Examples#

Please visit spark-rapids-examples repo for ETL, ML/DL, UDF related examples using the RAPIDS Accelerator for Apache Spark. It includes Scala/Python source code and related notebooks for different examples.

Benchmarks#

Please visit spark-rapids-benchmarks repo for Spark related benchmark sets and utilities using the RAPIDS Accelerator for Apache Spark.

Profiling Tool#

CLI Samples#

This section shows samples of Profiling CLI cmd assuming the following inputs:

  • CLUTER_NAME: The GPU cluster name on CSP (dataproc, Databricks, or EMR)

  • PROP_FILE: Path to a GPU cluster property file. The path can be a local filesystem, HDFS, S3, ABFS, or GCS. The file can be formatted according to gcloud specs (DATAPROC_PROP), EMR (EMR_PROP)

  • EVENTLOG: Path to Spark event logs without the scheme part. The scheme can be a local filesystem, HDFS, S3, ABFS, or GCS.

The following table shows samples of CLI cmds along with the expected functionalities and the platform based on what the analysis performed.

List of arguments and options for profiling CLI cmd#

CMD

Platform

Auto-Tuner

Comments

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

Dataproc

☑️

Auto-Tuner recommendations are based on accelerated Dataproc cluster because EVENTLOG is stored on GCS

 
spark_rapids profiling \
  --cluster $DATAPROC_PROP \
  --eventlogs file://$EVENTLOG

Dataproc

☑️

Auto-Tuner recommendations are based on accelerated Dataproc cluster because cluster arguments to a property file matching the Dataproc specs

 
spark_rapids profiling \
  --eventlogs file://$EVENTLOG

On-prem

The recommendations can’t be generated without cluster argument while EVENTLOG is stored on a local filesystem
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