In the machine learning and MLOps world, GPUs are widely used to speed up model training and inference, but what about the other stages of the workflow like ETL pipelines or hyperparameter optimization? Within the RAPIDS data science framework, ETL tools are designed to have a familiar look and feel to data scientists working in Python. Do you currently use Pandas, NumPy, Scikit-learn, or other parts of the PyData stack within your KubeFlow workflows? If so, you can use RAPIDS to accelerate those parts of your workflow by leveraging the GPUs likely already available in your cluster. In this post, I demonstrate how to drop RAPIDS into a KubeFlow environment.

More machine learning models are being taught and put into production, however with the explosion of data, the traditional pipeline used for training these models, which is pandas for data management and DataFrame manipulation feature processing, and selection, is becoming obsolete. Scickit learn, which is used for training and testing machine learning models, can be very sluggish because it is mostly based on the CPU and does not take use of the power supplied by GPUs. And then the Nvidia Rapids come to the rescue! RAPIDS is a collection of Nvidia libraries based on CUDA-X AI. This allows whole data science and analysis pipelines to be run on GPUs.

PyCaret is a low-code Python machine learning library based on the popular Caret library for R. It automates the data science process from data preprocessing to insights, such that short lines of code can accomplish each step with minimal manual effort. In addition, the ability to compare and tune many models with simple commands streamlines efficiency and productivity with less time spent in the weeds of creating useful models. The PyCaret team added NVIDIA GPU support in version 2.2, including all the latest and greatest from RAPIDS. With GPU acceleration, PyCaret modeling times can be between 2 and 200 times faster depending on the workload. This blog article will go over how to use PyCaret on GPUs to save both development and computation costs by an order of magnitude.

Fishermen gaffing catfish at the Madeira River rapids in the state of Rondônia, Brazil. These rapids are now completely submerged, and a nearby …

Abstract: In this article, we demonstrate how to use RAPIDS libraries to improve machine learning CPU-based libraries such as pandas, sklearn and NetworkX. We use a recommendation study case, which executed 44x faster in the GPU-based library when running the PageRank algorithm and 39x faster for the Personalized PageRank. Scikit-learn and Pandas are part of most data scientists' toolbox because of their friendly API and wide range of useful resources-- from model implementations to data transformation methods. However, many of these libraries still rely on CPU processing and, as far as this thread goes, libraries like Scikit-learn do not intend to scale up to GPU processing or scale out to cluster processing. To overcome this drawback, RAPIDS offers a suite of Python open source libraries that takes these widely used data science solutions and boost them up by including GPU-accelerated implementations while still providing a similar API.

You do a lot of data wrangling, cleaning, statistical tests, visualizations on a regular basis. You also tinker with a lot of linear models fitting data and occasionally venture into RandomForest. You are also into clustering large datasets. However, given the nature of the datasets you work on (mostly tabular and structured), you don't venture into deep learning that much. You would rather put all the hardware resources you have into the things that you actually do on a day-to-day basis, than spending on some fancy deep learning model.

Pandas library comes in handy while performing data-related operations. Everyone starting with their Data Science journey has to get a good understanding of this library. Pandas can handle a significant amount of data and process it most efficiently. But at the core, it is still running on CPUs. Parallel processing can be achieved to speed up the process but it is still not efficient to handle large amounts of data.

Did you miss the opportunity to join the conversation on Artificial Intelligence and how we impact the next frontier of our humanity? First, we're so sorry that you missed it! The event took place on Saturday, 20 February 2021 at 09:00 AM Pacific Time (US & Canada). We had an incredible time together discussing our role with black leaders, top experts, and innovators from the world's best tech companies and our community. That's EXACTLY why we'll make the replay available.

We're pleased to announce that Plotly and NVIDIA are partnering to bring GPU-accelerated Artificial Intelligence (AI) & Machine Learning (ML) to a vastly wider audience of business users. By integrating the Plotly Dash frontend with the NVIDIA RAPIDS backend, we are offering one of the highest performance AI & ML stacks available in Python today. This is all open-source and accessible in a few lines of Python code. Once you've created a Dash RAPIDS app on your desktop, get it into the hands of business users by uploading it to DEK. No IT or devops team required .

Researchers have switched on the world's fastest AI supercomputer, delivering nearly four exaFLOPS of AI performance for more than 7,000 researchers. Perlmutter, officially dedicated today at the National Energy Research Scientific Computing Centre (NERSC), is a supercomputer that will help piece together a 3D map of the universe, probe subatomic interactions for green energy sources, and much more. The supercomputer is made up of 6,159 NVIDIA A100 Tensor Core GPUs, which makes it the largest A100-powered system in the world. Over two dozen applications are getting ready to be among the first to use the system based at Lawrence Berkeley National Lab. In one project, the supercomputer will help assemble the largest 3D map of the visible universe to date.