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GETTING STARTED
Getting Started. The RAPIDS data science framework includes a collection of libraries for executing end-to-end data science pipelines completely in the GPU. It is designed to have a familiar look and feel to data scientists working in Python. OPEN GPU DATA SCIENCE The RAPIDS suite of open source software libraries and APIs gives you the ability to execute end-to-end data science and analytics pipelines entirely on GPUs. Licensed under Apache 2.0, RAPIDS is incubated by NVIDIA® based on extensive hardware and data science experience. RAPIDS utilizes NVIDIA CUDA® primitives for low-level computeRAPIDS + DASK
Pandas, Numpy, and scikit-learn packages are efficient, intuitive, and widely trusted—but they weren’t designed to scale. Dask is an open-source tool that can scale Python packages to multiple machines. Developed by core NumPy, pandas, scikit-learn, Jupyter, Dask is freely available and deployed in production across numerous Fortune 500companies.
RAPIDS + PLOTLY DASH Plotly’s Dash enables Data Science teams to focus on the data and models, while producing and sharing enterprise-ready analytic apps that sit on top of RAPIDS-accelerated Python dataframes. What would typically require a team of back-end developers, front-end developers, and IT can all be done by Data Science teams with Dash.GET STARTED
Get Started. The RAPIDS data science framework is a collection of libraries for running end-to-end data science pipelines completely on the GPU. The interaction is designed to have a familiar look and feel to working in Python, but utilizes optimized NVIDIA® CUDA®primitives and
RAPIDS DEMO CONTAINERMORTGAGE DATA
RAPIDS MAINTAINER DOCS RAPIDS Maintainers Docs. RAPIDS projects use an established set of guidelines and procedures for all projects. These are available for the community to review and provide feedback on. TIDY DATA A FOUNDATION FOR WRANGLING IN PANDAS INGESTING INGESTING AND RESHAPING DATA Change the layout of a data set LOGIC IN PYTHON (AND PANDAS) < Less than!= Not equal to > Greater than df.column.isin(values) Group membership == Equals pd.isnull(obj) Is NaN = Greater than or OPEN GPU DATA SCIENCE BlazingSQL is an open source project providing distributed SQL for analytics that enables the integration of enterprise data at scale. RAPIDS is actively contributing to BlazingSQL, and it integrates with RAPIDS cuDF, XGBoost, and RAPIDS cuML for GPU-accelerated data analytics and machine learning. Learn more on our BlazingSQL page.GETTING STARTED
Getting Started. The RAPIDS data science framework includes a collection of libraries for executing end-to-end data science pipelines completely in the GPU. It is designed to have a familiar look and feel to data scientists working in Python. OPEN GPU DATA SCIENCE The RAPIDS suite of open source software libraries and APIs gives you the ability to execute end-to-end data science and analytics pipelines entirely on GPUs. Licensed under Apache 2.0, RAPIDS is incubated by NVIDIA® based on extensive hardware and data science experience. RAPIDS utilizes NVIDIA CUDA® primitives for low-level computeRAPIDS + DASK
Pandas, Numpy, and scikit-learn packages are efficient, intuitive, and widely trusted—but they weren’t designed to scale. Dask is an open-source tool that can scale Python packages to multiple machines. Developed by core NumPy, pandas, scikit-learn, Jupyter, Dask is freely available and deployed in production across numerous Fortune 500companies.
RAPIDS + PLOTLY DASH Plotly’s Dash enables Data Science teams to focus on the data and models, while producing and sharing enterprise-ready analytic apps that sit on top of RAPIDS-accelerated Python dataframes. What would typically require a team of back-end developers, front-end developers, and IT can all be done by Data Science teams with Dash.GET STARTED
Get Started. The RAPIDS data science framework is a collection of libraries for running end-to-end data science pipelines completely on the GPU. The interaction is designed to have a familiar look and feel to working in Python, but utilizes optimized NVIDIA® CUDA®primitives and
RAPIDS DEMO CONTAINERMORTGAGE DATA
RAPIDS MAINTAINER DOCS RAPIDS Maintainers Docs. RAPIDS projects use an established set of guidelines and procedures for all projects. These are available for the community to review and provide feedback on. TIDY DATA A FOUNDATION FOR WRANGLING IN PANDAS INGESTING INGESTING AND RESHAPING DATA Change the layout of a data set LOGIC IN PYTHON (AND PANDAS) < Less than!= Not equal to > Greater than df.column.isin(values) Group membership == Equals pd.isnull(obj) Is NaN = Greater than orRAPIDS + CLOUD
RAPIDS GPU accelerated data science tools can be deployed on all of the major clouds, allowing anyone to take advantage of the speed increases and TCO reductions that RAPIDS enables. RAPIDS can be deployed in a number of ways, from hosted Jupyter notebooks, to the major HPO services, all the way up to large-scale clusters via Dask orKubernetes.
RAPIDS + HPO
RAPIDS supports hyperparameter optimization and AutoML solutions based on AWS SageMaker, Azure ML, Google Cloud AI, Dask ML, Optuna, Ray Tune and TPOT frameworks, so you can easily integrate with whichever framework you use today. RAPIDS also integrates easily with MLflow to track and orchestrate experiments from any of these frameworks.RAPIDS + XGBOOST
The RAPIDS team is developing GPU enhancements to open-source XGBoost, working closely with the DCML/XGBoost organization to improve the larger ecosystem. Since RAPIDS is iterating ahead of upstream XGBoost releases, some enhancements will be available earlier from the RAPIDS branch, or from RAPIDS-provided installers.GET STARTED
Get Started. The RAPIDS data science framework is a collection of libraries for running end-to-end data science pipelines completely on the GPU. The interaction is designed to have a familiar look and feel to working in Python, but utilizes optimized NVIDIA® CUDA®primitives and
OVERVIEW - RAPIDS DOCS Release Overview. We strive to release an update to the RAPIDS data science framework about every 6 weeks. Much can change between releases, so we like to consolidate the latest overview of our libraries, benchmarks, and updates in a release deck. Find the latest version below in both .pdf and .pptx format.GPUCI - RAPIDS DOCS
gpuCI. gpuCI is made of two pieces: definitions for standardized build and test scripts in all RAPIDS projects’ repositories and code to generate the Jenkins build jobs which use those standardized scripts. The build and scripts are used for. This standardization allows for scripts to create and config jobs. RAPIDS MAINTAINER DOCS RAPIDS Maintainers Docs. RAPIDS projects use an established set of guidelines and procedures for all projects. These are available for the community to review and provide feedback on. RELEASE 0.13 THE PLATFORM INSIDE AND OUT Joshua Patterson – Director, RAPIDS Engineering The Platform Inside and Out Release 0.13 UPDATES TO FROM-SOURCE BUILDS WITH CONDA FOR GCC '7.5.0 NOTE: The docker equivalent for this code can be found here With the install of gcc 7.5.0 and an updated libstdc++6 pkg, you should be able to build RAPIDS from-source with conda dependencies. If you encounter any GLIBC errors during the build process, be sure the check the version of the libstdc++6 package outlined below. Check libstdc++6 package version. In order to build with THE PLATFORM INSIDE AND OUT RELEASE 0 Joshua Patterson – Director, RAPIDS Engineering The Platform Inside and Out Release 0.11 OPEN GPU DATA SCIENCE BlazingSQL is an open source project providing distributed SQL for analytics that enables the integration of enterprise data at scale. RAPIDS is actively contributing to BlazingSQL, and it integrates with RAPIDS cuDF, XGBoost, and RAPIDS cuML for GPU-accelerated data analytics and machine learning. Learn more on our BlazingSQL page. OPEN GPU DATA SCIENCE The RAPIDS suite of open source software libraries and APIs gives you the ability to execute end-to-end data science and analytics pipelines entirely on GPUs. Licensed under Apache 2.0, RAPIDS is incubated by NVIDIA® based on extensive hardware and data science experience. RAPIDS utilizes NVIDIA CUDA® primitives for low-level computeGETTING STARTED
Getting Started. The RAPIDS data science framework includes a collection of libraries for executing end-to-end data science pipelines completely in the GPU. It is designed to have a familiar look and feel to data scientists working in Python.RAPIDS + CLOUD
RAPIDS GPU accelerated data science tools can be deployed on all of the major clouds, allowing anyone to take advantage of the speed increases and TCO reductions that RAPIDS enables. RAPIDS can be deployed in a number of ways, from hosted Jupyter notebooks, to the major HPO services, all the way up to large-scale clusters via Dask orKubernetes.
RAPIDS + XGBOOST
The RAPIDS team is developing GPU enhancements to open-source XGBoost, working closely with the DCML/XGBoost organization to improve the larger ecosystem. Since RAPIDS is iterating ahead of upstream XGBoost releases, some enhancements will be available earlier from the RAPIDS branch, or from RAPIDS-provided installers.RAPIDS + DASK
Pandas, Numpy, and scikit-learn packages are efficient, intuitive, and widely trusted—but they weren’t designed to scale. Dask is an open-source tool that can scale Python packages to multiple machines. Developed by core NumPy, pandas, scikit-learn, Jupyter, Dask is freely available and deployed in production across numerous Fortune 500companies.
GET STARTED
Get Started. The RAPIDS data science framework is a collection of libraries for running end-to-end data science pipelines completely on the GPU. The interaction is designed to have a familiar look and feel to working in Python, but utilizes optimized NVIDIA® CUDA®primitives and
RAPIDS DEMO CONTAINERHOME - RAPIDS DOCS
RAPIDS Docs. This site serves as a collection of all the documentation for RAPIDS. Whether you’re new to RAPIDS, looking to contribute, or are a part of the RAPIDS team, the docs here will help guide you. Visit rapids.ai for more information on the overall project. Documentation RAPIDS Notices Get Started RAPIDS onGIT METHODOLOGY
These help communicate the actions that are being taken, and help others when trying to review changes in a PR. First line. The first line should be a short description of the changes made, keeping the following in mind:. Limit the first line to 72 characters or less; use a third line for a detailed description; Begin the first line with a commit tag from the table above OPEN GPU DATA SCIENCE BlazingSQL is an open source project providing distributed SQL for analytics that enables the integration of enterprise data at scale. RAPIDS is actively contributing to BlazingSQL, and it integrates with RAPIDS cuDF, XGBoost, and RAPIDS cuML for GPU-accelerated data analytics and machine learning. Learn more on our BlazingSQL page. OPEN GPU DATA SCIENCE The RAPIDS suite of open source software libraries and APIs gives you the ability to execute end-to-end data science and analytics pipelines entirely on GPUs. Licensed under Apache 2.0, RAPIDS is incubated by NVIDIA® based on extensive hardware and data science experience. RAPIDS utilizes NVIDIA CUDA® primitives for low-level computeGETTING STARTED
Getting Started. The RAPIDS data science framework includes a collection of libraries for executing end-to-end data science pipelines completely in the GPU. It is designed to have a familiar look and feel to data scientists working in Python.RAPIDS + CLOUD
RAPIDS GPU accelerated data science tools can be deployed on all of the major clouds, allowing anyone to take advantage of the speed increases and TCO reductions that RAPIDS enables. RAPIDS can be deployed in a number of ways, from hosted Jupyter notebooks, to the major HPO services, all the way up to large-scale clusters via Dask orKubernetes.
RAPIDS + XGBOOST
The RAPIDS team is developing GPU enhancements to open-source XGBoost, working closely with the DCML/XGBoost organization to improve the larger ecosystem. Since RAPIDS is iterating ahead of upstream XGBoost releases, some enhancements will be available earlier from the RAPIDS branch, or from RAPIDS-provided installers.RAPIDS + DASK
Pandas, Numpy, and scikit-learn packages are efficient, intuitive, and widely trusted—but they weren’t designed to scale. Dask is an open-source tool that can scale Python packages to multiple machines. Developed by core NumPy, pandas, scikit-learn, Jupyter, Dask is freely available and deployed in production across numerous Fortune 500companies.
GET STARTED
Get Started. The RAPIDS data science framework is a collection of libraries for running end-to-end data science pipelines completely on the GPU. The interaction is designed to have a familiar look and feel to working in Python, but utilizes optimized NVIDIA® CUDA®primitives and
RAPIDS DEMO CONTAINERHOME - RAPIDS DOCS
RAPIDS Docs. This site serves as a collection of all the documentation for RAPIDS. Whether you’re new to RAPIDS, looking to contribute, or are a part of the RAPIDS team, the docs here will help guide you. Visit rapids.ai for more information on the overall project. Documentation RAPIDS Notices Get Started RAPIDS onGIT METHODOLOGY
These help communicate the actions that are being taken, and help others when trying to review changes in a PR. First line. The first line should be a short description of the changes made, keeping the following in mind:. Limit the first line to 72 characters or less; use a third line for a detailed description; Begin the first line with a commit tag from the table aboveRAPIDS + HPO
RAPIDS supports hyperparameter optimization and AutoML solutions based on AWS SageMaker, Azure ML, Google Cloud AI, Dask ML, Optuna, Ray Tune and TPOT frameworks, so you can easily integrate with whichever framework you use today. RAPIDS also integrates easily with MLflow to track and orchestrate experiments from any of these frameworks. RAPIDS DEVELOPER AND CONTRIBUTOR COMMUNITY RAPIDS + Dask. Dask is an open source project providing advanced parallelism for analytics that enables performance at scale. RAPIDS is actively contributing to Dask, and it integrates with both RAPIDS cuDF, XGBoost, and RAPIDS cuML for GPU-accelerated data analytics andmachine learning.
HOME - RAPIDS DOCS
RAPIDS Docs. This site serves as a collection of all the documentation for RAPIDS. Whether you’re new to RAPIDS, looking to contribute, or are a part of the RAPIDS team, the docs here will help guide you. Visit rapids.ai for more information on the overall project. Documentation RAPIDS Notices Get Started RAPIDS on OVERVIEW - RAPIDS DOCS Release Overview. We strive to release an update to the RAPIDS data science framework about every 6 weeks. Much can change between releases, so we like to consolidate the latest overview of our libraries, benchmarks, and updates in a release deck. Find the latest version below in both .pdf and .pptx format. RAPIDS BRANDING AND GUIDES Guides and Themes. This page contains useful guides, assets, fonts, and themes to help you style RAPIDS communications consistently and clearly. RAPIDS Citation Guide. We welcome citations!If you use RAPIDS in a publication, please use citations in the following format (BibTeXentry for LaTeX):
RAPIDS + PLOTLY DASH Plotly’s Dash enables Data Science teams to focus on the data and models, while producing and sharing enterprise-ready analytic apps that sit on top of RAPIDS-accelerated Python dataframes. What would typically require a team of back-end developers, front-end developers, and IT can all be done by Data Science teams with Dash. RAPIDS + NVIDIA MERLIN NVIDIA Merlin is an open source library designed to accelerate recommender systems on NVIDIA GPUs. It enables data scientists, machine learning engineers, and researchers to build high-performing recommenders at scale. Merlin includes tools to address common ETL,training, and
GPUCI - RAPIDS DOCS
gpuCI. gpuCI is made of two pieces: definitions for standardized build and test scripts in all RAPIDS projects’ repositories and code to generate the Jenkins build jobs which use those standardized scripts. The build and scripts are used for. This standardization allows for scripts to create and config jobs.0.19 RELEASE
5 cuDF Updates: Deep Dive Features added in 0.19 Decimal data type is now supported for joins, read_parquet, and column comparison functionsin Python
RAPIDS MAINTAINER DOCS RAPIDS Maintainers Docs. RAPIDS projects use an established set of guidelines and procedures for all projects. These are available for the community to review and provide feedback on. OPEN GPU DATA SCIENCE BlazingSQL is an open source project providing distributed SQL for analytics that enables the integration of enterprise data at scale. RAPIDS is actively contributing to BlazingSQL, and it integrates with RAPIDS cuDF, XGBoost, and RAPIDS cuML for GPU-accelerated data analytics and machine learning. Learn more on our BlazingSQL page.GETTING STARTED
Getting Started. The RAPIDS data science framework includes a collection of libraries for executing end-to-end data science pipelines completely in the GPU. It is designed to have a familiar look and feel to data scientists working in Python. OPEN GPU DATA SCIENCE The RAPIDS suite of open source software libraries and APIs gives you the ability to execute end-to-end data science and analytics pipelines entirely on GPUs. Licensed under Apache 2.0, RAPIDS is incubated by NVIDIA® based on extensive hardware and data science experience. RAPIDS utilizes NVIDIA CUDA® primitives for low-level computeRAPIDS + DASK
Pandas, Numpy, and scikit-learn packages are efficient, intuitive, and widely trusted—but they weren’t designed to scale. Dask is an open-source tool that can scale Python packages to multiple machines. Developed by core NumPy, pandas, scikit-learn, Jupyter, Dask is freely available and deployed in production across numerous Fortune 500companies.
GET STARTED
Get Started. The RAPIDS data science framework is a collection of libraries for running end-to-end data science pipelines completely on the GPU. The interaction is designed to have a familiar look and feel to working in Python, but utilizes optimized NVIDIA® CUDA®primitives and
RAPIDS DEMO CONTAINERGIT METHODOLOGY
These help communicate the actions that are being taken, and help others when trying to review changes in a PR. First line. The first line should be a short description of the changes made, keeping the following in mind:. Limit the first line to 72 characters or less; use a third line for a detailed description; Begin the first line with a commit tag from the table above TIDY DATA A FOUNDATION FOR WRANGLING IN PANDAS INGESTING INGESTING AND RESHAPING DATA Change the layout of a data set LOGIC IN PYTHON (AND PANDAS) < Less than!= Not equal to > Greater than df.column.isin(values) Group membership == Equals pd.isnull(obj) Is NaN = Greater than or GPUCI - RAPIDS DOCSSEE MORE ON DOCS.RAPIDS.AI RAPIDS MAINTAINER DOCS RAPIDS Maintainers Docs. RAPIDS projects use an established set of guidelines and procedures for all projects. These are available for the community to review and provide feedback on. OPEN GPU DATA SCIENCE BlazingSQL is an open source project providing distributed SQL for analytics that enables the integration of enterprise data at scale. RAPIDS is actively contributing to BlazingSQL, and it integrates with RAPIDS cuDF, XGBoost, and RAPIDS cuML for GPU-accelerated data analytics and machine learning. Learn more on our BlazingSQL page.GETTING STARTED
Getting Started. The RAPIDS data science framework includes a collection of libraries for executing end-to-end data science pipelines completely in the GPU. It is designed to have a familiar look and feel to data scientists working in Python. OPEN GPU DATA SCIENCE The RAPIDS suite of open source software libraries and APIs gives you the ability to execute end-to-end data science and analytics pipelines entirely on GPUs. Licensed under Apache 2.0, RAPIDS is incubated by NVIDIA® based on extensive hardware and data science experience. RAPIDS utilizes NVIDIA CUDA® primitives for low-level computeRAPIDS + DASK
Pandas, Numpy, and scikit-learn packages are efficient, intuitive, and widely trusted—but they weren’t designed to scale. Dask is an open-source tool that can scale Python packages to multiple machines. Developed by core NumPy, pandas, scikit-learn, Jupyter, Dask is freely available and deployed in production across numerous Fortune 500companies.
GET STARTED
Get Started. The RAPIDS data science framework is a collection of libraries for running end-to-end data science pipelines completely on the GPU. The interaction is designed to have a familiar look and feel to working in Python, but utilizes optimized NVIDIA® CUDA®primitives and
RAPIDS DEMO CONTAINERGIT METHODOLOGY
These help communicate the actions that are being taken, and help others when trying to review changes in a PR. First line. The first line should be a short description of the changes made, keeping the following in mind:. Limit the first line to 72 characters or less; use a third line for a detailed description; Begin the first line with a commit tag from the table above TIDY DATA A FOUNDATION FOR WRANGLING IN PANDAS INGESTING INGESTING AND RESHAPING DATA Change the layout of a data set LOGIC IN PYTHON (AND PANDAS) < Less than!= Not equal to > Greater than df.column.isin(values) Group membership == Equals pd.isnull(obj) Is NaN = Greater than or GPUCI - RAPIDS DOCSSEE MORE ON DOCS.RAPIDS.AI RAPIDS MAINTAINER DOCS RAPIDS Maintainers Docs. RAPIDS projects use an established set of guidelines and procedures for all projects. These are available for the community to review and provide feedback on.RAPIDS + CLOUD
RAPIDS GPU accelerated data science tools can be deployed on all of the major clouds, allowing anyone to take advantage of the speed increases and TCO reductions that RAPIDS enables. RAPIDS can be deployed in a number of ways, from hosted Jupyter notebooks, to the major HPO services, all the way up to large-scale clusters via Dask orKubernetes.
RAPIDS + XGBOOST
The RAPIDS team is developing GPU enhancements to open-source XGBoost, working closely with the DCML/XGBoost organization to improve the larger ecosystem. Since RAPIDS is iterating ahead of upstream XGBoost releases, some enhancements will be available earlier from the RAPIDS branch, or from RAPIDS-provided installers. OVERVIEW - RAPIDS DOCS Release Overview. We strive to release an update to the RAPIDS data science framework about every 6 weeks. Much can change between releases, so we like to consolidate the latest overview of our libraries, benchmarks, and updates in a release deck. Find the latest version below in both .pdf and .pptx format. RAPIDS BRANDING AND GUIDES Guides and Themes. This page contains useful guides, assets, fonts, and themes to help you style RAPIDS communications consistently and clearly. RAPIDS Citation Guide. We welcome citations!If you use RAPIDS in a publication, please use citations in the following format (BibTeXentry for LaTeX):
RAPIDS + PLOTLY DASH Plotly’s Dash enables Data Science teams to focus on the data and models, while producing and sharing enterprise-ready analytic apps that sit on top of RAPIDS-accelerated Python dataframes. What would typically require a team of back-end developers, front-end developers, and IT can all be done by Data Science teams with Dash.GPUCI - RAPIDS DOCS
gpuCI. gpuCI is made of two pieces: definitions for standardized build and test scripts in all RAPIDS projects’ repositories and code to generate the Jenkins build jobs which use those standardized scripts. The build and scripts are used for. This standardization allows for scripts to create and config jobs. RAPIDS MAINTAINER DOCS RAPIDS Maintainers Docs. RAPIDS projects use an established set of guidelines and procedures for all projects. These are available for the community to review and provide feedback on. RELEASE 0.13 THE PLATFORM INSIDE AND OUT Joshua Patterson – Director, RAPIDS Engineering The Platform Inside and Out Release 0.13 UPDATES TO FROM-SOURCE BUILDS WITH CONDA FOR GCC '7.5.0 NOTE: The docker equivalent for this code can be found here With the install of gcc 7.5.0 and an updated libstdc++6 pkg, you should be able to build RAPIDS from-source with conda dependencies. If you encounter any GLIBC errors during the build process, be sure the check the version of the libstdc++6 package outlined below. Check libstdc++6 package version. In order to build with THE PLATFORM INSIDE AND OUT RELEASE 0 Joshua Patterson – Director, RAPIDS Engineering The Platform Inside and Out Release 0.11 OPEN GPU DATA SCIENCE BlazingSQL is an open source project providing distributed SQL for analytics that enables the integration of enterprise data at scale. RAPIDS is actively contributing to BlazingSQL, and it integrates with RAPIDS cuDF, XGBoost, and RAPIDS cuML for GPU-accelerated data analytics and machine learning. Learn more on our BlazingSQL page. OPEN GPU DATA SCIENCE The RAPIDS suite of open source software libraries and APIs gives you the ability to execute end-to-end data science and analytics pipelines entirely on GPUs. Licensed under Apache 2.0, RAPIDS is incubated by NVIDIA® based on extensive hardware and data science experience. RAPIDS utilizes NVIDIA CUDA® primitives for low-level computeGETTING STARTED
Getting Started. The RAPIDS data science framework includes a collection of libraries for executing end-to-end data science pipelines completely in the GPU. It is designed to have a familiar look and feel to data scientists working in Python.RAPIDS + XGBOOST
The RAPIDS team is developing GPU enhancements to open-source XGBoost, working closely with the DCML/XGBoost organization to improve the larger ecosystem. Since RAPIDS is iterating ahead of upstream XGBoost releases, some enhancements will be available earlier from the RAPIDS branch, or from RAPIDS-provided installers.RAPIDS + DASK
Pandas, Numpy, and scikit-learn packages are efficient, intuitive, and widely trusted—but they weren’t designed to scale. Dask is an open-source tool that can scale Python packages to multiple machines. Developed by core NumPy, pandas, scikit-learn, Jupyter, Dask is freely available and deployed in production across numerous Fortune 500companies.
GET STARTED
Get Started. The RAPIDS data science framework is a collection of libraries for running end-to-end data science pipelines completely on the GPU. The interaction is designed to have a familiar look and feel to working in Python, but utilizes optimized NVIDIA® CUDA®primitives and
OVERVIEW - RAPIDS DOCS Release Overview. We strive to release an update to the RAPIDS data science framework about every 6 weeks. Much can change between releases, so we like to consolidate the latest overview of our libraries, benchmarks, and updates in a release deck. Find the latest version below in both .pdf and .pptx format.HOME - RAPIDS DOCS
RAPIDS Docs. This site serves as a collection of all the documentation for RAPIDS. Whether you’re new to RAPIDS, looking to contribute, or are a part of the RAPIDS team, the docs here will help guide you. Visit rapids.ai for more information on the overall project. Documentation RAPIDS Notices Get Started RAPIDS onHOTFIX PROCESS
Create your branch from the branch-M.A branch. Implement the fix succinctly. Change the minimal amount of code required. Update related documentation and unit tests. It is acceptable to implement a quick fix and open a new issue for a more in depth solution. Once complete, create a pull request targeting branch-M.A. RAPIDS MAINTAINER DOCS RAPIDS Maintainers Docs. RAPIDS projects use an established set of guidelines and procedures for all projects. These are available for the community to review and provide feedback on. OPEN GPU DATA SCIENCE BlazingSQL is an open source project providing distributed SQL for analytics that enables the integration of enterprise data at scale. RAPIDS is actively contributing to BlazingSQL, and it integrates with RAPIDS cuDF, XGBoost, and RAPIDS cuML for GPU-accelerated data analytics and machine learning. Learn more on our BlazingSQL page. OPEN GPU DATA SCIENCE The RAPIDS suite of open source software libraries and APIs gives you the ability to execute end-to-end data science and analytics pipelines entirely on GPUs. Licensed under Apache 2.0, RAPIDS is incubated by NVIDIA® based on extensive hardware and data science experience. RAPIDS utilizes NVIDIA CUDA® primitives for low-level computeGETTING STARTED
Getting Started. The RAPIDS data science framework includes a collection of libraries for executing end-to-end data science pipelines completely in the GPU. It is designed to have a familiar look and feel to data scientists working in Python.RAPIDS + XGBOOST
The RAPIDS team is developing GPU enhancements to open-source XGBoost, working closely with the DCML/XGBoost organization to improve the larger ecosystem. Since RAPIDS is iterating ahead of upstream XGBoost releases, some enhancements will be available earlier from the RAPIDS branch, or from RAPIDS-provided installers.RAPIDS + DASK
Pandas, Numpy, and scikit-learn packages are efficient, intuitive, and widely trusted—but they weren’t designed to scale. Dask is an open-source tool that can scale Python packages to multiple machines. Developed by core NumPy, pandas, scikit-learn, Jupyter, Dask is freely available and deployed in production across numerous Fortune 500companies.
GET STARTED
Get Started. The RAPIDS data science framework is a collection of libraries for running end-to-end data science pipelines completely on the GPU. The interaction is designed to have a familiar look and feel to working in Python, but utilizes optimized NVIDIA® CUDA®primitives and
OVERVIEW - RAPIDS DOCS Release Overview. We strive to release an update to the RAPIDS data science framework about every 6 weeks. Much can change between releases, so we like to consolidate the latest overview of our libraries, benchmarks, and updates in a release deck. Find the latest version below in both .pdf and .pptx format.HOME - RAPIDS DOCS
RAPIDS Docs. This site serves as a collection of all the documentation for RAPIDS. Whether you’re new to RAPIDS, looking to contribute, or are a part of the RAPIDS team, the docs here will help guide you. Visit rapids.ai for more information on the overall project. Documentation RAPIDS Notices Get Started RAPIDS onHOTFIX PROCESS
Create your branch from the branch-M.A branch. Implement the fix succinctly. Change the minimal amount of code required. Update related documentation and unit tests. It is acceptable to implement a quick fix and open a new issue for a more in depth solution. Once complete, create a pull request targeting branch-M.A. RAPIDS MAINTAINER DOCS RAPIDS Maintainers Docs. RAPIDS projects use an established set of guidelines and procedures for all projects. These are available for the community to review and provide feedback on.RAPIDS + CLOUD
RAPIDS GPU accelerated data science tools can be deployed on all of the major clouds, allowing anyone to take advantage of the speed increases and TCO reductions that RAPIDS enables. RAPIDS can be deployed in a number of ways, from hosted Jupyter notebooks, to the major HPO services, all the way up to large-scale clusters via Dask orKubernetes.
RAPIDS BRANDING AND GUIDES Guides and Themes. This page contains useful guides, assets, fonts, and themes to help you style RAPIDS communications consistently and clearly. RAPIDS Citation Guide. We welcome citations!If you use RAPIDS in a publication, please use citations in the following format (BibTeXentry for LaTeX):
RAPIDS + BLAZINGSQL
Blazing fast SQL on Rapids. BlazingSQL is an incredibly fast distributed SQL engine on GPUs. BlazingSQL enables data scientists to easily connect large-scale data lakes to GPU-accelerated analytics. With a few lines of code, you can directly query raw file formats such as CSV and Apache Parquet inside Data Lakes like HDFS and AWS S3, andCONTRIBUTING
Contributing. RAPIDS can only grow with community support and it is vital to include developers at all levels. Reporting bugs, fixing them, and/or creating new features are all vital for the success of RAPIDS. The following pages outline our approach for contributing toRAPIDS.
RAPIDS MAINTAINER DOCS RAPIDS Maintainers Docs. RAPIDS projects use an established set of guidelines and procedures for all projects. These are available for the community to review and provide feedback on. DATASETS - RAPIDS DOCS Datasets. Datasets listed here are used in RAPIDS containers, demos, and notebooks. Each dataset describes the data, the data source, how to use the data, and links for download. File an issue here for any issues encountered with the datasets.DEMOS - RAPIDS DOCS
Demos. RAPIDS demos allow users to test drive RAPIDS with sample datasets and notebooks.RAPIDS NOTICES
RAPIDS General Notice. RGN. Everyone. Project wide announcements and updates, including breaking changes. RAPIDS Support Notice. RSN. Everyone. Updates on RAPIDS support for specific versions of CUDA, Python, OS, platforms, and compliers.GPUCI - RAPIDS DOCS
gpuCI. gpuCI is made of two pieces: definitions for standardized build and test scripts in all RAPIDS projects’ repositories and code to generate the Jenkins build jobs which use those standardized scripts. The build and scripts are used for. This standardization allows for scripts to create and config jobs. UPDATES TO FROM-SOURCE BUILDS WITH CONDA FOR GCC '7.5.0 NOTE: The docker equivalent for this code can be found here With the install of gcc 7.5.0 and an updated libstdc++6 pkg, you should be able to build RAPIDS from-source with conda dependencies. If you encounter any GLIBC errors during the build process, be sure the check the version of the libstdc++6 package outlined below. Check libstdc++6 package version. In order to build with☰
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OPEN GPU DATA SCIENCEGET STARTED
GPU DATA SCIENCE
__ ACCELERATED DATA SCIENCE The RAPIDS suite of open source software libraries gives you the freedom to execute end-to-end data science and analytics pipelinesentirely on GPUs.
LEARN MORE ABOUT RAPIDS __ __ SCALE OUT ON GPUS Seamlessly scale from GPU workstations to multi-GPU servers and multi-node clusters with Dask. LEARN MORE ABOUT DASK __ __ PYTHON INTEGRATION Accelerate your Python data science toolchain with minimal code changes and no new tools to learn. LEARN MORE ABOUT OUR LIBRARIES __ __ TOP MODEL ACCURACY Increase machine learning model accuracy by iterating on models faster and deploying them more frequently. LEARN MORE ABOUT DEPLOYMENT __ __ REDUCED TRAINING TIME Drastically improve your productivity with more interactive data science tools like XGBoost. LEARN MORE ABOUT XGBOOST ____ OPEN SOURCE
RAPIDS is an open source project. Supported by NVIDIA, it also relies on numba, apache arrow, and many more open source projects. LEARN MORE ABOUT OUR PROJECTS __GETTING STARTED
The RAPIDS data science framework is designed to have a familiar look and feel to data scientist working in Python. Here’s a code snippet where we read in a CSV file and output some descriptive statistics:import cudf
gdf = cudf.read_csv('path/to/file.csv') for column in gdf.columns:print(gdf.mean())
Find more details on our GET STARTED SECTION ____ TRY NOW IN COLAB
Jump right into a GPU powered RAPIDS notebook with COLABRATORYfor free.
GO TO EXAMPLE NOTEBOOK __ __ 10 MINUTES TO CUDF Modeled after 10 Minutes to Pandas, this is a short introduction to cuDF that is geared mainly for new users.GO TO GUIDE __
__ 10 MINUTES TO DASK-XGBOOST A short introduction to XGBoost with a distributed CUDA DataFrame viaDask-cuDF.
GO TO GUIDE __
__ EXAMPLE NOTEBOOKS A Github repository with our introductory examples of XGBoost, cuML demos, cuGraph demos, and more.GO TO REPO __
__ EXAMPLE NOTEBOOKS EXTENDED A second Github repository with our extended collection of notebookexamples.
GO TO REPO __
RAPIDS NEWS
__ USING RAPIDS MEMORY MANAGER WITH CUPY Coauthored by John Kirkham and Mads R. B. KristensenIn Python workflows, it's common to use multiple libraries based on their strengths. For example, you might start with the RAPIDS GPU Dataframe library, cuDF, to load data from disk and... __ post by John Kirkham __ FAST SUPPORT VECTOR CLASSIFICATION WITH RAPIDS CUML __ post by Tamás Fehér __ INPUT AND OUTPUT CONFIGURABILITY IN RAPIDS CUML The RAPIDS machine learning library, cuML, supports several types of input data formats while attempting to return results in the output format that fits best into users' workflows. The RAPIDS team has added functionality to cuML to supp... __ post by Dante Gama Dessavre__ BLAZINGSQL
RT @blazingsql: BlazingSQL + @RAPIDSai Stack is now live on @awsmarketplace! https://t.co/QXBoz4PrvS Launch a high-performance@ProjectJu...
__ retweet by @rapidsai__ PYTORCH
RT @PyTorch: The Chainer team introduced pytorch-pfn-extras (PPE), which bridges the gap between Chainer and PyTorch. Check out thisguide...
__ retweet by @rapidsai__ ANACONDA
RT @anacondainc: Looking to up-skill from home? Don't miss our live, virtual tutorials on @dask_dev, @HoloViz_org, @rapidsai,#DeepLearning...
__ retweet by @rapidsaiRAPIDS REPOSITORIES
RAPIDS is committed to open source. We strive for a 6 WEEK RELEASE SCHEDULE , below is a generalized release schedule. Learn more on our ROAD TO 1.0 POST ____ RELEASE SCHEDULE
Release Schedule FEB 2020 MAR 2020 MAY 2020 LEGACY STABLE NIGHTLY0.12 0.13 0.14
__ RAPIDS APIS AND LIBRARIES RAPIDS is open source licensed under Apache 2.0, spanning multiple projects that range from GPU dataframes to GPU accelerated ML algorithms. Its also provides native array_interface support, allowing Apache Arrow data to be pushed to deep learning frameworks.LEARN MORE __
__ CONTRIBUTING
Whether you are new to RAPIDS, looking to help, or are part of the team, learn about our contributing guidelines on our contributingpage.
GO TO DOCS __
__ CUDF API
GITHUB / DOCS
/ CHANGE LOG
cuDF is a Python GPU DataFrame library (built on the APACHE ARROW columnar memory format) for loading, joining, aggregating, filtering, and otherwise manipulating data all in a PANDAS-LIKE API familiar to datascientists.
__ LIBCUDF LIB
GITHUB / DOCS
/ CHANGE LOG
libcudf is a C/C++ CUDA library for implementing standard dataframe operations. It is part of the cuDF repository.__ CUML API
GITHUB / DOCS
/ CHANGE LOG
cuML is a suite of libraries that implement machine learning algorithms and mathematical primitives functions that are compatible with other RAPIDS projects, all in a SCIKIT-LEARN-LIKE API familiar to datascientists.
__ CUGRAPH API
GITHUB / DOCS
/ CHANGE LOG
cuGraph is a GPU accelerated graph analytics library, with functionality like NETWORKX , which is seamlessly integrated into the RAPIDS data science platform.__ CUSGINAL API
GITHUB / DOCS
/ CHANGE LOG
cuSignal is a GPU accelerated signal processing library built around aSCIPY SIGNAL-LIKE
API, CuPy,
and custom Numba and CuPy CUDA kernels. cuSignal is written exclusively in Python and demonstrates GPU speeds without a C++software layer.
__ CUSPATIAL API
GITHUB / DOCS
/ CHANGE LOG
cuSpatial is an efficient C++ library accelerated on GPUs with Python bindings to enable use by the data science community. cuSpatial provides significant GPU-acceleration to common spatial and spatiotemporal operations such as point-in-polygon tests, distances between trajectories, and trajectory clustering when compared to CPU-based implementations.__ CUXFILTER API
GITHUB / DOCS
/ CHANGE LOG
cuxfilter is a framework to connect web visualizations to GPU accelerated crossfiltering. Inspired by the javascript version of the ORIGINAL , it enables interactive and super fast multi-dimensional filtering of 100 million+ row tabular datasets via CUDF .__ CLX API
GITHUB / DOCS
/ CHANGE LOG
Cyber Log Accelerators (CLX), also pronounced “clicks”, provides a collection of RAPIDS examples for security analysts, data scientists, and engineers to quickly get started applying RAPIDS and GPU acceleration to real-world cybersecurity use cases.__ NVSTRINGS API
GITHUB / DOCS
/ CHANGE LOG
nvStrings, the Python bindings for CUSTRINGS , provides a pandas-like API that will be familiar to data engineers & data scientists, so they can use it to easily accelerate their workflows without going into the details of CUDA programming.__ RMM LIB
GITHUB / DOCS
/ CHANGE LOG
RAPIDS Memory Manager (RMM) is a central place for all device memory allocations in cuDF (C++ and Python) and other RAPIDS libraries. In addition, it is a replacement allocator for CUDA Device Memory (and CUDA Managed Memory) and a pool allocator to make CUDA device memory allocation / deallocation faster and asynchronous.COMMUNITY PROJECTS
__ RAPIDS + BLAZINGSQL BlazingSQL is an open source project providing distributed SQL for analytics that enables the integration of enterprise data at scale. RAPIDS is actively contributing to BlazingSQL, and it integrates with RAPIDS cuDF, XGBoost, and RAPIDS cuML for GPU-accelerated data analytics and machine learning. LEARN MORE ON OUR BLAZINGSQL PAGE ____ RAPIDS + DASK
Dask is an open source project providing advanced parallelism for analytics that enables performance at scale. RAPIDS is actively contributing to Dask, and it integrates with both RAPIDS cuDF, XGBoost, and RAPIDS cuML for GPU-accelerated data analytics andmachine learning.
LEARN MORE ON OUR DASK PAGE ____ RAPIDS + XGBOOST
XGBoost is a well-known gradient boosted decision trees (GBDT) machine learning package used to tackle regression, classification, and ranking problems. The RAPIDS team works closely with the Distributed Machine Learning Common (DMLC) XGBoost organization to upstream code and ensure that all components of the GPU-accelerated analytics ecosystem work together. LEARN MORE ON OUR XGBOOST PAGE ____ RAPIDS + SPARK
Coming soon: NVIDIA will be bringing RAPIDS to Apache Spark. LEARN MORE ON OUR BLOG POST __CONTRIBUTORS
ADOPTERS
OPEN SOURCE
EXPERIENCE DATA SCIENCE ON GPUS WITH RAPIDSGET STARTED
2020 RAPIDS
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