RAPIDS, a suite of NVIDIA CUDA-X libraries for Python data science, released version 25.06, introducing exciting new features. These include a Polars GPU streaming engine, a unified API for graph neural networks (GNNs), and acceleration for support vector machines with zero code changes required. In this blog post, we’ll explore a few of these updates. In September 2024…
]]>In high-stakes fields such as quant finance, algorithmic trading, and fraud detection, data practitioners frequently need to process hundreds of gigabytes (GB) of data to make quick, informed decisions. Polars, one of the fastest-growing data processing libraries, meets this need with a GPU engine powered by NVIDIA cuDF that accelerates compute-bound queries that are common in these fields.
]]>Scikit-learn, the most widely used ML library, is popular for processing tabular data because of its simple API, diversity of algorithms, and compatibility with popular Python libraries such as pandas and NumPy. NVIDIA cuML now enables you to continue using familiar scikit-learn APIs and Python libraries while enabling data scientists and machine learning engineers to harness the power of CUDA on…
]]>Time series forecasting is a powerful data science technique used to predict future values based on data points from the past Open source Python libraries like skforecast make it easy to run time series forecasts on your data. They allow you to “bring your own” regressor that is compatible with the scikit-learn API, giving you the flexibility to work seamlessly with the model of your choice.
]]>