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* Add UDF compiler implementations * Update related docs Signed-off-by: Allen Xu <allxu@nvidia.com> Co-authored-by: Sean Lee <selee@nvidia.com> Co-authored-by: Nicholas Edelman <nedelman@nvidia.com> Co-authored-by: Alessandro Bellina <abellina@nvidia.com>
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docs/get-started/getting-started-with-rapids-accelerator-on-databricks.md
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,88 +1,88 @@ | ||
| --- | ||
| layout: page | ||
| title: Databricks | ||
| nav_order: 3 | ||
| parent: Getting-Started | ||
| --- | ||
|
|
||
| # Getting started with RAPIDS Accelerator on Databricks | ||
| This guide will run through how to set up the RAPIDS Accelerator for Apache Spark 3.0 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. | ||
|
|
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| ## Prerequisites | ||
| * Apache Spark 3.0 running in DataBricks Runtime 7.0 ML with GPU | ||
| * AWS: 7.0 ML (includes Apache Spark 3.0.0, GPU, Scala 2.12) | ||
| * Azure: 7.0 ML (GPU, Scala 2.12, Spark 3.0.0) | ||
|
|
||
| The number of GPUs per node dictates the number of Spark executors that can run in that node. | ||
|
|
||
| ## Start a Databricks Cluster | ||
| Create a Databricks cluster by going to Clusters, then clicking “+ Create Cluster”. Ensure the cluster meets the prerequisites above by configuring it as follows: | ||
| 1. On AWS, make sure to use 7.0 ML (GPU, Scala 2.12, Spark 3.0.0), or for Azure, choose 7.0 ML (GPU, Scala 2.12, Spark 3.0.0). | ||
| 2. Under Autopilot Options, disable auto scaling. | ||
| 3. Choose the number of workers that matches the number of GPUs you want to use. | ||
| 4. Select a worker type. On AWS, use nodes with 1 GPU each such as `p3.xlarge` or `g4dn.xlarge`. p2 nodes do not meet the architecture requirements for the Spark worker (although they can be used for the driver node). For Azure, choose GPU nodes such as Standard_NC6s_v3. | ||
| 5. Select the driver type. Generally this can be set to be the same as the worker. | ||
| 6. Start the cluster | ||
|
|
||
| ## Advanced Cluster Configuration | ||
|
|
||
| We will need to create an initialization script for the cluster that installs the RAPIDS jars to the cluster. | ||
|
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||
| 1. To create the initialization script, import the initialization script notebook from the repo [generate-init-script.ipynb](../demo/Databricks/generate-init-script.ipynb) to your workspace. See [Managing Notebooks](https://docs.databricks.com/notebooks/notebooks-manage.html#id2) on how to import a notebook, then open the notebook. | ||
| 2. Once you are in the notebook, click the “Run All” button. | ||
| 3. Ensure that the newly created init.sh script is present in the output from cell 2 and that the contents of the script are correct. | ||
| 4. Go back and edit your cluster to configure it to use the init script. To do this, click the “Clusters” button on the left panel, then select your cluster. | ||
| 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 initialization script: `dbfs:/databricks/init_scripts/init.sh`, then click “Add”. | ||
|
|
||
|  | ||
|
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||
| 6. Now select the “Spark” tab, and paste the following config options into the Spark Config section. Change the config values based on the workers you choose. See Apache Spark [configuration](https://spark.apache.org/docs/latest/configuration.html) and RAPIDS Accelerator for Apache Spark [descriptions](../configs) for each config. | ||
|
|
||
| The [`spark.task.resource.gpu.amount`](https://spark.apache.org/docs/latest/configuration.html#scheduling) 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. | ||
|
|
||
| ```bash | ||
| spark.plugins com.nvidia.spark.SQLPlugin | ||
| spark.sql.parquet.filterPushdown false | ||
| spark.rapids.sql.incompatibleOps.enabled true | ||
| spark.rapids.memory.pinnedPool.size 2G | ||
| spark.task.resource.gpu.amount 0.1 | ||
| spark.rapids.sql.concurrentGpuTasks 2 | ||
| spark.locality.wait 0s | ||
| spark.hadoop.mapreduce.fileoutputcommitter.algorithm.version 2 | ||
| spark.executor.extraJavaOptions "-Dai.rapids.cudf.prefer-pinned=true" | ||
| ``` | ||
|
|
||
|  | ||
|
|
||
| 7. Once you’ve added the Spark config, click “Confirm and Restart”. | ||
| 8. Once the cluster comes back up, it is now enabled for GPU-accelerated Spark with RAPIDS and cuDF. | ||
|
|
||
| ## Import the GPU Mortgage Example Notebook | ||
| Import the example [notebook](../demo/gpu-mortgage_accelerated.ipynb) from the repo into your workspace, then open the notebook. | ||
| Modify the first cell to point to your workspace, and download a larger dataset if needed. You can find the links to the datasets at [docs.rapids.ai](https://docs.rapids.ai/datasets/mortgage-data). | ||
|
|
||
| ```bash | ||
| %sh | ||
| wget http://rapidsai-data.s3-website.us-east-2.amazonaws.com/notebook-mortgage-data/mortgage_2000.tgz -P /Users/<your user id>/ | ||
| mkdir -p /dbfs/FileStore/tables/mortgage | ||
| mkdir -p /dbfs/FileStore/tables/mortgage_parquet_gpu/perf | ||
| mkdir /dbfs/FileStore/tables/mortgage_parquet_gpu/acq | ||
| mkdir /dbfs/FileStore/tables/mortgage_parquet_gpu/output | ||
| tar xfvz /Users/<your user id>/mortgage_2000.tgz --directory /dbfs/FileStore/tables/mortgage | ||
| ``` | ||
|
|
||
| In Cell 3, update the data paths if necessary. The example notebook merges the columns and prepares the data for XGoost training. The temp and final output results are written back to the dbfs. | ||
| ```bash | ||
| orig_perf_path='dbfs:///FileStore/tables/mortgage/perf/*' | ||
| orig_acq_path='dbfs:///FileStore/tables/mortgage/acq/*' | ||
| tmp_perf_path='dbfs:///FileStore/tables/mortgage_parquet_gpu/perf/' | ||
| tmp_acq_path='dbfs:///FileStore/tables/mortgage_parquet_gpu/acq/' | ||
| output_path='dbfs:///FileStore/tables/mortgage_parquet_gpu/output/' | ||
| ``` | ||
| Run the notebook by clicking “Run All”. | ||
|
|
||
| ## Hints | ||
| Spark logs in Databricks are removed upon cluster shutdown. It is possible to save logs in a cloud storage location using Databricks [cluster log delivery](https://docs.databricks.com/clusters/configure.html#cluster-log-delivery-1). Enable this option before starting the cluster to capture the logs. | ||
| --- | ||
| layout: page | ||
| title: Databricks | ||
| nav_order: 3 | ||
| parent: Getting-Started | ||
| --- | ||
|
|
||
| # Getting started with RAPIDS Accelerator on Databricks | ||
| This guide will run through how to set up the RAPIDS Accelerator for Apache Spark 3.0 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 3.0 running in DataBricks Runtime 7.0 ML with GPU | ||
| * AWS: 7.0 ML (includes Apache Spark 3.0.0, GPU, Scala 2.12) | ||
| * Azure: 7.0 ML (GPU, Scala 2.12, Spark 3.0.0) | ||
|
|
||
| The number of GPUs per node dictates the number of Spark executors that can run in that node. | ||
|
|
||
| ## Start a Databricks Cluster | ||
| Create a Databricks cluster by going to Clusters, then clicking “+ Create Cluster”. Ensure the cluster meets the prerequisites above by configuring it as follows: | ||
| 1. On AWS, make sure to use 7.0 ML (GPU, Scala 2.12, Spark 3.0.0), or for Azure, choose 7.0 ML (GPU, Scala 2.12, Spark 3.0.0). | ||
| 2. Under Autopilot Options, disable auto scaling. | ||
| 3. Choose the number of workers that matches the number of GPUs you want to use. | ||
| 4. Select a worker type. On AWS, use nodes with 1 GPU each such as `p3.xlarge` or `g4dn.xlarge`. p2 nodes do not meet the architecture requirements for the Spark worker (although they can be used for the driver node). For Azure, choose GPU nodes such as Standard_NC6s_v3. | ||
| 5. Select the driver type. Generally this can be set to be the same as the worker. | ||
| 6. Start the cluster | ||
|
|
||
| ## Advanced Cluster Configuration | ||
|
|
||
| We will need to create an initialization script for the cluster that installs the RAPIDS jars to the cluster. | ||
|
|
||
| 1. To create the initialization script, import the initialization script notebook from the repo [generate-init-script.ipynb](../demo/Databricks/generate-init-script.ipynb) to your workspace. See [Managing Notebooks](https://docs.databricks.com/notebooks/notebooks-manage.html#id2) on how to import a notebook, then open the notebook. | ||
| 2. Once you are in the notebook, click the “Run All” button. | ||
| 3. Ensure that the newly created init.sh script is present in the output from cell 2 and that the contents of the script are correct. | ||
| 4. Go back and edit your cluster to configure it to use the init script. To do this, click the “Clusters” button on the left panel, then select your cluster. | ||
| 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 initialization script: `dbfs:/databricks/init_scripts/init.sh`, then click “Add”. | ||
|
|
||
|  | ||
|
|
||
| 6. Now select the “Spark” tab, and paste the following config options into the Spark Config section. Change the config values based on the workers you choose. See Apache Spark [configuration](https://spark.apache.org/docs/latest/configuration.html) and RAPIDS Accelerator for Apache Spark [descriptions](../configs) for each config. | ||
|
|
||
| The [`spark.task.resource.gpu.amount`](https://spark.apache.org/docs/latest/configuration.html#scheduling) 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. | ||
|
|
||
| ```bash | ||
| spark.plugins com.nvidia.spark.SQLPlugin | ||
| spark.sql.parquet.filterPushdown false | ||
| spark.rapids.sql.incompatibleOps.enabled true | ||
| spark.rapids.memory.pinnedPool.size 2G | ||
| spark.task.resource.gpu.amount 0.1 | ||
| spark.rapids.sql.concurrentGpuTasks 2 | ||
| spark.locality.wait 0s | ||
| spark.hadoop.mapreduce.fileoutputcommitter.algorithm.version 2 | ||
| spark.executor.extraJavaOptions "-Dai.rapids.cudf.prefer-pinned=true" | ||
| ``` | ||
|
|
||
|  | ||
|
|
||
| 7. Once you’ve added the Spark config, click “Confirm and Restart”. | ||
| 8. Once the cluster comes back up, it is now enabled for GPU-accelerated Spark with RAPIDS and cuDF. | ||
|
|
||
| ## Import the GPU Mortgage Example Notebook | ||
| Import the example [notebook](../demo/gpu-mortgage_accelerated.ipynb) from the repo into your workspace, then open the notebook. | ||
| Modify the first cell to point to your workspace, and download a larger dataset if needed. You can find the links to the datasets at [docs.rapids.ai](https://docs.rapids.ai/datasets/mortgage-data). | ||
|
|
||
| ```bash | ||
| %sh | ||
| wget http://rapidsai-data.s3-website.us-east-2.amazonaws.com/notebook-mortgage-data/mortgage_2000.tgz -P /Users/<your user id>/ | ||
| mkdir -p /dbfs/FileStore/tables/mortgage | ||
| mkdir -p /dbfs/FileStore/tables/mortgage_parquet_gpu/perf | ||
| mkdir /dbfs/FileStore/tables/mortgage_parquet_gpu/acq | ||
| mkdir /dbfs/FileStore/tables/mortgage_parquet_gpu/output | ||
| tar xfvz /Users/<your user id>/mortgage_2000.tgz --directory /dbfs/FileStore/tables/mortgage | ||
| ``` | ||
|
|
||
| In Cell 3, update the data paths if necessary. The example notebook merges the columns and prepares the data for XGoost training. The temp and final output results are written back to the dbfs. | ||
| ```bash | ||
| orig_perf_path='dbfs:///FileStore/tables/mortgage/perf/*' | ||
| orig_acq_path='dbfs:///FileStore/tables/mortgage/acq/*' | ||
| tmp_perf_path='dbfs:///FileStore/tables/mortgage_parquet_gpu/perf/' | ||
| tmp_acq_path='dbfs:///FileStore/tables/mortgage_parquet_gpu/acq/' | ||
| output_path='dbfs:///FileStore/tables/mortgage_parquet_gpu/output/' | ||
| ``` | ||
| Run the notebook by clicking “Run All”. | ||
|
|
||
| ## Hints | ||
| Spark logs in Databricks are removed upon cluster shutdown. It is possible to save logs in a cloud storage location using Databricks [cluster log delivery](https://docs.databricks.com/clusters/configure.html#cluster-log-delivery-1). Enable this option before starting the cluster to capture the logs. |
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