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Question: How do I profile performance bottlenecks? #191

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Winterflower opened this issue Jul 9, 2015 · 2 comments
Closed

Question: How do I profile performance bottlenecks? #191

Winterflower opened this issue Jul 9, 2015 · 2 comments

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@Winterflower
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Hello Jupyter community,
apologies for asking this here. I've been trying to find some information about performance profiling online but my googling skills have failed me.
I use the Jupyter notebook daily to plot and analyse timeseries (mainly using seaborn and pandas).
Sometimes when the number of datapoints exceeds a certain amount, the kernel starts to hang (10+ minutes) or dies.
Have there been any efforts to document the performance of the kernel for large scale data visualization/analysis? I would like to establish some guidelines to help me decide when plotting something using the notebook is not a good idea.

@minrk minrk added this to the no action milestone Jul 9, 2015
@minrk
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minrk commented Jul 9, 2015

Depending on what you are doing, mostly standard Python profiling tools should work, such as the stdlib profile module, and line_profiler.

Some operations may cause hanging in the browser (e.g. SVG plots with too many elements), in which case using the browser's own profiler tools is a good idea.

@Carreau
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Carreau commented Sep 15, 2015

Closing as nothing to do here.

@Carreau Carreau closed this as completed Sep 15, 2015
kevin-bates added a commit to kevin-bates/notebook that referenced this issue Mar 25, 2020
This commit uses the approach used in jupyter_server jupyter#191 first proposed
by David Brochart.  This reduces code duplication and alleviates redundancy
relative to configurable options.

Also, the startup message now includes the version information.

Co-authored-by: David Brochart <[email protected]>
kevin-bates added a commit to kevin-bates/notebook that referenced this issue Mar 27, 2020
This commit uses the approach used in jupyter_server jupyter#191 first proposed
by David Brochart.  This reduces code duplication and alleviates redundancy
relative to configurable options.

Also, the startup message now includes the version information.

Co-authored-by: David Brochart <[email protected]>
toonijn pushed a commit to toonijn/notebook that referenced this issue Apr 9, 2020
This commit uses the approach used in jupyter_server jupyter#191 first proposed
by David Brochart.  This reduces code duplication and alleviates redundancy
relative to configurable options.

Also, the startup message now includes the version information.

Co-authored-by: David Brochart <[email protected]>
@github-actions github-actions bot locked as resolved and limited conversation to collaborators May 3, 2021
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