Top Tweets for #ploomber_engine
Try out ploomber-engine today! π©
As a #DataScientist, reproducibility is an important part of your work. With #ploomber_engine, you can re-run your notebooks and ensure that their outputs still match.
Get started with this guide:
π https://t.co/UCqUnnK2w9
#OpenSource
Have you tried #ploomber_engine's CLI? βοΈ
With ploomber-engine you can profile #Jupyter notebook's memory usage. This is one of our newest features: try our CLI for running notebooks and profiling them! β‘
Get started: https://t.co/bbbMpvaECq
#DataScience #Ploomber
Profile your notebooks with #ploomber_engine! π
We developed a tool that allows you to find memory leaks and runtime bottlenecks so you can optimize your code.
Read our guide on how to profile the memory usage across cell runs in #Jupyter:
π https://t.co/1wiOxI7FoG
With #ploomber_engine you can profile #Jupyter notebookβs memory usage. Unlike papermill, which isnβt capable of doing it. π₯
We're glad to present one of the newest features:
The #CLI for running notebooks, profiling them, and more!
Check it out: https://t.co/psQgNr0FJD
#ploomber_engine allows you to profile your #Jupyter notebooks to find memory leaks, and runtime bottlenecks, so you can optimize your code. π
We prepared a guide to show you how to profile the memory usage across cell runs in your #notebooks:
π https://t.co/mUNW3LbDFR
We got a new #ploomber_engine release! π
This release includes major improvements over the docs and CLI for your #notebooks, which we will cover during this week's posts. π
Get started with ploomber-engine: https://t.co/MnbMBVVbTG
#DataScience #Jupyter #OpenSource #Ploomber
Reproduce and test results with #ploomber_engine! βοΈ
As a data scientist, reproducibility is an important part of your work. With ploomber-engine, you can re-run your notebooks and ensure that their outputs still match.
Get started with this guide:
π https://t.co/RMptMWJNdC
Writing unit tests for #notebooks has never been easier!
With #ploomber_engine you can extract your function or class definitions and test them in the unit tests. This can lead you to also use parameterized tests!
Get started here! https://t.co/2wdOPytW2s
#DataScience
Notebook testing made easy! π
#ploomber_engine provides several utilities for testing notebooks. In particular, ploomber-engine allows you to test the output cells from your #notebooks to validate the expected outputs of your code.
Learn how: https://t.co/a8sDvHXLzM
#ploomber_engine allows you to profile your #Jupyter notebooks to find memory leaks, and runtime bottlenecks, so you can optimize your code. π
In this guide, we show you how to profile memory usage across cell runs in your #notebooks: https://t.co/xNlcTCfT4U
#DataScience
#ploomber_engine 0.0.14 is out! π
ploomber-engine is a toolbox for executing notebooks.
This release adds:
βͺοΈ Adds a memory profiler
βͺοΈ #PloomberClient records cell timestamps
βͺοΈ Improves documentation with more examples π₯
Start using ploomber-engine: https://t.co/wARt87aQCN
The power of profiling and debugging #Jupyter notebooks in the palm of your hand! ποΈ
Our package #ploomber_engine adds new capabilities to #papermill via custom engines. We prepared a demo notebook for engine profiling, so you can monitor memory usage: https://t.co/99JNCXFcHu
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