Quickstart

The simplest way to run the tool is using the spark-rapids-user-tools CLI. This enables you to run for logs from a number of CSP platforms in addition to on-prem.

In using standalone on Spark event logs, the tool can be run as a user tool command via RAPIDS user tools pip package or as a java application for CSP environments (Google Dataproc, AWS EMR, and Databricks Azure/AWS) and on-prem. More details on how to use the java application is described in java API.

Prerequisites

• Set up a Python environment with a version between 3.8 and 3.10

• Java 8+

• The developer machine used to host the CLI tools needs internet access to download JAR dependencies from mvn: spark-*.jar, hadoop-aws-*.jar, and aws-java-sdk-bundle*.jar. If the host machine is behind a proxy, then it is recommended to install the CLI package from source using the fat mode as described in Install the CLI Package section.

• Set the development environment for your CSP or on-prem

  • Dataproc
  • EMR
  • Databricks-AWS
  • Databricks-Azure

  The tools CLI depends on Python implementation of PyArrow which relies on some environment variables to bind with HDFS:

  • HADOOP_HOME: the root of your installed Hadoop distribution. Often has “lib/native/libhdfs.so”.

  • JAVA_HOME: the location of your Java SDK installation.

  • ARROW_LIBHDFS_DIR (optional): explicit location of “libhdfs.so” if it is installed somewhere other than $HADOOP_HOME/lib/native.

  • Add the Hadoop jars to your CLASSPATH.

    • Local filesystem
    • Linux
    • Windows

    No more steps required to run the tools on on-premises environment including standalone/local machines.

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    export CLASSPATH=`$HADOOP_HOME/bin/hadoop classpath --glob`
            

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    %HADOOP_HOME%/bin/hadoop classpath --glob > %CLASSPATH%
            

  For more information on HDFS requirements, refer to the PyArrow HDFS documentation

  • Install gcloud CLI. Follow the instructions on gcloud-sdk-install

  • Set the configuration settings and credentials of the gcloud CLI:

    • Initialize the gcloud CLI by following these instructions

    • Grant authorization to the gcloud CLI with a user account

    • Set up “application default credentials” to the gcloud CLI by logging in

    • Manage gcloud CLI configurations. For more details, visit gcloud-sdk-configurations

    • Verify that the following gcloud CLI properties are properly defined:

      • dataproc/region

      • compute/zone

      • compute/region

      • core/project

    • If the configuration is not set to default values, then make sure to explicitly set some environment variables to be picked up by the tools cmd such as: CLOUDSDK_DATAPROC_REGION, and CLOUDSDK_COMPUTE_REGION.

    • The tools CLI follows the process described in this doc to resolve the credentials. If not running on (GCP), the environment variable GOOGLE_APPLICATION_CREDENTIALS is required to point to a JSON file containing credentials.

  • Install the AWS CLI version 2. Follow the instructions on aws-cli-getting-started

  • Set the configuration settings and credentials of the AWS CLI by creating credentials and config files as described in aws-cli-configure-files.

  • If the AWS CLI configuration is not set to the default values, then make sure to explicitly set some environment variables tp be picked up by the tools cmd such as: AWS_PROFILE, AWS_DEFAULT_REGION, AWS_CONFIG_FILE, AWS_SHARED_CREDENTIALS_FILE. See the full list of variables in aws-cli-configure-envvars

  • Note that it is important to configure with the correct region for the bucket being used on S3. If region is not set, the AWS SDK will choose a default value that may not be valid. In addition, the tools CLI by inspects AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY emvironment variables if the credentials could not be pulled from the credential files.

  Note

  In order to be able to run tools that require SSH on the EMR nodes (i.e., bootstrap), then:

  • make sure that you have SSH access to the cluster nodes; and

  • create a key pair using Amazon EC2 through the AWS CLI command aws ec2 create-key-pair as instructed in aws-cli-create-key-pairs.

  The tool currently only supports event logs stored on S3 (no DBFS paths). The remote output storage is also expected to be S3.

  • Install Databricks CLI

    • Install the Databricks CLI version 0.200+. Follow the instructions on Install the CLI.

    • Set the configuration settings and credentials of the Databricks CLI:

    • Set up authentication by following these instructions

    • Verify that the access credentials are stored in the file ~/.databrickscfg on Unix, Linux, or macOS, or in another file defined by environment variable DATABRICKS_CONFIG_FILE.

    • If the configuration is not set to default values, then make sure to explicitly set some environment variables to be picked up by the tools cmd such as: DATABRICKS_CONFIG_FILE, DATABRICKS_HOST and DATABRICKS_TOKEN. See the description of the variables in environment variables docs.

  • Setup the environment to access S3

    • Install the AWS CLI version 2. Follow the instructions on aws-cli-getting-started

    • Set the configuration settings and credentials of the AWS CLI by creating credentials and config files as described in aws-cli-configure-files.

    • If the AWS CLI configuration is not set to the default values, then make sure to explicitly set some environment variables tp be picked up by the tools cmd such as: AWS_PROFILE, AWS_DEFAULT_REGION, AWS_CONFIG_FILE, AWS_SHARED_CREDENTIALS_FILE. See the full list of variables in aws-cli-configure-envvars

    • Note that it is important to configure with the correct region for the bucket being used on S3. If region is not set, the AWS SDK will choose a default value that may not be valid. In addition, the tools CLI by inspects AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY emvironment variables if the credentials could not be pulled from the credential files.

    Note

    In order to be able to run tools that require SSH on the EMR nodes (i.e., bootstrap), then:

    • make sure that you have SSH access to the cluster nodes; and

    • create a key pair using Amazon EC2 through the AWS CLI command aws ec2 create-key-pair as instructed in aws-cli-create-key-pairs.

  The tool currently only supports event logs stored on ABFS. The remote output storage is also expected to be ABFS (no DBFS paths).

  • Install Databricks CLI

    • Install the Databricks CLI version 0.200+. Follow the instructions on Install the CLI.

    • Set the configuration settings and credentials of the Databricks CLI:

    • Set up authentication by following these instructions

    • Verify that the access credentials are stored in the file ~/.databrickscfg on Unix, Linux, or macOS, or in another file defined by environment variable DATABRICKS_CONFIG_FILE.

    • If the configuration is not set to default values, then make sure to explicitly set some environment variables to be picked up by the tools cmd such as: DATABRICKS_CONFIG_FILE, DATABRICKS_HOST and DATABRICKS_TOKEN. See the description of the variables in environment variables docs.

  • Install Azure CLI

    • Install the Azure CLI. Follow the instructions on How to install the Azure CLI.

    • Set the configuration settings and credentials of the Azure CLI:

      • Set up the authentication by following these instructions.

      • Configure the Azure CLI by following these instructions.

        • location is used for retreving instance type description (default is westus).

        • output should use default of json in core section.

        • Verify that the configurations are stored in the file $AZURE_CONFIG_DIR/config where the default value of AZURE_CONFIG_DIR is $HOME/.azure on Linux or macOS.

    • If the configuration is not set to default values, then make sure to explicitly set some environment variables to be picked up by the tools cmd such as: AZURE_CONFIG_DIR and AZURE_DEFAULTS_LOCATION.

Install the CLI Package

Install spark-rapids-user-tools with one of the options below

            

            
pip install spark-rapids-user-tools
        

            

            
pip install <wheel-file>
        

A typical workflow to successfully run the qualification command in local mode is described as follows:

1. Follow the instructions to setup the CLI

2. Spark event logs from prior runs of the applications on Spark 2.x or later. Get the location of the Apache Spark eventlogs generated from CPU based Spark applications. In addition to local storage, the eventlogs should be stored in a valid remote storage:

Finally, run the qualification command on the set of selected eventlogs. The cmd helps quantify the expected acceleration and costs savings of migrating a Spark application or query to GPU. The cmd will process each app individually, but will group apps with the same name into the same output row after averaging duration metrics accordingly.

            

            
spark_rapids qualification <flag>
        

Environment Variables

In addition to the environment variables used to configure the CSP environment, the CLI has its own set of environment variables.

Before running any command, you can set environment variables to specify configurations. RAPIDS variables have a naming pattern RAPIDS_USER_TOOLS_*:

Command Options

You can list all the options using the help argument

            

            
spark_rapids qualification -- --help
        

Available options are listed in the following table.

Cost-Savings

By default, the tool generates estimated speedups of the CPU application. In order to generate the estimated cost-savings, then you need to point to input the CPU cluster information.

The tool allows to pass the cluster properties (including for on-prem cluster) using one of the following scenarios:

Sample Commands

To see a full list of commands in details, please visit Qualification-cmd CLI examples.

Qualification Output

The Qualification tool will run against logs from your CSP environment and then will output the applications recommended for acceleration along with Estimated GPU Speedup and cost saving metrics.

The command creates a directory with UUID that contains the following:

• Directory generated by the RAPIDS qualification tool rapids_4_spark_qualification_output;

• A CSV file that contains the summary of all the applications along with estimated absolute costs (qualification_summary.csv)

  Sample output directory structure.
  

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    qual_20230314145334_d2CaFA34
    ├── qualification_summary.csv
    └── rapids_4_spark_qualification_output/
        ├── ui/
        │   └── html/
        ...
          

  
  

In qualification_summary.csv, the command output lists the following fields for each application:

App ID

An application is referenced by its application ID, app-id. When running on YARN, each application may have multiple attempts, but there are attempt IDs only for applications in cluster mode, not applications in client mode. Applications in YARN cluster mode can be identified by their attempt-id.

App Name

Name of the application

App Duration

Wall-Clock time measured since the application starts till it is completed. If an app is not completed an estimated completion time would be computed.

Estimated GPU Duration

Predicted runtime of the app if it was run on GPU.
It is the sum of the accelerated operator durations and ML functions duration(if applicable) along with durations that could not run on GPU because they are unsupported operators or not SQL/Dataframe.

Estimated GPU Speedup

That will estimate how much faster the application would run on GPU. It is calculated as the ratio between App Duration and Estimated GPU Duration.

Estimated GPU Savings(%)

Percentage of cost savings of the app if it migrates to an accelerated cluster. It is calculated as:
\(\texttt{estimated}\_\texttt{saving} = 100 - (\frac{100 \times \texttt{gpu}\_\texttt{cost}}{\texttt{cpu}\_\texttt{cost}})\)

Savings Based Recommendation

Recommendation based on Estimated GPU Savings.

• Strongly Recommended: An app with savings \(\geq\) 40%

• Recommended: An app with savings between (1, 40) %

• Not Recommended: An app with no savings

• Not Applicable: An app that has job or stage failures.

Speedup Based Recommendation

Recommendation based on Estimated GPU Speedup. Note that an application that has job or stage failures will be labeled Not Applicable

Sample of Qualification cmd output on the STDOUT

            

            
  +----+----------+---------------------+----------------------+----------------------+---------------+-----------------+-----------------+-----------------+
  |    | App ID   | App Name            | Speedup Based        | Savings Based        |           App |   Estimated GPU |   Estimated GPU |   Estimated GPU |
  |    |          |                     | Recommendation       | Recommendation       |   Duration(s) |     Duration(s) |         Speedup |      Savings(%) |
  |----+----------+---------------------+----------------------+----------------------+---------------+-----------------+-----------------+-----------------|
  |  0 | app-0002 | spark_data_utils.py | Strongly Recommended | Strongly Recommended |       1201.72 |          220.85 |            5.44 |           44.33 |
  |  3 | app-0001 | Spark shell         | Strongly Recommended | Recommended          |       1783.65 |          533.05 |            3.35 |            9.48 |
  +----+----------+---------------------+----------------------+----------------------+---------------+-----------------+-----------------+-----------------+
        

For more information on the detailed output of the Qualification tool, go here: Output Details.

TCO Calculator

In addition to the above fields, Estimated Job Frequency (monthly) and Annual Cost Savings are to be used as part of a TCO calculator to see the long-term benefit of using Spark RAPIDS with your applications.

Copy the GSheet template and then follow the instructions listed in the Instructions tab.