Skip to content

Latest commit

 

History

History
176 lines (140 loc) · 8.89 KB

databricks.md

File metadata and controls

176 lines (140 loc) · 8.89 KB
Raw

Get Started with XGBoost4J-Spark on Databricks

This is a getting started guide to XGBoost4J-Spark on Databricks. At the end of this guide, the reader will be able to run a sample Apache Spark application that runs on NVIDIA GPUs on Databricks.

Prerequisites

* Apache Spark 3.x running in Databricks Runtime 10.4 ML or 11.3 ML with GPU
* AWS: 10.4 LTS ML (GPU, Scala 2.12, Spark 3.2.1) or 11.3 LTS ML (GPU, Scala 2.12, Spark 3.3.0)
* Azure: 10.4 LTS ML (GPU, Scala 2.12, Spark 3.2.1) or 11.3 LTS ML (GPU, Scala 2.12, Spark 3.3.0)

The number of GPUs per node dictates the number of Spark executors that can run in that node. Each executor should only be allowed to run 1 task at any given time.

Start A Databricks Cluster

Before creating the cluster, we will need to create an initialization script for the cluster to install the RAPIDS jars. Databricks recommends storing all cluster-scoped init scripts using workspace files. Each user has a Home directory configured under the /Users directory in the workspace. Navigate to your home directory in the UI and select Create > File from the menu, create an init.sh scripts with contents:

#!/bin/bash
sudo wget -O /databricks/jars/rapids-4-spark_2.12-24.08.1.jar https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/24.08.1/rapids-4-spark_2.12-24.08.1.jar

  1. Select the Databricks Runtime Version from one of the supported runtimes specified in the Prerequisites section.

  2. Choose the number of workers that matches the number of GPUs you want to use.

  3. Select a worker type. On AWS, use nodes with 1 GPU each such as p3.2xlarge or g4dn.xlarge. For Azure, choose GPU nodes such as Standard_NC6s_v3. For GCP, choose N1 or A2 instance types with GPUs.

  4. Select the driver type. Generally this can be set to be the same as the worker.

  5. Click the “Edit” button, then navigate down to the “Advanced Options” section. Select the “Init Scripts” tab in the advanced options section, and paste the workspace path to the initialization script:/Users/user@domain/init.sh, then click “Add”. Init Script

  6. Now select the “Spark” tab, and paste the following config options into the Spark Config section. Change the config values based on the workers you choose. See Apache Spark configuration and RAPIDS Accelerator for Apache Spark descriptions for each config.

    The spark.task.resource.gpu.amount configuration is defaulted to 1 by Databricks. That means that only 1 task can run on an executor with 1 GPU, which is limiting, especially on the reads and writes from Parquet. Set this to 1/(number of cores per executor) which will allow multiple tasks to run in parallel just like the CPU side. Having the value smaller is fine as well. Note: Please remove the spark.task.resource.gpu.amount config for a single-node Databricks cluster because Spark local mode does not support GPU scheduling.

    spark.plugins com.nvidia.spark.SQLPlugin
spark.task.resource.gpu.amount 0.1
spark.rapids.memory.pinnedPool.size 2G
spark.rapids.sql.concurrentGpuTasks 2

    Spark Config

    If running Pandas UDFs with GPU support from the plugin, at least three additional options as below are required. The spark.python.daemon.module option is to choose the right daemon module of python for Databricks. On Databricks, the python runtime requires different parameters than the Spark one, so a dedicated python demon module rapids.daemon_databricks is created and should be specified here. Set the config spark.rapids.sql.python.gpu.enabled to true to enable GPU support for python. Add the path of the plugin jar (supposing it is placed under /databricks/jars/) to the spark.executorEnv.PYTHONPATH option. For more details please go to GPU Scheduling For Pandas UDF

    spark.rapids.sql.python.gpu.enabled true
spark.python.daemon.module rapids.daemon_databricks
spark.executorEnv.PYTHONPATH /databricks/jars/rapids-4-spark_2.12-24.08.1.jar:/databricks/spark/python

    Note that since python memory pool require installing the cudf library, so you need to install cudf library in each worker nodes pip install cudf-cu11 --extra-index-url=https://pypi.nvidia.com or disable python memory pool spark.rapids.python.memory.gpu.pooling.enabled=false.

  7. Click Create Cluster, it is now enabled for GPU-accelerated Spark.

Install the xgboost4j_spark jar in the cluster

  1. See Libraries for how to install jars from DBFS
  2. Go to "Libraries" tab under your cluster and install dbfs:/FileStore/jars/${XGBOOST4J_SPARK_JAR} in your cluster by selecting the "DBFS" option for installing jars

These steps will ensure you are able to import xgboost libraries in python notebooks.

Import the GPU Mortgage Example Notebook

  1. See Managing Notebooks on how to import a notebook.
  2. Import the example notebook: XGBoost4j-Spark mortgage notebook
  3. Inside the mortgage example notebook, update the data paths from "/data/datasets/mortgage-small/train" to "dbfs:/FileStore/tables/mortgage/csv/train/mortgage_train_merged.csv" "/data/datasets/mortgage-small/eval" to "dbfs:/FileStore/tables/mortgage/csv/test/mortgage_eval_merged.csv"

The example notebook comes with the following configuration, you can adjust this according to your setup. See supported configuration options here: xgboost parameters

params = { 
    'eta': 0.1,
    'gamma': 0.1,
    'missing': 0.0,
    'treeMethod': 'gpu_hist',
    'maxDepth': 10, 
    'maxLeaves': 256,
    'growPolicy': 'depthwise',
    'minChildWeight': 30.0,
    'lambda_': 1.0,
    'scalePosWeight': 2.0,
    'subsample': 1.0,
    'nthread': 1,
    'numRound': 100,
    'numWorkers': 1,
}

  1. Run all the cells in the notebook.

  2. View the results In the cell 5 (Training), 7 (Transforming) and 8 (Accuracy of Evaluation) you will see the output.

--------------
==> Benchmark: 
Training takes 6.48 seconds
--------------

--------------
==> Benchmark: Transformation takes 3.2 seconds

--------------

------Accuracy of Evaluation------
Accuracy is 0.9980699597729774

Limitations

  1. When selecting GPU nodes, Databricks UI requires the driver node to be a GPU node. However you can use Databricks API to create a cluster with CPU driver node. Outside of Databricks the plugin can operate with the driver as a CPU node and workers as GPU nodes.

  2. Cannot spin off multiple executors on a multi-GPU node.

    Even though it is possible to set spark.executor.resource.gpu.amount=1 in the in Spark Configuration tab, Databricks overrides this to spark.executor.resource.gpu.amount=N (where N is the number of GPUs per node). This will result in failed executors when starting the cluster.

  3. Parquet rebase mode is set to "LEGACY" by default.

    The following Spark configurations are set to LEGACY by default on Databricks:

    spark.sql.legacy.parquet.datetimeRebaseModeInWrite
spark.sql.legacy.parquet.int96RebaseModeInWrite

    These settings will cause a CPU fallback for Parquet writes involving dates and timestamps. If you do not need LEGACY write semantics, set these configs to EXCEPTION which is the default value in Apache Spark 3.0 and higher.

  4. Databricks makes changes to the runtime without notification.

    Databricks makes changes to existing runtimes, applying patches, without notification. Issue-3098 is one example of this. We run regular integration tests on the Databricks environment to catch these issues and fix them once detected.

  5. In Databricks 11.3, an incorrect result is returned for window frames defined by a range in case of DecimalTypes with precision greater than 38. There is a bug filed in Apache Spark for it here, whereas when using the plugin the correct result will be returned.