From 48a1afb820c0c2704f3628b2f4ba0bcf98cd40ed Mon Sep 17 00:00:00 2001 From: liyuan Date: Wed, 21 Dec 2022 18:27:11 +0800 Subject: [PATCH 1/4] update for emr690 release Signed-off-by: liyuan --- docs/get-started/getting-started-aws-emr.md | 7 ++++--- docs/img/AWS-EMR/RAPIDS_EMR_GUI_1-bak.png | Bin 0 -> 115267 bytes docs/img/AWS-EMR/RAPIDS_EMR_GUI_1.png | Bin 115267 -> 123442 bytes docs/img/AWS-EMR/RAPIDS_EMR_GUI_5-bak.png | Bin 0 -> 608477 bytes docs/img/AWS-EMR/RAPIDS_EMR_GUI_5.png | Bin 608477 -> 170569 bytes 5 files changed, 4 insertions(+), 3 deletions(-) create mode 100644 docs/img/AWS-EMR/RAPIDS_EMR_GUI_1-bak.png create mode 100644 docs/img/AWS-EMR/RAPIDS_EMR_GUI_5-bak.png diff --git a/docs/get-started/getting-started-aws-emr.md b/docs/get-started/getting-started-aws-emr.md index ad2c789f389..bbada4d5cad 100644 --- a/docs/get-started/getting-started-aws-emr.md +++ b/docs/get-started/getting-started-aws-emr.md @@ -14,6 +14,7 @@ Different versions of EMR ship with different versions of Spark, RAPIDS Accelera | EMR | Spark | RAPIDS Accelerator jar | cuDF jar | xgboost4j-spark jar | --- | --- | --- | ---| --- | +| 6.9 | 3.3.0 | rapids-4-spark_2.12-22.08.0.jar | Bundled with rapids-4-spark | xgboost4j-spark_3.0-1.4.2-0.3.0.jar | | 6.8 | 3.3.0 | rapids-4-spark_2.12-22.06.0.jar | Bundled with rapids-4-spark | xgboost4j-spark_3.0-1.4.2-0.3.0.jar | | 6.7 | 3.2.1 | rapids-4-spark_2.12-22.02.0.jar | cudf-22.02.0-cuda11.jar | xgboost4j-spark_3.0-1.2.0-0.1.0.jar | | 6.6 | 3.2.0 | rapids-4-spark_2.12-22.02.0.jar | cudf-22.02.0-cuda11.jar | xgboost4j-spark_3.0-1.2.0-0.1.0.jar | @@ -40,7 +41,7 @@ g4dn.2xlarge nodes: ``` aws emr create-cluster \ ---release-label emr-6.7.0 \ +--release-label emr-6.9.0 \ --applications Name=Hadoop Name=Spark Name=Livy Name=JupyterEnterpriseGateway \ --service-role EMR_DefaultRole \ --ec2-attributes KeyName=my-key-pair,InstanceProfile=EMR_EC2_DefaultRole \ @@ -80,8 +81,8 @@ detailed cluster configuration page. #### Step 1: Software Configuration and Steps -Select **emr-6.8.0** for the release, uncheck all the software options, and then check **Hadoop -3.2.1**, **Spark 3.3.0**, **Livy 0.7.1** and **JupyterEnterpriseGateway 2.1.0**. +Select **emr-6.9.0** for the release, uncheck all the software options, and then check **Hadoop +3.2.1**, **Spark 3.3.0**, **Livy 0.7.1** and **JupyterEnterpriseGateway 2.6.0**. In the "Edit software settings" field, copy and paste the configuration from the [EMR document](https://docs.aws.amazon.com/emr/latest/ReleaseGuide/emr-spark-rapids.html). 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liyuan Date: Wed, 21 Dec 2022 18:31:13 +0800 Subject: [PATCH 3/4] update hadoop version Signed-off-by: liyuan --- docs/get-started/getting-started-aws-emr.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/get-started/getting-started-aws-emr.md b/docs/get-started/getting-started-aws-emr.md index bbada4d5cad..67553a01267 100644 --- a/docs/get-started/getting-started-aws-emr.md +++ b/docs/get-started/getting-started-aws-emr.md @@ -82,7 +82,7 @@ detailed cluster configuration page. #### Step 1: Software Configuration and Steps Select **emr-6.9.0** for the release, uncheck all the software options, and then check **Hadoop -3.2.1**, **Spark 3.3.0**, **Livy 0.7.1** and **JupyterEnterpriseGateway 2.6.0**. +3.3.3**, **Spark 3.3.0**, **Livy 0.7.1** and **JupyterEnterpriseGateway 2.6.0**. In the "Edit software settings" field, copy and paste the configuration from the [EMR document](https://docs.aws.amazon.com/emr/latest/ReleaseGuide/emr-spark-rapids.html). You can also From 2c4f3c64f2b6d8d341abf2f18966b4879a170075 Mon Sep 17 00:00:00 2001 From: liyuan Date: Mon, 26 Dec 2022 11:09:10 +0800 Subject: [PATCH 4/4] update getting started on db since we support autoscaling Signed-off-by: liyuan --- .../get-started/getting-started-databricks.md | 27 +++++++++---------- 1 file changed, 13 insertions(+), 14 deletions(-) diff --git a/docs/get-started/getting-started-databricks.md b/docs/get-started/getting-started-databricks.md index 11fb9b356ac..f5a9d7177ed 100644 --- a/docs/get-started/getting-started-databricks.md +++ b/docs/get-started/getting-started-databricks.md @@ -55,18 +55,17 @@ The number of GPUs per node dictates the number of Spark executors that can run detected. ## Start a Databricks Cluster -Create a Databricks cluster by going to Clusters, then clicking `+ Create Cluster`. Ensure the +Create a Databricks cluster by going to "Compute", then clicking `+ Create compute`. Ensure the cluster meets the prerequisites above by configuring it as follows: 1. Select the Databricks Runtime Version from one of the supported runtimes specified in the Prerequisites section. -2. Under Autopilot Options, disable autoscaling. -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 `p3.2xlarge` or `g4dn.xlarge`. +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`. p2 nodes do not meet the architecture requirements (Pascal or higher) for the Spark worker (although they can be used for the driver node). For Azure, choose GPU nodes such as - Standard_NC6s_v3. For GCP, choose N1 or A2 instance types with GPUs. -5. Select the driver type. Generally this can be set to be the same as the worker. -6. Start the cluster. + 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. Start the cluster. ## Advanced Cluster Configuration @@ -93,25 +92,25 @@ cluster. 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 +4. Go back and edit your cluster to configure it to use the init script. To do this, click the + “Compute” 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”. ![Init Script](../img/Databricks/initscript.png) 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 + 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.md) 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 + 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. + 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. @@ -184,7 +183,7 @@ 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 +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.