![]() ![]() RAPIDS with the GPU-powered workflow alleviates all these hurdles. Typical workday for a developer using a GPU- vs. This results in lost productivity and, likely, a coffee addiction if we take a look at the chart below.įigure 1. Anyone who has tried to read and process a 2GB dataset on a CPU knows what we’re talking about.Īdditionally, since we’re human and we make mistakes, rerunning a pipeline might quickly turn into a full day exercise. One of the reasons is because the questions we ask the dataset take too long to answer. The fundamental data science task, and the one that all data scientists complain about, is cleaning, featurizing and getting familiar with the dataset. In fact, cuDF can store data in all the formats it can read.Īll of these capabilities make it possible to get up and running quickly no matter what your task is or where your data lives.Įxtracting, transforming, and summarizing data In addition, cuDF supports saving the data stored in a DataFrame into multiple formats and file systems. To converting to and from pandas DataFrames and Series. ![]() Through DLPack memory objects used to share tensors between deep learning frameworks and Apache Arrow format that facilitates a much more convenient way of manipulating memory objects from various programming languages,.From an internal GPU matrix represented as an DeviceNDArray,.You can also convert to and from other memory representations: Switching from CPU to GPU Data Science stack has never been easier: with as little change as importing cuDF instead of pandas, you can harness the enormous power of NVIDIA GPUs, speeding up the workloads 10-100x (on the low end), and enjoying more productivity - all while using your favorite tools.Ĭheck the sample code below that presents how familiar cuDF API is to anyone using pandas. Whether you’re performing ETL, building ML models, or processing graphs, if you know pandas, NumPy, scikit-learn or NetworkX, you will feel at home when using RAPIDS. The core premise of RAPIDS is to provide a familiar user experience to popular data science tools so that the power of NVIDIA GPUs is easily accessible for all practitioners. cuDF speeds up these tasks significantly, allowing you to gain insights faster when splitting and aggregating your data GroupBy operations: GroupBy operations are a staple in data analysis but can be resource-intensive.These operations are made effortless with GPU-acceleration String data processing : Traditionally, string data processing has been a challenging and slow task due to the complex nature of textual data.Large-scale data filtering and transformation: For large datasets exceeding several gigabytes, you can perform filtering and transformation tasks using cuDF in a fraction of the time it takes with pandas.This integration allows for real-time interactivity to become a vital component of your analytics cycle. Large-scale data visualization : Whether you're creating heat maps for geographic data or visualizing complex financial trends, developers can deploy data visualization libraries with high-performance and high-FPS data visualization by using cuDF and cuxfilter.Efficient processing means quicker model development and allows you to get towards the deployment quicker. Machine learning (ML) data preparation : Speed up data transformation tasks and prepare your data for commonly used ML algorithms, such as regression, classification and clustering, with cuDF's acceleration capabilities.Real-time exploratory data analysis (EDA): Browsing through large datasets can be a chore with traditional tools, but cuDF's GPU-accelerated processing power makes real-time exploration of even the biggest data sets possible.Time series analysis : Whether you're resampling data, extracting features, or conducting complex computations, cuDF offers a substantial speed-up, potentially up to 880x faster than pandas for time-series analysis. ![]() ![]() How cuDF Can Make Your Data Science Work FasterĪre you tired of watching the clock while your script runs? Whether you're handling string data or working with time series, there are many ways you can use cuDF to drive your data work forward. However, with an easy and familiar Python interface, cuDF users don't need to interact directly with that layer. Like all components in the RAPIDS suite, cuDF employs the CUDA backend to power GPU computations. As a fundamental component within the RAPIDS suite, cuDF underpins the other libraries, solidifying its role as a common building block. It is an EDA workhorse you can use to build allowing data pipelines to process data and derive new features. Want a handy summary of these tips? Follow along with the downloadable cuDF cheat sheet.ĬuDF is a data science building block for the RAPIDS suite of GPU-accelerated libraries. ![]()
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