liveBook · Manning

CuPy

As explained in Starting Data Analytics with Generative AI and Python

CuPy is a Python array-computing library that provides GPU-accelerated operations with an interface modeled on NumPy.

CuPy provides GPU-accelerated versions of many NumPy functions, letting compatible numerical code run on an Nvidia GPU through CUDA. It is useful when larger datasets or computationally intensive operations make GPU acceleration worthwhile. section 7.2.2

Move bootstrap confidence intervals to CUDA

Install CuPy with pip, replace NumPy with CuPy, generate samples with cp.random, calculate percentiles with cp.percentile, and convert returned bounds to NumPy using cp.asnumpy. The statistical function passed as func must also accept CuPy arrays. section 7.2.2

How Starting Data Analytics with Generative AI and Python covers it

How deep it goes
Demonstrates installation and code conversion, but defers detailed CPU–GPU architecture and compatibility mechanics.
Examples
Uses a CUDA bootstrap_CI function, pip installation, CuPy percentile operations, and execution-time comparisons against optimized CPU code.
What it assumes
Assumes an Nvidia graphics card with CUDA and requires functions passed to the routine to accept CuPy arrays.
What you can do after
Apply it via CuPy by adapting NumPy-based array code for CUDA and returning results to NumPy when needed.
How it connects
Connects NumPy-style array operations with CUDA, Nvidia GPUs, bootstrap resampling, and GPU performance comparisons.
Cover of Starting Data Analytics with Generative AI and Python
liveBookmanning.com
Read Starting Data Analytics with Generative AI and Python on liveBook Get the book at manning.com

Also explained in

Compare how 2 books explain CuPy →

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Starting Data Analytics with Generative AI and Python. The section links point to the parts of the book it draws on.