Model developers no longer face a steep learning curve to accelerate model training. By utilizing two open-source software projects, Determined AI’s Deep Learning Training Platform and the RAPIDS accelerated data science toolkit, they can easily achieve up to 10x speedups in data preprocessing and train models at scale. Making GPUs accessible As the field of … Continued
Search Results for rapids
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
In this post we take a look at how to use cuDF, the RAPIDS dataframe library, to do some of the preprocessing steps required to get the mortgage data in a format that PyTorch can process so that we can explore the performance of deep learning on tabular data and compare it to the xgboost … Continued
Kick off 2021 with a listen to the newest episode of RAPIDSFire: the Accelerated Data Science Podcast.
Machine learning (ML) data is big and messy. Organizations have increasingly adopted RAPIDS and cuML to help their teams run experiments faster and achieve better model performance on larger datasets. That, in turn, accelerates the training of ML models using GPUs. With RAPIDS, data scientists can now train models 100X faster and more frequently. Like … Continued
With RAPIDS, data scientists can now train models 100X faster and more frequently. Like RAPIDS, we’ve ensured that our data logging solution at WhyLabs empowers users working with larger than memory datasets.
When I joined the RAPIDS team in 2018, NVIDIA CUDA device memory allocation was a performance problem. RAPIDS cuDF allocates and deallocates memory at high frequency, because its APIs generally create new Series and DataFrames rather than modifying them in place. The overhead of cudaMalloc and synchronization of cudaFree was holding RAPIDS back. My first … Continued
RAPIDS aims to democratize accelerated data science through accessibility and innovation.
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
RAPIDS makes it possible to perform interactive data analysis on large datasets using Python APIs that closely resemble NumPy, Pandas, and scikit-learn.
The human body is made up of nearly 40 trillion cells, of many different types. Recent advances in experimental biology have made it possible to explore the genetic material of single cells. With the birth of this new field of single-cell genomics, scientists can now probe the DNA and RNA of individual cells in the … Continued
The COVID-19 pandemic brings the efforts of the data science community to the forefront. Real-time, interactive visualizations of the novel coronavirus’ spread across populations help researchers, scientists, health officials and governments understand, validate, and communicate important insights hidden among hundreds of millions of rows of records.
In this post, the team shows how the new RAPIDS Accelerator for Apache Spark enables GPU acceleration of end-to-end data analytic pipelines, Spark SQL operations, and Spark shuffle operations.
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
Given the parallel nature of many data processing tasks, it’s only natural that the massively parallel architecture of a GPU should be able to parallelize and accelerate Apache Spark data processing queries, in the same way that a GPU accelerates deep learning (DL) in artificial intelligence (AI). NVIDIA has worked with the Apache Spark community … Continued