Accelerated Data Analytics: A Guide to Data Visualization with RAPIDS
Learn how to use RAPIDS to integrate powerful visualizations into your workflows.
Search Results for rapids
GPU-Accelerated JSON Data Processing with RAPIDS
JSON is a widely adopted format for text-based information working interoperably between systems, most commonly in web applications. While the JSON format is human-readable, it is complex to process with data science and data engineering tools. To bridge that gap, RAPIDS cuDF provides a GPU-accelerated JSON reader (cudf.read_json) that is efficient and robust for many … Continued
Limit Order Book Dataset Generation for Accelerated Short-Term Price Prediction with RAPIDS
Hardware acceleration using GPUs reduces the time required for financial ML researchers to obtain prediction results.
Accelerated Data Analytics: Faster Time Series Analysis with RAPIDS cuDF
This post walks you through the common steps of time series data processing with RAPIDS cuDF.
Faster HDBSCAN Soft Clustering with RAPIDS cuML
Discover the importance of using soft clustering to better capture nuance in downstream analysis and the performance gains possible with RAPIDS.
Achieving 100x Faster Single-Cell Modality Prediction with NVIDIA RAPIDS cuML
Single-cell measurement technologies have advanced rapidly, revolutionizing the life sciences. We have scaled from measuring dozens to millions of cells and from one modality to multiple high dimensional modalities. The vast amounts of information at the level of individual cells present a great opportunity to train machine learning models to help us better understand the … Continued
Using the RAPIDS VM Image for Google Cloud Platform
NVIDIA’s Ty McKercher and Google’s Viacheslav Kovalevskyi and Gonzalo Gasca Meza jointly authored a post on using the new the RAPIDS VM Image for Google Cloud Platform. Following is a short summary. For the full post, please see the full Google article. If you’re a data scientist, researcher, engineer, or developer using pandas, Dask, scikit-learn, … Continued
Accelerated Data Analytics: Speed Up Data Exploration with RAPIDS cuDF
This post is part of a series on accelerated data analytics. Digital advancements in climate modeling, healthcare, finance, and retail are generating unprecedented volumes and types of data. IDC says that by 2025, there will be 180 ZB of data compared to 64 ZB in 2020, scaling up the need for data analytics to turn … Continued
Building an Accelerated Data Science Ecosystem: RAPIDS Hits Two Years
GTC Fall 2020 marked the second anniversary of the initial release of RAPIDS. Created out of the GPU Open Analytics Initiative (GoAi) aimed at making accelerated, end-to-end analytics on GPUs easy, RAPIDS has proven GPUs are performant, easy to use, and transformative to the future of data analytics. By thinking about the relationship between software … Continued
Running Python UDFs in Native NVIDIA CUDA Kernels with the RAPIDS cuDF
In this post, I introduce a design and implementation of a framework within RAPIDS cuDF that enables compiling Python user-defined functions (UDF) and inlining them into native CUDA kernels. This framework uses the Numba Python compiler and Jitify CUDA just-in-time (JIT) compilation library to provide cuDF users the flexibility of Python with the performance of … Continued
Accelerating Vector Search: Using GPU-Powered Indexes with RAPIDS RAFT
In the AI landscape of 2023, vector search is one of the hottest topics due to its applications in large language models (LLM) and generative AI. Semantic vector search enables a broad range of important tasks like detecting fraudulent transactions, recommending products to users, using contextual information to augment full-text searches, and finding actors that … Continued
Reusable Computational Patterns for Machine Learning and Information Retrieval with RAPIDS RAFT
RAPIDS is a suite of accelerated libraries for data science and machine learning on GPUs: cuDF for pandas-like data structures cuGraph for graph data cuML for machine learning In many data analytics and machine learning algorithms, computational bottlenecks tend to come from a small subset of steps that dominate the end-to-end performance. Reusable solutions for … Continued
Streamline ETL Workflows with Nested Data Types in RAPIDS libcudf
Nested data types are a convenient way to represent hierarchical relationships within columnar data. They are frequently used as part of extract, transform, load (ETL) workloads in business intelligence, recommender systems, cybersecurity, geospatial, and other applications. List types can be used to easily attach multiple transactions to a user without creating a new lookup table, … Continued
10 Minutes to Data Science: Transitioning Between RAPIDS cuDF and CuPy Libraries
RAPIDS is about creating bridges, connections, and clean handoffs between GPU PyData libraries. Interoperability with functionality is our goal. For example, if you’re working with RAPIDS cuDF but need a more linear-algebra oriented function that exists in CuPy, you can leverage the interoperability of the GPU PyData ecosystem to use that function. Just like you … Continued
RAPIDS Accelerates Data Science End-to-End
Today’s data science problems demand a dramatic increase in the scale of data as well as the computational power required to process it. Unfortunately, the end of Moore’s law means that handling large data sizes in today’s data science ecosystem requires scaling out to many CPU nodes, which brings its own problems of communication bottlenecks, energy, and … Continued