Parallelizing computation with Dask
Episode #207,
published Sun, Apr 14, 2019, recorded Wed, Feb 20, 2019
What if you could write standard numpy and pandas code but have it run on a distributed computing grid for incredible parallel processing right from Python? How about just splitting it across multiprocessing to escape the limitations of the GIL on your local machine? That's what Dask was built to do.
On this episode, you'll meet Matthew Rocklin to talk about its origins, use-cases, and a whole bunch of other interesting topics.
On this episode, you'll meet Matthew Rocklin to talk about its origins, use-cases, and a whole bunch of other interesting topics.
Links from the show
Dask: dask.org
Matthew on Twitter: @mrocklin
Matthew's website: matthewrocklin.com
Dask examples: github.com
PyCon presentation: youtube.com
PyCon presentation slides: matthewrocklin.com/slides
Episode transcripts: talkpython.fm
--- Stay in touch with us ---
Subscribe to Talk Python on YouTube: youtube.com
Talk Python on Bluesky: @talkpython.fm at bsky.app
Talk Python on Mastodon: talkpython
Michael on Bluesky: @mkennedy.codes at bsky.app
Michael on Mastodon: mkennedy
Matthew on Twitter: @mrocklin
Matthew's website: matthewrocklin.com
Dask examples: github.com
PyCon presentation: youtube.com
PyCon presentation slides: matthewrocklin.com/slides
Episode transcripts: talkpython.fm
--- Stay in touch with us ---
Subscribe to Talk Python on YouTube: youtube.com
Talk Python on Bluesky: @talkpython.fm at bsky.app
Talk Python on Mastodon: talkpython
Michael on Bluesky: @mkennedy.codes at bsky.app
Michael on Mastodon: mkennedy
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