Channel: Coding Tech

Category: Education

Tags: performant pythonpythonfast python

Description: Data Scientists often have large datasets and powerful hardware at their disposal. However, the excitement of fast computation in Python slows against a steep learning curve. This talk will build your confidence and intuition around high performance computing with Python. We step through a complete example while also covering the core concepts so you can generalize to your own work. Description An example data science pipeline with numpy and pandas Common heuristics for when to accelerate your code Quick survey of common approaches An example data processing pipeline with numpy How to accelerate on a single machine with Numba Brief introduction to Numba Quick comparison to cython Accelerating our example pipeline with numba How to distribute on a cluster with Numba and Dask Brief introduction to Dask Quick comparison to PySpark, Ray Accelerating our example pipeline with numba and dask How to accelerate and distribute with Numba, Dask, and Rapids Brief introduction to Rapids & GPUs Quick comparison to other GPU computing methods Accelerating our example pipeline with numba, dask, and rapids Conclusion Review of performance gains Summary of when to apply each to your project Where to find hardware and example costs for various pipelines and data volumes PUBLICATION PERMISSIONS: PyData provided Coding Tech with the permission to republish PyData tech talks. CREDITS: PyData YouTube channel: youtube.com/c/PyDataTV/videos