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
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.
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
Learn how RAPIDS cuDF accelerates data science with the help of GPUDirect Storage. Dive into the techniques that minimize the time to upload data to the GPU.
RAPIDS cuGraph is on a mission to provide multi-GPU graph analytics to allow users to scale to billion and even trillion scale graphs.
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.
With RAPIDS, practitioners can quickly accelerate data science workloads on NVIDIA GPUs, and with Saturn Cloud focus on solving their business challenges.
The GPUs powering Colab were upgraded to the new NVIDIA T4 GPUs.
Running PyCarert on GPU not only streamline model building but offsets the time cost.
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.
Learn how the use of RAPIDS to accelerate the analysis of single-cell RNA-sequence on a single NVIDIA V100 GPU shows a massive performance increase.
Model Interpretability aids developers and other stakeholders to understand model characteristics and the underlying reasons for the decisions, thus making the process more transparent.
Here’s how you can get up and running quickly using the RAPIDS machine learning pipeline with the NVIDIA NGC catalog and Google Vertex AI.
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
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
