keys=[quarter#2233, loan_id#2202L], functions=[partial_gpumax(current_loan_delinquency_status#2212), partial_gpumin(delinquency_30#1975), partial_gpumin(delinquency_90#1976), partial_gpumin(delinquency_180#1977)]), filters=ArrayBuffer(None, None, None, None))\n", + " : : : : +- GpuRowToColumnar targetsize(536870912)\n", + " : : : : +- *(4) Project [quarter#2233, loan_id#2202L, current_loan_delinquency_status#2212, CASE WHEN (current_loan_delinquency_status#2212 >= 1) THEN cast(gettimestamp(monthly_reporting_period#2203, MM/dd/yyyy, Some(America/Los_Angeles), false) as date) END AS delinquency_30#1975, CASE WHEN (current_loan_delinquency_status#2212 >= 3) THEN cast(gettimestamp(monthly_reporting_period#2203, MM/dd/yyyy, Some(America/Los_Angeles), false) as date) END AS delinquency_90#1976, CASE WHEN (current_loan_delinquency_status#2212 >= 6) THEN cast(gettimestamp(monthly_reporting_period#2203, MM/dd/yyyy, Some(America/Los_Angeles), false) as date) END AS delinquency_180#1977]\n", + " : : : : +- GpuColumnarToRow false\n", + " : : : : +- GpuFilter (gpuisnotnull(loan_id#2202L) AND gpuisnotnull(quarter#2233)), true\n", + " : : : : +- GpuFileGpuScan parquet [loan_id#2202L,monthly_reporting_period#2203,current_loan_delinquency_status#2212,quarter#2233] Batched: true, DataFilters: [isnotnull(loan_id#2202L), isnotnull(quarter#2233)], Format: Parquet, Location: InMemoryFileIndex[file:/local/saralihalli/HOME/mortgage/perf], PartitionFilters: [], PushedFilters: [IsNotNull(loan_id), IsNotNull(quarter)], ReadSchema: struct\n", + " : : : +- GpuColumnarToRow false\n", + " : : : +- GpuSort [quarter#2484 ASC NULLS FIRST, loan_id#2453L ASC NULLS FIRST, timestamp_year#2307L ASC NULLS FIRST, timestamp_month#2336L ASC NULLS FIRST], false, com.nvidia.spark.rapids.OutOfCoreSort$@163d9f7d\n", + " : : : +- GpuShuffleCoalesce 536870912\n", + " : : : +- GpuColumnarExchange gpuhashpartitioning(quarter#2484, loan_id#2453L, timestamp_year#2307L, timestamp_month#2336L, 192), ENSURE_REQUIREMENTS, [id=#1114]\n", + " : : : +- GpuRowToColumnar targetsize(536870912)\n", + " : : : +- *(6) HashAggregate(keys=[quarter#2484, loan_id#2453L, josh_mody_n#2264L, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997, month_y#2248], functions=[])\n", + " : : : +- GpuColumnarToRow false\n", + " : : : +- GpuShuffleCoalesce 536870912\n", + " : : : +- GpuColumnarExchange gpuhashpartitioning(quarter#2484, loan_id#2453L, josh_mody_n#2264L, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997, month_y#2248, 192), ENSURE_REQUIREMENTS, [id=#1107]\n", + " : : : +- GpuHashAggregate(keys=[quarter#2484, loan_id#2453L, josh_mody_n#2264L, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997, month_y#2248], functions=[]), filters=ArrayBuffer())\n", + " : : : +- GpuRowToColumnar targetsize(536870912)\n", + " : : : +- *(5) Project [quarter#2484, FLOOR((cast(((((timestamp_year#2087 * 12) + timestamp_month#2051) - 24000) - month_y#2248) as double) / 12.0)) AS josh_mody_n#2264L, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997, loan_id#2453L, month_y#2248]\n", + " : : : +- *(5) Filter (isnotnull(FLOOR((cast(((24000 + (FLOOR((cast(((((timestamp_year#2087 * 12) + timestamp_month#2051) - 24000) - month_y#2248) as double) / 12.0)) * 12)) + cast((month_y#2248 - 1) as bigint)) as double) / 12.0))) AND isnotnull(CASE WHEN (pmod(((24000 + (FLOOR((cast(((((timestamp_year#2087 * 12) + timestamp_month#2051) - 24000) - month_y#2248) as double) / 12.0)) * 12)) + cast(month_y#2248 as bigint)), 12) = 0) THEN 12 ELSE pmod(((24000 + (FLOOR((cast(((((timestamp_year#2087 * 12) + timestamp_month#2051) - 24000) - month_y#2248) as double) / 12.0)) * 12)) + cast(month_y#2248 as bigint)), 12) END))\n", + " : : : +- GpuColumnarToRow false\n", + " : : : +- GpuGenerate gpuexplode([0,1,2,3,4,5,6,7,8,9,10,11]), [loan_id#2453L, quarter#2484, timestamp_month#2051, timestamp_year#2087, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997], false, [month_y#2248]\n", + " : : : +- GpuProject [loan_id#2453L, quarter#2484, timestamp_month#2051, timestamp_year#2087, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997]\n", + " : : : +- GpuBroadcastHashJoin [loan_id#2453L, quarter#2484], [loan_id#2202L, quarter#2233], LeftOuter, GpuBuildRight\n", + " : : : :- GpuRowToColumnar targetsize(536870912)\n", + " : : : : +- *(3) Project [quarter#2484, loan_id#2453L, month(cast(gettimestamp(monthly_reporting_period#2454, MM/dd/yyyy, Some(America/Los_Angeles), false) as date)) AS timestamp_month#2051, year(cast(gettimestamp(monthly_reporting_period#2454, MM/dd/yyyy, Some(America/Los_Angeles), false) as date)) AS timestamp_year#2087]\n", + " : : : : +- GpuColumnarToRow false\n", + " : : : : +- GpuFilter (gpuisnotnull(quarter#2484) AND gpuisnotnull(loan_id#2453L)), true\n", + " : : : : +- GpuFileGpuScan parquet [loan_id#2453L,monthly_reporting_period#2454,quarter#2484] Batched: true, DataFilters: [isnotnull(quarter#2484), isnotnull(loan_id#2453L)], Format: Parquet, Location: InMemoryFileIndex[file:/local/saralihalli/HOME/mortgage/perf], PartitionFilters: [], PushedFilters: [IsNotNull(quarter), IsNotNull(loan_id)], ReadSchema: struct\n", + " : : : +- GpuBroadcastExchange HashedRelationBroadcastMode(List(input[1, bigint, true], input[0, string, true]),false), [id=#1096]\n", + " : : : +- GpuHashAggregate(keys=[quarter#2233, loan_id#2202L], functions=[gpumax(current_loan_delinquency_status#2212), gpumin(delinquency_30#1975), gpumin(delinquency_90#1976), gpumin(delinquency_180#1977)]), filters=ArrayBuffer(None, None, None, None))\n", + " : : : +- GpuShuffleCoalesce 536870912\n", + " : : : +- GpuColumnarExchange gpuhashpartitioning(quarter#2233, loan_id#2202L, 192), ENSURE_REQUIREMENTS, [id=#1093]\n", + " : : : +- GpuHashAggregate(keys=[quarter#2233, loan_id#2202L], functions=[partial_gpumax(current_loan_delinquency_status#2212), partial_gpumin(delinquency_30#1975), partial_gpumin(delinquency_90#1976), partial_gpumin(delinquency_180#1977)]), filters=ArrayBuffer(None, None, None, None))\n", + " : : : +- GpuRowToColumnar targetsize(536870912)\n", + " : : : +- *(4) Project [quarter#2233, loan_id#2202L, current_loan_delinquency_status#2212, CASE WHEN (current_loan_delinquency_status#2212 >= 1) THEN cast(gettimestamp(monthly_reporting_period#2203, MM/dd/yyyy, Some(America/Los_Angeles), false) as date) END AS delinquency_30#1975, CASE WHEN (current_loan_delinquency_status#2212 >= 3) THEN cast(gettimestamp(monthly_reporting_period#2203, MM/dd/yyyy, Some(America/Los_Angeles), false) as date) END AS delinquency_90#1976, CASE WHEN (current_loan_delinquency_status#2212 >= 6) THEN cast(gettimestamp(monthly_reporting_period#2203, MM/dd/yyyy, Some(America/Los_Angeles), false) as date) END AS delinquency_180#1977]\n", + " : : : +- GpuColumnarToRow false\n", + " : : : +- GpuFilter (gpuisnotnull(loan_id#2202L) AND gpuisnotnull(quarter#2233)), true\n", + " : : : +- GpuFileGpuScan parquet [loan_id#2202L,monthly_reporting_period#2203,current_loan_delinquency_status#2212,quarter#2233] Batched: true, DataFilters: [isnotnull(loan_id#2202L), isnotnull(quarter#2233)], Format: Parquet, Location: InMemoryFileIndex[file:/local/saralihalli/HOME/mortgage/perf], PartitionFilters: [], PushedFilters: [IsNotNull(loan_id), IsNotNull(quarter)], ReadSchema: struct\n", + " : : +- GpuColumnarToRow false\n", + " : : +- GpuSort [quarter#2484 ASC NULLS FIRST, loan_id#2453L ASC NULLS FIRST, timestamp_year#2307L ASC NULLS FIRST, timestamp_month#2336L ASC NULLS FIRST], false, com.nvidia.spark.rapids.OutOfCoreSort$@163d9f7d\n", + " : : +- GpuShuffleCoalesce 536870912\n", + " : : +- GpuColumnarExchange gpuhashpartitioning(quarter#2484, loan_id#2453L, timestamp_year#2307L, timestamp_month#2336L, 192), ENSURE_REQUIREMENTS, [id=#1114]\n", + " : : +- GpuRowToColumnar targetsize(536870912)\n", + " : : +- *(6) HashAggregate(keys=[quarter#2484, loan_id#2453L, josh_mody_n#2264L, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997, month_y#2248], functions=[])\n", + " : : +- GpuColumnarToRow false\n", + " : : +- GpuShuffleCoalesce 536870912\n", + " : : +- GpuColumnarExchange gpuhashpartitioning(quarter#2484, loan_id#2453L, josh_mody_n#2264L, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997, month_y#2248, 192), ENSURE_REQUIREMENTS, [id=#1107]\n", + " : : +- GpuHashAggregate(keys=[quarter#2484, loan_id#2453L, josh_mody_n#2264L, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997, month_y#2248], functions=[]), filters=ArrayBuffer())\n", + " : : +- GpuRowToColumnar targetsize(536870912)\n", + " : : +- *(5) Project [quarter#2484, FLOOR((cast(((((timestamp_year#2087 * 12) + timestamp_month#2051) - 24000) - month_y#2248) as double) / 12.0)) AS josh_mody_n#2264L, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997, loan_id#2453L, month_y#2248]\n", + " : : +- *(5) Filter (isnotnull(FLOOR((cast(((24000 + (FLOOR((cast(((((timestamp_year#2087 * 12) + timestamp_month#2051) - 24000) - month_y#2248) as double) / 12.0)) * 12)) + cast((month_y#2248 - 1) as bigint)) as double) / 12.0))) AND isnotnull(CASE WHEN (pmod(((24000 + (FLOOR((cast(((((timestamp_year#2087 * 12) + timestamp_month#2051) - 24000) - month_y#2248) as double) / 12.0)) * 12)) + cast(month_y#2248 as bigint)), 12) = 0) THEN 12 ELSE pmod(((24000 + (FLOOR((cast(((((timestamp_year#2087 * 12) + timestamp_month#2051) - 24000) - month_y#2248) as double) / 12.0)) * 12)) + cast(month_y#2248 as bigint)), 12) END))\n", + " : : +- GpuColumnarToRow false\n", + " : : +- GpuGenerate gpuexplode([0,1,2,3,4,5,6,7,8,9,10,11]), [loan_id#2453L, quarter#2484, timestamp_month#2051, timestamp_year#2087, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997], false, [month_y#2248]\n", + " : : +- GpuProject [loan_id#2453L, quarter#2484, timestamp_month#2051, timestamp_year#2087, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997]\n", + " : : +- GpuBroadcastHashJoin [loan_id#2453L, quarter#2484], [loan_id#2202L, quarter#2233], LeftOuter, GpuBuildRight\n", + " : : :- GpuRowToColumnar targetsize(536870912)\n", + " : : : +- *(3) Project [quarter#2484, loan_id#2453L, month(cast(gettimestamp(monthly_reporting_period#2454, MM/dd/yyyy, Some(America/Los_Angeles), false) as date)) AS timestamp_month#2051, year(cast(gettimestamp(monthly_reporting_period#2454, MM/dd/yyyy, Some(America/Los_Angeles), false) as date)) AS timestamp_year#2087]\n", + " : : : +- GpuColumnarToRow false\n", + " : : : +- GpuFilter (gpuisnotnull(quarter#2484) AND gpuisnotnull(loan_id#2453L)), true\n", + " : : : +- GpuFileGpuScan parquet [loan_id#2453L,monthly_reporting_period#2454,quarter#2484] Batched: true, DataFilters: [isnotnull(quarter#2484), isnotnull(loan_id#2453L)], Format: Parquet, Location: InMemoryFileIndex[file:/local/saralihalli/HOME/mortgage/perf], PartitionFilters: [], PushedFilters: [IsNotNull(quarter), IsNotNull(loan_id)], ReadSchema: struct\n", + " : : +- GpuBroadcastExchange HashedRelationBroadcastMode(List(input[1, bigint, true], input[0, string, true]),false), [id=#1096]\n", + " : : +- GpuHashAggregate(keys=[quarter#2233, loan_id#2202L], functions=[gpumax(current_loan_delinquency_status#2212), gpumin(delinquency_30#1975), gpumin(delinquency_90#1976), gpumin(delinquency_180#1977)]), filters=ArrayBuffer(None, None, None, None))\n", + " : : +- GpuShuffleCoalesce 536870912\n", + " : : +- GpuColumnarExchange gpuhashpartitioning(quarter#2233, loan_id#2202L, 192), ENSURE_REQUIREMENTS, [id=#1093]\n", + " : : +- GpuHashAggregate(keys=[quarter#2233, loan_id#2202L], functions=[partial_gpumax(current_loan_delinquency_status#2212), partial_gpumin(delinquency_30#1975), partial_gpumin(delinquency_90#1976), partial_gpumin(delinquency_180#1977)]), filters=ArrayBuffer(None, None, None, None))\n", + " : : +- GpuRowToColumnar targetsize(536870912)\n", + " : : +- *(4) Project [quarter#2233, loan_id#2202L, current_loan_delinquency_status#2212, CASE WHEN (current_loan_delinquency_status#2212 >= 1) THEN cast(gettimestamp(monthly_reporting_period#2203, MM/dd/yyyy, Some(America/Los_Angeles), false) as date) END AS delinquency_30#1975, CASE WHEN (current_loan_delinquency_status#2212 >= 3) THEN cast(gettimestamp(monthly_reporting_period#2203, MM/dd/yyyy, Some(America/Los_Angeles), false) as date) END AS delinquency_90#1976, CASE WHEN (current_loan_delinquency_status#2212 >= 6) THEN cast(gettimestamp(monthly_reporting_period#2203, MM/dd/yyyy, Some(America/Los_Angeles), false) as date) END AS delinquency_180#1977]\n", + " : : +- GpuColumnarToRow false\n", + " : : +- GpuFilter (gpuisnotnull(loan_id#2202L) AND gpuisnotnull(quarter#2233)), true\n", + " : : +- GpuFileGpuScan parquet [loan_id#2202L,monthly_reporting_period#2203,current_loan_delinquency_status#2212,quarter#2233] Batched: true, DataFilters: [isnotnull(loan_id#2202L), isnotnull(quarter#2233)], Format: Parquet, Location: InMemoryFileIndex[file:/local/saralihalli/HOME/mortgage/perf], PartitionFilters: [], PushedFilters: [IsNotNull(loan_id), IsNotNull(quarter)], ReadSchema: struct\n", + " : +- GpuColumnarToRow false\n", + " : +- GpuSort [quarter#2484 ASC NULLS FIRST, loan_id#2453L ASC NULLS FIRST, timestamp_year#2307L ASC NULLS FIRST, timestamp_month#2336L ASC NULLS FIRST], false, com.nvidia.spark.rapids.OutOfCoreSort$@163d9f7d\n", + " : +- GpuShuffleCoalesce 536870912\n", + " : +- GpuColumnarExchange gpuhashpartitioning(quarter#2484, loan_id#2453L, timestamp_year#2307L, timestamp_month#2336L, 192), ENSURE_REQUIREMENTS, [id=#1114]\n", + " : +- GpuRowToColumnar targetsize(536870912)\n", + " : +- *(6) HashAggregate(keys=[quarter#2484, loan_id#2453L, josh_mody_n#2264L, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997, month_y#2248], functions=[])\n", + " : +- GpuColumnarToRow false\n", + " : +- GpuShuffleCoalesce 536870912\n", + " : +- GpuColumnarExchange gpuhashpartitioning(quarter#2484, loan_id#2453L, josh_mody_n#2264L, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997, month_y#2248, 192), ENSURE_REQUIREMENTS, [id=#1107]\n", + " : +- GpuHashAggregate(keys=[quarter#2484, loan_id#2453L, josh_mody_n#2264L, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997, month_y#2248], functions=[]), filters=ArrayBuffer())\n", + " : +- GpuRowToColumnar targetsize(536870912)\n", + " : +- *(5) Project [quarter#2484, FLOOR((cast(((((timestamp_year#2087 * 12) + timestamp_month#2051) - 24000) - month_y#2248) as double) / 12.0)) AS josh_mody_n#2264L, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997, loan_id#2453L, month_y#2248]\n", + " : +- *(5) Filter (isnotnull(FLOOR((cast(((24000 + (FLOOR((cast(((((timestamp_year#2087 * 12) + timestamp_month#2051) - 24000) - month_y#2248) as double) / 12.0)) * 12)) + cast((month_y#2248 - 1) as bigint)) as double) / 12.0))) AND isnotnull(CASE WHEN (pmod(((24000 + (FLOOR((cast(((((timestamp_year#2087 * 12) + timestamp_month#2051) - 24000) - month_y#2248) as double) / 12.0)) * 12)) + cast(month_y#2248 as bigint)), 12) = 0) THEN 12 ELSE pmod(((24000 + (FLOOR((cast(((((timestamp_year#2087 * 12) + timestamp_month#2051) - 24000) - month_y#2248) as double) / 12.0)) * 12)) + cast(month_y#2248 as bigint)), 12) END))\n", + " : +- GpuColumnarToRow false\n", + " : +- GpuGenerate gpuexplode([0,1,2,3,4,5,6,7,8,9,10,11]), [loan_id#2453L, quarter#2484, timestamp_month#2051, timestamp_year#2087, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997], false, [month_y#2248]\n", + " : +- GpuProject [loan_id#2453L, quarter#2484, timestamp_month#2051, timestamp_year#2087, ever_30#2004, ever_90#2005, ever_180#2006, delinquency_30#1993, delinquency_90#1995, delinquency_180#1997]\n", + " : +- GpuBroadcastHashJoin [loan_id#2453L, quarter#2484], [loan_id#2202L, quarter#2233], LeftOuter, GpuBuildRight\n", + " : :- GpuRowToColumnar targetsize(536870912)\n", + " : : +- *(3) Project [quarter#2484, loan_id#2453L, month(cast(gettimestamp(monthly_reporting_period#2454, MM/dd/yyyy, Some(America/Los_Angeles), false) as date)) AS timestamp_month#2051, year(cast(gettimestamp(monthly_reporting_period#2454, MM/dd/yyyy, Some(America/Los_Angeles), false) as date)) AS timestamp_year#2087]\n", + " : : +- GpuColumnarToRow false\n", + " : : +- GpuFilter (gpuisnotnull(quarter#2484) AND gpuisnotnull(loan_id#2453L)), true\n", + " : : +- GpuFileGpuScan parquet [loan_id#2453L,monthly_reporting_period#2454,quarter#2484] Batched: true, DataFilters: [isnotnull(quarter#2484), isnotnull(loan_id#2453L)], Format: Parquet, Location: InMemoryFileIndex[file:/local/saralihalli/HOME/mortgage/perf], PartitionFilters: [], PushedFilters: [IsNotNull(quarter), IsNotNull(loan_id)], ReadSchema: struct\n", + " : +- GpuBroadcastExchange HashedRelationBroadcastMode(List(input[1, bigint, true], input[0, string, true]),false), [id=#1096]\n", + " : +- GpuHashAggregate(keys=[quarter#2233, loan_id#2202L], functions=[gpumax(current_loan_delinquency_status#2212), gpumin(delinquency_30#1975), gpumin(delinquency_90#1976), gpumin(delinquency_180#1977)]), filters=ArrayBuffer(None, None, None, None))\n", + " : +- GpuShuffleCoalesce 536870912\n", + " : +- GpuColumnarExchange gpuhashpartitioning(quarter#2233, loan_id#2202L, 192), ENSURE_REQUIREMENTS, [id=#1093]\n", + " : +- GpuHashAggregate(keys=[quarter#2233, loan_id#2202L], functions=[partial_gpumax(current_loan_delinquency_status#2212), partial_gpumin(delinquency_30#1975), partial_gpumin(delinquency_90#1976), partial_gpumin(delinquency_180#1977)]), filters=ArrayBuffer(None, None, None, None))\n", + " : +- GpuRowToColumnar targetsize(536870912)\n", + " : +- *(4) Project [quarter#2233, loan_id#2202L, current_loan_delinquency_status#2212, CASE WHEN (current_loan_delinquency_status#2212 >= 1) THEN cast(gettimestamp(monthly_reporting_period#2203, MM/dd/yyyy, Some(America/Los_Angeles), false) as date) END AS delinquency_30#1975, CASE WHEN (current_loan_delinquency_status#2212 >= 3) THEN cast(gettimestamp(monthly_reporting_period#2203, MM/dd/yyyy, Some(America/Los_Angeles), false) as date) END AS delinquency_90#1976, CASE WHEN (current_loan_delinquency_status#2212 >= 6) THEN cast(gettimestamp(monthly_reporting_period#2203, MM/dd/yyyy, Some(America/Los_Angeles), false) as date) END AS delinquency_180#1977]\n", + " : +- GpuColumnarToRow false\n", + " : +- GpuFilter (gpuisnotnull(loan_id#2202L) AND gpuisnotnull(quarter#2233)), true\n", + " : +- GpuFileGpuScan parquet [loan_id#2202L,monthly_reporting_period#2203,current_loan_delinquency_status#2212,quarter#2233] Batched: true, DataFilters: [isnotnull(loan_id#2202L), isnotnull(quarter#2233)], Format: Parquet, Location: InMemoryFileIndex[file:/local/saralihalli/HOME/mortgage/perf], PartitionFilters: [], PushedFilters: [IsNotNull(loan_id), IsNotNull(quarter)], ReadSchema: struct\n", + "Download Single-Family Loan Performance Data from [Fannie Mae](https://datadynamics.fanniemae.com/data-dynamics/#/reportMenu;category=HP) website. \n", "\n", "### 2. Download needed jars\n", "* [rapids-4-spark_2.12-22.06.0.jar](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/22.06.0/rapids-4-spark_2.12-22.06.0.jar)\n", @@ -66,38 +67,15 @@ }, { "cell_type": "code", - "execution_count": 2, - "id": "674771a8", + "execution_count": null, + "id": "b2834c06", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "dataRoot = /data\n", - "perfPath = /data/mortgage/Performance/\n", - "acqPath = /data/mortgage/Acquisition/\n", - "outPath = /data/mortgage/output/\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "/data" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "val dataRoot = sys.env.getOrElse(\"DATA_ROOT\", \"/data\")\n", - "val perfPath = dataRoot + \"/mortgage/Performance/\"\n", - "val acqPath = dataRoot + \"/mortgage/Acquisition/\"\n", - "val outPath = dataRoot + \"/mortgage/output/\"\n" + "val dataRoot = sys.env.getOrElse(\"DATA_ROOT\", \"/mortgage\")\n", + "val dataPath = dataRoot + \"/input\"\n", + "val outPath = dataRoot + \"/output\"\n", + "val saveTrainEvalDataset = true" ] }, { @@ -120,7 +98,7 @@ { "data": { "text/plain": [ - "performanceSchema = StructType(StructField(loan_id,LongType,true), StructField(monthly_reporting_period,StringType,true), StructField(servicer,StringType,true), StructField(interest_rate,DoubleType,true), StructField(current_actual_upb,DoubleType,true), StructField(loan_age,DoubleType,true), StructField(remaining_months_to_legal_maturity,DoubleType,true), StructField(adj_remaining_months_to_maturity,DoubleType,true), StructField(maturity_date,StringType,true), StructField(msa,DoubleType,true), StructField(current_loan_delinquency_status,IntegerType,true), StructField(mod_flag,StringType,true), StructField(zero_balance_code,StringType,true), StructField(zero_balance_effective_date,StringType,true), StructField(last_paid_installment_date,StringType,t...\n" + "rawSchema = StructType(StructField(reference_pool_id,StringType,true), StructField(loan_id,LongType,true), StructField(monthly_reporting_period,StringType,true), StructField(orig_channel,StringType,true), StructField(seller_name,StringType,true), StructField(servicer,StringType,true), StructField(master_servicer,StringType,true), StructField(orig_interest_rate,DoubleType,true), StructField(interest_rate,DoubleType,true), StructField(orig_upb,IntegerType,true), StructField(upb_at_issuance,StringType,true), StructField(current_actual_upb,DoubleType,true), StructField(orig_loan_term,IntegerType,true), StructField(orig_date,StringType,true), StructField(first_pay_date,StringType,true), StructField(loan_age,DoubleType,true), StructField(remaining_months...\n" ] }, "metadata": {}, @@ -129,7 +107,7 @@ { "data": { "text/plain": [ - "StructType(StructField(loan_id,LongType,true), StructField(monthly_reporting_period,StringType,true), StructField(servicer,StringType,true), StructField(interest_rate,DoubleType,true), StructField(current_actual_upb,DoubleType,true), StructField(loan_age,DoubleType,true), StructField(remaining_months_to_legal_maturity,DoubleType,true), StructField(adj_remaining_months_to_maturity,DoubleType,true), StructField(maturity_date,StringType,true), StructField(msa,DoubleType,true), StructField(current_loan_delinquency_status,IntegerType,true), StructField(mod_flag,StringType,true), StructField(zero_balance_code,StringType,true), StructField(zero_balance_effective_date,StringType,true), StructField(last_paid_installment_date,StringType,t..." + "StructType(StructField(reference_pool_id,StringType,true), StructField(loan_id,LongType,true), StructField(monthly_reporting_period,StringType,true), StructField(orig_channel,StringType,true), StructField(seller_name,StringType,true), StructField(servicer,StringType,true), StructField(master_servicer,StringType,true), StructField(orig_interest_rate,DoubleType,true), StructField(interest_rate,DoubleType,true), StructField(orig_upb,IntegerType,true), StructField(upb_at_issuance,StringType,true), StructField(current_actual_upb,DoubleType,true), StructField(orig_loan_term,IntegerType,true), StructField(orig_date,StringType,true), StructField(first_pay_date,StringType,true), StructField(loan_age,DoubleType,true), StructField(remaining_months..." ] }, "execution_count": 3, @@ -139,21 +117,57 @@ ], "source": [ "// File schema\n", - "val performanceSchema = StructType(Array(\n", + "val rawSchema = StructType(Array(\n", + " StructField(\"reference_pool_id\", StringType),\n", " StructField(\"loan_id\", LongType),\n", " StructField(\"monthly_reporting_period\", StringType),\n", + " StructField(\"orig_channel\", StringType),\n", + " StructField(\"seller_name\", StringType),\n", " StructField(\"servicer\", StringType),\n", + " StructField(\"master_servicer\", StringType),\n", + " StructField(\"orig_interest_rate\", DoubleType),\n", " StructField(\"interest_rate\", DoubleType),\n", + " StructField(\"orig_upb\", DoubleType),\n", + " StructField(\"upb_at_issuance\", StringType),\n", " StructField(\"current_actual_upb\", DoubleType),\n", + " StructField(\"orig_loan_term\", IntegerType),\n", + " StructField(\"orig_date\", StringType),\n", + " StructField(\"first_pay_date\", StringType), \n", " StructField(\"loan_age\", DoubleType),\n", " StructField(\"remaining_months_to_legal_maturity\", DoubleType),\n", " StructField(\"adj_remaining_months_to_maturity\", DoubleType),\n", " StructField(\"maturity_date\", StringType),\n", + " StructField(\"orig_ltv\", DoubleType),\n", + " StructField(\"orig_cltv\", DoubleType),\n", + " StructField(\"num_borrowers\", DoubleType),\n", + " StructField(\"dti\", DoubleType),\n", + " StructField(\"borrower_credit_score\", DoubleType),\n", + " StructField(\"coborrow_credit_score\", DoubleType),\n", + " StructField(\"first_home_buyer\", StringType),\n", + " StructField(\"loan_purpose\", StringType),\n", + " StructField(\"property_type\", StringType),\n", + " StructField(\"num_units\", IntegerType),\n", + " StructField(\"occupancy_status\", StringType),\n", + " StructField(\"property_state\", StringType),\n", " StructField(\"msa\", DoubleType),\n", + " StructField(\"zip\", IntegerType),\n", + " StructField(\"mortgage_insurance_percent\", DoubleType),\n", + " StructField(\"product_type\", StringType),\n", + " StructField(\"prepayment_penalty_indicator\", StringType),\n", + " StructField(\"interest_only_loan_indicator\", StringType),\n", + " StructField(\"interest_only_first_principal_and_interest_payment_date\", StringType),\n", + " StructField(\"months_to_amortization\", StringType),\n", " StructField(\"current_loan_delinquency_status\", IntegerType),\n", + " StructField(\"loan_payment_history\", StringType),\n", " StructField(\"mod_flag\", StringType),\n", + " StructField(\"mortgage_insurance_cancellation_indicator\", StringType),\n", " StructField(\"zero_balance_code\", StringType),\n", " StructField(\"zero_balance_effective_date\", StringType),\n", + " StructField(\"upb_at_the_time_of_removal\", StringType),\n", + " StructField(\"repurchase_date\", StringType),\n", + " StructField(\"scheduled_principal_current\", StringType),\n", + " StructField(\"total_principal_current\", StringType),\n", + " StructField(\"unscheduled_principal_current\", StringType),\n", " StructField(\"last_paid_installment_date\", StringType),\n", " StructField(\"foreclosed_after\", StringType),\n", " StructField(\"disposition_date\", StringType),\n", @@ -168,37 +182,51 @@ " StructField(\"other_foreclosure_proceeds\", DoubleType),\n", " StructField(\"non_interest_bearing_upb\", DoubleType),\n", " StructField(\"principal_forgiveness_upb\", StringType),\n", - " StructField(\"repurchase_make_whole_proceeds_flag\", StringType),\n", - " StructField(\"foreclosure_principal_write_off_amount\", StringType),\n", - " StructField(\"servicing_activity_indicator\", StringType))\n", - " )\n", - "\n", - "val acquisitionSchema = StructType(Array(\n", - " StructField(\"loan_id\", LongType),\n", - " StructField(\"orig_channel\", StringType),\n", - " StructField(\"seller_name\", StringType),\n", - " StructField(\"orig_interest_rate\", DoubleType),\n", - " StructField(\"orig_upb\", IntegerType),\n", - " StructField(\"orig_loan_term\", IntegerType),\n", - " StructField(\"orig_date\", StringType),\n", - " StructField(\"first_pay_date\", StringType),\n", - " StructField(\"orig_ltv\", DoubleType),\n", - " StructField(\"orig_cltv\", DoubleType),\n", - " StructField(\"num_borrowers\", DoubleType),\n", - " StructField(\"dti\", DoubleType),\n", - " StructField(\"borrower_credit_score\", DoubleType),\n", - " StructField(\"first_home_buyer\", StringType),\n", - " StructField(\"loan_purpose\", StringType),\n", - " StructField(\"property_type\", StringType),\n", - " StructField(\"num_units\", IntegerType),\n", - " StructField(\"occupancy_status\", StringType),\n", - " StructField(\"property_state\", StringType),\n", - " StructField(\"zip\", IntegerType),\n", - " StructField(\"mortgage_insurance_percent\", DoubleType),\n", - " StructField(\"product_type\", StringType),\n", - " StructField(\"coborrow_credit_score\", DoubleType),\n", + " StructField(\"original_list_start_date\", StringType),\n", + " StructField(\"original_list_price\", StringType),\n", + " StructField(\"current_list_start_date\", StringType),\n", + " StructField(\"current_list_price\", StringType),\n", + " StructField(\"borrower_credit_score_at_issuance\", StringType),\n", + " StructField(\"co-borrower_credit_score_at_issuance\", StringType),\n", + " StructField(\"borrower_credit_score_current\", StringType),\n", + " StructField(\"co-Borrower_credit_score_current\", StringType),\n", " StructField(\"mortgage_insurance_type\", DoubleType),\n", - " StructField(\"relocation_mortgage_indicator\", StringType))\n", + " StructField(\"servicing_activity_indicator\", StringType),\n", + " StructField(\"current_period_modification_loss_amount\", StringType),\n", + " StructField(\"cumulative_modification_loss_amount\", StringType),\n", + " StructField(\"current_period_credit_event_net_gain_or_loss\", StringType),\n", + " StructField(\"cumulative_credit_event_net_gain_or_loss\", StringType),\n", + " StructField(\"homeready_program_indicator\", StringType),\n", + " StructField(\"foreclosure_principal_write_off_amount\", StringType),\n", + " StructField(\"relocation_mortgage_indicator\", StringType),\n", + " StructField(\"zero_balance_code_change_date\", StringType),\n", + " StructField(\"loan_holdback_indicator\", StringType),\n", + " StructField(\"loan_holdback_effective_date\", StringType),\n", + " StructField(\"delinquent_accrued_interest\", StringType),\n", + " StructField(\"property_valuation_method\", StringType),\n", + " StructField(\"high_balance_loan_indicator\", StringType),\n", + " StructField(\"arm_initial_fixed-rate_period_lt_5_yr_indicator\", StringType),\n", + " StructField(\"arm_product_type\", StringType),\n", + " StructField(\"initial_fixed-rate_period\", StringType),\n", + " StructField(\"interest_rate_adjustment_frequency\", StringType),\n", + " StructField(\"next_interest_rate_adjustment_date\", StringType),\n", + " StructField(\"next_payment_change_date\", StringType),\n", + " StructField(\"index\", StringType),\n", + " StructField(\"arm_cap_structure\", StringType),\n", + " StructField(\"initial_interest_rate_cap_up_percent\", StringType),\n", + " StructField(\"periodic_interest_rate_cap_up_percent\", StringType),\n", + " StructField(\"lifetime_interest_rate_cap_up_percent\", StringType),\n", + " StructField(\"mortgage_margin\", StringType),\n", + " StructField(\"arm_balloon_indicator\", StringType),\n", + " StructField(\"arm_plan_number\", StringType),\n", + " StructField(\"borrower_assistance_plan\", StringType),\n", + " StructField(\"hltv_refinance_option_indicator\", StringType),\n", + " StructField(\"deal_name\", StringType),\n", + " StructField(\"repurchase_make_whole_proceeds_flag\", StringType),\n", + " StructField(\"alternative_delinquency_resolution\", StringType),\n", + " StructField(\"alternative_delinquency_resolution_count\", StringType),\n", + " StructField(\"total_deferral_amount\", StringType)\n", + " )\n", " )" ] }, @@ -356,7 +384,7 @@ " // So we strip off the .txt and everything after it\n", " // and then take everything after the last remaining _\n", " def apply(): Column = substring_index(\n", - " substring_index(input_file_name(), \".\", 1), \"_\", -1)\n", + " substring_index(input_file_name(), \".\", 1), \"/\", -1)\n", "}" ] }, @@ -413,7 +441,7 @@ "\n", "val numericCols = List(\n", " (\"orig_interest_rate\", FloatType),\n", - " (\"orig_upb\", IntegerType),\n", + " (\"orig_upb\", DoubleType),\n", " (\"orig_loan_term\", IntegerType),\n", " (\"orig_ltv\", FloatType),\n", " (\"orig_cltv\", FloatType),\n", @@ -556,6 +584,120 @@ " }" ] }, + { + "cell_type": "code", + "execution_count": 9, + "id": "9e1fbb61", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "defined object extractPerfColumns\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "object extractPerfColumns{\n", + " def apply(rawDf : DataFrame) : DataFrame = {\n", + " val perfDf = rawDf.select(\n", + " col(\"loan_id\"),\n", + " date_format(to_date(col(\"monthly_reporting_period\"),\"MMyyyy\"), \"MM/dd/yyyy\").as(\"monthly_reporting_period\"),\n", + " upper(col(\"servicer\")).as(\"servicer\"),\n", + " col(\"interest_rate\"),\n", + " col(\"current_actual_upb\"),\n", + " col(\"loan_age\"),\n", + " col(\"remaining_months_to_legal_maturity\"),\n", + " col(\"adj_remaining_months_to_maturity\"),\n", + " date_format(to_date(col(\"maturity_date\"),\"MMyyyy\"), \"MM/yyyy\").as(\"maturity_date\"),\n", + " col(\"msa\"),\n", + " col(\"current_loan_delinquency_status\"),\n", + " col(\"mod_flag\"),\n", + " col(\"zero_balance_code\"),\n", + " date_format(to_date(col(\"zero_balance_effective_date\"),\"MMyyyy\"), \"MM/yyyy\").as(\"zero_balance_effective_date\"),\n", + " date_format(to_date(col(\"last_paid_installment_date\"),\"MMyyyy\"), \"MM/dd/yyyy\").as(\"last_paid_installment_date\"),\n", + " date_format(to_date(col(\"foreclosed_after\"),\"MMyyyy\"), \"MM/dd/yyyy\").as(\"foreclosed_after\"),\n", + " date_format(to_date(col(\"disposition_date\"),\"MMyyyy\"), \"MM/dd/yyyy\").as(\"disposition_date\"),\n", + " col(\"foreclosure_costs\"),\n", + " col(\"prop_preservation_and_repair_costs\"),\n", + " col(\"asset_recovery_costs\"),\n", + " col(\"misc_holding_expenses\"),\n", + " col(\"holding_taxes\"),\n", + " col(\"net_sale_proceeds\"),\n", + " col(\"credit_enhancement_proceeds\"),\n", + " col(\"repurchase_make_whole_proceeds\"),\n", + " col(\"other_foreclosure_proceeds\"),\n", + " col(\"non_interest_bearing_upb\"),\n", + " col(\"principal_forgiveness_upb\"),\n", + " col(\"repurchase_make_whole_proceeds_flag\"),\n", + " col(\"foreclosure_principal_write_off_amount\"),\n", + " col(\"servicing_activity_indicator\"),\n", + " col(\"quarter\")\n", + " )\n", + " \n", + " perfDf.select(\"*\").filter(\"current_actual_upb != 0.0\")\n", + " }\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "ce429163", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "defined object extractAcqColumns\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "object extractAcqColumns{\n", + " def apply(rawDf : DataFrame) : DataFrame = {\n", + " val acqDf = rawDf.select(\n", + " col(\"loan_id\"),\n", + " col(\"orig_channel\"),\n", + " upper(col(\"seller_name\")).as(\"seller_name\"),\n", + " col(\"orig_interest_rate\"),\n", + " col(\"orig_upb\"),\n", + " col(\"orig_loan_term\"),\n", + " date_format(to_date(col(\"orig_date\"),\"MMyyyy\"), \"MM/yyyy\").as(\"orig_date\"),\n", + " date_format(to_date(col(\"first_pay_date\"),\"MMyyyy\"), \"MM/yyyy\").as(\"first_pay_date\"),\n", + " col(\"orig_ltv\"),\n", + " col(\"orig_cltv\"),\n", + " col(\"num_borrowers\"),\n", + " col(\"dti\"),\n", + " col(\"borrower_credit_score\"),\n", + " col(\"first_home_buyer\"),\n", + " col(\"loan_purpose\"),\n", + " col(\"property_type\"),\n", + " col(\"num_units\"),\n", + " col(\"occupancy_status\"),\n", + " col(\"property_state\"),\n", + " col(\"zip\"),\n", + " col(\"mortgage_insurance_percent\"),\n", + " col(\"product_type\"),\n", + " col(\"coborrow_credit_score\"),\n", + " col(\"mortgage_insurance_type\"),\n", + " col(\"relocation_mortgage_indicator\"),\n", + " col(\"quarter\"),\n", + " dense_rank().over(Window.partitionBy(\"loan_id\").orderBy(to_date(col(\"monthly_reporting_period\"),\"MMyyyy\"))).as(\"rank\")\n", + " )\n", + "\n", + " acqDf.select(\"*\").filter(col(\"rank\") === 1)\n", + " }\n", + "\n", + "}" + ] + }, { "cell_type": "markdown", "id": "37c64d85", @@ -566,15 +708,15 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "id": "98d37174", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "sparkSession = org.apache.spark.sql.SparkSession@1d87c1c2\n", - "reader = org.apache.spark.sql.DataFrameReader@2e8a7a69\n" + "sparkSession = org.apache.spark.sql.SparkSession@694178ec\n", + "reader = org.apache.spark.sql.DataFrameReader@4b2afd51\n" ] }, "metadata": {}, @@ -583,18 +725,30 @@ { "data": { "text/plain": [ - "org.apache.spark.sql.DataFrameReader@2e8a7a69" + "org.apache.spark.sql.DataFrameReader@4b2afd51" ] }, - "execution_count": 9, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "// Build the spark session and data reader as usual\n", - "val sparkSession = SparkSession.builder.appName(\"mortgage-gpu\").getOrCreate\n", - "val reader = sparkSession.read.option(\"header\", true).schema(performanceSchema)" + "val sparkSession = SparkSession.builder.appName(\"mortgage-gpu\").config(\"spark.sql.cache.serializer\", \"com.nvidia.spark.ParquetCachedBatchSerializer\").getOrCreate\n", + "\n", + "// GPU run, set to true\n", + "sparkSession.conf.set(\"spark.rapids.sql.enabled\", true)\n", + "// CPU run, set to false\n", + "// sparkSession.conf.set('spark.rapids.sql.enabled', 'false')\n", + "// remove config(\"spark.sql.cache.serializer\", \"com.nvidia.spark.ParquetCachedBatchSerializer\") for CPU\n", + "sparkSession.conf.set(\"spark.sql.files.maxPartitionBytes\", \"1G\")\n", + "sparkSession.conf.set(\"spark.sql.broadcastTimeout\", 700)\n", + "sparkSession.conf.set(\"spark.rapids.sql.hasNans\", false)\n", + "// use GPU to read CSV\n", + "sparkSession.conf.set(\"spark.rapids.sql.csv.read.double.enabled\", true)\n", + "\n", + "val reader = sparkSession.read.schema(rawSchema)" ] }, { @@ -607,7 +761,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "id": "5bac2301", "metadata": {}, "outputs": [ @@ -615,8 +769,9 @@ "data": { "text/plain": [ "optionsMap = Map(header -> true)\n", + "rawDf = [reference_pool_id: string, loan_id: bigint ... 107 more fields]\n", "perfSet = [loan_id: bigint, monthly_reporting_period: string ... 30 more fields]\n", - "acqSet = [loan_id: bigint, orig_channel: string ... 24 more fields]\n" + "acqSet = [loan_id: bigint, orig_channel: string ... 25 more fields]\n" ] }, "metadata": {}, @@ -625,28 +780,25 @@ { "data": { "text/plain": [ - "[loan_id: bigint, orig_channel: string ... 24 more fields]" + "[loan_id: bigint, orig_channel: string ... 25 more fields]" ] }, - "execution_count": 10, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "val optionsMap = Map(\"header\" -> \"true\")\n", - "val perfSet = reader.options(optionsMap)\n", + "val rawDf = reader.option(\"header\", false)\n", " .option(\"nullValue\", \"\")\n", " .option(\"delimiter\", \"|\")\n", " .option(\"parserLib\", \"univocity\")\n", - " .schema(performanceSchema)\n", - " .csv(perfPath)\n", + " .schema(rawSchema)\n", + " .csv(dataPath)\n", " .withColumn(\"quarter\", GetQuarterFromCsvFileName())\n", - "val acqSet = reader.options(optionsMap)\n", - " .option(\"delimiter\", \"|\")\n", - " .schema(acquisitionSchema)\n", - " .csv(acqPath)\n", - " .withColumn(\"quarter\", GetQuarterFromCsvFileName())" + "\n", + "val perfSet = extractPerfColumns(rawDf)\n", + "val acqSet = extractAcqColumns(rawDf)" ] }, { @@ -659,7 +811,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, "id": "a16155cb", "metadata": {}, "outputs": [ @@ -681,7 +833,7 @@ "List(orig_channel, first_home_buyer, loan_purpose, property_type, occupancy_status, property_state, product_type, relocation_mortgage_indicator, seller_name, mod_flag, orig_interest_rate, orig_upb, orig_loan_term, orig_ltv, orig_cltv, num_borrowers, dti, borrower_credit_score, num_units, zip, mortgage_insurance_percent, current_loan_delinquency_status, current_actual_upb, interest_rate, loan_age, msa, non_interest_bearing_upb, delinquency_12)" ] }, - "execution_count": 11, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -816,7 +968,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "id": "78b76252", "metadata": {}, "outputs": [ @@ -859,7 +1011,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 15, "id": "ffdb0a62", "metadata": {}, "outputs": [ @@ -867,16 +1019,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "Elapsed time : 35.638s\n" + "Elapsed time : 399.241s\n" ] }, { "data": { "text/plain": [ - "t0 = 1654138715501\n", + "t0 = 1656695479451\n", "optionsMap = Map(header -> true)\n", "rawDF = [orig_channel: int, first_home_buyer: int ... 26 more fields]\n", - "t1 = 1654138751139\n" + "t1 = 1656695878692\n" ] }, "metadata": {}, @@ -885,42 +1037,47 @@ { "data": { "text/plain": [ - "1654138751139" + "1656695878692" ] }, - "execution_count": 13, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "val t0 = System.currentTimeMillis\n", - "val optionsMap = Map(\"header\" -> \"true\")\n", "val rawDF = transform(\n", " perfSet,\n", " acqSet,\n", " sparkSession\n", " )\n", - "rawDF.write.mode(\"overwrite\").parquet(new Path(outPath, \"data\").toString)\n", + "\n", + "val etlDataPath = new Path(outPath, \"data\").toString\n", + "rawDF.write.mode(\"overwrite\").parquet(etlDataPath)\n", + "\n", + "if(saveTrainEvalDataset == true)\n", + "{\n", + " val etlDf = sparkSession.read.parquet(etlDataPath)\n", + " val sets = etlDf.randomSplit(Array[Double](0.8, 0.2))\n", + " val train = sets(0)\n", + " val eval = sets(1)\n", + " train.write.mode(\"overwrite\").parquet(new Path(outPath, \"train\").toString)\n", + " eval.write.mode(\"overwrite\").parquet(new Path(outPath, \"eval\").toString)\n", + "}\n", + "\n", + "\n", "val t1 = System.currentTimeMillis\n", "println(\"Elapsed time : \" + ((t1 - t0).toFloat / 1000) + \"s\")\n", "sparkSession.stop()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4388fe96", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "XGBoost4j-Spark - Scala", + "display_name": "XGBoost4j-Spark_new2 - Scala", "language": "scala", - "name": "XGBoost4j-Spark_scala" + "name": "xgboost4j-spark_new2_scala" }, "language_info": { "codemirror_mode": "text/x-scala", @@ -933,4 +1090,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-gpu.ipynb b/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-gpu.ipynb index 9376f0de4..398f2b3e8 100644 --- a/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-gpu.ipynb +++ b/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-gpu.ipynb @@ -47,39 +47,15 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "dataRoot = /data\n", - "trainPath = /data/mortgage/csv/train/\n", - "evalPath = /data/mortgage/csv/test/\n", - "transPath = /data/mortgage/csv/test/\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "/data/mortgage/csv/test/" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "// You need to update them to your real paths! The input data files can be the output of mortgage-etl jobs, or you can\n", - "// just use the provided sample datasets upder datasets path. \n", - "val dataRoot = sys.env.getOrElse(\"DATA_ROOT\", \"/data\")\n", - "val trainPath = dataRoot + \"/mortgage/csv/train/\"\n", - "val evalPath = dataRoot + \"/mortgage/csv/test/\"\n", - "val transPath = dataRoot + \"/mortgage/csv/test/\"" + "// You need to update them to your real paths! The input data files is the output of mortgage-etl jobs\n", + "val dataRoot = sys.env.getOrElse(\"DATA_ROOT\", \"/mortgage\")\n", + "val trainPath = dataRoot + \"/train/\"\n", + "val evalPath = dataRoot + \"/eval/\"\n", + "val transPath = dataRoot + \"/test/\"" ] }, { @@ -132,7 +108,7 @@ " StructField(\"seller_name\", DoubleType),\n", " StructField(\"mod_flag\", DoubleType),\n", " StructField(\"orig_interest_rate\", DoubleType),\n", - " StructField(\"orig_upb\", IntegerType),\n", + " StructField(\"orig_upb\", DoubleType),\n", " StructField(\"orig_loan_term\", IntegerType),\n", " StructField(\"orig_ltv\", DoubleType),\n", " StructField(\"orig_cltv\", DoubleType),\n", @@ -208,7 +184,7 @@ "source": [ "// Build the spark session and data reader as usual\n", "val sparkSession = SparkSession.builder.appName(\"mortgage-gpu\").getOrCreate\n", - "val reader = sparkSession.read.option(\"header\", true).schema(schema)" + "val reader = sparkSession.read" ] }, { @@ -239,10 +215,9 @@ } ], "source": [ - "// Please make sure to change the api to reader.parquet if you load parquet files.\n", - "val trainSet = reader.csv(trainPath)\n", - "val evalSet = reader.csv(evalPath)\n", - "val transSet = reader.csv(transPath)" + "val trainSet = reader.parquet(trainPath)\n", + "val evalSet = reader.parquet(evalPath)\n", + "val transSet = reader.parquet(transPath)" ] }, { @@ -588,9 +563,9 @@ } ], "source": [ - "xgbClassificationModel.write.overwrite.save(dataRoot + \"/model/mortgage\")\n", + "xgbClassificationModel.write.overwrite.save(dataRoot + \"/model/\")\n", "\n", - "val modelFromDisk = XGBoostClassificationModel.load(dataRoot + \"/model/mortgage\")\n", + "val modelFromDisk = XGBoostClassificationModel.load(dataRoot + \"/model/\")\n", "\n", "val (results2, _) = Benchmark.time(\"transform2\") {\n", " modelFromDisk.transform(transSet)\n", @@ -606,13 +581,6 @@ "source": [ "sparkSession.close()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -632,4 +600,4 @@ }, "nbformat": 4, "nbformat_minor": 2 -} \ No newline at end of file +} diff --git a/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage_gpu_crossvalidation.ipynb b/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage_gpu_crossvalidation.ipynb index 74bb38f9d..d91c74dcf 100644 --- a/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage_gpu_crossvalidation.ipynb +++ b/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage_gpu_crossvalidation.ipynb @@ -23,7 +23,7 @@ "import org.apache.spark.sql.SparkSession\n", "import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator\n", "import org.apache.spark.ml.tuning.{ParamGridBuilder,CrossValidator}\n", - "import org.apache.spark.sql.types.{FloatType, IntegerType, StructField, StructType}" + "import org.apache.spark.sql.types.{FloatType, IntegerType, StructField, StructType, DoubleType}" ] }, { @@ -42,36 +42,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "dataRoot = /data\n", - "trainParquetPath = /data/mortgage/parquet/train\n", - "evalParquetPath = /data/mortgage/parquet/eval\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "/data/mortgage/parquet/eval" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "// You need to update them to your real paths!\n", - "val dataRoot = sys.env.getOrElse(\"DATA_ROOT\", \"/data\")\n", - "val trainParquetPath=dataRoot + \"/mortgage/parquet/train\"\n", - "val evalParquetPath=dataRoot + \"/mortgage/parquet/eval\"" + "val dataRoot = sys.env.getOrElse(\"DATA_ROOT\", \"/mortgage\")\n", + "val trainParquetPath=dataRoot + \"/train\"\n", + "val evalParquetPath=dataRoot + \"/eval\"" ] }, { @@ -83,30 +61,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "labelColName = delinquency_12\n", - "schema = StructType(StructField(orig_channel,FloatType,true), StructField(first_home_buyer,FloatType,true), StructField(loan_purpose,FloatType,true), StructField(property_type,FloatType,true), StructField(occupancy_status,FloatType,true), StructField(property_state,FloatType,true), StructField(product_type,FloatType,true), StructField(relocation_mortgage_indicator,FloatType,true), StructField(seller_name,FloatType,true), StructField(mod_flag,FloatType,true), StructField(orig_interest_rate,FloatType,true), StructField(orig_upb,IntegerType,true), StructField(orig_loan_term,IntegerType,true), StructField(orig_ltv,FloatType,true), StructField(orig_cltv,FloatType,true), StructField(num_borrowers,FloatType,true), Str...\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "StructType(StructField(orig_channel,FloatType,true), StructField(first_home_buyer,FloatType,true), StructField(loan_purpose,FloatType,true), StructField(property_type,FloatType,true), StructField(occupancy_status,FloatType,true), StructField(property_state,FloatType,true), StructField(product_type,FloatType,true), StructField(relocation_mortgage_indicator,FloatType,true), StructField(seller_name,FloatType,true), StructField(mod_flag,FloatType,true), StructField(orig_interest_rate,FloatType,true), StructField(orig_upb,IntegerType,true), StructField(orig_loan_term,IntegerType,true), StructField(orig_ltv,FloatType,true), StructField(orig_cltv,FloatType,true), StructField(num_borrowers,FloatType,true), Str..." - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "val labelColName = \"delinquency_12\"\n", "val schema = StructType(List(\n", @@ -121,7 +78,7 @@ " StructField(\"seller_name\", FloatType),\n", " StructField(\"mod_flag\", FloatType),\n", " StructField(\"orig_interest_rate\", FloatType),\n", - " StructField(\"orig_upb\", IntegerType),\n", + " StructField(\"orig_upb\", DoubleType),\n", " StructField(\"orig_loan_term\", IntegerType),\n", " StructField(\"orig_ltv\", FloatType),\n", " StructField(\"orig_cltv\", FloatType),\n", @@ -480,13 +437,6 @@ "source": [ "spark.close()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -506,4 +456,4 @@ }, "nbformat": 4, "nbformat_minor": 2 -} \ No newline at end of file +} diff --git a/examples/XGBoost-Examples/mortgage/python/com/nvidia/spark/examples/mortgage/consts.py b/examples/XGBoost-Examples/mortgage/python/com/nvidia/spark/examples/mortgage/consts.py index 7782d84c5..1cca6e6d8 100644 --- a/examples/XGBoost-Examples/mortgage/python/com/nvidia/spark/examples/mortgage/consts.py +++ b/examples/XGBoost-Examples/mortgage/python/com/nvidia/spark/examples/mortgage/consts.py @@ -30,7 +30,7 @@ StructField('seller_name', FloatType()), StructField('mod_flag', FloatType()), StructField('orig_interest_rate', FloatType()), - StructField('orig_upb', IntegerType()), + StructField('orig_upb', DoubleType()), StructField('orig_loan_term', IntegerType()), StructField('orig_ltv', FloatType()), StructField('orig_cltv', FloatType()), @@ -133,67 +133,117 @@ 'Other REFINANCE': 'OTHER REFINANCE', } -performance_schema = StructType([ - StructField('loan_id', LongType()), - StructField('monthly_reporting_period', StringType()), - StructField('servicer', StringType()), - StructField('interest_rate', DoubleType()), - StructField('current_actual_upb', DoubleType()), - StructField('loan_age', DoubleType()), - StructField('remaining_months_to_legal_maturity', DoubleType()), - StructField('adj_remaining_months_to_maturity', DoubleType()), - StructField('maturity_date', StringType()), - StructField('msa', DoubleType()), - StructField('current_loan_delinquency_status', IntegerType()), - StructField('mod_flag', StringType()), - StructField('zero_balance_code', StringType()), - StructField('zero_balance_effective_date', StringType()), - StructField('last_paid_installment_date', StringType()), - StructField('foreclosed_after', StringType()), - StructField('disposition_date', StringType()), - StructField('foreclosure_costs', DoubleType()), - StructField('prop_preservation_and_repair_costs', DoubleType()), - StructField('asset_recovery_costs', DoubleType()), - StructField('misc_holding_expenses', DoubleType()), - StructField('holding_taxes', DoubleType()), - StructField('net_sale_proceeds', DoubleType()), - StructField('credit_enhancement_proceeds', DoubleType()), - StructField('repurchase_make_whole_proceeds', StringType()), - StructField('other_foreclosure_proceeds', DoubleType()), - StructField('non_interest_bearing_upb', DoubleType()), - StructField('principal_forgiveness_upb', StringType()), - StructField('repurchase_make_whole_proceeds_flag', StringType()), - StructField('foreclosure_principal_write_off_amount', StringType()), - StructField('servicing_activity_indicator', StringType()), -]) -acquisition_schema = StructType([ - StructField('loan_id', LongType()), - StructField('orig_channel', StringType()), - StructField('seller_name', StringType()), - StructField('orig_interest_rate', DoubleType()), - StructField('orig_upb', IntegerType()), - StructField('orig_loan_term', IntegerType()), - StructField('orig_date', StringType()), - StructField('first_pay_date', StringType()), - StructField('orig_ltv', DoubleType()), - StructField('orig_cltv', DoubleType()), - StructField('num_borrowers', DoubleType()), - StructField('dti', DoubleType()), - StructField('borrower_credit_score', DoubleType()), - StructField('first_home_buyer', StringType()), - StructField('loan_purpose', StringType()), - StructField('property_type', StringType()), - StructField('num_units', IntegerType()), - StructField('occupancy_status', StringType()), - StructField('property_state', StringType()), - StructField('zip', IntegerType()), - StructField('mortgage_insurance_percent', DoubleType()), - StructField('product_type', StringType()), - StructField('coborrow_credit_score', DoubleType()), - StructField('mortgage_insurance_type', DoubleType()), - StructField('relocation_mortgage_indicator', StringType()), -]) +rawSchema = StructType([ + StructField("reference_pool_id", StringType()), + StructField("loan_id", LongType()), + StructField("monthly_reporting_period", StringType()), + StructField("orig_channel", StringType()), + StructField("seller_name", StringType()), + StructField("servicer", StringType()), + StructField("master_servicer", StringType()), + StructField("orig_interest_rate", DoubleType()), + StructField("interest_rate", DoubleType()), + StructField("orig_upb", DoubleType()), + StructField("upb_at_issuance", StringType()), + StructField("current_actual_upb", DoubleType()), + StructField("orig_loan_term", IntegerType()), + StructField("orig_date", StringType()), + StructField("first_pay_date", StringType()), + StructField("loan_age", DoubleType()), + StructField("remaining_months_to_legal_maturity", DoubleType()), + StructField("adj_remaining_months_to_maturity", DoubleType()), + StructField("maturity_date", StringType()), + StructField("orig_ltv", DoubleType()), + StructField("orig_cltv", DoubleType()), + StructField("num_borrowers", DoubleType()), + StructField("dti", DoubleType()), + StructField("borrower_credit_score", DoubleType()), + StructField("coborrow_credit_score", DoubleType()), + StructField("first_home_buyer", StringType()), + StructField("loan_purpose", StringType()), + StructField("property_type", StringType()), + StructField("num_units", IntegerType()), + StructField("occupancy_status", StringType()), + StructField("property_state", StringType()), + StructField("msa", DoubleType()), + StructField("zip", IntegerType()), + StructField("mortgage_insurance_percent", DoubleType()), + StructField("product_type", StringType()), + StructField("prepayment_penalty_indicator", StringType()), + StructField("interest_only_loan_indicator", StringType()), + StructField("interest_only_first_principal_and_interest_payment_date", StringType()), + StructField("months_to_amortization", StringType()), + StructField("current_loan_delinquency_status", IntegerType()), + StructField("loan_payment_history", StringType()), + StructField("mod_flag", StringType()), + StructField("mortgage_insurance_cancellation_indicator", StringType()), + StructField("zero_balance_code", StringType()), + StructField("zero_balance_effective_date", StringType()), + StructField("upb_at_the_time_of_removal", StringType()), + StructField("repurchase_date", StringType()), + StructField("scheduled_principal_current", StringType()), + StructField("total_principal_current", StringType()), + StructField("unscheduled_principal_current", StringType()), + StructField("last_paid_installment_date", StringType()), + StructField("foreclosed_after", StringType()), + StructField("disposition_date", StringType()), + StructField("foreclosure_costs", DoubleType()), + StructField("prop_preservation_and_repair_costs", DoubleType()), + StructField("asset_recovery_costs", DoubleType()), + StructField("misc_holding_expenses", DoubleType()), + StructField("holding_taxes", DoubleType()), + StructField("net_sale_proceeds", DoubleType()), + StructField("credit_enhancement_proceeds", DoubleType()), + StructField("repurchase_make_whole_proceeds", StringType()), + StructField("other_foreclosure_proceeds", DoubleType()), + StructField("non_interest_bearing_upb", DoubleType()), + StructField("principal_forgiveness_upb", StringType()), + StructField("original_list_start_date", StringType()), + StructField("original_list_price", StringType()), + StructField("current_list_start_date", StringType()), + StructField("current_list_price", StringType()), + StructField("borrower_credit_score_at_issuance", StringType()), + StructField("co-borrower_credit_score_at_issuance", StringType()), + StructField("borrower_credit_score_current", StringType()), + StructField("co-Borrower_credit_score_current", StringType()), + StructField("mortgage_insurance_type", DoubleType()), + StructField("servicing_activity_indicator", StringType()), + StructField("current_period_modification_loss_amount", StringType()), + StructField("cumulative_modification_loss_amount", StringType()), + StructField("current_period_credit_event_net_gain_or_loss", StringType()), + StructField("cumulative_credit_event_net_gain_or_loss", StringType()), + StructField("homeready_program_indicator", StringType()), + StructField("foreclosure_principal_write_off_amount", StringType()), + StructField("relocation_mortgage_indicator", StringType()), + StructField("zero_balance_code_change_date", StringType()), + StructField("loan_holdback_indicator", StringType()), + StructField("loan_holdback_effective_date", StringType()), + StructField("delinquent_accrued_interest", StringType()), + StructField("property_valuation_method", StringType()), + StructField("high_balance_loan_indicator", StringType()), + StructField("arm_initial_fixed-rate_period_lt_5_yr_indicator", StringType()), + StructField("arm_product_type", StringType()), + StructField("initial_fixed-rate_period", StringType()), + StructField("interest_rate_adjustment_frequency", StringType()), + StructField("next_interest_rate_adjustment_date", StringType()), + StructField("next_payment_change_date", StringType()), + StructField("index", StringType()), + StructField("arm_cap_structure", StringType()), + StructField("initial_interest_rate_cap_up_percent", StringType()), + StructField("periodic_interest_rate_cap_up_percent", StringType()), + StructField("lifetime_interest_rate_cap_up_percent", StringType()), + StructField("mortgage_margin", StringType()), + StructField("arm_balloon_indicator", StringType()), + StructField("arm_plan_number", StringType()), + StructField("borrower_assistance_plan", StringType()), + StructField("hltv_refinance_option_indicator", StringType()), + StructField("deal_name", StringType()), + StructField("repurchase_make_whole_proceeds_flag", StringType()), + StructField("alternative_delinquency_resolution", StringType()), + StructField("alternative_delinquency_resolution_count", StringType()), + StructField("total_deferral_amount", StringType()) + ]) categorical_columns = [ 'orig_channel', diff --git a/examples/XGBoost-Examples/mortgage/python/com/nvidia/spark/examples/mortgage/etl.py b/examples/XGBoost-Examples/mortgage/python/com/nvidia/spark/examples/mortgage/etl.py index eb3f40aef..d1f157833 100644 --- a/examples/XGBoost-Examples/mortgage/python/com/nvidia/spark/examples/mortgage/etl.py +++ b/examples/XGBoost-Examples/mortgage/python/com/nvidia/spark/examples/mortgage/etl.py @@ -16,9 +16,10 @@ from com.nvidia.spark.examples.mortgage.consts import * from pyspark.sql.functions import * from pyspark.sql.types import * +from pyspark.sql.window import Window from sys import exit -get_quarter = udf(lambda path: path.split(r'.')[0].split('_')[-1], StringType()) +get_quarter = udf(lambda path: path.split(r'.')[0].split('/')[-1], StringType()) standardize_name = udf(lambda name: name_mapping.get(name), StringType()) def load_data(spark, paths, schema, args, extra_csv_opts={}): @@ -31,18 +32,60 @@ def load_data(spark, paths, schema, args, extra_csv_opts={}): (reader .schema(schema) .option('delimiter', '|') - .option('header', args.hasHeader)) + .option('header', False)) for k, v in extra_csv_opts.items(): reader.option(k, v) return reader.load(paths) -def prepare_performance(spark, args): +def prepare_rawDf(spark, args): extra_csv_options = { 'nullValue': '', 'parserLib': 'univocity', } - paths = extract_paths(args.dataPaths, 'perf::') - performance = (load_data(spark, paths, performance_schema, args, extra_csv_options) + paths = extract_paths(args.dataPaths, 'data::') + rawDf = load_data(spark, paths, rawSchema, args, extra_csv_options) + + return rawDf + +def extract_perf_columns(rawDf): + perfDf = rawDf.select( + col("loan_id"), + date_format(to_date(col("monthly_reporting_period"),"MMyyyy"), "MM/dd/yyyy").alias("monthly_reporting_period"), + upper(col("servicer")).alias("servicer"), + col("interest_rate"), + col("current_actual_upb"), + col("loan_age"), + col("remaining_months_to_legal_maturity"), + col("adj_remaining_months_to_maturity"), + date_format(to_date(col("maturity_date"),"MMyyyy"), "MM/yyyy").alias("maturity_date"), + col("msa"), + col("current_loan_delinquency_status"), + col("mod_flag"), + col("zero_balance_code"), + date_format(to_date(col("zero_balance_effective_date"),"MMyyyy"), "MM/yyyy").alias("zero_balance_effective_date"), + date_format(to_date(col("last_paid_installment_date"),"MMyyyy"), "MM/dd/yyyy").alias("last_paid_installment_date"), + date_format(to_date(col("foreclosed_after"),"MMyyyy"), "MM/dd/yyyy").alias("foreclosed_after"), + date_format(to_date(col("disposition_date"),"MMyyyy"), "MM/dd/yyyy").alias("disposition_date"), + col("foreclosure_costs"), + col("prop_preservation_and_repair_costs"), + col("asset_recovery_costs"), + col("misc_holding_expenses"), + col("holding_taxes"), + col("net_sale_proceeds"), + col("credit_enhancement_proceeds"), + col("repurchase_make_whole_proceeds"), + col("other_foreclosure_proceeds"), + col("non_interest_bearing_upb"), + col("principal_forgiveness_upb"), + col("repurchase_make_whole_proceeds_flag"), + col("foreclosure_principal_write_off_amount"), + col("servicing_activity_indicator")) + + return perfDf.select("*").filter("current_actual_upb != 0.0") + + +def prepare_performance(spark, args, rawDf): + performance = (extract_perf_columns(rawDf) .withColumn('quarter', get_quarter(input_file_name())) .withColumn('timestamp', to_date(col('monthly_reporting_period'), 'MM/dd/yyyy')) .withColumn('timestamp_year', year(col('timestamp'))) @@ -133,8 +176,42 @@ def prepare_performance(spark, args): .join(to_join, ['quarter', 'loan_id', 'timestamp_year', 'timestamp_month'], 'left') .drop('timestamp_year', 'timestamp_month')) -def prepare_acquisition(spark, args): - return (load_data(spark, extract_paths(args.dataPaths, 'acq::'), acquisition_schema, args) +def extract_acq_columns(rawDf): + acqDf = rawDf.select( + col("loan_id"), + col("orig_channel"), + upper(col("seller_name")).alias("seller_name"), + col("orig_interest_rate"), + col("orig_upb"), + col("orig_loan_term"), + date_format(to_date(col("orig_date"),"MMyyyy"), "MM/yyyy").alias("orig_date"), + date_format(to_date(col("first_pay_date"),"MMyyyy"), "MM/yyyy").alias("first_pay_date"), + col("orig_ltv"), + col("orig_cltv"), + col("num_borrowers"), + col("dti"), + col("borrower_credit_score"), + col("first_home_buyer"), + col("loan_purpose"), + col("property_type"), + col("num_units"), + col("occupancy_status"), + col("property_state"), + col("zip"), + col("mortgage_insurance_percent"), + col("product_type"), + col("coborrow_credit_score"), + col("mortgage_insurance_type"), + col("relocation_mortgage_indicator"), + dense_rank().over(Window.partitionBy("loan_id").orderBy(to_date(col("monthly_reporting_period"),"MMyyyy"))).alias("rank") + ) + + return acqDf.select("*").filter(col("rank")==1) + + + +def prepare_acquisition(spark, args, rawDf): + return (extract_acq_columns(rawDf) .withColumn('quarter', get_quarter(input_file_name())) .withColumn('seller_name', standardize_name(col('seller_name')))) @@ -147,8 +224,9 @@ def extract_paths(paths, prefix): return results def etl(spark, args): - performance = prepare_performance(spark, args) - acquisition = prepare_acquisition(spark, args) + rawDf = prepare_rawDf(spark, args) + performance = prepare_performance(spark, args, rawDf) + acquisition = prepare_acquisition(spark, args, rawDf) return (performance .join(acquisition, ['loan_id', 'quarter'], 'left_outer') .select( diff --git a/examples/XGBoost-Examples/mortgage/python/com/nvidia/spark/examples/mortgage/etl_main.py b/examples/XGBoost-Examples/mortgage/python/com/nvidia/spark/examples/mortgage/etl_main.py index 6002f5056..55f5df5fc 100644 --- a/examples/XGBoost-Examples/mortgage/python/com/nvidia/spark/examples/mortgage/etl_main.py +++ b/examples/XGBoost-Examples/mortgage/python/com/nvidia/spark/examples/mortgage/etl_main.py @@ -16,7 +16,6 @@ from com.nvidia.spark.examples.mortgage.consts import * from com.nvidia.spark.examples.mortgage.etl import etl, extract_paths from com.nvidia.spark.examples.utility.utils import * -from ml.dmlc.xgboost4j.scala.spark import * from pyspark.sql import SparkSession def main(args, xgboost_args): diff --git a/examples/XGBoost-Examples/mortgage/scala/src/com/nvidia/spark/examples/mortgage/ETLMain.scala b/examples/XGBoost-Examples/mortgage/scala/src/com/nvidia/spark/examples/mortgage/ETLMain.scala index f54d3d67c..0b798739b 100644 --- a/examples/XGBoost-Examples/mortgage/scala/src/com/nvidia/spark/examples/mortgage/ETLMain.scala +++ b/examples/XGBoost-Examples/mortgage/scala/src/com/nvidia/spark/examples/mortgage/ETLMain.scala @@ -31,17 +31,17 @@ object ETLMain extends Mortgage { val spark = SparkSession.builder().appName(appInfo.mkString("-")).getOrCreate() try { - val (perfPaths, acqPaths, outPath) = checkAndGetPaths(xgbArgs.dataPaths) + val (dataPaths, outPath) = checkAndGetPaths(xgbArgs.dataPaths) println("\n------ Start ETL ------") benchmark.time("ETL") { // ETL the raw data val rawDF = xgbArgs.format match { - case "csv" => XGBoostETL.csv(spark, perfPaths, acqPaths, xgbArgs.hasHeader) - case "orc" => XGBoostETL.orc(spark, perfPaths, acqPaths) - case "parquet" => XGBoostETL.parquet(spark, perfPaths, acqPaths) + case "csv" => XGBoostETL.csv(spark, dataPaths, false) + case "orc" => XGBoostETL.orc(spark, dataPaths) + case "parquet" => XGBoostETL.parquet(spark, dataPaths) case _ => throw new IllegalArgumentException("Unsupported data file format!") } - rawDF.write.mode("overwrite").parquet(new Path(outPath, "data").toString) + rawDF.write.mode("overwrite").parquet(outPath) } if (xgbArgs.saveDict) { XGBoostETL.saveDictTable(new Path(outPath, ".dict").toString) @@ -52,32 +52,26 @@ object ETLMain extends Mortgage { } } - private def checkAndGetPaths(paths: Seq[String]): (Seq[String], Seq[String], String) = { - val prefixes = Array("perf::", "acq::", "out::") + private def checkAndGetPaths(paths: Seq[String]): (Seq[String], String) = { + val prefixes = Array("data::", "out::") val validPaths = paths.filter(_.nonEmpty).map(_.trim) // get and check perf data paths - val perfPaths = validPaths.filter(_.startsWith(prefixes.head)) - require(perfPaths.nonEmpty, s"$appName ETL requires at least one path for performance data file." + - s" Please specify it by '-dataPath=perf::your_perf_path'") - - // get and check acq data paths - val acqPaths = validPaths.filter(_.startsWith(prefixes(1))) - require(acqPaths.nonEmpty, s"$appName ETL requires at least one path for acquisition data file." + - s" Please specify it by '-dataPath=acq::your_acq_path'") + val dataPaths = validPaths.filter(_.startsWith(prefixes.head)) + require(dataPaths.nonEmpty, s"$appName ETL requires at least one path for data file." + + s" Please specify it by '-dataPath=data::your_data_path'") // get and check out path - val outPath = validPaths.filter(_.startsWith(prefixes(2))) + val outPath = validPaths.filter(_.startsWith(prefixes(1))) require(outPath.nonEmpty, s"$appName ETL requires a path to save the ETLed data file. Please specify it" + " by '-dataPath=out::your_out_path', only the first path is used if multiple paths are found.") // check data paths not specified type val unknownPaths = validPaths.filterNot(p => prefixes.exists(p.contains(_))) require(unknownPaths.isEmpty, s"Unknown type for data path: ${unknownPaths.head}, $appName requires to specify" + - " the type for each data path by adding the prefix 'perf::' or 'acq::' or 'out::'.") + " the type for each data path by adding the prefix 'data::' or 'out::'.") - (perfPaths.map(_.stripPrefix(prefixes.head)), - acqPaths.map(_.stripPrefix(prefixes(1))), - outPath.head.stripPrefix(prefixes(2))) + (dataPaths.map(_.stripPrefix(prefixes.head)), + outPath.head.stripPrefix(prefixes(1))) } } diff --git a/examples/XGBoost-Examples/mortgage/scala/src/com/nvidia/spark/examples/mortgage/Mortgage.scala b/examples/XGBoost-Examples/mortgage/scala/src/com/nvidia/spark/examples/mortgage/Mortgage.scala index 582492006..c051cff07 100644 --- a/examples/XGBoost-Examples/mortgage/scala/src/com/nvidia/spark/examples/mortgage/Mortgage.scala +++ b/examples/XGBoost-Examples/mortgage/scala/src/com/nvidia/spark/examples/mortgage/Mortgage.scala @@ -16,7 +16,7 @@ package com.nvidia.spark.examples.mortgage -import org.apache.spark.sql.types.{FloatType, IntegerType, StructField, StructType} +import org.apache.spark.sql.types.{FloatType, IntegerType, StructField, StructType, DoubleType} private[mortgage] trait Mortgage { val appName = "Mortgage" @@ -37,7 +37,7 @@ private[mortgage] trait Mortgage { protected val numericCols = List( ("orig_interest_rate", FloatType), - ("orig_upb", IntegerType), + ("orig_upb", DoubleType), ("orig_loan_term", IntegerType), ("orig_ltv", FloatType), ("orig_cltv", FloatType), diff --git a/examples/XGBoost-Examples/mortgage/scala/src/com/nvidia/spark/examples/mortgage/XGBoostETL.scala b/examples/XGBoost-Examples/mortgage/scala/src/com/nvidia/spark/examples/mortgage/XGBoostETL.scala index 0ef25ea2e..55f40799d 100644 --- a/examples/XGBoost-Examples/mortgage/scala/src/com/nvidia/spark/examples/mortgage/XGBoostETL.scala +++ b/examples/XGBoost-Examples/mortgage/scala/src/com/nvidia/spark/examples/mortgage/XGBoostETL.scala @@ -27,27 +27,64 @@ object GetQuarterFromCsvFileName { // So we strip off the .txt and everything after it // and then take everything after the last remaining _ def apply(): Column = substring_index( - substring_index(input_file_name(), ".", 1), "_", -1) + substring_index(input_file_name(), ".", 1), "/", -1) } private object CsvReader { - def readPerformance(spark: SparkSession, paths: Seq[String], optionsMap: Map[String, String]): DataFrame = { - val performanceSchema = StructType(Array( + def readRaw(spark: SparkSession, paths: Seq[String], optionsMap: Map[String, String]): DataFrame = { + + val rawSchema = StructType(Array( + StructField("reference_pool_id", StringType), StructField("loan_id", LongType), StructField("monthly_reporting_period", StringType), + StructField("orig_channel", StringType), + StructField("seller_name", StringType), StructField("servicer", StringType), + StructField("master_servicer", StringType), + StructField("orig_interest_rate", DoubleType), StructField("interest_rate", DoubleType), + StructField("orig_upb", DoubleType), + StructField("upb_at_issuance", StringType), StructField("current_actual_upb", DoubleType), + StructField("orig_loan_term", IntegerType), + StructField("orig_date", StringType), + StructField("first_pay_date", StringType), StructField("loan_age", DoubleType), StructField("remaining_months_to_legal_maturity", DoubleType), StructField("adj_remaining_months_to_maturity", DoubleType), StructField("maturity_date", StringType), + StructField("orig_ltv", DoubleType), + StructField("orig_cltv", DoubleType), + StructField("num_borrowers", DoubleType), + StructField("dti", DoubleType), + StructField("borrower_credit_score", DoubleType), + StructField("coborrow_credit_score", DoubleType), + StructField("first_home_buyer", StringType), + StructField("loan_purpose", StringType), + StructField("property_type", StringType), + StructField("num_units", IntegerType), + StructField("occupancy_status", StringType), + StructField("property_state", StringType), StructField("msa", DoubleType), + StructField("zip", IntegerType), + StructField("mortgage_insurance_percent", DoubleType), + StructField("product_type", StringType), + StructField("prepayment_penalty_indicator", StringType), + StructField("interest_only_loan_indicator", StringType), + StructField("interest_only_first_principal_and_interest_payment_date", StringType), + StructField("months_to_amortization", StringType), StructField("current_loan_delinquency_status", IntegerType), + StructField("loan_payment_history", StringType), StructField("mod_flag", StringType), + StructField("mortgage_insurance_cancellation_indicator", StringType), StructField("zero_balance_code", StringType), StructField("zero_balance_effective_date", StringType), + StructField("upb_at_the_time_of_removal", StringType), + StructField("repurchase_date", StringType), + StructField("scheduled_principal_current", StringType), + StructField("total_principal_current", StringType), + StructField("unscheduled_principal_current", StringType), StructField("last_paid_installment_date", StringType), StructField("foreclosed_after", StringType), StructField("disposition_date", StringType), @@ -62,59 +99,141 @@ private object CsvReader { StructField("other_foreclosure_proceeds", DoubleType), StructField("non_interest_bearing_upb", DoubleType), StructField("principal_forgiveness_upb", StringType), - StructField("repurchase_make_whole_proceeds_flag", StringType), + StructField("original_list_start_date", StringType), + StructField("original_list_price", StringType), + StructField("current_list_start_date", StringType), + StructField("current_list_price", StringType), + StructField("borrower_credit_score_at_issuance", StringType), + StructField("co-borrower_credit_score_at_issuance", StringType), + StructField("borrower_credit_score_current", StringType), + StructField("co-Borrower_credit_score_current", StringType), + StructField("mortgage_insurance_type", DoubleType), + StructField("servicing_activity_indicator", StringType), + StructField("current_period_modification_loss_amount", StringType), + StructField("cumulative_modification_loss_amount", StringType), + StructField("current_period_credit_event_net_gain_or_loss", StringType), + StructField("cumulative_credit_event_net_gain_or_loss", StringType), + StructField("homeready_program_indicator", StringType), StructField("foreclosure_principal_write_off_amount", StringType), - StructField("servicing_activity_indicator", StringType)) + StructField("relocation_mortgage_indicator", StringType), + StructField("zero_balance_code_change_date", StringType), + StructField("loan_holdback_indicator", StringType), + StructField("loan_holdback_effective_date", StringType), + StructField("delinquent_accrued_interest", StringType), + StructField("property_valuation_method", StringType), + StructField("high_balance_loan_indicator", StringType), + StructField("arm_initial_fixed-rate_period_lt_5_yr_indicator", StringType), + StructField("arm_product_type", StringType), + StructField("initial_fixed-rate_period", StringType), + StructField("interest_rate_adjustment_frequency", StringType), + StructField("next_interest_rate_adjustment_date", StringType), + StructField("next_payment_change_date", StringType), + StructField("index", StringType), + StructField("arm_cap_structure", StringType), + StructField("initial_interest_rate_cap_up_percent", StringType), + StructField("periodic_interest_rate_cap_up_percent", StringType), + StructField("lifetime_interest_rate_cap_up_percent", StringType), + StructField("mortgage_margin", StringType), + StructField("arm_balloon_indicator", StringType), + StructField("arm_plan_number", StringType), + StructField("borrower_assistance_plan", StringType), + StructField("hltv_refinance_option_indicator", StringType), + StructField("deal_name", StringType), + StructField("repurchase_make_whole_proceeds_flag", StringType), + StructField("alternative_delinquency_resolution", StringType), + StructField("alternative_delinquency_resolution_count", StringType), + StructField("total_deferral_amount", StringType) + ) ) spark.read .options(optionsMap) .option("nullValue", "") .option("delimiter", "|") - .option("parserLib", "univocity") - .schema(performanceSchema) + .schema(rawSchema) .csv(paths: _*) .withColumn("quarter", GetQuarterFromCsvFileName()) } +} - def readAcquisition(spark: SparkSession, paths: Seq[String], optionsMap: Map[String, String]): DataFrame = { - val acquisitionSchema = StructType(Array( - StructField("loan_id", LongType), - StructField("orig_channel", StringType), - StructField("seller_name", StringType), - StructField("orig_interest_rate", DoubleType), - StructField("orig_upb", IntegerType), - StructField("orig_loan_term", IntegerType), - StructField("orig_date", StringType), - StructField("first_pay_date", StringType), - StructField("orig_ltv", DoubleType), - StructField("orig_cltv", DoubleType), - StructField("num_borrowers", DoubleType), - StructField("dti", DoubleType), - StructField("borrower_credit_score", DoubleType), - StructField("first_home_buyer", StringType), - StructField("loan_purpose", StringType), - StructField("property_type", StringType), - StructField("num_units", IntegerType), - StructField("occupancy_status", StringType), - StructField("property_state", StringType), - StructField("zip", IntegerType), - StructField("mortgage_insurance_percent", DoubleType), - StructField("product_type", StringType), - StructField("coborrow_credit_score", DoubleType), - StructField("mortgage_insurance_type", DoubleType), - StructField("relocation_mortgage_indicator", StringType)) +object extractPerfColumns{ + def apply(rawDf : DataFrame) : DataFrame = { + val perfDf = rawDf.select( + col("loan_id"), + date_format(to_date(col("monthly_reporting_period"),"MMyyyy"), "MM/dd/yyyy").as("monthly_reporting_period"), + upper(col("servicer")).as("servicer"), + col("interest_rate"), + col("current_actual_upb"), + col("loan_age"), + col("remaining_months_to_legal_maturity"), + col("adj_remaining_months_to_maturity"), + date_format(to_date(col("maturity_date"),"MMyyyy"), "MM/yyyy").as("maturity_date"), + col("msa"), + col("current_loan_delinquency_status"), + col("mod_flag"), + col("zero_balance_code"), + date_format(to_date(col("zero_balance_effective_date"),"MMyyyy"), "MM/yyyy").as("zero_balance_effective_date"), + date_format(to_date(col("last_paid_installment_date"),"MMyyyy"), "MM/dd/yyyy").as("last_paid_installment_date"), + date_format(to_date(col("foreclosed_after"),"MMyyyy"), "MM/dd/yyyy").as("foreclosed_after"), + date_format(to_date(col("disposition_date"),"MMyyyy"), "MM/dd/yyyy").as("disposition_date"), + col("foreclosure_costs"), + col("prop_preservation_and_repair_costs"), + col("asset_recovery_costs"), + col("misc_holding_expenses"), + col("holding_taxes"), + col("net_sale_proceeds"), + col("credit_enhancement_proceeds"), + col("repurchase_make_whole_proceeds"), + col("other_foreclosure_proceeds"), + col("non_interest_bearing_upb"), + col("principal_forgiveness_upb"), + col("repurchase_make_whole_proceeds_flag"), + col("foreclosure_principal_write_off_amount"), + col("servicing_activity_indicator"), + col("quarter") ) - spark.read - .options(optionsMap) - .option("delimiter", "|") - .schema(acquisitionSchema) - .csv(paths: _*) - .withColumn("quarter", GetQuarterFromCsvFileName()) + perfDf.select("*").filter("current_actual_upb != 0.0") } } +object extractAcqColumns{ + def apply(rawDf : DataFrame) : DataFrame = { + val acqDf = rawDf.select( + col("loan_id"), + col("orig_channel"), + upper(col("seller_name")).as("seller_name"), + col("orig_interest_rate"), + col("orig_upb"), + col("orig_loan_term"), + date_format(to_date(col("orig_date"),"MMyyyy"), "MM/yyyy").as("orig_date"), + date_format(to_date(col("first_pay_date"),"MMyyyy"), "MM/yyyy").as("first_pay_date"), + col("orig_ltv"), + col("orig_cltv"), + col("num_borrowers"), + col("dti"), + col("borrower_credit_score"), + col("first_home_buyer"), + col("loan_purpose"), + col("property_type"), + col("num_units"), + col("occupancy_status"), + col("property_state"), + col("zip"), + col("mortgage_insurance_percent"), + col("product_type"), + col("coborrow_credit_score"), + col("mortgage_insurance_type"), + col("relocation_mortgage_indicator"), + col("quarter"), + dense_rank().over(Window.partitionBy("loan_id").orderBy(to_date(col("monthly_reporting_period"),"MMyyyy"))).as("rank") + ) + + acqDf.select("*").filter(col("rank") === 1).drop("rank") + } + +} + object NameMapping { /** * Returns a dataframe with two columns named based off of the column names passed in. @@ -414,27 +533,36 @@ object XGBoostETL extends Mortgage { } } - def csv(spark: SparkSession, perfPaths: Seq[String], acqPaths: Seq[String], hasHeader: Boolean): DataFrame = { + def csv(spark: SparkSession, dataPaths: Seq[String], hasHeader: Boolean): DataFrame = { val optionsMap = Map("header" -> hasHeader.toString) + val rawDf = CsvReader.readRaw(spark, dataPaths, optionsMap) + val perfDf = extractPerfColumns(rawDf) + val acqDf = extractAcqColumns(rawDf) transform( - CsvReader.readPerformance(spark, perfPaths, optionsMap), - CsvReader.readAcquisition(spark, acqPaths, optionsMap), + perfDf, + acqDf, spark ) } - def parquet(spark: SparkSession, perfPaths: Seq[String], acqPaths: Seq[String]): DataFrame = { + def parquet(spark: SparkSession, dataPaths: Seq[String]): DataFrame = { + val rawDf = spark.read.parquet(dataPaths: _*) + val perfDf = extractPerfColumns(rawDf) + val acqDf = extractAcqColumns(rawDf) transform( - spark.read.parquet(perfPaths: _*), - spark.read.parquet(acqPaths: _*), + perfDf, + acqDf, spark ) } - def orc(spark: SparkSession, perfPaths: Seq[String], acqPaths: Seq[String]): DataFrame = { + def orc(spark: SparkSession, dataPaths: Seq[String]): DataFrame = { + val rawDf = spark.read.orc(dataPaths: _*) + val perfDf = extractPerfColumns(rawDf) + val acqDf = extractAcqColumns(rawDf) transform( - spark.read.orc(perfPaths: _*), - spark.read.orc(acqPaths: _*), + perfDf, + acqDf, spark ) } From 7a407389b6fbf31430ba5357359177c4bb411e2d Mon Sep 17 00:00:00 2001 From: Peixin Date: Wed, 27 Jul 2022 13:19:11 +0800 Subject: [PATCH 09/17] Enable automerge from 22.08 to 22.10 (#197) Signed-off-by: Peixin Li --- .github/workflows/auto-merge.yml | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/.github/workflows/auto-merge.yml b/.github/workflows/auto-merge.yml index f89865c3f..b292dca00 100644 --- a/.github/workflows/auto-merge.yml +++ b/.github/workflows/auto-merge.yml @@ -18,7 +18,7 @@ name: auto-merge HEAD to BASE on: pull_request_target: branches: - - branch-22.06 + - branch-22.08 types: [closed] jobs: @@ -29,13 +29,13 @@ jobs: steps: - uses: actions/checkout@v2 with: - ref: branch-22.06 # force to fetch from latest upstream instead of PR ref + ref: branch-22.08 # 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-22.06 - BASE: branch-22.08 + HEAD: branch-22.08 + BASE: branch-22.10 AUTOMERGE_TOKEN: ${{ secrets.AUTOMERGE_TOKEN }} # use to merge PR From 88c726403a227ba865754b941f57fedd229aa3e1 Mon Sep 17 00:00:00 2001 From: Suraj Aralihalli Date: Thu, 28 Jul 2022 23:27:38 -0400 Subject: [PATCH 10/17] Fix deprecated parameter spark.rapids.memory.gpu.pooling.enabled and other bugs in readme (#200) * initial commit: correct readMe files for notebooks and applications Signed-off-by: Suraj Aralihalli * fixed more bugs Signed-off-by: Suraj Aralihalli * added link to download datasets in notebooks Signed-off-by: Suraj Aralihalli * test -> eval Signed-off-by: Suraj Aralihalli --- .../notebook/python-notebook.md | 28 ++++++++++++++++++- .../xgboost-examples/notebook/toree.md | 25 +++++++++++++++-- .../on-prem-cluster/kubernetes-scala.md | 8 ++++-- .../on-prem-cluster/standalone-python.md | 8 ++++-- .../on-prem-cluster/standalone-scala.md | 8 ++++-- .../on-prem-cluster/yarn-python.md | 8 +++++- .../on-prem-cluster/yarn-scala.md | 9 ++++-- .../python/MortgageETL+XGBoost.ipynb | 4 +++ .../notebooks/python/MortgageETL.ipynb | 3 +- .../notebooks/scala/mortgage-ETL.ipynb | 2 +- .../notebooks/scala/mortgage-gpu.ipynb | 3 +- 11 files changed, 90 insertions(+), 16 deletions(-) diff --git a/docs/get-started/xgboost-examples/notebook/python-notebook.md b/docs/get-started/xgboost-examples/notebook/python-notebook.md index 037353254..3bfd71174 100644 --- a/docs/get-started/xgboost-examples/notebook/python-notebook.md +++ b/docs/get-started/xgboost-examples/notebook/python-notebook.md @@ -20,6 +20,10 @@ and the home directory for Apache Spark respectively. 3. Launch the notebook: + Note: For ETL jobs, Set `spark.task.resource.gpu.amount` to `1/spark.executor.cores`. + + For ETL: + ``` bash PYSPARK_DRIVER_PYTHON=jupyter \ PYSPARK_DRIVER_PYTHON_OPTS=notebook \ @@ -28,16 +32,38 @@ and the home directory for Apache Spark respectively. --jars ${RAPIDS_JAR},${XGBOOST4J_JAR},${XGBOOST4J_SPARK_JAR}\ --py-files ${XGBOOST4J_SPARK_JAR},${SAMPLE_ZIP} \ --conf spark.plugins=com.nvidia.spark.SQLPlugin \ - --conf spark.rapids.memory.gpu.pooling.enabled=false \ --conf spark.executor.resource.gpu.amount=1 \ + --conf spark.executor.cores=10 \ + --conf spark.task.resource.gpu.amount=0.1 \ --conf spark.sql.cache.serializer=com.nvidia.spark.ParquetCachedBatchSerializer \ --conf spark.rapids.sql.hasNans=false \ + --conf spark.executor.resource.gpu.discoveryScript=./getGpusResources.sh \ + --files $SPARK_HOME/examples/src/main/scripts/getGpusResources.sh + ``` + + For XGBoost: + + ``` bash + PYSPARK_DRIVER_PYTHON=jupyter \ + PYSPARK_DRIVER_PYTHON_OPTS=notebook \ + pyspark \ + --master ${SPARK_MASTER} \ + --jars ${RAPIDS_JAR},${XGBOOST4J_JAR},${XGBOOST4J_SPARK_JAR}\ + --py-files ${XGBOOST4J_SPARK_JAR},${SAMPLE_ZIP} \ + --conf spark.plugins=com.nvidia.spark.SQLPlugin \ + --conf spark.rapids.memory.gpu.pool=NONE \ + --conf spark.executor.resource.gpu.amount=1 \ + --conf spark.executor.cores=10 \ --conf spark.task.resource.gpu.amount=1 \ + --conf spark.rapids.sql.hasNans=false \ --conf spark.executor.resource.gpu.discoveryScript=./getGpusResources.sh \ --files $SPARK_HOME/examples/src/main/scripts/getGpusResources.sh ``` + + 4. Launch ETL Part + - Mortgage ETL Notebook: [Python](../../../../examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL.ipynb) - Taxi ETL Notebook: [Python](../../../../examples/XGBoost-Examples/taxi/notebooks/python/taxi-ETL.ipynb) - Note: Agaricus does not have ETL part. diff --git a/docs/get-started/xgboost-examples/notebook/toree.md b/docs/get-started/xgboost-examples/notebook/toree.md index 5be31c180..e338fd909 100644 --- a/docs/get-started/xgboost-examples/notebook/toree.md +++ b/docs/get-started/xgboost-examples/notebook/toree.md @@ -29,18 +29,39 @@ and the home directory for Apache Spark respectively. 4. Install a new kernel with gpu enabled and launch the notebook + Note: For ETL jobs, Set `spark.task.resource.gpu.amount` to `1/spark.executor.cores`. + + For ETL: ``` bash jupyter toree install \ --spark_home=${SPARK_HOME} \ --user \ --toree_opts='--nosparkcontext' \ - --kernel_name="XGBoost4j-Spark" \ + --kernel_name="ETL-Spark" \ + --spark_opts='--master ${SPARK_MASTER} \ + --jars ${RAPIDS_JAR},${SAMPLE_JAR} \ + --conf spark.plugins=com.nvidia.spark.SQLPlugin \ + --conf spark.executor.extraClassPath=${RAPIDS_JAR} \ + --conf spark.executor.cores=10 \ + --conf spark.task.resource.gpu.amount=0.1 \ + --conf spark.executor.resource.gpu.discoveryScript=./getGpusResources.sh \ + --files $SPARK_HOME/examples/src/main/scripts/getGpusResources.sh' + ``` + + For XGBoost: + ``` bash + jupyter toree install \ + --spark_home=${SPARK_HOME} \ + --user \ + --toree_opts='--nosparkcontext' \ + --kernel_name="XGBoost-Spark" \ --spark_opts='--master ${SPARK_MASTER} \ --jars ${RAPIDS_JAR},${SAMPLE_JAR} \ --conf spark.plugins=com.nvidia.spark.SQLPlugin \ --conf spark.executor.extraClassPath=${RAPIDS_JAR} \ - --conf spark.rapids.memory.gpu.pooling.enabled=false \ + --conf spark.rapids.memory.gpu.pool=NONE \ --conf spark.executor.resource.gpu.amount=1 \ + --conf spark.executor.cores=10 \ --conf spark.task.resource.gpu.amount=1 \ --conf spark.executor.resource.gpu.discoveryScript=./getGpusResources.sh \ --files $SPARK_HOME/examples/src/main/scripts/getGpusResources.sh' diff --git a/docs/get-started/xgboost-examples/on-prem-cluster/kubernetes-scala.md b/docs/get-started/xgboost-examples/on-prem-cluster/kubernetes-scala.md index 5b46b51e6..e7de73508 100644 --- a/docs/get-started/xgboost-examples/on-prem-cluster/kubernetes-scala.md +++ b/docs/get-started/xgboost-examples/on-prem-cluster/kubernetes-scala.md @@ -92,13 +92,16 @@ to execute using a GPU which is already in use -- causing undefined behavior and --------------------------- Use the ETL app to process raw Mortgage data. You can either use this ETLed data to split into training and evaluation data or run the ETL on different subsets of the dataset to produce training and evaluation datasets. +Note: For ETL jobs, Set `spark.task.resource.gpu.amount` to `1/spark.executor.cores`. + Run spark-submit ``` bash ${SPARK_HOME}/bin/spark-submit \ --conf spark.plugins=com.nvidia.spark.SQLPlugin \ --conf spark.executor.resource.gpu.amount=1 \ - --conf spark.task.resource.gpu.amount=1 \ + --conf spark.executor.cores=10 \ + --conf spark.task.resource.gpu.amount=0.1 \ --conf spark.rapids.sql.incompatibleDateFormats.enabled=true \ --conf spark.rapids.sql.csv.read.double.enabled=true \ --conf spark.executor.resource.gpu.discoveryScript=./getGpusResources.sh \ @@ -178,9 +181,10 @@ Run spark-submit: ``` bash ${SPARK_HOME}/bin/spark-submit \ --conf spark.plugins=com.nvidia.spark.SQLPlugin \ - --conf spark.rapids.memory.gpu.pooling.enabled=false \ + --conf spark.rapids.memory.gpu.pool=NONE \ --conf spark.executor.resource.gpu.amount=1 \ --conf spark.task.resource.gpu.amount=1 \ + --conf spark.rapids.sql.hasNans=false \ --conf spark.executor.resource.gpu.discoveryScript=./getGpusResources.sh \ --files $SPARK_HOME/examples/src/main/scripts/getGpusResources.sh \ --jars ${RAPIDS_JAR} \ diff --git a/docs/get-started/xgboost-examples/on-prem-cluster/standalone-python.md b/docs/get-started/xgboost-examples/on-prem-cluster/standalone-python.md index ae06e201c..7bfc78d89 100644 --- a/docs/get-started/xgboost-examples/on-prem-cluster/standalone-python.md +++ b/docs/get-started/xgboost-examples/on-prem-cluster/standalone-python.md @@ -91,13 +91,16 @@ Launch a Standalone Spark Cluster Launch Mortgage or Taxi ETL Part --------------------------- Use the ETL app to process raw Mortgage data. You can either use this ETLed data to split into training and evaluation data or run the ETL on different subsets of the dataset to produce training and evaluation datasets. + +Note: For ETL jobs, Set `spark.task.resource.gpu.amount` to `1/spark.executor.cores`. ### ETL on GPU ``` bash ${SPARK_HOME}/bin/spark-submit \ --master spark://$HOSTNAME:7077 \ --executor-memory 32G \ --conf spark.executor.resource.gpu.amount=1 \ - --conf spark.task.resource.gpu.amount=1 \ + --conf spark.executor.cores=10 \ + --conf spark.task.resource.gpu.amount=0.1 \ --conf spark.plugins=com.nvidia.spark.SQLPlugin \ --conf spark.rapids.sql.incompatibleDateFormats.enabled=true \ --conf spark.rapids.sql.csv.read.double.enabled=true \ @@ -182,9 +185,10 @@ Run spark-submit: ``` bash ${SPARK_HOME}/bin/spark-submit \ --conf spark.plugins=com.nvidia.spark.SQLPlugin \ - --conf spark.rapids.memory.gpu.pooling.enabled=false \ + --conf spark.rapids.memory.gpu.pool=NONE \ --conf spark.executor.resource.gpu.amount=1 \ --conf spark.task.resource.gpu.amount=1 \ + --conf spark.rapids.sql.hasNans=false \ --master ${SPARK_MASTER} \ --driver-memory ${SPARK_DRIVER_MEMORY} \ --executor-memory ${SPARK_EXECUTOR_MEMORY} \ diff --git a/docs/get-started/xgboost-examples/on-prem-cluster/standalone-scala.md b/docs/get-started/xgboost-examples/on-prem-cluster/standalone-scala.md index 35a2ed238..c946b2e5d 100644 --- a/docs/get-started/xgboost-examples/on-prem-cluster/standalone-scala.md +++ b/docs/get-started/xgboost-examples/on-prem-cluster/standalone-scala.md @@ -95,13 +95,16 @@ Launch a Standalone Spark Cluster Use the ETL app to process raw Mortgage data. You can either use this ETLed data to split into training and evaluation data or run the ETL on different subsets of the dataset to produce training and evaluation datasets. Run spark-submit +Note: For ETL jobs, Set `spark.task.resource.gpu.amount` to `1/spark.executor.cores`. + ### ETL on GPU ``` bash ${SPARK_HOME}/bin/spark-submit \ --master spark://$HOSTNAME:7077 \ --executor-memory 32G \ --conf spark.executor.resource.gpu.amount=1 \ - --conf spark.task.resource.gpu.amount=1 \ + --conf spark.executor.cores=10 \ + --conf spark.task.resource.gpu.amount=0.1 \ --conf spark.plugins=com.nvidia.spark.SQLPlugin \ --conf spark.rapids.sql.incompatibleDateFormats.enabled=true \ --conf spark.rapids.sql.csv.read.double.enabled=true \ @@ -187,7 +190,8 @@ Run spark-submit: ``` bash ${SPARK_HOME}/bin/spark-submit \ --conf spark.plugins=com.nvidia.spark.SQLPlugin \ - --conf spark.rapids.memory.gpu.pooling.enabled=false \ + --conf spark.rapids.memory.gpu.pool=NONE \ + --conf spark.rapids.sql.hasNans=false \ --conf spark.executor.resource.gpu.amount=1 \ --conf spark.task.resource.gpu.amount=1 \ --master ${SPARK_MASTER} \ diff --git a/docs/get-started/xgboost-examples/on-prem-cluster/yarn-python.md b/docs/get-started/xgboost-examples/on-prem-cluster/yarn-python.md index fe0605aef..69a903cf6 100644 --- a/docs/get-started/xgboost-examples/on-prem-cluster/yarn-python.md +++ b/docs/get-started/xgboost-examples/on-prem-cluster/yarn-python.md @@ -48,6 +48,9 @@ Launch Mortgage or Taxi ETL Part --------------------------- Use the ETL app to process raw Mortgage data. You can either use this ETLed data to split into training and evaluation data or run the ETL on different subsets of the dataset to produce training and evaluation datasets. + +Note: For ETL jobs, Set `spark.task.resource.gpu.amount` to `1/spark.executor.cores`. + ``` bash # location where data was downloaded export DATA_PATH=hdfs:/tmp/xgboost4j_spark_python/ @@ -55,6 +58,8 @@ export DATA_PATH=hdfs:/tmp/xgboost4j_spark_python/ ${SPARK_HOME}/bin/spark-submit \ --master yarn \ --deploy-mode cluster \ + --conf spark.executor.cores=10 \ + --conf spark.task.resource.gpu.amount=0.1 \ --conf spark.rapids.sql.incompatibleDateFormats.enabled=true \ --conf spark.rapids.sql.csv.read.double.enabled=true \ --conf spark.sql.cache.serializer=com.nvidia.spark.ParquetCachedBatchSerializer \ @@ -114,9 +119,10 @@ Run spark-submit: ``` bash ${SPARK_HOME}/bin/spark-submit \ --conf spark.plugins=com.nvidia.spark.SQLPlugin \ - --conf spark.rapids.memory.gpu.pooling.enabled=false \ + --conf spark.rapids.memory.gpu.pool=NONE \ --conf spark.executor.resource.gpu.amount=1 \ --conf spark.task.resource.gpu.amount=1 \ + --conf spark.rapids.sql.hasNans=false \ --conf spark.executor.resource.gpu.discoveryScript=./getGpusResources.sh \ --files ${SPARK_HOME}/examples/src/main/scripts/getGpusResources.sh \ --master yarn \ diff --git a/docs/get-started/xgboost-examples/on-prem-cluster/yarn-scala.md b/docs/get-started/xgboost-examples/on-prem-cluster/yarn-scala.md index 052d62ec2..0c407dd96 100644 --- a/docs/get-started/xgboost-examples/on-prem-cluster/yarn-scala.md +++ b/docs/get-started/xgboost-examples/on-prem-cluster/yarn-scala.md @@ -52,13 +52,17 @@ Create a directory in HDFS, and copy: Use the ETL app to process raw Mortgage data. You can either use this ETLed data to split into training and evaluation data or run the ETL on different subsets of the dataset to produce training and evaluation datasets. +Note: For ETL jobs, Set `spark.task.resource.gpu.amount` to `1/spark.executor.cores`. + + Run spark-submit ``` bash ${SPARK_HOME}/bin/spark-submit \ --conf spark.plugins=com.nvidia.spark.SQLPlugin \ --conf spark.executor.resource.gpu.amount=1 \ - --conf spark.task.resource.gpu.amount=1 \ + --conf spark.executor.cores=10 \ + --conf spark.task.resource.gpu.amount=0.1 \ --conf spark.rapids.sql.incompatibleDateFormats.enabled=true \ --conf spark.rapids.sql.csv.read.double.enabled=true \ --conf spark.executor.resource.gpu.discoveryScript=./getGpusResources.sh \ @@ -122,9 +126,10 @@ Run spark-submit: ``` bash ${SPARK_HOME}/bin/spark-submit \ --conf spark.plugins=com.nvidia.spark.SQLPlugin \ - --conf spark.rapids.memory.gpu.pooling.enabled=false \ + --conf spark.rapids.memory.gpu.pool=NONE \ --conf spark.executor.resource.gpu.amount=1 \ --conf spark.task.resource.gpu.amount=1 \ + --conf spark.rapids.sql.hasNans=false \ --conf spark.executor.resource.gpu.discoveryScript=./getGpusResources.sh \ --files $SPARK_HOME/examples/src/main/scripts/getGpusResources.sh \ --jars ${RAPIDS_JAR} \ diff --git a/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL+XGBoost.ipynb b/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL+XGBoost.ipynb index 7b29bed7b..756c0d982 100644 --- a/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL+XGBoost.ipynb +++ b/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL+XGBoost.ipynb @@ -4,6 +4,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "# Dataset\n", + "\n", + "Dataset is derived from Fannie Mae’s [Single-Family Loan Performance Data](http://www.fanniemae.com/portal/funding-the-market/data/loan-performance-data.html) with all rights reserved by Fannie Mae. Refer to these [instructions](https://github.com/NVIDIA/spark-rapids-examples/blob/branch-22.08/docs/get-started/xgboost-examples/dataset/mortgage.md) to download the dataset.\n", + "\n", "# ETL + XGBoost train & transform\n", "\n", "This notebook is an end-to-end example of ETL + XGBoost Train & Transform by using [Spark-Rapids](https://github.com/NVIDIA/spark-rapids) and [XGBoost](https://github.com/nvidia/spark-xgboost) with GPU accelerated.\n", diff --git a/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL.ipynb b/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL.ipynb index c029770e1..6b2b919a3 100644 --- a/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL.ipynb +++ b/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL.ipynb @@ -6,8 +6,7 @@ "source": [ "## Prerequirement\n", "### 1. Download data\n", - "\n", - "Download Single-Family Loan Performance Data from [Fannie Mae](https://datadynamics.fanniemae.com/data-dynamics/#/reportMenu;category=HP) website. \n", + "Dataset is derived from Fannie Mae’s [Single-Family Loan Performance Data](http://www.fanniemae.com/portal/funding-the-market/data/loan-performance-data.html) with all rights reserved by Fannie Mae. Refer to these [instructions](https://github.com/NVIDIA/spark-rapids-examples/blob/branch-22.08/docs/get-started/xgboost-examples/dataset/mortgage.md) to download the dataset.\n", "\n", "### 2. Download needed jars\n", "* [rapids-4-spark_2.12-22.06.0.jar](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/22.06.0/rapids-4-spark_2.12-22.06.0.jar)\n", diff --git a/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-ETL.ipynb b/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-ETL.ipynb index 28889a05b..269534325 100644 --- a/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-ETL.ipynb +++ b/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-ETL.ipynb @@ -17,7 +17,7 @@ "## Prerequirement\n", "### 1. Download data\n", "\n", - "Download Single-Family Loan Performance Data from [Fannie Mae](https://datadynamics.fanniemae.com/data-dynamics/#/reportMenu;category=HP) website. \n", + "Refer to these [instructions](https://github.com/NVIDIA/spark-rapids-examples/blob/branch-22.08/docs/get-started/xgboost-examples/dataset/mortgage.md) to download the dataset.\n", "\n", "### 2. Download needed jars\n", "* [rapids-4-spark_2.12-22.06.0.jar](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/22.06.0/rapids-4-spark_2.12-22.06.0.jar)\n", diff --git a/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-gpu.ipynb b/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-gpu.ipynb index 398f2b3e8..8fa003648 100644 --- a/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-gpu.ipynb +++ b/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-gpu.ipynb @@ -55,7 +55,7 @@ "val dataRoot = sys.env.getOrElse(\"DATA_ROOT\", \"/mortgage\")\n", "val trainPath = dataRoot + \"/train/\"\n", "val evalPath = dataRoot + \"/eval/\"\n", - "val transPath = dataRoot + \"/test/\"" + "val transPath = dataRoot + \"/eval/\"" ] }, { @@ -184,6 +184,7 @@ "source": [ "// Build the spark session and data reader as usual\n", "val sparkSession = SparkSession.builder.appName(\"mortgage-gpu\").getOrCreate\n", + "sparkSession.conf.set(\"spark.rapids.sql.hasNans\", false)\n", "val reader = sparkSession.read" ] }, From ed6cfc22b29ef113edf511a173602148f9768aea Mon Sep 17 00:00:00 2001 From: liyuan <84758614+nvliyuan@users.noreply.github.com> Date: Wed, 3 Aug 2022 10:04:28 +0800 Subject: [PATCH 11/17] add convert raw csv files to parquet to improve performance for both CPU/GPU (#202) * update python/scala mortgage notebooks/codes to convert csv to parquet and save to tmpPath Signed-off-by: liyuan * fix build error Signed-off-by: liyuan * fix build error Signed-off-by: liyuan * fix build error Signed-off-by: liyuan * fix build error Signed-off-by: liyuan * fix build error Signed-off-by: liyuan * update getting started docs for add converting raw csv files to parquet Signed-off-by: liyuan * update new mortgage perf Signed-off-by: liyuan --- .../on-prem-cluster/kubernetes-scala.md | 4 +++- .../on-prem-cluster/standalone-python.md | 8 ++++++-- .../on-prem-cluster/standalone-scala.md | 7 +++++-- .../on-prem-cluster/yarn-python.md | 4 +++- .../on-prem-cluster/yarn-scala.md | 4 +++- docs/img/guides/mortgage-perf.png | Bin 34062 -> 210760 bytes .../supported_xgboost_parameters_python.md | 1 + .../supported_xgboost_parameters_scala.md | 1 + .../python/MortgageETL+XGBoost.ipynb | 8 ++++++-- .../notebooks/python/MortgageETL.ipynb | 6 +++++- .../notebooks/scala/mortgage-ETL.ipynb | 6 +++++- .../com/nvidia/spark/examples/mortgage/etl.py | 3 +++ .../spark/examples/mortgage/ETLMain.scala | 16 +++++++++++----- .../spark/examples/mortgage/XGBoostETL.scala | 10 ++++++++-- 14 files changed, 60 insertions(+), 18 deletions(-) diff --git a/docs/get-started/xgboost-examples/on-prem-cluster/kubernetes-scala.md b/docs/get-started/xgboost-examples/on-prem-cluster/kubernetes-scala.md index e7de73508..97b133922 100644 --- a/docs/get-started/xgboost-examples/on-prem-cluster/kubernetes-scala.md +++ b/docs/get-started/xgboost-examples/on-prem-cluster/kubernetes-scala.md @@ -118,11 +118,13 @@ ${SPARK_HOME}/bin/spark-submit \ $SAMPLE_JAR \ -format=csv \ -dataPath="data::${SPARK_XGBOOST_DIR}/mortgage/input/" \ - -dataPath="out::${SPARK_XGBOOST_DIR}/mortgage/output/train/" + -dataPath="out::${SPARK_XGBOOST_DIR}/mortgage/output/train/" \ + -dataPath="tmp::${SPARK_XGBOOST_DIR}/mortgage/output/tmp/" # if generating eval data, change the data path to eval # -dataPath="data::${SPARK_XGBOOST_DIR}/mortgage/input/" # -dataPath="out::${SPARK_XGBOOST_DIR}/mortgage/output/eval/" +# -dataPath="tmp::${SPARK_XGBOOST_DIR}/mortgage/output/tmp/" # if running Taxi ETL benchmark, change the class and data path params to # -class com.nvidia.spark.examples.taxi.ETLMain # -dataPath="raw::${SPARK_XGBOOST_DIR}/taxi/your-path" diff --git a/docs/get-started/xgboost-examples/on-prem-cluster/standalone-python.md b/docs/get-started/xgboost-examples/on-prem-cluster/standalone-python.md index 7bfc78d89..6132a7563 100644 --- a/docs/get-started/xgboost-examples/on-prem-cluster/standalone-python.md +++ b/docs/get-started/xgboost-examples/on-prem-cluster/standalone-python.md @@ -111,11 +111,13 @@ ${SPARK_HOME}/bin/spark-submit \ --mainClass='com.nvidia.spark.examples.mortgage.etl_main' \ --format=csv \ --dataPath="data::${SPARK_XGBOOST_DIR}/mortgage/input/" \ - --dataPath="out::${SPARK_XGBOOST_DIR}/mortgage/output/train/" + --dataPath="out::${SPARK_XGBOOST_DIR}/mortgage/output/train/" \ + --dataPath="tmp::${SPARK_XGBOOST_DIR}/mortgage/output/tmp/" # if generating eval data, change the data path to eval # --dataPath="data::${SPARK_XGBOOST_DIR}/mortgage/input/" # --dataPath="out::${SPARK_XGBOOST_DIR}/mortgage/output/eval/" +# --dataPath="tmp::${SPARK_XGBOOST_DIR}/mortgage/output/tmp/" # if running Taxi ETL benchmark, change the class and data path params to # -class com.nvidia.spark.examples.taxi.ETLMain # -dataPath="raw::${SPARK_XGBOOST_DIR}/taxi/your-path" @@ -132,11 +134,13 @@ ${SPARK_HOME}/bin/spark-submit \ --mainClass='com.nvidia.spark.examples.mortgage.etl_main' \ --format=csv \ --dataPath="data::${SPARK_XGBOOST_DIR}/mortgage/input/" \ - --dataPath="out::${SPARK_XGBOOST_DIR}/mortgage/output/train/" + --dataPath="out::${SPARK_XGBOOST_DIR}/mortgage/output/train/" \ + --dataPath="tmp::${SPARK_XGBOOST_DIR}/mortgage/output/tmp/" # if generating eval data, change the data path to eval # --dataPath="data::${SPARK_XGBOOST_DIR}/mortgage/input/" # --dataPath="out::${SPARK_XGBOOST_DIR}/mortgage/output/eval/" +# --dataPath="tmp::${SPARK_XGBOOST_DIR}/mortgage/output/tmp/" # if running Taxi ETL benchmark, change the class and data path params to # -class com.nvidia.spark.examples.taxi.ETLMain # -dataPath="raw::${SPARK_XGBOOST_DIR}/taxi/your-path" diff --git a/docs/get-started/xgboost-examples/on-prem-cluster/standalone-scala.md b/docs/get-started/xgboost-examples/on-prem-cluster/standalone-scala.md index c946b2e5d..ccad55e47 100644 --- a/docs/get-started/xgboost-examples/on-prem-cluster/standalone-scala.md +++ b/docs/get-started/xgboost-examples/on-prem-cluster/standalone-scala.md @@ -114,11 +114,13 @@ ${SPARK_HOME}/bin/spark-submit \ $SAMPLE_JAR \ -format=csv \ -dataPath="data::${SPARK_XGBOOST_DIR}/mortgage/input/" \ - -dataPath="out::${SPARK_XGBOOST_DIR}/mortgage/output/train/" + -dataPath="out::${SPARK_XGBOOST_DIR}/mortgage/output/train/" \ + -dataPath="tmp::${SPARK_XGBOOST_DIR}/mortgage/output/tmp/" # if generating eval data, change the data path to eval # -dataPath="data::${SPARK_XGBOOST_DIR}/mortgage/input/" # -dataPath="out::${SPARK_XGBOOST_DIR}/mortgage/output/eval/" +# -dataPath="tmp::${SPARK_XGBOOST_DIR}/mortgage/output/tmp/" # if running Taxi ETL benchmark, change the class and data path params to # -class com.nvidia.spark.examples.taxi.ETLMain # -dataPath="raw::${SPARK_XGBOOST_DIR}/taxi/your-path" @@ -137,7 +139,8 @@ ${SPARK_HOME}/bin/spark-submit \ $SAMPLE_JAR \ -format=csv \ -dataPath="data::${SPARK_XGBOOST_DIR}/mortgage/input/" \ --dataPath="out::${SPARK_XGBOOST_DIR}/mortgage/output/train/" +-dataPath="out::${SPARK_XGBOOST_DIR}/mortgage/output/train/" \ +-dataPath="tmp::${SPARK_XGBOOST_DIR}/mortgage/output/tmp/" # if generating eval data, change the data path to eval # -dataPath="data::${SPARK_XGBOOST_DIR}/mortgage/input/" diff --git a/docs/get-started/xgboost-examples/on-prem-cluster/yarn-python.md b/docs/get-started/xgboost-examples/on-prem-cluster/yarn-python.md index 69a903cf6..9d92da01a 100644 --- a/docs/get-started/xgboost-examples/on-prem-cluster/yarn-python.md +++ b/docs/get-started/xgboost-examples/on-prem-cluster/yarn-python.md @@ -69,11 +69,13 @@ ${SPARK_HOME}/bin/spark-submit \ --mainClass='com.nvidia.spark.examples.mortgage.etl_main' \ --format=csv \ --dataPath="data::${DATA_PATH}/mortgage/data/mortgage/input/" \ - --dataPath="out::${DATA_PATH}/mortgage/data/mortgage/output/train/" + --dataPath="out::${DATA_PATH}/mortgage/data/mortgage/output/train/" \ + --dataPath="tmp::${SPARK_XGBOOST_DIR}/mortgage/output/tmp/" # if generating eval data, change the data path to eval # --dataPath="data::${SPARK_XGBOOST_DIR}/mortgage/input/" # --dataPath="out::${SPARK_XGBOOST_DIR}/mortgage/output/eval/" +# --dataPath="tmp::${SPARK_XGBOOST_DIR}/mortgage/output/tmp/" # if running Taxi ETL benchmark, change the class and data path params to # -class com.nvidia.spark.examples.taxi.ETLMain # -dataPath="raw::${SPARK_XGBOOST_DIR}/taxi/your-path" diff --git a/docs/get-started/xgboost-examples/on-prem-cluster/yarn-scala.md b/docs/get-started/xgboost-examples/on-prem-cluster/yarn-scala.md index 0c407dd96..dc6918ee4 100644 --- a/docs/get-started/xgboost-examples/on-prem-cluster/yarn-scala.md +++ b/docs/get-started/xgboost-examples/on-prem-cluster/yarn-scala.md @@ -79,11 +79,13 @@ ${SPARK_HOME}/bin/spark-submit \ $SAMPLE_JAR \ -format=csv \ -dataPath="data::${SPARK_XGBOOST_DIR}/mortgage/input/" \ - -dataPath="out::${SPARK_XGBOOST_DIR}/mortgage/output/train/" + -dataPath="out::${SPARK_XGBOOST_DIR}/mortgage/output/train/" \ + -dataPath="tmp::${SPARK_XGBOOST_DIR}/mortgage/output/tmp/" # if generating eval data, change the data path to eval # -dataPath="data::${SPARK_XGBOOST_DIR}/mortgage/input/" # -dataPath="out::${SPARK_XGBOOST_DIR}/mortgage/output/eval/" +# -dataPath="tmp::${SPARK_XGBOOST_DIR}/mortgage/output/tmp/" # if running Taxi ETL benchmark, change the class and data path params to # -class com.nvidia.spark.examples.taxi.ETLMain # -dataPath="raw::${SPARK_XGBOOST_DIR}/taxi/your-path" diff --git a/docs/img/guides/mortgage-perf.png b/docs/img/guides/mortgage-perf.png index 0548ffd0ed53a38a6a13ccd1e3ba64495deffd82..23715ce9a5149dfd3c786e93f2e27be7ae921316 100644 GIT binary patch literal 210760 zcmZU4byyou*lmhii#x?#iWO^t;O?}=-AZvO(gHyX6bbHD3dP-vySux)dyphI{e9oP z|J*#!?oM`Rb|&+_bKY}iCgB=t@-ML{umAwSOGO14EdT(88UR3w#y~^dd4ad21OVWe z+e%AoC`wDyYPdRC+uBu1r<*S**_V>@=6L2W~SlzzE0y$pLtl+&V*!`O|eTf 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--- a/examples/XGBoost-Examples/app-parameters/supported_xgboost_parameters_python.md +++ b/examples/XGBoost-Examples/app-parameters/supported_xgboost_parameters_python.md @@ -30,6 +30,7 @@ This is a description of all the parameters available when you are running examp * `--dataPath=raw::[path]`: Path to the raw data files to be transformed by taxi/ETLMain. * `--dataPath=perf::[path]`,`-dataPath=acq::[path]`: Paths to the raw data files to be transformed by mortgage/ETLMain. * `--dataPath=out::`: Path where to place the output data files for both mortgage/ETLMain and taxi/ETLMain. + * `--dataPath=tmp::`: Path where to place the output data files for converting raw csv format to parquet. 6. `--modelPath=[path]`: Path to save model after training, or where to load model for transforming only. Required only when mode is 'transform'. 7. `--overwrite=[true|false]`: Whether to overwrite the current model data under 'modelPath'. Default is false. You may need to set to true to avoid IOException when saving the model to a path already exists. 8. `--hasHeader=[true|false]`: Indicate whether the csv file has header. diff --git a/examples/XGBoost-Examples/app-parameters/supported_xgboost_parameters_scala.md b/examples/XGBoost-Examples/app-parameters/supported_xgboost_parameters_scala.md index 5b1d77410..838404342 100644 --- a/examples/XGBoost-Examples/app-parameters/supported_xgboost_parameters_scala.md +++ b/examples/XGBoost-Examples/app-parameters/supported_xgboost_parameters_scala.md @@ -19,6 +19,7 @@ This is a description of all the parameters available when you are running examp * `-dataPath=raw::[path]`: Path to the raw data files to be transformed by taxi/ETLMain. * `-dataPath=perf::[path]`,`-dataPath=acq::[path]`: Paths to the raw data files to be transformed by mortgage/ETLMain. * `-dataPath=out::`: Path where to place the output data files for both mortgage/ETLMain and taxi/ETLMain. + * `-dataPath=tmp::`: Path where to place the output data files for converting raw csv format to parquet. 5. `-modelPath=[path]`: Path to save model after training, or where to load model for transforming only. Required only when mode is 'transform'. 6. `-overwrite=[true|false]`: Whether to overwrite the current model data under 'modelPath'. Default is false. You may need to set to true to avoid IOException when saving the model to a path already exists. 7. `-hasHeader=[true|false]`: Indicate whether the csv file has header. diff --git a/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL+XGBoost.ipynb b/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL+XGBoost.ipynb index 756c0d982..2b3cfd98e 100644 --- a/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL+XGBoost.ipynb +++ b/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL+XGBoost.ipynb @@ -55,7 +55,8 @@ "# The input path of dataset\n", "# dataRoot = os.getenv(\"DATA_ROOT\", \"/data\")\n", "dataRoot = os.getenv(\"DATA_ROOT\", \"/mortgage\")\n", - "orig_raw_path = dataRoot + \"/input/\"" + "orig_raw_path = dataRoot + \"/input/\"\n", + "orig_raw_path_csv2parquet = dataRoot + \"/output/csv2parquet/\"" ] }, { @@ -768,6 +769,9 @@ "\n", "# read raw dataset\n", "rawDf = read_raw_csv(spark, orig_raw_path)\n", + "rawDf.write.parquet(orig_raw_path_csv2parquet, mode='overwrite')\n", + "rawDf = spark.read.parquet(orig_raw_path_csv2parquet)\n", + "\n", "acq = extract_acq_columns(rawDf)\n", "perf = extract_perf_columns(rawDf)\n", "\n", @@ -1015,7 +1019,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.9" + "version": "3.8.2" }, "name": "gpu-mortgage", "notebookId": 4440374682851873 diff --git a/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL.ipynb b/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL.ipynb index 6b2b919a3..7f580740d 100644 --- a/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL.ipynb +++ b/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL.ipynb @@ -751,6 +751,7 @@ "outputs": [], "source": [ "output_path = dataRoot + '/output/data/'\n", + "output_csv2parquet = dataRoot + '/output/csv2parquet/'\n", "output_path_train = dataRoot + '/output/train/'\n", "output_path_eval = dataRoot + '/output/eval/'\n", "save_train_eval_dataset = True" @@ -795,6 +796,9 @@ "outputs": [], "source": [ "rawDf = read_raw_csv(spark, orig_raw_path)\n", + "rawDf.write.parquet(output_csv2parquet, mode='overwrite')\n", + "rawDf = spark.read.parquet(output_csv2parquet)\n", + "\n", "acq = extract_acq_columns(rawDf)\n", "perf = extract_perf_columns(rawDf)" ] @@ -1756,7 +1760,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.9" + "version": "3.8.2" }, "name": "gpu-mortgage", "notebookId": 4440374682851873 diff --git a/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-ETL.ipynb b/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-ETL.ipynb index 269534325..edad901b4 100644 --- a/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-ETL.ipynb +++ b/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-ETL.ipynb @@ -75,6 +75,7 @@ "val dataRoot = sys.env.getOrElse(\"DATA_ROOT\", \"/mortgage\")\n", "val dataPath = dataRoot + \"/input\"\n", "val outPath = dataRoot + \"/output\"\n", + "val output_csv2parquet = dataRoot + \"/output/csv2parquet/\"\n", "val saveTrainEvalDataset = true" ] }, @@ -789,7 +790,7 @@ } ], "source": [ - "val rawDf = reader.option(\"header\", false)\n", + "val rawDf_csv = reader.option(\"header\", false)\n", " .option(\"nullValue\", \"\")\n", " .option(\"delimiter\", \"|\")\n", " .option(\"parserLib\", \"univocity\")\n", @@ -797,6 +798,9 @@ " .csv(dataPath)\n", " .withColumn(\"quarter\", GetQuarterFromCsvFileName())\n", "\n", + "rawDf_csv.write.mode(\"overwrite\").parquet(output_csv2parquet)\n", + "val rawDf = spark.read.parquet(output_csv2parquet)\n", + "\n", "val perfSet = extractPerfColumns(rawDf)\n", "val acqSet = extractAcqColumns(rawDf)" ] diff --git a/examples/XGBoost-Examples/mortgage/python/com/nvidia/spark/examples/mortgage/etl.py b/examples/XGBoost-Examples/mortgage/python/com/nvidia/spark/examples/mortgage/etl.py index d1f157833..47052737c 100644 --- a/examples/XGBoost-Examples/mortgage/python/com/nvidia/spark/examples/mortgage/etl.py +++ b/examples/XGBoost-Examples/mortgage/python/com/nvidia/spark/examples/mortgage/etl.py @@ -225,6 +225,9 @@ def extract_paths(paths, prefix): def etl(spark, args): rawDf = prepare_rawDf(spark, args) + rawDf.write.parquet(extract_paths(args.dataPaths, 'tmp::')[0], mode='overwrite') + rawDf = spark.read.parquet(extract_paths(args.dataPaths, 'tmp::')[0]) + performance = prepare_performance(spark, args, rawDf) acquisition = prepare_acquisition(spark, args, rawDf) return (performance diff --git a/examples/XGBoost-Examples/mortgage/scala/src/com/nvidia/spark/examples/mortgage/ETLMain.scala b/examples/XGBoost-Examples/mortgage/scala/src/com/nvidia/spark/examples/mortgage/ETLMain.scala index 0b798739b..d6b5db30a 100644 --- a/examples/XGBoost-Examples/mortgage/scala/src/com/nvidia/spark/examples/mortgage/ETLMain.scala +++ b/examples/XGBoost-Examples/mortgage/scala/src/com/nvidia/spark/examples/mortgage/ETLMain.scala @@ -31,12 +31,12 @@ object ETLMain extends Mortgage { val spark = SparkSession.builder().appName(appInfo.mkString("-")).getOrCreate() try { - val (dataPaths, outPath) = checkAndGetPaths(xgbArgs.dataPaths) + val (dataPaths, outPath, tmpPath) = checkAndGetPaths(xgbArgs.dataPaths) println("\n------ Start ETL ------") benchmark.time("ETL") { // ETL the raw data val rawDF = xgbArgs.format match { - case "csv" => XGBoostETL.csv(spark, dataPaths, false) + case "csv" => XGBoostETL.csv(spark, dataPaths, tmpPath, false) case "orc" => XGBoostETL.orc(spark, dataPaths) case "parquet" => XGBoostETL.parquet(spark, dataPaths) case _ => throw new IllegalArgumentException("Unsupported data file format!") @@ -52,8 +52,8 @@ object ETLMain extends Mortgage { } } - private def checkAndGetPaths(paths: Seq[String]): (Seq[String], String) = { - val prefixes = Array("data::", "out::") + def checkAndGetPaths(paths: Seq[String]): (Seq[String], String, String) = { + val prefixes = Array("data::", "out::", "tmp::") val validPaths = paths.filter(_.nonEmpty).map(_.trim) // get and check perf data paths @@ -65,6 +65,11 @@ object ETLMain extends Mortgage { val outPath = validPaths.filter(_.startsWith(prefixes(1))) require(outPath.nonEmpty, s"$appName ETL requires a path to save the ETLed data file. Please specify it" + " by '-dataPath=out::your_out_path', only the first path is used if multiple paths are found.") + + // get and check tmp path + val tmpPath = validPaths.filter(_.startsWith(prefixes(2))) + require(tmpPath.nonEmpty, s"$appName ETL requires a path to save the temp parquet files. Please specify it" + + " by '-dataPath=tmp::your_out_path'.") // check data paths not specified type val unknownPaths = validPaths.filterNot(p => prefixes.exists(p.contains(_))) @@ -72,6 +77,7 @@ object ETLMain extends Mortgage { " the type for each data path by adding the prefix 'data::' or 'out::'.") (dataPaths.map(_.stripPrefix(prefixes.head)), - outPath.head.stripPrefix(prefixes(1))) + outPath.head.stripPrefix(prefixes(1)), + tmpPath.head.stripPrefix(prefixes(2))) } } diff --git a/examples/XGBoost-Examples/mortgage/scala/src/com/nvidia/spark/examples/mortgage/XGBoostETL.scala b/examples/XGBoost-Examples/mortgage/scala/src/com/nvidia/spark/examples/mortgage/XGBoostETL.scala index 55f40799d..7c21b9dbe 100644 --- a/examples/XGBoost-Examples/mortgage/scala/src/com/nvidia/spark/examples/mortgage/XGBoostETL.scala +++ b/examples/XGBoost-Examples/mortgage/scala/src/com/nvidia/spark/examples/mortgage/XGBoostETL.scala @@ -533,9 +533,13 @@ object XGBoostETL extends Mortgage { } } - def csv(spark: SparkSession, dataPaths: Seq[String], hasHeader: Boolean): DataFrame = { + def csv(spark: SparkSession, dataPaths: Seq[String], tmpPath: String, hasHeader: Boolean): DataFrame = { val optionsMap = Map("header" -> hasHeader.toString) - val rawDf = CsvReader.readRaw(spark, dataPaths, optionsMap) + val rawDf_csv = CsvReader.readRaw(spark, dataPaths, optionsMap) + + rawDf_csv.write.mode("overwrite").parquet(tmpPath) + val rawDf = spark.read.parquet(tmpPath) + val perfDf = extractPerfColumns(rawDf) val acqDf = extractAcqColumns(rawDf) transform( @@ -566,4 +570,6 @@ object XGBoostETL extends Mortgage { spark ) } + + } From 60092f213fc98227dcb3f921ef3f241c75532de0 Mon Sep 17 00:00:00 2001 From: Suraj Aralihalli Date: Thu, 4 Aug 2022 21:02:06 -0400 Subject: [PATCH 12/17] fix bug GPUMain -> Main (#206) Signed-off-by: Suraj Aralihalli --- .../xgboost-examples/on-prem-cluster/standalone-scala.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/get-started/xgboost-examples/on-prem-cluster/standalone-scala.md b/docs/get-started/xgboost-examples/on-prem-cluster/standalone-scala.md index ccad55e47..c1b512b07 100644 --- a/docs/get-started/xgboost-examples/on-prem-cluster/standalone-scala.md +++ b/docs/get-started/xgboost-examples/on-prem-cluster/standalone-scala.md @@ -181,8 +181,8 @@ export SPARK_EXECUTOR_MEMORY=8g # example class to use export EXAMPLE_CLASS=com.nvidia.spark.examples.mortgage.Main -# or change to com.nvidia.spark.examples.taxi.GPUMain to run Taxi Xgboost benchmark -# or change to com.nvidia.spark.examples.agaricus.GPUMain to run Agaricus Xgboost benchmark +# or change to com.nvidia.spark.examples.taxi.Main to run Taxi Xgboost benchmark +# or change to com.nvidia.spark.examples.agaricus.Main to run Agaricus Xgboost benchmark # tree construction algorithm export TREE_METHOD=gpu_hist From eea48e95a40f90af78e595736995c440e323ed77 Mon Sep 17 00:00:00 2001 From: Tim Liu Date: Sat, 6 Aug 2022 13:39:09 +0800 Subject: [PATCH 13/17] Update Dataset and jars path for automatic tests (#205) Update cuspatial Dataset and jars path for automatic tests Signed-off-by: Tim Liu --- examples/UDF-Examples/Spark-cuSpatial/gpu-run.sh | 6 +++--- .../notebooks/cuspatial_sample_standalone.ipynb | 13 ++++++++----- 2 files changed, 11 insertions(+), 8 deletions(-) diff --git a/examples/UDF-Examples/Spark-cuSpatial/gpu-run.sh b/examples/UDF-Examples/Spark-cuSpatial/gpu-run.sh index ce1168a29..00fdc0c36 100755 --- a/examples/UDF-Examples/Spark-cuSpatial/gpu-run.sh +++ b/examples/UDF-Examples/Spark-cuSpatial/gpu-run.sh @@ -15,10 +15,10 @@ # # change to your spark folder -SPARK_HOME=/data/spark-3.2.0-bin-hadoop3.2 +SPARK_HOME=${SPARK_HOME:-/data/spark-3.2.0-bin-hadoop3.2} # change this path to your root path for the dataset -ROOT_PATH=/data/cuspatial_data +ROOT_PATH=${ROOT_PATH:-/data/cuspatial_data} # Extract the sample dataset in ../../datasets/cuspatial_data.tar.gz # Copy the polygons and points data into the root path or change the root path to where they are SHAPE_FILE_DIR=$ROOT_PATH/polygons @@ -31,7 +31,7 @@ rm -rf $DATA_OUT_PATH # the path to keep the jars of spark-rapids & spark-cuspatial JARS=$ROOT_PATH/jars -JARS_PATH=$JARS/rapids-4-spark_2.12-22.06.0.jar,$JARS/spark-cuspatial-22.08.0-SNAPSHOT.jar +JARS_PATH=${JARS_PATH:-$JARS/rapids-4-spark_2.12-22.06.0.jar,$JARS/spark-cuspatial-22.08.0-SNAPSHOT.jar} $SPARK_HOME/bin/spark-submit --master spark://$HOSTNAME:7077 \ --name "Gpu Spatial Join UDF" \ diff --git a/examples/UDF-Examples/Spark-cuSpatial/notebooks/cuspatial_sample_standalone.ipynb b/examples/UDF-Examples/Spark-cuSpatial/notebooks/cuspatial_sample_standalone.ipynb index 62b48cc74..6e2d4ae6f 100644 --- a/examples/UDF-Examples/Spark-cuSpatial/notebooks/cuspatial_sample_standalone.ipynb +++ b/examples/UDF-Examples/Spark-cuSpatial/notebooks/cuspatial_sample_standalone.ipynb @@ -8,8 +8,10 @@ "outputs": [], "source": [ "from pyspark.sql import SparkSession\n", + "import os\n", + "jarsPath = os.getenv(\"JARS_PATH\", \"/data/cuspatial_data/jars/rapids-4-spark_2.12-22.06.0.jar,/data/cuspatial_data/jars/spark-cuspatial-22.08.0-SNAPSHOT.jar\")\n", "spark = SparkSession.builder \\\n", - " .config(\"spark.jars\", \"/data/cuspatial_data/jars/rapids-4-spark_2.12-22.06.0.jar,/data/cuspatial_data/jars/spark-cuspatial-22.08.0-SNAPSHOT.jar\") \\\n", + " .config(\"spark.jars\", jarsPath) \\\n", " .config(\"spark.sql.adaptive.enabled\", \"false\") \\\n", " .config(\"spark.executor.memory\", \"20GB\") \\\n", " .config(\"spark.executor.cores\", \"6\") \\\n", @@ -28,11 +30,12 @@ "outputs": [], "source": [ "# prepare shape files\n", - "spark.sparkContext.addFile(\"/data/cuspatial_data/polygons/polygons.shp\")\n", - "spark.sparkContext.addFile(\"/data/cuspatial_data/polygons/polygons.shx\")\n", + "rootPath = os.getenv(\"ROOT_PATH\", \"/data/cuspatial_data\")\n", + "spark.sparkContext.addFile(rootPath + \"/polygons/polygons.shp\")\n", + "spark.sparkContext.addFile(rootPath + \"/polygons/polygons.shx\")\n", "\n", - "inputPath = \"/data/cuspatial_data/points/\"\n", - "outputPath = \"/data/cuspatial_data/output/\"" + "inputPath = rootPath + \"/points/\"\n", + "outputPath = rootPath + \"/output/\"" ] }, { From ff38ff75d51936a92b575389f83227b83b963d22 Mon Sep 17 00:00:00 2001 From: Tim Liu Date: Tue, 9 Aug 2022 19:50:47 +0800 Subject: [PATCH 14/17] Update Mortgage datasets and notebook tests dataset path (#203) * Update Mortgage datasets and notebook tests dateset path Suppose there are mortgage/taxi/agaricus sample datasets under the /data(DATA_ROOT) path under customers test cases. Append mortgage/taxi/agaricus to "DATA_ROOT" dir, then we would not mess up them together, Otherwise "input" subdir under "DATA_ROOT" would contains all the mortgage/taxi/agaricus datasets, Or we need to set "DATA_ROOT" diff values one by one pointing to diff subdirs for each of mortgage/taxi/agaricus Signed-off-by: Tim Liu * Remove Fannie Mae dataset --- .../notebooks/python/MortgageETL+XGBoost.ipynb | 11 +++++------ .../mortgage/notebooks/python/MortgageETL.ipynb | 12 ++++++------ .../notebooks/python/cv-mortgage-gpu.ipynb | 6 +++--- .../mortgage/notebooks/python/mortgage-gpu.ipynb | 10 +++++----- .../mortgage/notebooks/scala/mortgage-ETL.ipynb | 14 +++++++------- .../mortgage/notebooks/scala/mortgage-gpu.ipynb | 12 ++++++------ .../scala/mortgage_gpu_crossvalidation.ipynb | 6 +++--- .../taxi/notebooks/scala/taxi-ETL.ipynb | 4 ++-- 8 files changed, 37 insertions(+), 38 deletions(-) diff --git a/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL+XGBoost.ipynb b/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL+XGBoost.ipynb index 2b3cfd98e..83adaf96a 100644 --- a/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL+XGBoost.ipynb +++ b/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL+XGBoost.ipynb @@ -53,10 +53,9 @@ "outputs": [], "source": [ "# The input path of dataset\n", - "# dataRoot = os.getenv(\"DATA_ROOT\", \"/data\")\n", - "dataRoot = os.getenv(\"DATA_ROOT\", \"/mortgage\")\n", - "orig_raw_path = dataRoot + \"/input/\"\n", - "orig_raw_path_csv2parquet = dataRoot + \"/output/csv2parquet/\"" + "dataRoot = os.getenv(\"DATA_ROOT\", \"/data\")\n", + "orig_raw_path = dataRoot + \"/mortgage/input/\"" + "orig_raw_path_csv2parquet = dataRoot + \"/mortgage/output/csv2parquet/\"" ] }, { @@ -81,9 +80,9 @@ "# Set False, the dataset after ETL will be directly used in XGBoost train and transform\n", "\n", "is_save_dataset=True\n", - "output_path_data=dataRoot + \"/output/data/\"\n", + "output_path_data=dataRoot + \"/mortgage/output/data/\"\n", "# the path to save the xgboost model\n", - "output_path_model=dataRoot + \"/output/model/\"" + "output_path_model=dataRoot + \"/mortgage/output/model/\"" ] }, { diff --git a/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL.ipynb b/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL.ipynb index 7f580740d..abf1fc761 100644 --- a/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL.ipynb +++ b/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL.ipynb @@ -733,8 +733,8 @@ "outputs": [], "source": [ "# You need to update them to your real paths!\n", - "dataRoot = os.getenv(\"DATA_ROOT\", \"/mortgage\")\n", - "orig_raw_path = dataRoot + '/input/'" + "dataRoot = os.getenv(\"DATA_ROOT\", \"/data\")\n", + "orig_raw_path = dataRoot + '/mortgage/input/'" ] }, { @@ -750,10 +750,10 @@ "metadata": {}, "outputs": [], "source": [ - "output_path = dataRoot + '/output/data/'\n", - "output_csv2parquet = dataRoot + '/output/csv2parquet/'\n", - "output_path_train = dataRoot + '/output/train/'\n", - "output_path_eval = dataRoot + '/output/eval/'\n", + "output_path = dataRoot + '/mortgage/output/data/'\n", + "output_csv2parquet = dataRoot + '/mortgage/output/csv2parquet/'\n", + "output_path_train = dataRoot + '/mortgage/output/train/'\n", + "output_path_eval = dataRoot + '/mortgage/output/eval/'\n", "save_train_eval_dataset = True" ] }, diff --git a/examples/XGBoost-Examples/mortgage/notebooks/python/cv-mortgage-gpu.ipynb b/examples/XGBoost-Examples/mortgage/notebooks/python/cv-mortgage-gpu.ipynb index 5249179af..07cffb6af 100644 --- a/examples/XGBoost-Examples/mortgage/notebooks/python/cv-mortgage-gpu.ipynb +++ b/examples/XGBoost-Examples/mortgage/notebooks/python/cv-mortgage-gpu.ipynb @@ -113,9 +113,9 @@ "features = [ x.name for x in schema if x.name != label ]\n", "\n", "# You need to update them to your real paths!\n", - "dataRoot = os.getenv(\"DATA_ROOT\", \"/mortgage\")\n", - "train_path = dataRoot + \"/train\"\n", - "eval_path = dataRoot + \"/eval\"\n", + "dataRoot = os.getenv(\"DATA_ROOT\", \"/data\")\n", + "train_path = dataRoot + \"/mortgage/train\"\n", + "eval_path = dataRoot + \"/mortgage/eval\"\n", "\n", "train_data = spark.read.parquet(train_path)\n", "trans_data = spark.read.parquet(eval_path)" diff --git a/examples/XGBoost-Examples/mortgage/notebooks/python/mortgage-gpu.ipynb b/examples/XGBoost-Examples/mortgage/notebooks/python/mortgage-gpu.ipynb index 597a2b3d3..b7fbbe2e9 100644 --- a/examples/XGBoost-Examples/mortgage/notebooks/python/mortgage-gpu.ipynb +++ b/examples/XGBoost-Examples/mortgage/notebooks/python/mortgage-gpu.ipynb @@ -117,9 +117,9 @@ "features = [ x.name for x in schema if x.name != label ]\n", "\n", "# You need to update them to your real paths!\n", - "dataRoot = os.getenv(\"DATA_ROOT\", \"/mortgage\")\n", - "train_path = dataRoot + \"/train\"\n", - "eval_path = dataRoot + \"/eval\"\n", + "dataRoot = os.getenv(\"DATA_ROOT\", \"/data\")\n", + "train_path = dataRoot + \"/mortgage/train\"\n", + "eval_path = dataRoot + \"/mortgage/eval\"\n", "\n", "train_data = reader.parquet(train_path)\n", "trans_data = reader.parquet(eval_path)" @@ -235,8 +235,8 @@ "metadata": {}, "outputs": [], "source": [ - "model.write().overwrite().save(dataRoot + '/model')\n", - "loaded_model = XGBoostClassificationModel().load(dataRoot + '/model')" + "model.write().overwrite().save(dataRoot + '/mortgage/model')\n", + "loaded_model = XGBoostClassificationModel().load(dataRoot + '/mortgage/model')" ] }, { diff --git a/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-ETL.ipynb b/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-ETL.ipynb index edad901b4..fb6018240 100644 --- a/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-ETL.ipynb +++ b/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-ETL.ipynb @@ -31,7 +31,7 @@ "\n", "```\n", "\n", - "### 5.Start Jupyter Notebook with spylon-kernal or toree\n", + "### 5.Start Jupyter Notebook with spylon-kernel or toree\n", "\n", "```\n", "$ jupyter notebook --allow-root --notebook-dir=${your-dir} --config=${your-configs}\n", @@ -72,10 +72,10 @@ "metadata": {}, "outputs": [], "source": [ - "val dataRoot = sys.env.getOrElse(\"DATA_ROOT\", \"/mortgage\")\n", - "val dataPath = dataRoot + \"/input\"\n", - "val outPath = dataRoot + \"/output\"\n", - "val output_csv2parquet = dataRoot + \"/output/csv2parquet/\"\n", + "val dataRoot = sys.env.getOrElse(\"DATA_ROOT\", \"/data\")\n", + "val dataPath = dataRoot + \"/mortgage/input\"\n", + "val outPath = dataRoot + \"/mortgage/output\"\n", + "val output_csv2parquet = dataRoot + \"/mortgage/output/csv2parquet/\"\n", "val saveTrainEvalDataset = true" ] }, @@ -1079,9 +1079,9 @@ ], "metadata": { "kernelspec": { - "display_name": "XGBoost4j-Spark_new2 - Scala", + "display_name": "XGBoost4j-Spark Scala", "language": "scala", - "name": "xgboost4j-spark_new2_scala" + "name": "XGBoost4j-Spark_scala" }, "language_info": { "codemirror_mode": "text/x-scala", diff --git a/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-gpu.ipynb b/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-gpu.ipynb index 8fa003648..bcf29e770 100644 --- a/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-gpu.ipynb +++ b/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-gpu.ipynb @@ -52,10 +52,10 @@ "outputs": [], "source": [ "// You need to update them to your real paths! The input data files is the output of mortgage-etl jobs\n", - "val dataRoot = sys.env.getOrElse(\"DATA_ROOT\", \"/mortgage\")\n", - "val trainPath = dataRoot + \"/train/\"\n", - "val evalPath = dataRoot + \"/eval/\"\n", - "val transPath = dataRoot + \"/eval/\"" + "val dataRoot = sys.env.getOrElse(\"DATA_ROOT\", \"/data\")\n", + "val trainPath = dataRoot + \"/mortgage/train/\"\n", + "val evalPath = dataRoot + \"/mortgage/eval/\"\n", + "val transPath = dataRoot + \"/mortgage/eval/\"" ] }, { @@ -564,9 +564,9 @@ } ], "source": [ - "xgbClassificationModel.write.overwrite.save(dataRoot + \"/model/\")\n", + "xgbClassificationModel.write.overwrite.save(dataRoot + \"/mortgage/model/\")\n", "\n", - "val modelFromDisk = XGBoostClassificationModel.load(dataRoot + \"/model/\")\n", + "val modelFromDisk = XGBoostClassificationModel.load(dataRoot + \"/mortgage/model/\")\n", "\n", "val (results2, _) = Benchmark.time(\"transform2\") {\n", " modelFromDisk.transform(transSet)\n", diff --git a/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage_gpu_crossvalidation.ipynb b/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage_gpu_crossvalidation.ipynb index d91c74dcf..e2de7c1ca 100644 --- a/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage_gpu_crossvalidation.ipynb +++ b/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage_gpu_crossvalidation.ipynb @@ -47,9 +47,9 @@ "outputs": [], "source": [ "// You need to update them to your real paths!\n", - "val dataRoot = sys.env.getOrElse(\"DATA_ROOT\", \"/mortgage\")\n", - "val trainParquetPath=dataRoot + \"/train\"\n", - "val evalParquetPath=dataRoot + \"/eval\"" + "val dataRoot = sys.env.getOrElse(\"DATA_ROOT\", \"/data\")\n", + "val trainParquetPath=dataRoot + \"/mortgage/train\"\n", + "val evalParquetPath=dataRoot + \"/mortgage/eval\"" ] }, { diff --git a/examples/XGBoost-Examples/taxi/notebooks/scala/taxi-ETL.ipynb b/examples/XGBoost-Examples/taxi/notebooks/scala/taxi-ETL.ipynb index 9532f1a1c..42b3913f6 100644 --- a/examples/XGBoost-Examples/taxi/notebooks/scala/taxi-ETL.ipynb +++ b/examples/XGBoost-Examples/taxi/notebooks/scala/taxi-ETL.ipynb @@ -30,7 +30,7 @@ "\n", "```\n", "\n", - "### 5.Start Jupyter Notebook with spylon-kernal or toree\n", + "### 5.Start Jupyter Notebook with spylon-kernel or toree\n", "\n", "```\n", "$ jupyter notebook --allow-root --notebook-dir=${your-dir} --config=${your-configs}\n", @@ -578,4 +578,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} From b401baaefe2606c14bbdb89188965eb367b1cf42 Mon Sep 17 00:00:00 2001 From: Tim Liu Date: Wed, 10 Aug 2022 13:18:26 +0800 Subject: [PATCH 15/17] Keep the paths of Mortgage consistent in ETL and its downstream jobs (#210) Signed-off-by: Tim Liu --- .../mortgage/notebooks/python/cv-mortgage-gpu.ipynb | 4 ++-- .../mortgage/notebooks/python/mortgage-gpu.ipynb | 4 ++-- .../mortgage/notebooks/scala/mortgage-ETL.ipynb | 5 +++-- .../mortgage/notebooks/scala/mortgage-gpu.ipynb | 6 +++--- .../notebooks/scala/mortgage_gpu_crossvalidation.ipynb | 4 ++-- 5 files changed, 12 insertions(+), 11 deletions(-) diff --git a/examples/XGBoost-Examples/mortgage/notebooks/python/cv-mortgage-gpu.ipynb b/examples/XGBoost-Examples/mortgage/notebooks/python/cv-mortgage-gpu.ipynb index 07cffb6af..94a682cef 100644 --- a/examples/XGBoost-Examples/mortgage/notebooks/python/cv-mortgage-gpu.ipynb +++ b/examples/XGBoost-Examples/mortgage/notebooks/python/cv-mortgage-gpu.ipynb @@ -114,8 +114,8 @@ "\n", "# You need to update them to your real paths!\n", "dataRoot = os.getenv(\"DATA_ROOT\", \"/data\")\n", - "train_path = dataRoot + \"/mortgage/train\"\n", - "eval_path = dataRoot + \"/mortgage/eval\"\n", + "train_path = dataRoot + \"/mortgage/output/train\"\n", + "eval_path = dataRoot + \"/mortgage/output/eval\"\n", "\n", "train_data = spark.read.parquet(train_path)\n", "trans_data = spark.read.parquet(eval_path)" diff --git a/examples/XGBoost-Examples/mortgage/notebooks/python/mortgage-gpu.ipynb b/examples/XGBoost-Examples/mortgage/notebooks/python/mortgage-gpu.ipynb index b7fbbe2e9..e2c64c15e 100644 --- a/examples/XGBoost-Examples/mortgage/notebooks/python/mortgage-gpu.ipynb +++ b/examples/XGBoost-Examples/mortgage/notebooks/python/mortgage-gpu.ipynb @@ -118,8 +118,8 @@ "\n", "# You need to update them to your real paths!\n", "dataRoot = os.getenv(\"DATA_ROOT\", \"/data\")\n", - "train_path = dataRoot + \"/mortgage/train\"\n", - "eval_path = dataRoot + \"/mortgage/eval\"\n", + "train_path = dataRoot + \"/mortgage/output/train\"\n", + "eval_path = dataRoot + \"/mortgage/output/eval\"\n", "\n", "train_data = reader.parquet(train_path)\n", "trans_data = reader.parquet(eval_path)" diff --git a/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-ETL.ipynb b/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-ETL.ipynb index fb6018240..05698f394 100644 --- a/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-ETL.ipynb +++ b/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-ETL.ipynb @@ -73,9 +73,10 @@ "outputs": [], "source": [ "val dataRoot = sys.env.getOrElse(\"DATA_ROOT\", \"/data\")\n", + "val dataOut = sys.env.getOrElse(\"DATA_OUT\", \"/data\")\n", "val dataPath = dataRoot + \"/mortgage/input\"\n", - "val outPath = dataRoot + \"/mortgage/output\"\n", - "val output_csv2parquet = dataRoot + \"/mortgage/output/csv2parquet/\"\n", + "val outPath = dataOut + \"/mortgage/output\"\n", + "val output_csv2parquet = dataOut + \"/mortgage/output/csv2parquet/\"\n", "val saveTrainEvalDataset = true" ] }, diff --git a/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-gpu.ipynb b/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-gpu.ipynb index bcf29e770..872e09c2d 100644 --- a/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-gpu.ipynb +++ b/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-gpu.ipynb @@ -53,9 +53,9 @@ "source": [ "// You need to update them to your real paths! The input data files is the output of mortgage-etl jobs\n", "val dataRoot = sys.env.getOrElse(\"DATA_ROOT\", \"/data\")\n", - "val trainPath = dataRoot + \"/mortgage/train/\"\n", - "val evalPath = dataRoot + \"/mortgage/eval/\"\n", - "val transPath = dataRoot + \"/mortgage/eval/\"" + "val trainPath = dataRoot + \"/mortgage/output/train/\"\n", + "val evalPath = dataRoot + \"/mortgage/output/eval/\"\n", + "val transPath = dataRoot + \"/mortgage/output/eval/\"" ] }, { diff --git a/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage_gpu_crossvalidation.ipynb b/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage_gpu_crossvalidation.ipynb index e2de7c1ca..812436087 100644 --- a/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage_gpu_crossvalidation.ipynb +++ b/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage_gpu_crossvalidation.ipynb @@ -48,8 +48,8 @@ "source": [ "// You need to update them to your real paths!\n", "val dataRoot = sys.env.getOrElse(\"DATA_ROOT\", \"/data\")\n", - "val trainParquetPath=dataRoot + \"/mortgage/train\"\n", - "val evalParquetPath=dataRoot + \"/mortgage/eval\"" + "val trainParquetPath=dataRoot + \"/mortgage/output/train\"\n", + "val evalParquetPath=dataRoot + \"/mortgage/output/eval\"" ] }, { From f661b76b72a0caeae24bf9d69f5fd5524ee3f7ee Mon Sep 17 00:00:00 2001 From: liyuan <84758614+nvliyuan@users.noreply.github.com> Date: Thu, 11 Aug 2022 14:47:57 +0800 Subject: [PATCH 16/17] fix notebook display issue (#212) Signed-off-by: liyuan Signed-off-by: liyuan --- .../mortgage/notebooks/python/MortgageETL+XGBoost.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL+XGBoost.ipynb b/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL+XGBoost.ipynb index 83adaf96a..63a11ccfd 100644 --- a/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL+XGBoost.ipynb +++ b/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL+XGBoost.ipynb @@ -54,7 +54,7 @@ "source": [ "# The input path of dataset\n", "dataRoot = os.getenv(\"DATA_ROOT\", \"/data\")\n", - "orig_raw_path = dataRoot + \"/mortgage/input/\"" + "orig_raw_path = dataRoot + \"/mortgage/input/\"", "orig_raw_path_csv2parquet = dataRoot + \"/mortgage/output/csv2parquet/\"" ] }, From da21bae99ff987f9a6ae5b0d203ef6d0ca780669 Mon Sep 17 00:00:00 2001 From: liyuan <84758614+nvliyuan@users.noreply.github.com> Date: Wed, 31 Aug 2022 15:50:18 +0800 Subject: [PATCH 17/17] update version and fix some document error, add more comments for running xgboost notebooks on GCP (#215) Signed-off-by: liyuan Signed-off-by: liyuan --- .../xgboost-examples/csp/databricks/databricks.md | 2 +- .../csp/databricks/generate-init-script-10.4.ipynb | 6 +++--- .../csp/databricks/generate-init-script.ipynb | 6 +++--- .../xgboost-examples/on-prem-cluster/kubernetes-scala.md | 2 +- .../prepare-package-data/preparation-python.md | 4 ++-- .../prepare-package-data/preparation-scala.md | 4 ++-- examples/ML+DL-Examples/Spark-cuML/pca/Dockerfile | 2 +- examples/ML+DL-Examples/Spark-cuML/pca/README.md | 4 ++-- .../micro-benchmarks/notebooks/micro-benchmarks-gpu.ipynb | 2 +- examples/UDF-Examples/RAPIDS-accelerated-UDFs/README.md | 2 +- examples/UDF-Examples/RAPIDS-accelerated-UDFs/pom.xml | 2 +- examples/UDF-Examples/Spark-cuSpatial/Dockerfile | 2 +- examples/UDF-Examples/Spark-cuSpatial/Dockerfile.awsdb | 2 +- examples/UDF-Examples/Spark-cuSpatial/README.md | 2 +- examples/UDF-Examples/Spark-cuSpatial/gpu-run.sh | 2 +- .../notebooks/cuspatial_sample_standalone.ipynb | 2 +- examples/UDF-Examples/Spark-cuSpatial/pom.xml | 2 +- .../Spark-cuSpatial/src/main/native/CMakeLists.txt | 2 +- examples/XGBoost-Examples/README.md | 7 ++++++- .../mortgage/notebooks/python/MortgageETL.ipynb | 4 ++-- .../mortgage/notebooks/scala/mortgage-ETL.ipynb | 4 ++-- .../XGBoost-Examples/taxi/notebooks/python/taxi-ETL.ipynb | 4 ++-- .../XGBoost-Examples/taxi/notebooks/scala/taxi-ETL.ipynb | 4 ++-- 23 files changed, 39 insertions(+), 34 deletions(-) diff --git a/docs/get-started/xgboost-examples/csp/databricks/databricks.md b/docs/get-started/xgboost-examples/csp/databricks/databricks.md index 073b1902e..c09576d0e 100644 --- a/docs/get-started/xgboost-examples/csp/databricks/databricks.md +++ b/docs/get-started/xgboost-examples/csp/databricks/databricks.md @@ -49,7 +49,7 @@ cluster. - [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 22.06.0 or later on Databricks 10.4 LTS ML. In this case use + 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. diff --git a/docs/get-started/xgboost-examples/csp/databricks/generate-init-script-10.4.ipynb b/docs/get-started/xgboost-examples/csp/databricks/generate-init-script-10.4.ipynb index 53d61c456..3be77a4b5 100644 --- a/docs/get-started/xgboost-examples/csp/databricks/generate-init-script-10.4.ipynb +++ b/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_2.12-22.06.0.jar https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/22.06.0/rapids-4-spark_2.12-22.06.0.jar\n", + "sudo wget -O rapids-4-spark_2.12-22.08.0.jar https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/22.08.0/rapids-4-spark_2.12-22.08.0.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", "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_2.12-1.6.1.jar /databricks/jars/\n", - "sudo cp /dbfs/FileStore/jars/rapids-4-spark_2.12-22.06.0.jar /databricks/jars/\n", + "sudo cp /dbfs/FileStore/jars/rapids-4-spark_2.12-22.08.0.jar /databricks/jars/\n", "sudo cp /dbfs/FileStore/jars/xgboost4j-spark-gpu_2.12-1.6.1.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_2.12-1.6.1.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-22.06/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-22.08/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')`" diff --git a/docs/get-started/xgboost-examples/csp/databricks/generate-init-script.ipynb b/docs/get-started/xgboost-examples/csp/databricks/generate-init-script.ipynb index e81611b43..540132062 100644 --- a/docs/get-started/xgboost-examples/csp/databricks/generate-init-script.ipynb +++ b/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_2.12-22.06.0.jar https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/22.06.0/rapids-4-spark_2.12-22.06.0.jar\n", + "sudo wget -O rapids-4-spark_2.12-22.08.0.jar https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/22.08.0/rapids-4-spark_2.12-22.08.0.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", "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_2.12-1.6.1.jar /databricks/jars/\n", - "sudo cp /dbfs/FileStore/jars/rapids-4-spark_2.12-22.06.0.jar /databricks/jars/\n", + "sudo cp /dbfs/FileStore/jars/rapids-4-spark_2.12-22.08.0.jar /databricks/jars/\n", "sudo cp /dbfs/FileStore/jars/xgboost4j-spark-gpu_2.12-1.6.1.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_2.12-1.6.1.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-22.06/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-22.08/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')`" diff --git a/docs/get-started/xgboost-examples/on-prem-cluster/kubernetes-scala.md b/docs/get-started/xgboost-examples/on-prem-cluster/kubernetes-scala.md index 97b133922..11d1fb4dd 100644 --- a/docs/get-started/xgboost-examples/on-prem-cluster/kubernetes-scala.md +++ b/docs/get-started/xgboost-examples/on-prem-cluster/kubernetes-scala.md @@ -40,7 +40,7 @@ export SPARK_DOCKER_IMAGE= export SPARK_DOCKER_TAG= pushd ${SPARK_HOME} -wget https://github.com/NVIDIA/spark-rapids-examples/raw/branch-22.06/dockerfile/Dockerfile +wget https://github.com/NVIDIA/spark-rapids-examples/raw/branch-22.08/dockerfile/Dockerfile # Optionally install additional jars into ${SPARK_HOME}/jars/ diff --git a/docs/get-started/xgboost-examples/prepare-package-data/preparation-python.md b/docs/get-started/xgboost-examples/prepare-package-data/preparation-python.md index 48558b409..6f511be5b 100644 --- a/docs/get-started/xgboost-examples/prepare-package-data/preparation-python.md +++ b/docs/get-started/xgboost-examples/prepare-package-data/preparation-python.md @@ -9,7 +9,7 @@ For simplicity export the location to these jars. All examples assume the packag * [XGBoost4j-Spark Package](https://repo1.maven.org/maven2/com/nvidia/xgboost4j-spark_3.0/1.4.2-0.3.0/) 2. Download the RAPIDS Accelerator for Apache Spark plugin jar - * [RAPIDS Spark Package](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/22.06.0/rapids-4-spark_2.12-22.06.0.jar) + * [RAPIDS Spark Package](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/22.08.0/rapids-4-spark_2.12-22.08.0.jar) ### Build XGBoost Python Examples @@ -26,7 +26,7 @@ You need to copy the dataset to `/opt/xgboost`. Use the following links to downl ``` bash export SPARK_XGBOOST_DIR=/opt/xgboost -export RAPIDS_JAR=${SPARK_XGBOOST_DIR}/rapids-4-spark_2.12-22.06.0.jar +export RAPIDS_JAR=${SPARK_XGBOOST_DIR}/rapids-4-spark_2.12-22.08.0.jar export XGBOOST4J_JAR=${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 diff --git a/docs/get-started/xgboost-examples/prepare-package-data/preparation-scala.md b/docs/get-started/xgboost-examples/prepare-package-data/preparation-scala.md index e2cd6daa5..e5bf88571 100644 --- a/docs/get-started/xgboost-examples/prepare-package-data/preparation-scala.md +++ b/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_2.12/22.06.0/rapids-4-spark_2.12-22.06.0.jar) + * [RAPIDS Spark Package](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/22.08.0/rapids-4-spark_2.12-22.08.0.jar) ### Build XGBoost Scala Examples @@ -22,6 +22,6 @@ You need to copy the dataset to `/opt/xgboost`. Use the following links to downl ``` bash export SPARK_XGBOOST_DIR=/opt/xgboost -export RAPIDS_JAR=${SPARK_XGBOOST_DIR}/rapids-4-spark_2.12-22.06.0.jar +export RAPIDS_JAR=${SPARK_XGBOOST_DIR}/rapids-4-spark_2.12-22.08.0.jar export SAMPLE_JAR=${SPARK_XGBOOST_DIR}/sample_xgboost_apps-0.2.3-jar-with-dependencies.jar ``` diff --git a/examples/ML+DL-Examples/Spark-cuML/pca/Dockerfile b/examples/ML+DL-Examples/Spark-cuML/pca/Dockerfile index 3f0ae80e8..ea40e1ed4 100644 --- a/examples/ML+DL-Examples/Spark-cuML/pca/Dockerfile +++ b/examples/ML+DL-Examples/Spark-cuML/pca/Dockerfile @@ -17,7 +17,7 @@ ARG CUDA_VER=11.5.1 FROM nvidia/cuda:${CUDA_VER}-devel-ubuntu20.04 -ARG BRANCH_VER=22.06 +ARG BRANCH_VER=22.08 RUN apt-get update RUN apt-get install -y wget ninja-build git diff --git a/examples/ML+DL-Examples/Spark-cuML/pca/README.md b/examples/ML+DL-Examples/Spark-cuML/pca/README.md index f844553b5..4c1d9e861 100644 --- a/examples/ML+DL-Examples/Spark-cuML/pca/README.md +++ b/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_2.12-22.06.0.jar](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/22.06.0/rapids-4-spark_2.12-22.06.0.jar) +[rapids-4-spark_2.12-22.08.0.jar](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/22.08.0/rapids-4-spark_2.12-22.08.0.jar) ## Sample code @@ -48,7 +48,7 @@ It is assumed that a Standalone Spark cluster has been set up, the `SPARK_MASTER ``` bash RAPIDS_ML_JAR=PATH_TO_rapids-4-spark-ml_2.12-22.02.0-cuda11.jar - PLUGIN_JAR=PATH_TO_rapids-4-spark_2.12-22.06.0.jar + PLUGIN_JAR=PATH_TO_rapids-4-spark_2.12-22.08.0.jar jupyter toree install \ --spark_home=${SPARK_HOME} \ diff --git a/examples/SQL+DF-Examples/micro-benchmarks/notebooks/micro-benchmarks-gpu.ipynb b/examples/SQL+DF-Examples/micro-benchmarks/notebooks/micro-benchmarks-gpu.ipynb index f37ae39fb..cc1d11331 100644 --- a/examples/SQL+DF-Examples/micro-benchmarks/notebooks/micro-benchmarks-gpu.ipynb +++ b/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_2.12-22.06.0.jar\")\n" + "RAPIDS_JAR = os.getenv(\"RAPIDS_JAR\", \"/your-path/rapids-4-spark_2.12-22.08.0.jar\")\n" ] }, { diff --git a/examples/UDF-Examples/RAPIDS-accelerated-UDFs/README.md b/examples/UDF-Examples/RAPIDS-accelerated-UDFs/README.md index d924f4b06..242719b2e 100644 --- a/examples/UDF-Examples/RAPIDS-accelerated-UDFs/README.md +++ b/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_2.12-22.06.0.jar](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/22.06.0/rapids-4-spark_2.12-22.06.0.jar) +[rapids-4-spark_2.12-22.08.0.jar](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/22.08.0/rapids-4-spark_2.12-22.08.0.jar) ### Launch a local mode Spark diff --git a/examples/UDF-Examples/RAPIDS-accelerated-UDFs/pom.xml b/examples/UDF-Examples/RAPIDS-accelerated-UDFs/pom.xml index d7b07f56b..95f18cbc4 100644 --- a/examples/UDF-Examples/RAPIDS-accelerated-UDFs/pom.xml +++ b/examples/UDF-Examples/RAPIDS-accelerated-UDFs/pom.xml @@ -37,7 +37,7 @@ cuda11 2.12 - 22.06.0 + 22.08.0 3.1.1 2.12.15 ${project.build.directory}/cpp-build diff --git a/examples/UDF-Examples/Spark-cuSpatial/Dockerfile b/examples/UDF-Examples/Spark-cuSpatial/Dockerfile index a37cdf22a..6d81a260c 100644 --- a/examples/UDF-Examples/Spark-cuSpatial/Dockerfile +++ b/examples/UDF-Examples/Spark-cuSpatial/Dockerfile @@ -39,7 +39,7 @@ RUN conda --version RUN conda install -c conda-forge openjdk=8 maven=3.8.1 -y # install cuDF dependency. -RUN conda install -c rapidsai -c nvidia -c conda-forge -c defaults libcuspatial=22.06 python=3.8 -y +RUN conda install -c rapidsai -c nvidia -c conda-forge -c defaults libcuspatial=22.08 python=3.8 -y RUN wget --quiet \ https://github.com/Kitware/CMake/releases/download/v3.21.3/cmake-3.21.3-linux-x86_64.tar.gz \ diff --git a/examples/UDF-Examples/Spark-cuSpatial/Dockerfile.awsdb b/examples/UDF-Examples/Spark-cuSpatial/Dockerfile.awsdb index b18ec97ad..98839d1ed 100644 --- a/examples/UDF-Examples/Spark-cuSpatial/Dockerfile.awsdb +++ b/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=22.06 +RUN conda install -c rapidsai-nightly -c nvidia -c conda-forge -c defaults libcuspatial=22.08 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 diff --git a/examples/UDF-Examples/Spark-cuSpatial/README.md b/examples/UDF-Examples/Spark-cuSpatial/README.md index 732fe527d..b90b34be7 100644 --- a/examples/UDF-Examples/Spark-cuSpatial/README.md +++ b/examples/UDF-Examples/Spark-cuSpatial/README.md @@ -86,7 +86,7 @@ Note: The docker env is just for building the jar, not for running the applicati 2. 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. 3. Download Spark RAPIDS JAR - * [Spark RAPIDS JAR v22.06.0](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/22.06.0/rapids-4-spark_2.12-22.06.0.jar) or above + * [Spark RAPIDS JAR v22.08.0](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/22.08.0/rapids-4-spark_2.12-22.08.0.jar) or above 4. 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-.jar` to `/data/cuspatial_data/jars/`. If you build the `spark-cuspatial-.jar` in docker, please copy the jar from docker to local: diff --git a/examples/UDF-Examples/Spark-cuSpatial/gpu-run.sh b/examples/UDF-Examples/Spark-cuSpatial/gpu-run.sh index 00fdc0c36..987a3ea52 100755 --- a/examples/UDF-Examples/Spark-cuSpatial/gpu-run.sh +++ b/examples/UDF-Examples/Spark-cuSpatial/gpu-run.sh @@ -31,7 +31,7 @@ rm -rf $DATA_OUT_PATH # the path to keep the jars of spark-rapids & spark-cuspatial JARS=$ROOT_PATH/jars -JARS_PATH=${JARS_PATH:-$JARS/rapids-4-spark_2.12-22.06.0.jar,$JARS/spark-cuspatial-22.08.0-SNAPSHOT.jar} +JARS_PATH=${JARS_PATH:-$JARS/rapids-4-spark_2.12-22.08.0.jar,$JARS/spark-cuspatial-22.08.0-SNAPSHOT.jar} $SPARK_HOME/bin/spark-submit --master spark://$HOSTNAME:7077 \ --name "Gpu Spatial Join UDF" \ diff --git a/examples/UDF-Examples/Spark-cuSpatial/notebooks/cuspatial_sample_standalone.ipynb b/examples/UDF-Examples/Spark-cuSpatial/notebooks/cuspatial_sample_standalone.ipynb index 6e2d4ae6f..f13889ed1 100644 --- a/examples/UDF-Examples/Spark-cuSpatial/notebooks/cuspatial_sample_standalone.ipynb +++ b/examples/UDF-Examples/Spark-cuSpatial/notebooks/cuspatial_sample_standalone.ipynb @@ -9,7 +9,7 @@ "source": [ "from pyspark.sql import SparkSession\n", "import os\n", - "jarsPath = os.getenv(\"JARS_PATH\", \"/data/cuspatial_data/jars/rapids-4-spark_2.12-22.06.0.jar,/data/cuspatial_data/jars/spark-cuspatial-22.08.0-SNAPSHOT.jar\")\n", + "jarsPath = os.getenv(\"JARS_PATH\", \"/data/cuspatial_data/jars/rapids-4-spark_2.12-22.08.0.jar,/data/cuspatial_data/jars/spark-cuspatial-22.08.0-SNAPSHOT.jar\")\n", "spark = SparkSession.builder \\\n", " .config(\"spark.jars\", jarsPath) \\\n", " .config(\"spark.sql.adaptive.enabled\", \"false\") \\\n", diff --git a/examples/UDF-Examples/Spark-cuSpatial/pom.xml b/examples/UDF-Examples/Spark-cuSpatial/pom.xml index 811e1d534..6f8f08a49 100644 --- a/examples/UDF-Examples/Spark-cuSpatial/pom.xml +++ b/examples/UDF-Examples/Spark-cuSpatial/pom.xml @@ -30,7 +30,7 @@ 1.8 1.8 8 - 22.06.0 + 22.08.0 2.12 3.2.0 ${project.build.directory}/cpp-build diff --git a/examples/UDF-Examples/Spark-cuSpatial/src/main/native/CMakeLists.txt b/examples/UDF-Examples/Spark-cuSpatial/src/main/native/CMakeLists.txt index 0ab52a7c8..40eff8c31 100755 --- a/examples/UDF-Examples/Spark-cuSpatial/src/main/native/CMakeLists.txt +++ b/examples/UDF-Examples/Spark-cuSpatial/src/main/native/CMakeLists.txt @@ -16,7 +16,7 @@ cmake_minimum_required(VERSION 3.20.1 FATAL_ERROR) -project(SPATIALUDJNI VERSION 22.06.0 LANGUAGES C CXX CUDA) +project(SPATIALUDJNI VERSION 22.08.0 LANGUAGES C CXX CUDA) ################################################################################################### # - build type ------------------------------------------------------------------------------------ diff --git a/examples/XGBoost-Examples/README.md b/examples/XGBoost-Examples/README.md index 4d064c8d7..f4e654530 100644 --- a/examples/XGBoost-Examples/README.md +++ b/examples/XGBoost-Examples/README.md @@ -6,7 +6,7 @@ For PySpark based XGBoost, please refer to the [Spark-RAPIDS-examples 22.04 bran uses [NVIDIA’s Spark XGBoost version](https://repo1.maven.org/maven2/com/nvidia/xgboost4j-spark_3.0/1.4.2-0.3.0/). Most data scientists spend a lot of time not only on Training models but also processing the large amounts of data needed to train these models. -As you can see below, XGBoost training on GPUs can be upto 7X and data processing using +As you can see below, XGBoost training on GPUs can be up to 10X and data processing using RAPIDS Accelerator can also be accelerated with an end-to-end speed-up of 7X on GPU compared to CPU. In the public cloud, better performance can lead to significantly lower costs as demonstrated in this [blog](https://developer.nvidia.com/blog/gpu-accelerated-spark-xgboost/). @@ -96,3 +96,8 @@ otherwise the customized CrossValidator may schedule more than 1 xgboost trainin For XGBoost job, if the number of shuffle stage tasks before training is less than the num_worker, the training tasks will be scheduled to run on part of nodes instead of all nodes due to Spark Data Locality feature. The workaround is to increase the partitions of the shuffle stage by setting `spark.sql.files.maxPartitionBytes=RightNum`. +If you are running XGBoost scala notebooks on Dataproc, please make sure to update below configs to avoid job failure: +``` +spark.dynamicAllocation.enabled=false +spark.task.resource.gpu.amount=1 +``` \ No newline at end of file diff --git a/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL.ipynb b/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL.ipynb index abf1fc761..433d35880 100644 --- a/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL.ipynb +++ b/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL.ipynb @@ -9,7 +9,7 @@ "Dataset is derived from Fannie Mae’s [Single-Family Loan Performance Data](http://www.fanniemae.com/portal/funding-the-market/data/loan-performance-data.html) with all rights reserved by Fannie Mae. Refer to these [instructions](https://github.com/NVIDIA/spark-rapids-examples/blob/branch-22.08/docs/get-started/xgboost-examples/dataset/mortgage.md) to download the dataset.\n", "\n", "### 2. Download needed jars\n", - "* [rapids-4-spark_2.12-22.06.0.jar](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/22.06.0/rapids-4-spark_2.12-22.06.0.jar)\n", + "* [rapids-4-spark_2.12-22.08.0.jar](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/22.08.0/rapids-4-spark_2.12-22.08.0.jar)\n", "\n", "\n", "### 3. Start Spark Standalone\n", @@ -17,7 +17,7 @@ "\n", "### 4. Add ENV\n", "```\n", - "$ export SPARK_JARS=rapids-4-spark_2.12-22.06.0.jar\n", + "$ export SPARK_JARS=rapids-4-spark_2.12-22.08.0.jar\n", "$ export PYSPARK_DRIVER_PYTHON=jupyter \n", "$ export PYSPARK_DRIVER_PYTHON_OPTS=notebook\n", "```\n", diff --git a/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-ETL.ipynb b/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-ETL.ipynb index 05698f394..4a9bee1ec 100644 --- a/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-ETL.ipynb +++ b/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-ETL.ipynb @@ -20,14 +20,14 @@ "Refer to these [instructions](https://github.com/NVIDIA/spark-rapids-examples/blob/branch-22.08/docs/get-started/xgboost-examples/dataset/mortgage.md) to download the dataset.\n", "\n", "### 2. Download needed jars\n", - "* [rapids-4-spark_2.12-22.06.0.jar](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/22.06.0/rapids-4-spark_2.12-22.06.0.jar)\n", + "* [rapids-4-spark_2.12-22.08.0.jar](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/22.08.0/rapids-4-spark_2.12-22.08.0.jar)\n", "\n", "### 3. Start Spark Standalone\n", "Before Running the script, please setup Spark standalone mode\n", "\n", "### 4. Add ENV\n", "```\n", - "$ export SPARK_JARS=rapids-4-spark_2.12-22.06.0.jar\n", + "$ export SPARK_JARS=rapids-4-spark_2.12-22.08.0.jar\n", "\n", "```\n", "\n", diff --git a/examples/XGBoost-Examples/taxi/notebooks/python/taxi-ETL.ipynb b/examples/XGBoost-Examples/taxi/notebooks/python/taxi-ETL.ipynb index 7abeac750..171f47f4c 100644 --- a/examples/XGBoost-Examples/taxi/notebooks/python/taxi-ETL.ipynb +++ b/examples/XGBoost-Examples/taxi/notebooks/python/taxi-ETL.ipynb @@ -19,14 +19,14 @@ "All data could be found at https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page\n", "\n", "### 2. Download needed jars\n", - "* [rapids-4-spark_2.12-22.06.0.jar](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/22.06.0/rapids-4-spark_2.12-22.06.0.jar)\n", + "* [rapids-4-spark_2.12-22.08.0.jar](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/22.08.0/rapids-4-spark_2.12-22.08.0.jar)\n", "\n", "### 3. Start Spark Standalone\n", "Before running the script, please setup Spark standalone mode\n", "\n", "### 4. Add ENV\n", "```\n", - "$ export SPARK_JARS=rapids-4-spark_2.12-22.06.0.jar\n", + "$ export SPARK_JARS=rapids-4-spark_2.12-22.08.0.jar\n", "$ export PYSPARK_DRIVER_PYTHON=jupyter \n", "$ export PYSPARK_DRIVER_PYTHON_OPTS=notebook\n", "```\n", diff --git a/examples/XGBoost-Examples/taxi/notebooks/scala/taxi-ETL.ipynb b/examples/XGBoost-Examples/taxi/notebooks/scala/taxi-ETL.ipynb index 42b3913f6..0f14cdc65 100644 --- a/examples/XGBoost-Examples/taxi/notebooks/scala/taxi-ETL.ipynb +++ b/examples/XGBoost-Examples/taxi/notebooks/scala/taxi-ETL.ipynb @@ -19,14 +19,14 @@ "All data could be found at https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page\n", "\n", "### 2. Download needed jar\n", - "* [rapids-4-spark_2.12-22.06.0.jar](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/22.06.0/rapids-4-spark_2.12-22.06.0.jar)\n", + "* [rapids-4-spark_2.12-22.08.0.jar](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/22.08.0/rapids-4-spark_2.12-22.08.0.jar)\n", "\n", "### 3. Start Spark Standalone\n", "Before running the script, please setup Spark standalone mode\n", "\n", "### 4. Add ENV\n", "```\n", - "$ export SPARK_JARS=rapids-4-spark_2.12-22.06.0.jar\n", + "$ export SPARK_JARS=rapids-4-spark_2.12-22.08.0.jar\n", "\n", "```\n", "\n",