Episódios

  • #516: Accelerating Python Data Science at NVIDIA

    #516: Accelerating Python Data Science at NVIDIA
    Aug 19 2025
    Python’s data stack is getting a serious GPU turbo boost. In this episode, Ben Zaitlen from NVIDIA joins us to unpack RAPIDS, the open source toolkit that lets pandas, scikit-learn, Spark, Polars, and even NetworkX execute on GPUs. We trace the project’s origin and why NVIDIA built it in the open, then dig into the pieces that matter in practice: cuDF for DataFrames, cuML for ML, cuGraph for graphs, cuXfilter for dashboards, and friends like cuSpatial and cuSignal. We talk real speedups, how the pandas accelerator works without a rewrite, and what becomes possible when jobs that used to take hours finish in minutes. You’ll hear strategies for datasets bigger than GPU memory, scaling out with Dask or Ray, Spark acceleration, and the growing role of vector search with cuVS for AI workloads. If you know the CPU tools, this is your on-ramp to the same APIs at GPU speed.

    Episode sponsors

    Posit
    Talk Python Courses

    Links from the show RAPIDS: github.com/rapidsai
    Example notebooks showing drop-in accelerators: github.com
    Benjamin Zaitlen - LinkedIn: linkedin.com
    RAPIDS Deployment Guide (Stable): docs.rapids.ai
    RAPIDS cuDF API Docs (Stable): docs.rapids.ai
    Asianometry YouTube Video: youtube.com
    cuDF pandas Accelerator (Stable): docs.rapids.ai
    Watch this episode on YouTube: youtube.com
    Episode #516 deep-dive: talkpython.fm/516
    Episode transcripts: talkpython.fm
    Developer Rap Theme Song: Served in a Flask: talkpython.fm/flasksong

    --- 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
    Exibir mais Exibir menos
    1 hora e 6 minutos
  • #515: Durable Python Execution with Temporal

    #515: Durable Python Execution with Temporal
    Aug 11 2025
    What if your code was crash-proof? That's the value prop for a framework called Temporal. Temporal is a durable execution platform that enables developers to build scalable applications without sacrificing productivity or reliability. The Temporal server executes units of application logic called Workflows in a resilient manner that automatically handles intermittent failures, and retries failed operations. We have Mason Egger from Temporal on to dive into durable execution. Episode sponsors Posit PyBay Talk Python Courses Links from the show Just Enough Python for Data Scientists Course: talkpython.fm Temporal Durable Execution Platform: temporal.io Temporal Learn Portal: learn.temporal.io Temporal GitHub Repository: github.com Temporal Python SDK GitHub Repository: github.com What Is Durable Execution, Temporal Blog: temporal.io Mason on Bluesky Profile: bsky.app Mason on Mastodon Profile: fosstodon.org Mason on Twitter Profile: twitter.com Mason on LinkedIn Profile: linkedin.com X Post by @skirano: x.com Temporal Docker Compose GitHub Repository: github.com Building a distributed asyncio event loop (Chad Retz) - PyTexas 2025: youtube.com Watch this episode on YouTube: youtube.com Episode #515 deep-dive: talkpython.fm/515 Episode transcripts: talkpython.fm Developer Rap Theme Song: Served in a Flask: talkpython.fm/flasksong --- 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
    Exibir mais Exibir menos
    1 hora e 11 minutos
  • #514: Python Language Summit 2025

    #514: Python Language Summit 2025
    Jul 18 2025
    Every year the core developers of Python convene in person to focus on high priority topics for CPython and beyond. This year they met at PyCon US 2025. Those meetings are closed door to keep focused and productive. But we're lucky that Seth Michael Larson was in attendance and wrote up each topic presented and the reactions and feedback to each. We'll be exploring this year's Language Summit with Seth. It's quite insightful to where Python is going and the pressing matters. Episode sponsors Seer: AI Debugging, Code TALKPYTHON Sentry AI Monitoring, Code TALKPYTHON Talk Python Courses Links from the show Seth on Mastodon: @sethmlarson@fosstodon.org Seth on Twitter: @sethmlarson Seth on Github: github.com Python Language Summit 2025: pyfound.blogspot.com WheelNext: wheelnext.dev Free-Threaded Wheels: hugovk.github.io Free-Threaded Python Compatibility Tracking: py-free-threading.github.io PEP 779: Criteria for supported status for free-threaded Python: discuss.python.org PyPI Data: py-code.org Senior Engineer tries Vibe Coding: youtube.com Watch this episode on YouTube: youtube.com Episode #514 deep-dive: talkpython.fm/514 Episode transcripts: talkpython.fm Developer Rap Theme Song: Served in a Flask: talkpython.fm/flasksong --- 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
    Exibir mais Exibir menos
    1 hora e 13 minutos
  • #513: Stories from Python History

    #513: Stories from Python History
    Jul 14 2025
    Why do people listen to this podcast? Sure, they're looking for technical explorations of new libraries and ideas. But often it's to hear the story behind them. If that speaks to you, then I have the perfect episode lined up. I have Barry Warsaw, Paul Everitt, Carol Willing, and Brett Cannon all back on the show to share stories from the history of Python. You'll hear about how import this came to be and how the first PyCon had around 30 attendees (two of whom are guests on this episode!). Sit back and enjoy the humorous stories from Python's past.

    Episode sponsors

    Posit
    Agntcy
    Talk Python Courses

    Links from the show Barry's Zen of Python song: youtube.com
    Jake Vanderplas - Keynote - PyCon 2017: youtube.com
    Why it’s called “Python” (Monty Python fan-reference): geeksforgeeks.org
    import antigravity: python-history.blogspot.com
    NIST Python Workshop Attendees: legacy.python.org
    Paul Everitt open-sources Zope: old.zope.dev
    Carol Willing wins ACM Software System Award: awards.acm.org
    Watch this episode on YouTube: youtube.com
    Episode #513 deep-dive: talkpython.fm/513
    Episode transcripts: talkpython.fm
    Developer Rap Theme Song: Served in a Flask: talkpython.fm/flasksong

    --- 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
    Exibir mais Exibir menos
    1 hora e 9 minutos
  • #512: Building a JIT Compiler for CPython

    #512: Building a JIT Compiler for CPython
    Jul 2 2025
    Do you like to dive into the details and intricacies of how Python executes and how we can optimize it? Well, do I have an episode for you. We welcome back Brandt Bucher to give us an update on the upcoming JIT compiler for Python and why it differs from JITs for languages such as C# and Java.

    Episode sponsors

    Posit
    Talk Python Courses

    Links from the show Brandt Bucher: github.com/brandtbucher

    PyCon Talk: What they don't tell you about building a JIT compiler for CPython: youtube.com
    Specializing, Adaptive Interpreter Episode: talkpython.fm
    Watch this episode on YouTube: youtube.com
    Episode #512 deep-dive: talkpython.fm/512
    Episode transcripts: talkpython.fm
    Developer Rap Theme Song: Served in a Flask: talkpython.fm/flasksong

    --- 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
    Exibir mais Exibir menos
    1 hora e 8 minutos
  • #511: From Notebooks to Production Data Science Systems

    #511: From Notebooks to Production Data Science Systems
    Jun 25 2025
    If you're doing data science and have mostly spent your time doing exploratory or just local development, this could be the episode for you. We are joined by Catherine Nelson to discuss techniques and tools to move your data science game from local notebooks to full-on production workflows. Episode sponsors Agntcy Sentry Error Monitoring, Code TALKPYTHON Talk Python Courses Links from the show New Course: LLM Building Blocks for Python: training.talkpython.fm Catherine Nelson LinkedIn Profile: linkedin.com Catherine Nelson Bluesky Profile: bsky.app Enter to win the book: forms.google.com Going From Notebooks to Scalable Systems - PyCon US 2025: us.pycon.org Going From Notebooks to Scalable Systems - Catherine Nelson – YouTube: youtube.com From Notebooks to Scalable Systems Code Repository: github.com Building Machine Learning Pipelines Book: oreilly.com Software Engineering for Data Scientists Book: oreilly.com Jupytext - Jupyter Notebooks as Markdown Documents: github.com Jupyter nbconvert - Notebook Conversion Tool: github.com Awesome MLOps - Curated List: github.com Watch this episode on YouTube: youtube.com Episode #511 deep-dive: talkpython.fm/511 Episode transcripts: talkpython.fm Developer Rap Theme Song: Served in a Flask: talkpython.fm/flasksong --- 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
    Exibir mais Exibir menos
    54 minutos
  • #510: 10 Polars Tools and Techniques To Level Up Your Data Science

    #510: 10 Polars Tools and Techniques To Level Up Your Data Science
    Jun 18 2025
    Are you using Polars for your data science work? Maybe you've been sticking with the tried-and-true Pandas? There are many benefits to Polars directly of course. But you might not be aware of all the excellent tools and libraries that make Polars even better. Examples include Patito which combines Pydantic and Polars for data validation and polars_encryption which adds AES encryption to selected columns. We have Christopher Trudeau back on Talk Python To Me to tell us about his list of excellent libraries to power up your Polars game and we also talk a bit about his new Polars course. Episode sponsors Agntcy Sentry Error Monitoring, Code TALKPYTHON Talk Python Courses Links from the show New Theme Song (Full-Length Download and backstory): talkpython.fm/blog Polars for Power Users Course: training.talkpython.fm Awesome Polars: github.com Polars Visualization with Plotly: docs.pola.rs Dataframely: github.com Patito: github.com polars_iptools: github.com polars-fuzzy-match: github.com Nucleo Fuzzy Matcher: github.com polars-strsim: github.com polars_encryption: github.com polars-xdt: github.com polars_ols: github.com Least Mean Squares Filter in Signal Processing: www.geeksforgeeks.org polars-pairing: github.com Pairing Function: en.wikipedia.org polars_list_utils: github.com Harley Schema Helpers: tomburdge.github.io Marimo Reactive Notebooks Episode: talkpython.fm Marimo: marimo.io Ahoy Narwhals Podcast Episode Links: talkpython.fm Watch this episode on YouTube: youtube.com Episode #510 deep-dive: talkpython.fm/510 Episode transcripts: talkpython.fm Developer Rap Theme Song: Served in a Flask: talkpython.fm/flasksong --- 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
    Exibir mais Exibir menos
    1 hora e 2 minutos
  • #509: GPU Programming in Pure Python

    #509: GPU Programming in Pure Python
    Jun 11 2025
    If you're looking to leverage the insane power of modern GPUs for data science and ML, you might think you'll need to use some low-level programming language such as C++. But the folks over at NVIDIA have been hard at work building Python SDKs which provide nearly native level of performance when doing Pythonic GPU programming. Bryce Adelstein Lelbach is here to tell us about programming your GPU in pure Python. Episode sponsors Posit Agntcy Talk Python Courses Links from the show Bryce Adelstein Lelbach on Twitter: @blelbach Episode Deep Dive write up: talkpython.fm/blog NVIDIA CUDA Python API: github.com Numba (JIT Compiler for Python): numba.pydata.org Applied Data Science Podcast: adspthepodcast.com NVIDIA Accelerated Computing Hub: github.com NVIDIA CUDA Python Math API Documentation: docs.nvidia.com CUDA Cooperative Groups (CCCL): nvidia.github.io Numba CUDA User Guide: nvidia.github.io CUDA Python Core API: nvidia.github.io Numba (JIT Compiler for Python): numba.pydata.org NVIDIA’s First Desktop AI PC ($3,000): arstechnica.com Google Colab: colab.research.google.com Compiler Explorer (“Godbolt”): godbolt.org CuPy: github.com RAPIDS User Guide: docs.rapids.ai Watch this episode on YouTube: youtube.com Episode #509 deep-dive: talkpython.fm/509 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
    Exibir mais Exibir menos
    57 minutos