Scaling-out RAPIDS cuML and XGBoost with Dask on Google Kubernetes Engine (GKE)
This guide will walk through how to easily train cuML models on multi-node, multi-GPU (MNMG) clusters managed by Google’s Kubernetes Engine (GKE) platform.
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Making a Plotly Dash Census Viz Powered by RAPIDS
The use of Plotly’s Dash, RAPIDS, and Data shader allows users to build viz dashboards that both render datasets of 300 million+ rows and remain highly interactive without the need for precomputed aggregations.
Accelerating Volkswagen Connected Car Data Pipelines 100x Faster with NVIDIA RAPIDS
Connected cars are vehicles that communicate with other vehicles using backend systems to enhance usability, enable convenient services, and keep distributed software maintained and up to date. At Volkswagen, we are working on connected car with NVIDIA to solve the challenges which have computational inefficiencies like Geospatial Indexing and K-Nearest Neighbors when implemented in native … Continued
Delivering Fast Fecommendations from Google Analytics 360 SQL Knowledge Graph with RAPIDS cuGraph
In this second part of our blog series, we take a deep dive into RAPIDS cuGraph, a collection of powerful graph algorithms implemented over NVIDIA GPUs.
Gauss Rank Transformation Is 100x Faster with RAPIDS and CuPy
As explained in the Batch Normalization paper, training neural networks becomes way easier if its input is Gaussian. This is clear. And if your model inputs are not Gaussian, RAPIDS will just transform it to Gaussian in the blink of an eye.
Accelerating k-nearest Neighbors 600x Using RAPIDS cuML
k-Nearest Neighbors classification is a straightforward machine learning technique that predicts an unknown observation by using the k most similar known observations in the training dataset. In the second row of the example pictured above, we find the seven digits 3, 3, 3, 3, 3, 5, 5 from the training data are most similar to … Continued
RAPIDS cuGraph: multi-GPU PageRank
RAPIDS cuGraph is on a mission to provide multi-GPU graph analytics to allow users to scale to billion and even trillion scale graphs.
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.
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
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.
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
Accelerating ETL on KubeFlow with RAPIDS
Using RAPIDS on your KubeFlow cluster empowers you to GPU-accelerate your ETL work in both your interactive sessions and ETL pipelines.
Building NVIDIA GPU-Accelerated Pipelines on Azure Synapse Analytics with RAPIDS
Azure recently announced support for NVIDIA’s T4 Tensor Core Graphics Processing Units (GPUs) which are optimized for deploying machine learning inferencing or analytical workloads in a cost-effective manner. With Apache Spark™ deployments tuned for NVIDIA GPUs, plus pre-installed libraries, Azure Synapse Analytics offers a simple way to leverage GPUs to power a variety of data … Continued
GPUs for ETL? Run Faster, Less Costly Workloads with NVIDIA RAPIDS Accelerator for Apache Spark and Databricks
We were stuck. Really stuck. With a hard delivery deadline looming, our team needed to figure out how to process a complex extract-transform-load (ETL) job on trillions of point-of-sale transaction records in a few hours. The results of this job would feed a series of downstream machine learning (ML) models that would make critical retail … Continued