Quickstart

User Guide (Latest Version)

spark-rapids-user-tools CLI enables user to run the tool for logs from a number of CSP platforms in addition to on-prem.

Tip

For most accurate results, it is recommended to run the latest version of the CLI tool.

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 needs internet access. If the machine is behind a proxy, it is recommended to install the CLI package from source using fat mode as described in Install the CLI Package.

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

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

    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.

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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

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pip install spark-rapids-user-tools

If you need more details, please find in RAPIDS user tools pip package.

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pip install <wheel-file>

  • Checkout the code repository

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    git clone git@github.com:NVIDIA/spark-rapids-tools.git cd spark-rapids-tools/user_tools

  • Optional: Run the project in a virtual environment

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    python -m venv .venv source .venv/bin/activate

  • Build wheel file using one of the following modes:

    Fat mode

    Similar to fat jar in Java, this mode solves the problem when web access is not available to download resources having Url-paths (http/https). The command builds the tools jar file and downloads the necessary dependencies and packages them with the source code into a single wheel file. You may consider this mode if the development environment has no access to download dependencies (i.e., Spark jars) during runtime.

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    ./build.sh fat

    Default mode

    This mode builds a wheel package without any jar dependencies

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    ./build.sh

  • Finally, install the package using the wheel file

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    pip install <wheel-file>


A typical workflow to successfully run the qualification command:

  1. Follow the instructions to set up the prerequisites and install the CLI

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

    • For Dataproc, it should be set to GCS path.

    • For EMR and Databricks-AWS, it should be set to S3 path.

    • For Databricks-Azure, it should be set to ABFS path.

  3. Run the qualification command on the set of selected eventlogs.

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spark_rapids qualification <flags>


The tool helps quantify the expected acceleration and costs savings of migrating a Spark application or query to GPU. The tool will process each app individually, but will group apps with the same name and cluster details into a single output row after averaging duration metrics accordingly.

Example Commands

This section shows examples of Qualification CLI commands assuming the following inputs:

  • CLUSTER_NAME: The cluster name on CSP (for Dataproc or EMR platform, not applicable for on-prem clusters)

  • CLUSTER_ID: The cluster ID on CSP (for Databricks-AWS or Databricks-Azure platform, not applicable for on-prem clusters)

  • CLUSTER_PROPS: The cluster properties file (for all platforms). See Cluster by Property File under Cost Savings on how to generate this file.

  • EVENTLOG: Path to Spark eventlogs without the scheme part. The scheme can be a local file system (file://), HDFS (hdfs://), S3 (s3://), ABFS (abfss://), or GCS (gs://).

The following table shows CLI command examples along with platform and expected functionalities based on which analysis is performed.

Examples of qualification CLI commands

CMD

Platform

Cost Savings

Speedups

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spark_rapids qualification \ --cluster $CLUSTER_NAME \ --eventlogs gs://$EVENTLOG

Dataproc ☑️ ☑️ Cost savings are calculated based on Dataproc cluster because EVENTLOG is stored on GCS
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spark_rapids qualification \ --cluster $CLUSTER_NAME \ --eventlogs s3://$EVENTLOG

EMR ☑️ ☑️ Cost savings are calculated based on EMR cluster because EVENTLOG is stored on S3
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spark_rapids qualification \ --cluster $CLUSTER_ID \ --platform databricks-azure \ --eventlogs file://$EVENTLOG

Databricks-Azure ☑️ ☑️ Cost savings are calculated based on Databricks-Azure cluster because platform is provided
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spark_rapids qualification \ --cluster $CLUSTER_PROPS \ --eventlogs file://$EVENTLOG

Databricks-AWS ☑️ ☑️ Cost savings are calculated based on Databricks-AWS cluster because platform is detected from CLUSTER_PROPS file
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spark_rapids qualification \ --eventlogs file://$EVENTLOG

On-prem ☑️ The cost-savings cannot be generated without cluster argument while EVENTLOG is stored on a local file system

Command Options

You can list all the options using the help argument

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spark_rapids qualification -- --help


Available options are listed in the following table.

List of options for qualification CLI command

Option

Description

Default

Required

--eventlogs Event log filenames or CSP storage directories containing event logs (comma separated). Skipping this argument requires that the cluster argument points to a valid cluster name on the CSP. N/A N
--cluster Name or ID of cluster or path to cluster property file. Further details described in Cost Savings. N/A N
--platform, -p Defines one of the following “on-prem”, “emr”, “dataproc”, “dataproc-gke”, “databricks-aws”, and “databricks-azure”. N/A N
--target_platform, -t Cost savings and speedup recommendation for comparable cluster in target_platform based on on-prem cluster configuration. Currently only dataproc is supported. If not provided, the final report will be limited to GPU speedups only without cost-savings. N/A N
--output_folder, -o Path to store the output. N/A N
--filter_apps, -f Requires cluster argument.
Filtering criteria of the applications listed in the final STDOUT table without affecting the CSV report:
  • ALL means no filter applied.
  • SPEEDUPS lists all the apps that are either Recommended, or Strongly Recommended based on speedups.
  • SAVINGS lists all the apps that have positive estimated GPU savings except for the apps that are Not Applicable.
  • TOP_CANDIDATES lists all apps that have unsupported operators stage duration less than 25% of app duration and speedups greater than 1.3x.
TOP_CANDIDATES N
--estimation_model Model used to calculate the estimated GPU duration and cost savings:
  • xgboost: an XGBoost model for GPU duration estimation
  • speedups: uses a simple static estimated speedup per operator, set by default.
speedups N
--tools_jar Path to a bundled jar including Rapids tool. The path is a local filesystem, or remote cloud storage url. If missing, the wrapper downloads the latest rapids-4-spark-tools_*.jar from maven repository. N/A N
--jvm_heap_size The maximum heap size of the JVM in gigabytes. Default is calculated based on a function of the total memory of the host. N/A N
--jvm_threads Number of thread to use for parallel processing on the eventlogs batch. Default is calculated as a function of the total number of cores and the heap size on the host. N/A N
--cpu_cluster_price The CPU cluster hourly price (float) provided by the user. N/A N
--estimated_gpu_cluster_price The GPU cluster hourly price provided by the user. N/A N
--cpu_discount A percent discount for the cpu cluster cost in the form of an integer value (e.g. 30 for 30% discount). N/A N
--gpu_discount A percent discount for the gpu cluster cost in the form of an integer value (e.g. 30 for 30% discount). N/A N
--global_discount A percent discount for both the cpu and gpu cluster costs in the form of an integer value (e.g. 30 for 30% discount). N/A N
--gpu_cluster_recommendation Requires cluster argument.
The type of GPU cluster recommendation to generate:
  • MATCH: keep GPU cluster same number of nodes as CPU cluster
  • CLUSTER: recommend optimal GPU cluster by cost for entire cluster
  • JOB: recommend optimal GPU cluster by cost per job
MATCH N
--verbose, -v True or False to enable verbosity of the script. N/A N

Cost-Savings

By default, the qualification tool generates estimated speedups of the CPU applications. To generate estimated cost-savings, you need to provide the CPU cluster information.

The specific type of parameter passed for the cluster is based on platform, see the following scenarios to determine which method to use for your platform:

User can pass name of the cluster CLUSTER_NAME to the command:

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spark_rapids qualification --cluster $CLUSTER_NAME [flags]


This is supported on Dataproc and EMR platform. The tool uses the CSP CLI to collect the cluster information.

User can pass ID of the cluster CLUSTER_ID to the command:

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spark_rapids qualification --cluster $CLUSTER_ID [flags]


This is supported on Databricks-AWS and Databricks-Azure platform. The tool uses the CSP CLI to collect the cluster information.

User can pass CLUSTER_PROPS - the path to cluster property file (in json/yaml formats) to the command. This is useful if the cluster is not accessible or permanently deleted.

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spark_rapids qualification --cluster $CLUSTER_PROPS [flags]


User defines the cluster configuration of on-prem platform. The following is a sample cluster property file CLUSTER_PROPS in yaml format.

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config: masterConfig: numCores: 2 memory: 7680MiB workerConfig: numCores: 8 memory: 7680MiB numWorkers: 2

target_platform is required for on-prem clusters. Currently only Dataproc is supported.

Given Dataproc CLUSTER_NAME, user can generate its cluster property file CLUSTER_PROPS using the following command. (See gcloud CLI docs)

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gcloud dataproc clusters describe $CLUSTER_NAME > $CLUSTER_PROPS

Given EMR CLUSTER_ID, user can generate its cluster property file CLUSTER_PROPS using the following command. (See AWS CLI docs)

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aws emr describe-cluster --cluster-id $CLUSTER_ID > $CLUSTER_PROPS

Given Databricks CLUSTER_ID, user can generate its cluster property file CLUSTER_PROPS using the following command. (See Databricks CLI docs)

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databricks clusters get $CLUSTER_ID > $CLUSTER_PROPS

Given Databricks CLUSTER_ID, user can generate its cluster property file CLUSTER_PROPS using the following command. (See Databricks CLI docs)

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databricks clusters get $CLUSTER_ID > $CLUSTER_PROPS


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/ ...


Note

See this listing for full details of the subdirectory rapids_4_spark_qualification_output.

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

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+----+----------+---------------------+----------------------+----------------------+---------------+-----------------+-----------------+-----------------+ | | 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.

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© Copyright 2024, NVIDIA. Last updated on Jun 19, 2024.