pyinfra VS orange

pyinfra – automates infrastructure using Python

There is https://pyinfra.com/

As a sidenote, I also made a small experiment a while ago : https://github.com/linkdd/tricorder/

But it's a bit of a chicken-and-egg problem. Without users, I don't know how it should be used, without features I won't get any users. So for now, it's in a state of "I'll address bug reports and feature requests, but I won't actively develop it".

I like https://github.com/pyinfra-dev/pyinfra. "pyinfra automates infrastructure using Python"

Only played with it for a little but it seems well designed an simpler alternative to ansible, chef and other such things.

Haven't used it in anger yet, but I have high hopes for PyInfra: https://github.com/pyinfra-dev/pyinfra

pyinfra - https://pyinfra.com/ - Pyinfra is simpler for me than Ansible. I completed the entire deployment in one afternoon, from installing and configuring the VPS server from scratch to deploying the application and automatically restoring the database from a backup.

I’ve replaced Ansible with PyInfra where ever possible. https://pyinfra.com/ is very clean, and fast but lacks the shear amount of automation that can be found with Ansible.

Some folks don't like YAML all that well, and I can understand where they are coming from. I wish Ansible provided a good Python API so that playbooks could be written in Python easier. But there is a project called PyInfra that is trying to do something similiar to Ansible, using Python as the configuration language. https://pyinfra.com/ It is still pretty new so not got nearly as many modules written for it yet.

I know I've tooted its horn before, but Orange3 is a pretty neat Python-based GUI platform that makes this and a metric buttload of other statistical/ML techniques available to non-programmer types.

Just watch out for null character `x00` in the corpus. That always seems to kill it stone dead.

https://orangedatamining.com/

https://orange3.readthedocs.io/projects/orange-visual-progra...

For all you folks who aren't ace programmer types, the Orange3[1] platform gives you a very miniaturized[2] ability to turn out these sorts of visualizations very rapidly. It's not the most stable thing in the world, but the node-based ML workflow designer is worth the price of admission all by itself.

[1] https://orangedatamining.com/

[2] The Wikipedia extension in Text limits each search result to 25 articles, so sucking all of Wikipedia is . . well, Orange text analytics crashes when I look at it sideways with a null character, so let's not think about what would happen.

First is identifying the "similar" things in a corpus. Best way I know to do that, for non-programmer audiences, is the Orange Data Mining tool, which gives you a node-based text mining interface to perform statistical analysis on text. Hierarchical Clustering shows - very rapidly - how similar your "modules" are, which ones are most similar. There's many other techniques (semantic viewer, similarity hash, etc) as well - the right one will depend on how your content is laying about.

Both tools are ripoffs of a data mining framework named Orange 3

You should also check out Orange Data Mining, it allows to create a lot of charts, filter data from a chart to another, build ML models, predictions and a lot more. And you can do it with zero code.

You can start with a free GUI based tool Orange. It is a component based data science workflow tool, which you can use to handle 60-75% of the traditional data science tasks from classification, regression, to basic neural networks.