11-11, 09:00–10:30 (America/New_York), Central Park West (6th floor)
Learn how to parallelize your Python code, first on your laptop and then on a distributed cluster.
This tutorial shows how to use Dask, a popular open source framework for parallel computing, to parallelize Python code. We start with parallelizing simple for loops, and move on to scaling out pandas code.
Along the way we will learn about concepts like partitioning data, parallel performance tracking, and managing exceptions and debugging on remote machines.
This will be a hands-on tutorial with Jupyter notebooks.
Learn how to parallelize your Python code, first on your laptop and then on a distributed cluster.
This tutorial shows how to use Dask, a popular open source framework for parallel computing, to parallelize Python code. We start with parallelizing simple for loops, and move on to scaling out pandas code.
Along the way we will learn about concepts like partitioning data, parallel performance tracking, and managing exceptions and debugging on remote machines.
This will be a hands-on tutorial with Jupyter notebooks.
No previous knowledge expected
Matthew is an open source software developer in the PyData ecosystem. He primarily works on Dask, a library for parallel computing in Python. Matthew worked for Anaconda and NVIDIA before starting a company, Coiled with a mission to enable scalable computing for the Python community.