spark-rapids-user-tools
User tools to help with the adoption, installation, execution, and tuning of the NVIDIA cuDF plugin for Apache Spark.
The wrapper improves end-user experience within the following dimensions:
- Qualification: Educate the CPU customer on the cost savings and acceleration potential of the cuDF plugin. The output shows a list of apps recommended for the cuDF plugin with estimated savings and speed-up.
- Tuning: Tune the cuDF plugin configs based on initial job run leveraging Spark event logs. The output shows recommended per-app cuDF plugin configuration settings.
- Diagnostics: Run diagnostic functions to validate the Dataproc environment with the cuDF plugin to make sure the cluster is healthy and ready for Spark jobs.
- Prediction: Predict the speedup of running a Spark application with the cuDF plugin on GPUs.
- Train: Train a model to predict the performance of a Spark job with the cuDF plugin. The output shows the model file that can be used to predict the performance of a Spark job.
Getting started
Set up a Python environment with a version between 3.10 and 3.12
-
Run the project in a virtual environment. Note, .venv is the directory created to put the virtual env in, so modify if you want a different location.
$ python -m venv .venv $ source .venv/bin/activate
-
Install spark-rapids-user-tools
-
Using released package.
$ pip install spark-rapids-user-tools
-
Install from source.
$ pip install -e .
Note:
- To install dependencies required for running unit tests, use the optional
testparameter:pip install -e '.[test]' - To install dependencies required for QualX training, use the optional
qualxparameterpip install -e '.[qualx]' - To install all the required dependencies, use the optional
dev-envparameter:pip install -e '.[dev-env]'
- To install dependencies required for running unit tests, use the optional
-
Using wheel package built from the repo (see the build steps below).
$ pip install <wheel-file>
-
-
Make sure to install CSP SDK if you plan to run the tool wrapper.
Building from source
Set up a Python environment similar to the steps above.
-
Create a virtual environment. Note, .venv is the directory created to put the virtual env in, so modify if you want a different location.
$ python -m venv .venv $ source .venv/bin/activate
-
Run the provided build script to compile the project.
$> ./build.sh -
Fat Mode: Similar to
fat jarin Java, this mode solves the problem when web access is not available to download resources having Url-paths (http/https). The command builds the tools jar file and downloads the necessary dependencies and packages them with the source code into a single 'wheel' file.$> ./build.sh fat
Logging Configuration
The core tools project uses Log4j for logging. Default log level is set to INFO.
You can configure logging settings in the log4j.properties file located in the
src/spark_rapids_pytools/resources/dev/ directory. This is applicable when
you clone the project and build it from source.
To change the logging level, modify the log4j.rootLogger property.
Possible levels include DEBUG, INFO, WARN, ERROR.
Usage and supported platforms
Please refer to spark-rapids-user-tools guide for details on how to use the tools and the platform.
Please refer to qualx guide for details on how to use the QualX tool for prediction and training.
What's new
Please refer to CHANGELOG.md for our latest changes.
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distributions
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file spark_rapids_user_tools-26.8.1-469_093fb2a9-py3-none-any.whl.
File metadata
- Download URL: spark_rapids_user_tools-26.8.1-469_093fb2a9-py3-none-any.whl
- Upload date:
- Size: 5.5 MB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via:
twine/6.1.0 CPython/3.10.17
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
a9c705daa01b353ef444e42af687c5df9105a003c9c2b0800d97b8c28744e7a1
|
|
| MD5 |
03799c61c1a735a8f586fcf9e6668931
|
|
| BLAKE2b-256 |
82526482d3feab71c172d1941c8c4835afc2e64a5d35f71f4cb6ec903c29d528
|
