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Key parameters and specifications
Key parameters and specifications
Parameter
Value
Language
Scala
Category
Infrastructure
Subcategory
Orchestration, Data systems
Keywords
Data processing, DevOps, Memory management, Optimization
Stars
1,011
Forks
311
Contributors
91
Created date
2020-05-14
Updated date
2026-10-16
Stars in 1 day
1
Stars in 7 days
2
Rank
5,933
Previous rank
18,817
Rank change
12,884
7-day stars rank
8,310
Previous 7-day stars rank
8,380
7-day stars rank change
70
Total stars rank
11,064
Previous total stars rank
11,051
Total stars rank change
-13
Country
USA
README summary
NVIDIA cuDF plugin for Apache Spark NOTE: For the latest stable README. The NVIDIA cuDF plugin for Apache Spark provides a plugin library that leverages GPUs to accelerate processing via the cuDF (CUDA DataFrame) libraries. Documentation on the current release can be found here. To get started and try the plugin out use the getting started guide. Note: The NVIDIA cuDF plugin for Apache Spark was formerly known as the RAPIDS Accelerator for Apache Spark. The RAPIDS name will be
sunset over time. Artifact names will remain the same for now. Compatibility The SQL plugin tries to produce results that are bit for bit identical with Apache Spark. Operator compatibility is documented here Tuning To get started tuning your job and get the most performance out of it please start with the tuning guide. Configuration The plugin has a set of Spark configs that control its behavior and are documented here. Issues & Questions We use github to track bugs, feature
requests, and answer questions. File an issue for a bug or feature request. Ask or answer a question on the discussion board. Download The jar files for the most recent release can be retrieved from the download page. Building From Source See the build instructions in the contributing guide. Integration The cuDF plugin provides some APIs for doing zero copy data transfer into other GPU enabled applications. Currently, we are working with XGBoost to try to provide this integration out
of the box. You may need to disable RMM caching when exporting data to an ML library as that library will likely want to use all of the GPU's memory and if it is not aware of RMM it will not have access to any of the memory that RMM is holding. Qualification and Profiling tools The Qualification and Profiling tools are available in the NVIDIA/cudf-spark-tools repository. Please refer to Qualification tool documentation and Profiling tool documentation
for more details on how to use the tools. Dependency for External Projects If you develop functionality on top of the cuDF plugin (we currently limit support to GPU-accelerated UDFs) we recommend you declare the distribution artifact as a dependency.
Frequently asked questions
How many stars and forks are recorded?
NVIDIA/cudf-spark has 1,011 stars and 311 forks.
What is the repository rank?
NVIDIA/cudf-spark is ranked 5,933.
What language and category are recorded?
NVIDIA/cudf-spark has 1,011 stars, uses Scala and belongs to Infrastructure.
Which repository is closest in rank?
NVIDIA/cudf-spark is compared with MoonshotAI/checkpoint-engine, ranked 5,934 with 1,010 stars.
README
NVIDIA cuDF plugin for Apache Spark
NOTE: For the latest stable README.md ensure you are on the main branch.
The NVIDIA cuDF plugin for Apache Spark provides a plugin library that
leverages GPUs to accelerate processing via the cuDF (CUDA
DataFrame) libraries.
Documentation on the current release can be found here.
Note: The NVIDIA cuDF plugin for Apache Spark was formerly known as the RAPIDS Accelerator for
Apache Spark. The RAPIDS name will be sunset over time. GitHub links from spark-rapids will
redirect to cudf-spark. Artifact names will remain the same for now.
Compatibility
The SQL plugin tries to produce results that are bit for bit identical with Apache Spark.
Operator compatibility is documented here
Tuning
To get started tuning your job and get the most performance out of it please start with the
tuning guide.
Configuration
The plugin has a set of Spark configs that control its behavior and are documented
here.
Issues & Questions
We use github to track bugs, feature requests, and answer questions. File an
issue for a bug or feature request. Ask
or answer a question on the discussion board.
Download
The jar files for the most recent release can be retrieved from the download
page.
The cuDF plugin provides some APIs for doing zero copy data transfer into other GPU enabled
applications. It is described
here.
Currently, we are working with XGBoost to try to provide this integration out of the box.
You may need to disable RMM caching when exporting data to an ML library as that library
will likely want to use all of the GPU's memory and if it is not aware of RMM it will not have
access to any of the memory that RMM is holding.
Qualification and Profiling tools
The Qualification and Profiling tools are available in the
NVIDIA/cudf-spark-tools repository.
If you develop functionality on top of the cuDF plugin (we currently limit support to
GPU-accelerated UDFs) we recommend you declare the distribution artifact as a provided dependency.
