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Getting started with RAPIDS Accelerator on GCP Dataproc

Google Cloud Dataproc is Google Cloud’s fully managed Apache Spark and Hadoop service. The quick start guide will go through:

The advanced guide will walk through the steps to:

Quick Start Prerequisites

  • gcloud CLI is installed: https://cloud.google.com/sdk/docs/install
  • python 3.8+
  • pip install spark-rapids-user-tools

Qualify CPU Workloads for GPU Acceleration

The qualification tool is launched on a Dataproc cluster that has applications that have already run. The tool will output the applications recommended for acceleration along with estimated speed-up and cost saving metrics. Additionally, it will provide information on how to launch a GPU- accelerated cluster to take advantage of the speed-up and cost savings.

Usage: spark_rapids_dataproc qualification --cluster <cluster-name> --region <region>

Help (to see all options available): spark_rapids_dataproc qualification --help

Example output:

+----+------------+--------------------------------+----------------------+-----------------+-----------------+---------------+-----------------+
|    | App Name   | App ID                         | Recommendation       |   Estimated GPU |   Estimated GPU |           App |   Estimated GPU |
|    |            |                                |                      |         Speedup |     Duration(s) |   Duration(s) |      Savings(%) |
|----+------------+--------------------------------+----------------------+-----------------+-----------------+---------------+-----------------|
|  0 | query24    | application_1664888311321_0011 | Strongly Recommended |            3.49 |          257.18 |        897.68 |           59.70 |
|  1 | query78    | application_1664888311321_0009 | Strongly Recommended |            3.35 |          113.89 |        382.35 |           58.10 |
|  2 | query23    | application_1664888311321_0010 | Strongly Recommended |            3.08 |          325.77 |       1004.28 |           54.37 |
|  3 | query64    | application_1664888311321_0008 | Strongly Recommended |            2.91 |          150.81 |        440.30 |           51.82 |
|  4 | query50    | application_1664888311321_0003 | Recommended          |            2.47 |          101.54 |        250.95 |           43.08 |
|  5 | query16    | application_1664888311321_0005 | Recommended          |            2.36 |          106.33 |        251.95 |           40.63 |
|  6 | query38    | application_1664888311321_0004 | Recommended          |            2.29 |           67.37 |        154.33 |           38.59 |
|  7 | query87    | application_1664888311321_0006 | Recommended          |            2.25 |           75.67 |        170.69 |           37.64 |
|  8 | query51    | application_1664888311321_0002 | Recommended          |            1.53 |           53.94 |         82.63 |            8.18 |
+----+------------+--------------------------------+----------------------+-----------------+-----------------+---------------+-----------------+
To launch a GPU-accelerated cluster with Spark RAPIDS, add the following to your cluster creation script:
        --initialization-actions=gs://goog-dataproc-initialization-actions-us-central1/gpu/install_gpu_driver.sh,gs://goog-dataproc-initialization-actions-us-central1/rapids/rapids.sh \
        --worker-accelerator type=nvidia-tesla-t4,count=2 \
        --metadata gpu-driver-provider="NVIDIA" \
        --metadata rapids-runtime=SPARK \
        --cuda-version=11.5

Bootstrap GPU Cluster with Optimized Settings

The bootstrap tool will apply optimized settings for the RAPIDS Accelerator on Apache Spark on a GPU cluster for Dataproc. The tool will fetch the characteristics of the cluster – including number of workers, worker cores, worker memory, and GPU accelerator type and count. It will use the cluster properties to then determine the optimal settings for running GPU-accelerated Spark applications.

Usage: spark_rapids_dataproc bootstrap --cluster <cluster-name> --region <region>

Help (to see all options available): spark_rapids_dataproc bootstrap --help

Example output:

##### BEGIN : RAPIDS bootstrap settings for gpu-cluster
spark.executor.cores=16
spark.executor.memory=32768m
spark.executor.memoryOverhead=7372m
spark.rapids.sql.concurrentGpuTasks=2
spark.rapids.memory.pinnedPool.size=4096m
spark.sql.files.maxPartitionBytes=512m
spark.task.resource.gpu.amount=0.0625
##### END : RAPIDS bootstrap settings for gpu-cluster

A detailed description for bootstrap settings with usage information is available in the RAPIDS Accelerator for Apache Spark Configuration and Spark Configuration page.

Tune Applications on GPU Cluster

Once Spark applications have been run on the GPU cluster, the profiling tool can be run to analyze the event logs of the applications to determine if more optimal settings should be configured. The tool will output a per-application set of config settings to be adjusted for enhanced performance.

Usage: spark_rapids_dataproc profiling --cluster <cluster-name> --region <region>

Help (to see all options available): spark_rapids_dataproc profiling --help

Example output:

+--------------------------------+--------------------------------------------------+--------------------------------------------------------------------------------------------------+
| App ID                         | Recommendations                                  | Comments                                                                                         |
+================================+==================================================+==================================================================================================+
| application_1664894105643_0011 | --conf spark.executor.cores=16                   | - 'spark.task.resource.gpu.amount' was not set.                                                  |
|                                | --conf spark.executor.memory=32768m              | - 'spark.rapids.sql.concurrentGpuTasks' was not set.                                             |
|                                | --conf spark.executor.memoryOverhead=7372m       | - 'spark.rapids.memory.pinnedPool.size' was not set.                                             |
|                                | --conf spark.rapids.memory.pinnedPool.size=4096m | - 'spark.executor.memoryOverhead' was not set.                                                   |
|                                | --conf spark.rapids.sql.concurrentGpuTasks=2     | - 'spark.sql.files.maxPartitionBytes' was not set.                                               |
|                                | --conf spark.sql.files.maxPartitionBytes=1571m   | - 'spark.sql.shuffle.partitions' was not set.                                                    |
|                                | --conf spark.sql.shuffle.partitions=200          |                                                                                                  |
|                                | --conf spark.task.resource.gpu.amount=0.0625     |                                                                                                  |
+--------------------------------+--------------------------------------------------+--------------------------------------------------------------------------------------------------+
| application_1664894105643_0002 | --conf spark.executor.cores=16                   | - 'spark.task.resource.gpu.amount' was not set.                                                  |
|                                | --conf spark.executor.memory=32768m              | - 'spark.rapids.sql.concurrentGpuTasks' was not set.                                             |
|                                | --conf spark.executor.memoryOverhead=7372m       | - 'spark.rapids.memory.pinnedPool.size' was not set.                                             |
|                                | --conf spark.rapids.memory.pinnedPool.size=4096m | - 'spark.executor.memoryOverhead' was not set.                                                   |
|                                | --conf spark.rapids.sql.concurrentGpuTasks=2     | - 'spark.sql.files.maxPartitionBytes' was not set.                                               |
|                                | --conf spark.sql.files.maxPartitionBytes=3844m   | - 'spark.sql.shuffle.partitions' was not set.                                                    |
|                                | --conf spark.sql.shuffle.partitions=200          |                                                                                                  |
|                                | --conf spark.task.resource.gpu.amount=0.0625     |                                                                                                  |
+--------------------------------+--------------------------------------------------+--------------------------------------------------------------------------------------------------+

Diagnose GPU Cluster

The diagnostic tool can be run to check a GPU cluster with RAPIDS Accelerator for Apache Spark is healthy and ready for Spark jobs, such as checking the version of installed NVIDIA driver, cuda-toolkit, RAPIDS Accelerator and running Spark test jobs etc. This tool also can be used by the frontline support team for basic diagnostic and troubleshooting before escalating to NVIDIA RAPIDS Accelerator for Apache Spark engineering team.

Usage: spark_rapids_dataproc diagnostic --cluster <cluster-name> --region <region>

Help (to see all options available): spark_rapids_dataproc diagnostic --help

Example output:

*** Running diagnostic function "nv_driver" ***
Warning: Permanently added 'compute.9009746126288801979' (ECDSA) to the list of known hosts.
Fri Oct 14 05:17:55 2022
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 460.106.00   Driver Version: 460.106.00   CUDA Version: 11.2     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  Tesla T4            On   | 00000000:00:04.0 Off |                    0 |
| N/A   48C    P8    10W /  70W |      0MiB / 15109MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+
NVRM version: NVIDIA UNIX x86_64 Kernel Module  460.106.00  Tue Sep 28 12:05:58 UTC 2021
GCC version:  gcc version 7.5.0 (Ubuntu 7.5.0-3ubuntu1~18.04)
Connection to 34.68.242.247 closed.
*** Check "nv_driver": PASS ***
*** Running diagnostic function "nv_driver" ***
Warning: Permanently added 'compute.6788823627063447738' (ECDSA) to the list of known hosts.
Fri Oct 14 05:18:02 2022
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 460.106.00   Driver Version: 460.106.00   CUDA Version: 11.2     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  Tesla T4            On   | 00000000:00:04.0 Off |                    0 |
| N/A   35C    P8     9W /  70W |      0MiB / 15109MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+
NVRM version: NVIDIA UNIX x86_64 Kernel Module  460.106.00  Tue Sep 28 12:05:58 UTC 2021
GCC version:  gcc version 7.5.0 (Ubuntu 7.5.0-3ubuntu1~18.04)
Connection to 34.123.223.104 closed.
*** Check "nv_driver": PASS ***
*** Running diagnostic function "cuda_version" ***
Connection to 34.68.242.247 closed.
found cuda major version: 11
*** Check "cuda_version": PASS ***
*** Running diagnostic function "cuda_version" ***
Connection to 34.123.223.104 closed.
found cuda major version: 11
*** Check "cuda_version": PASS ***
...
********************************************************************************
Overall check result: PASS

Please note that the diagnostic tool supports the following:

  • Dataproc 2.0 with image of Debian 10 or Ubuntu 18.04 (Rocky8 support is coming soon)
  • GPU cluster that must have 1 worker node at least. Single node cluster (1 master, 0 workers) is not supported

Create a Dataproc Cluster Accelerated by GPUs

You can use Cloud Shell to execute shell commands that will create a Dataproc cluster. Cloud Shell contains command line tools for interacting with Google Cloud Platform, including gcloud and gsutil. Alternatively, you can install GCloud SDK on your machine. From the Cloud Shell, users will need to enable services within your project. Enable the Compute and Dataproc APIs in order to access Dataproc, and enable the Storage API as you’ll need a Google Cloud Storage bucket to house your data. This may take several minutes.

gcloud services enable compute.googleapis.com
gcloud services enable dataproc.googleapis.com
gcloud services enable storage-api.googleapis.com

After the command line environment is setup, log in to your GCP account. You can now create a Dataproc cluster. Dataproc supports multiple different GPU types depending on your use case. Generally, T4 is a good option for use with the RAPIDS Accelerator for Spark. We also support MIG on the Ampere architecture GPUs like the A100. Using MIG you can request an A100 and split it up into multiple different compute instances and it runs like you have multiple separate GPUs.

The example configurations below will allow users to run any of the notebook demos on GCP. Adjust the sizes and number of GPU based on your needs.

The script below will initialize with the following:

  • GPU Driver and RAPIDS Acclerator for Apache Spark through initialization actions (please note it takes up to 1 week for the latest init script to be merged into the GCP Dataproc public GCS bucket)

    To make changes to example configuration, make a copy of spark-rapids.sh and add the RAPIDS Accelerator related parameters according to tuning guide and modify the --initialization-actions parameter to point to the updated version.

  • Configuration for GPU scheduling and isolation
  • Local SSD is recommended for Spark scratch space to improve IO
  • Component gateway enabled for accessing Web UIs hosted on the cluster

Create a Dataproc Cluster using T4’s

  • One 16-core master node and 5 32-core worker nodes
  • Four NVIDIA T4 for each worker node
    export REGION=[Your Preferred GCP Region]
    export GCS_BUCKET=[Your GCS Bucket]
    export CLUSTER_NAME=[Your Cluster Name]
    export NUM_GPUS=2
    export NUM_WORKERS=4

gcloud dataproc clusters create $CLUSTER_NAME  \
    --region=$REGION \
    --image-version=2.0-ubuntu18 \
    --master-machine-type=n1-standard-16 \
    --num-workers=$NUM_WORKERS \
    --worker-accelerator=type=nvidia-tesla-t4,count=$NUM_GPUS \
    --worker-machine-type=n1-highmem-32\
    --num-worker-local-ssds=4 \
    --initialization-actions=gs://goog-dataproc-initialization-actions-${REGION}/spark-rapids/spark-rapids.sh \
    --optional-components=JUPYTER,ZEPPELIN \
    --metadata=rapids-runtime=SPARK \
    --bucket=$GCS_BUCKET \
    --enable-component-gateway \
    --subnet=default

Explanation of parameters:

  • NUM_GPUS = number of GPUs to attach to each worker node in the cluster
  • NUM_WORKERS = number of Spark worker nodes in the cluster

This takes around 10-15 minutes to complete. You can navigate to the Dataproc clusters tab in the Google Cloud Console to see the progress.

Dataproc Cluster

If you’d like to further accelerate init time to 4-5 minutes, create a custom Dataproc image using this guide.

Create a Dataproc Cluster using MIG with A100’s

  • One 16-core master node and 5 12-core worker nodes
  • 1 NVIDIA A100 for each worker node, split into 2 MIG instances using instance profile 3g.20gb.
    export REGION=[Your Preferred GCP Region]
    export ZONE=[Your Preferred GCP Zone]
    export GCS_BUCKET=[Your GCS Bucket]
    export CLUSTER_NAME=[Your Cluster Name]
    export NUM_GPUS=1
    export NUM_WORKERS=4

gcloud dataproc clusters create $CLUSTER_NAME  \
    --region=$REGION \
    --zone=$ZONE \
    --image-version=2.0-ubuntu18 \
    --master-machine-type=n1-standard-16 \
    --num-workers=$NUM_WORKERS \
    --worker-accelerator=type=nvidia-tesla-a100,count=$NUM_GPUS \
    --worker-machine-type=a2-highgpu-1g \
    --num-worker-local-ssds=4 \
    --initialization-actions=gs://goog-dataproc-initialization-actions-${REGION}/spark-rapids/spark-rapids.sh \
    --metadata=startup-script-url=gs://goog-dataproc-initialization-actions-${REGION}/gpu/mig.sh \
    --optional-components=JUPYTER,ZEPPELIN \
    --metadata=rapids-runtime=SPARK \
    --bucket=$GCS_BUCKET \
    --enable-component-gateway \
    --subnet=default

Explanation of parameters:

  • NUM_GPUS = number of GPUs to attach to each worker node in the cluster
  • NUM_WORKERS = number of Spark worker nodes in the cluster

To change the MIG instance profile you can specify either the profile id or profile name via the metadata parameter MIG_CGI. Below is an example of using a profile name and a profile id.

    --metadata=^:^MIG_CGI='3g.20gb,9'

This may take around 10-15 minutes to complete. You can navigate to the Dataproc clusters tab in the Google Cloud Console to see the progress.

Dataproc Cluster

If you’d like to further accelerate init time to 4-5 minutes, create a custom Dataproc image using this guide.

Cluster creation troubleshooting

If you encounter an error related to GPUs not being available because of your account quotas, please go to this page for updating your quotas: Quotas and limits.

If you encounter an error related to GPUs not available in the specific region or zone, you will need to update the REGION or ZONE parameter in the cluster creation command.

Run PySpark or Scala Notebook on a Dataproc Cluster Accelerated by GPUs

To use notebooks with a Dataproc cluster, click on the cluster name under the Dataproc cluster tab and navigate to the “Web Interfaces” tab. Under “Web Interfaces”, click on the JupyterLab or Jupyter link. Download the sample Mortgage ETL on GPU Jupyter Notebook and upload it to Jupyter.

To get example data for the sample notebook, please refer to these instructions. Download the desired data, decompress it, and upload the csv files to a GCS bucket.

Dataproc Web Interfaces

The sample notebook will transcode the CSV files into Parquet files before running an ETL query that prepares the dataset for training. The ETL query splits the data, saving 20% of the data in a seaprate GCS location training for evaluation. Using the default notebook configuration the first stage should take ~110 seconds (1/3 of CPU execution time with same config) and the second stage takes ~170 seconds (1/7 of CPU execution time with same config). The notebook depends on the pre-compiled Spark RAPIDS SQL plugin which is pre-downloaded by the GCP Dataproc RAPIDS init script.

Once the data is prepared, we use the Mortgage XGBoost4j Scala Notebook in Dataproc’s jupyter notebook to execute the training job on GPUs. Scala based XGBoost examples use DLMC XGBoost. For a PySpark based XGBoost example, please refer to Spark-RAPIDS-examples that make sure the required libraries are installed.

The training time should be around 680 seconds (1/7 of CPU execution time with same config). This is shown under cell:

// Start training
println("\n------ Training ------")
val (xgbClassificationModel, _) = benchmark("train") {
  xgbClassifier.fit(trainSet)
}

Submit Spark jobs to a Dataproc Cluster Accelerated by GPUs

Similar to spark-submit for on-prem clusters, Dataproc supports submitting Spark applications to Dataproc clusters. The previous mortgage examples are also available as a spark application.

Follow these instructions to Build the xgboost-example jars. Upload the sample_xgboost_apps-${VERSION}-SNAPSHOT-jar-with-dependencies.jar to a GCS bucket by dragging and dropping the jar file from your local machine into the GCS web console or by running:

gsutil cp aggregator/target/sample_xgboost_apps-${VERSION}-SNAPSHOT-jar-with-dependencies.jar gs://${GCS_BUCKET}/scala/

Submit the Spark XGBoost application to dataproc using the following command:

export REGION=[Your Preferred GCP Region]
export GCS_BUCKET=[Your GCS Bucket]
export CLUSTER_NAME=[Your Cluster Name]
export VERSION=[Your jar version]
export SPARK_NUM_EXECUTORS=20
export SPARK_EXECUTOR_MEMORY=20G
export SPARK_EXECUTOR_MEMORYOVERHEAD=16G
export SPARK_NUM_CORES_PER_EXECUTOR=7
export DATA_PATH=gs://${GCS_BUCKET}/mortgage_full

gcloud dataproc jobs submit spark \
    --cluster=$CLUSTER_NAME \
    --region=$REGION \
    --class=com.nvidia.spark.examples.mortgage.GPUMain \
    --jars=gs://${GCS_BUCKET}/scala/sample_xgboost_apps-${VERSION}-SNAPSHOT-jar-with-dependencies.jar \
    --properties=spark.executor.cores=${SPARK_NUM_CORES_PER_EXECUTOR},spark.task.cpus=${SPARK_NUM_CORES_PER_EXECUTOR},spark.executor.memory=${SPARK_EXECUTOR_MEMORY},spark.executor.memoryOverhead=${SPARK_EXECUTOR_MEMORYOVERHEAD},spark.executor.resource.gpu.amount=1,spark.task.resource.gpu.amount=1,spark.rapids.sql.batchSizeBytes=512M,spark.rapids.sql.reader.batchSizeBytes=768M,spark.rapids.sql.variableFloatAgg.enabled=true,spark.rapids.memory.gpu.pooling.enabled=false,spark.dynamicAllocation.enabled=false \
    -- \
    -dataPath=train::${DATA_PATH}/train \
    -dataPath=trans::${DATA_PATH}/eval \
    -format=parquet \
    -numWorkers=${SPARK_NUM_EXECUTORS} \
    -treeMethod=gpu_hist \
    -numRound=100 \
    -maxDepth=8   

Dataproc Hub in AI Platform Notebook to Dataproc cluster

With the integration between AI Platform Notebooks and Dataproc, users can create a Dataproc Hub notebook. The AI platform will connect to a Dataproc cluster through a yaml configuration.

In the future, users will be able to provision a Dataproc cluster through DataprocHub notebook. You can use example pyspark notebooks to experiment.

Build custom dataproc image to accelerate cluster init time

In order to accelerate cluster init time to 3-4 minutes, we need to build a custom Dataproc image that already has NVIDIA drivers and CUDA toolkit installed, with RAPIDS deployed. The custom image could also be used in an air gap environment. In this section, we will be using these instructions from GCP to create a custom image.

Currently, we can directly download the spark-rapids.sh script to create the Dataproc image:

Google provides a generate_custom_image.py script that:

  • Launches a temporary Compute Engine VM instance with the specified Dataproc base image.
  • Then runs the customization script inside the VM instance to install custom packages and/or update configurations.
  • After the customization script finishes, it shuts down the VM instance and creates a Dataproc custom image from the disk of the VM instance.
  • The temporary VM is deleted after the custom image is created.
  • The custom image is saved and can be used to create Dataproc clusters.

Download spark-rapids.sh in this repo. The script uses Google’s generate_custom_image.py script. This step may take 20-25 minutes to complete.

git clone https://github.com/GoogleCloudDataproc/custom-images
cd custom-images

export CUSTOMIZATION_SCRIPT=/path/to/spark-rapids.sh
export ZONE=[Your Preferred GCP Zone]
export GCS_BUCKET=[Your GCS Bucket]
export IMAGE_NAME=sample-20-ubuntu18-gpu-t4
export DATAPROC_VERSION=2.0-ubuntu18
export GPU_NAME=nvidia-tesla-t4
export GPU_COUNT=1

python generate_custom_image.py \
    --image-name $IMAGE_NAME \
    --dataproc-version $DATAPROC_VERSION \
    --customization-script $CUSTOMIZATION_SCRIPT \
    --no-smoke-test \
    --zone $ZONE \
    --gcs-bucket $GCS_BUCKET \
    --machine-type n1-standard-4 \
    --accelerator type=$GPU_NAME,count=$GPU_COUNT \
    --disk-size 200 \
    --subnet default 

See here for more details on generate_custom_image.py script arguments and here for dataproc version description.

The image sample-20-ubuntu18-gpu-t4 is now ready and can be viewed in the GCP console under Compute Engine > Storage > Images. The next step is to launch the cluster using this new image and new initialization actions (that do not install NVIDIA drivers since we are already past that step).

Move this to your own bucket. Let’s launch the cluster:

export REGION=[Your Preferred GCP Region]
export GCS_BUCKET=[Your GCS Bucket]
export CLUSTER_NAME=[Your Cluster Name]
export NUM_GPUS=1
export NUM_WORKERS=2

gcloud dataproc clusters create $CLUSTER_NAME  \
    --region=$REGION \
    --image=sample-20-ubuntu18-gpu-t4 \
    --master-machine-type=n1-standard-4 \
    --num-workers=$NUM_WORKERS \
    --worker-accelerator=type=nvidia-tesla-t4,count=$NUM_GPUS \
    --worker-machine-type=n1-standard-4 \
    --num-worker-local-ssds=1 \
    --optional-components=JUPYTER,ZEPPELIN \
    --metadata=rapids-runtime=SPARK \
    --bucket=$GCS_BUCKET \
    --enable-component-gateway \
    --subnet=default 

The new cluster should be up and running within 3-4 minutes!