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reproducibility

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dvc
lotsoffood
lotsoffood commented Sep 16, 2019

Is there a method for setting a timeout period for an observer? For example, say the observer is a SQL database, and that database becomes inaccessible for a few minutes. Right now, the experiment simply fails. Is there an argument somewhere to avoid such a situation? Particularly for multi-day experiments.

jchen42703
jchen42703 commented Mar 2, 2020

(I will compile a list and hopefully open a PR if needed)

Describe the bug
This behavior is present in a plethora of catalyst's callbacks and losses. It's consistent, but it's definitely confusing for many new users.

To Reproduce
Steps to reproduce the behavior:
Use these functions/classes:
Callbacks

A comparison between some VPS providers. It uses Ansible to perform a series of automated benchmark tests over the VPS servers that you specify. It allows the reproducibility of those tests by anyone that wanted to compare these results to their own. All the tests results are available in order to provide independence and transparency.

  • Updated May 22, 2018
  • Python
cmeesters
cmeesters commented Feb 28, 2020

Is your feature request related to a problem? Please describe.
The current documentation states that the --cluster-config is deprecated and it refers to the --profile section, which in turn refers to https://github.com/snakemake-profiles/doc

This change apparently carries a few assumption, which do not necessarily hold:

  • clusters are by their very nature pretty heterogeneous, hence a clu
reprex
joukewitteveen
joukewitteveen commented Dec 25, 2019

Word of warning: This issue came up at an interesting talk by @annakrystalli. I have no time to help out, but she encouraged me to post this regardless.

Consider a hypothetical library X that, in version 1.0.0 contains an obscure bug where 0.5683/0 evaluates to -infinity, in violation of IEEE754. From the perspective of the library developers, this is a silly bug and a new version is releas

guaraqe
guaraqe commented Dec 11, 2019

We should have a default overlay containing a sort of LTS for standard data science libraries: tensorflow, pytorch, numpy, pandas, etc...

Many of these libraries are not always trivial to install, so this has an added value by itself, besides the convenience for the explorative data scientist who whishes to use JupyterWith, who does not have to be concerned with package setup.

emmjab
emmjab commented Apr 23, 2020

Is your feature request related to a problem? Please describe.
One of the reasons it's hard to write new documentation and update old documentation is because the documentation isn't well documented!

Describe the solution you'd like
Better document how to add documentation.

Additional context
One of the other reasons it's hard to add and update docs is because the organization

iamciera
iamciera commented Jun 17, 2016

This guide is getting pretty rusty. We should really think about what the function of it is. It has a lot of useful information, but I feel like the information is hard absorb. There are too many sections, uneven content, and outdated information.

The first question is, in the condition the site is in now, does it serve a valuable function? And/Or does it do any harm staying up in the conditi

pschloss
pschloss commented Jun 13, 2016

Admittedly, I'm not a pythonista, but I wonder whether there would be value in using bash versions of the three python scripts. For whatever reason, I'm running into problems with getting python installed correctly on my Mac. Once I got it pointed in the right direct, I ran into problems with installing numpy. It's quickly becoming a tutorial on installing python rather than make :)

I suspect the

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