cuxfilter ( ku-cross-filter ) is a RAPIDS 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. cuxfilter is one of the core projects of the “RAPIDS viz” team. Taking the axiom that “a slider is worth a thousand queries” from @lmeyerov to heart, we want to enable fast exploratory data analytics through an easier-to-use pythonic notebook interface.
https://docs.rapids.ai/api/cuxfilter/nightly/Tags | visualization gpu crossfilter rapids cudf |
Implementation | Jupyter Notebook |
License | Apache |
Platform |
The RAPIDS cuGraph library is a collection of GPU accelerated graph algorithms that process data found in GPU DataFrames. The vision of cuGraph is to make graph analysis ubiquitous to the point that users just think in terms of analysis and not technologies or frameworks. To realize that vision, cuGraph operates, at the Python layer, on GPU DataFrames, thereby allowing for seamless passing of data between ETL tasks in cuDF and machine learning tasks in cuML. Data scientists familiar with Python will quickly pick up how cuGraph integrates with the Pandas-like API of cuDF. Likewise, users familiar with NetworkX will quickly recognize the NetworkX-like API provided in cuGraph, with the goal to allow existing code to be ported with minimal effort into RAPIDS. For users familiar with C++/CUDA and graph structures, a C++ API is also provided. However, there is less type and structure checking at the C++ layer. For more project details, see rapids.ai.
NOTE: For the latest stable README.md ensure you are on the main branch. Built based on the Apache Arrow columnar memory format, cuDF is a GPU DataFrame library for loading, joining, aggregating, filtering, and otherwise manipulating data.
anaconda gpu arrow machine-learning-algorithms h2o cuda pandas python-api mapd gpu-dataframe rapids cudfDimensional charting built to work natively with crossfilter rendered using d3.js. MapD-Charting is a superfast charting library that works natively with crossfilter that is based off dc.js. It is designed to work with MapD-Connector and MapD-Crossfilter to create charts instantly with our MapD-Core SQL Database. Please see examples for further understanding to quickly create interactive charts.
visualization charting-library mapd crossfilter interactive-visualization gpu svg animation canvas chart dimensional d3Dimensional charting built to work natively with crossfilter rendered using d3.js. MapD-Charting is a superfast charting library that works natively with crossfilter that is based off dc.js. It is designed to work with MapD-Connector and MapD-Crossfilter to create charts instantly with our MapD-Core SQL Database. Please see examples for further understanding to quickly create interactive charts.
visualization charting-library gpu crossfilter interactive-visualization mapd svg animation canvas chart dimensional d3The RAPIDS cuSignal project leverages CuPy, Numba, and the RAPIDS ecosystem for GPU accelerated signal processing. In some cases, cuSignal is a direct port of Scipy Signal to leverage GPU compute resources via CuPy but also contains Numba CUDA and Raw CuPy CUDA kernels for additional speedups for selected functions. cuSignal achieves its best gains on large signals and compute intensive functions but stresses online processing with zero-copy memory (pinned, mapped) between CPU and GPU. NOTE: For the latest stable README.md ensure you are on the latest branch.
NOTE: cuSpatial depends on cuDF and RMM from RAPIDS. The rest of steps assume the environment variable CUDF_HOME points to the root directory of your clone of the cuDF repo, and that the cudf_dev Anaconda environment created in step 3 is active.
Crossfilter is a JavaScript library for exploring large multivariate datasets in the browser. Crossfilter supports extremely fast (<30ms) interaction with coordinated views, even with datasets containing a million or more records. Since most interactions only involve a single dimension, and then only small adjustments are made to the filter values, incremental filtering and reducing is significantly faster than starting from scratch. Crossfilter uses sorted indexes (and a few bit-twiddling hacks) to make this possible, dramatically increasing the performance of live histograms and top-K lists. Crossfilter is available under the Apache License.
analytics visualization crossfilterA multi-dimensional charting library built to work natively with crossfilter and rendered using d3.js
crossfilter visualization charting svg animation canvas chart dimensional d3 data-visualization charts charting-librarycuML is a suite of libraries that implement machine learning algorithms and mathematical primitives functions that share compatible APIs with other RAPIDS projects. cuML enables data scientists, researchers, and software engineers to run traditional tabular ML tasks on GPUs without going into the details of CUDA programming. In most cases, cuML's Python API matches the API from scikit-learn.
machine-learning gpu machine-learning-algorithms cuda nvidiaDatoviz is an open-source high-performance interactive scientific data visualization library leveraging the graphics processing unit (GPU) for speed, visual quality, and scalability. It supports both 2D and 3D rendering, as well as minimal graphical user interfaces (using the Dear ImGUI library). Written in C, Datoviz has been designed from the ground up for performance. It provides native Python bindings (based on Cython). Bindings to other languages could be developed thanks to community efforts (Julia, R, MATLAB, Rust, C#, and so on). Datoviz uses the Vulkan graphics API created by the Khronos consortium, successor of OpenGL. Supporting other modern graphics API, such as WebGPU, would constitute interesting developments.
visualization cpp gpu graphics rendering vulkan data-visualization scientific-visualization scientific-computing data-vizOmniSciDB is the foundation of the OmniSci platform. OmniSciDB is SQL-based, relational, columnar and specifically developed to harness the massive parallelism of modern CPU and GPU hardware. OmniSciDB can query up to billions of rows in milliseconds, and is capable of unprecedented ingestion speeds, making it the ideal SQL engine for the era of big, high-velocity data.
database visualization machine-learning real-time sql interactive gpu llvm olap mapd geodata distributed-database gpu-database analytics analytics-databaseFast multidimensional filtering for coordinated views.
square analytics visualizationluma.gl's provides efficient and easy-to-use WebGL2-based building blocks enabling high-performance GPU-based data visualizations and computations on your browser.See Examples and Documentation. Change Log.
webgl data-visualization uber visualization animation 3dPyrender is a pure Python (2.7, 3.4, 3.5, 3.6) library for physically-based rendering and visualization. It is designed to meet the glTF 2.0 specification from Khronos. Pyrender is lightweight, easy to install, and simple to use. It comes packaged with both an intuitive scene viewer and a headache-free offscreen renderer with support for GPU-accelerated rendering on headless servers, which makes it perfect for machine learning applications.
visualization opengl rendering 3d-graphics gltf-viewerMapD Core is an in-memory, column store, SQL relational database that was designed from the ground up to run on GPUs. MapD Core is the foundational element of a larger data exploration platform that emphasizes speed at scale. By taking advantage of the parallel processing power of the hardware, MapD Core can query billions of rows in milliseconds. Furthermore, by using the graphics pipelines of GPUs, MapD Core can render graphics directly from the server.
gpu database olap visualization sql machine-learning analytics column-store columnar-databaseGAN Lab is a novel interactive visualization tool for anyone to learn and experiment with Generative Adversarial Networks (GANs), a popular class of complex deep learning models. With GAN Lab, you can interactively train GAN models for 2D data distributions and visualize their inner-workings, similar to TensorFlow Playground. GAN Lab uses TensorFlow.js, an in-browser GPU-accelerated deep learning library. Everything, from model training to visualization, is implemented with JavaScript. Users only need a web browser like Chrome to run GAN Lab. Our implementation approach significantly broadens people's access to interactive tools for deep learning.
napari is a fast, interactive, multi-dimensional image viewer for Python. It’s designed for browsing, annotating, and analyzing large multi-dimensional images. It’s built on top of Qt (for the GUI), vispy (for performant GPU-based rendering), and the scientific Python stack (numpy, scipy).
image-viewer visualization numpy vispy imagesThis repository serves as a convenience for our developers and users as a colocation of all RAPIDS notebooks.
This guide should help fellow researchers and hobbyists to easily automate and accelerate there deep leaning training with their own Kubernetes GPU cluster. Therefore I will explain how to easily setup a GPU cluster on multiple Ubuntu 16.04 bare metal servers and provide some useful scripts and .yaml files that do the entire setup for you. By the way: If you need a Kubernetes GPU-cluster for other reasons, this guide might be helpful to you as well.
kubernetes kubernetes-cluster kubernetes-setup deep-learning gpu-computing distributed-systems guide kubernetes-gpu-cluster cluster gpu worker-nodesPyGDF implements the Python interface to access and manipulate the GPU Dataframe of GPU Open Analytics Initialive (GOAI). We aim to provide a simple interface that similar to the Pandas dataframe and hide the details of GPU programming.
gpu gpu-data-frame h2o python-api machine-learning-algorithms mapd anaconda analytics
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