GPU Python for the Real World: Practical Steps to GPU-Accelerated Python with RAPIDS
NVIDIA GPUs offer unmatched speed and efficiency for data processing and model training, significantly reducing the time and cost associated with these tasks. Using GPUs is even more tempting when you use zero-code-change plugins and libraries. You can use PyData libraries including pandas, polars and networkx without needing to rewrite your code to get the benefits of GPU acceleration. We can also mix in GPU native libraries like Numba, CuPy and pytorch to accelerate our workflows from end-to-end.
However, integrating GPUs into our workflow can be a new challenge where we need to learn about installation, dependency management, and deployment in the Python ecosystem. When writing code, we also need to monitor performance, leverage hardware effectively, and debug when things go wrong
This is where RAPIDS and its tooling ecosystem comes to the rescue. RAPIDS, is a collection of open source software libraries to execute end-to-end data pipelines on NVIDIA GPUs using familiar PyData APIs.