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I want to understand the difference between distributing a python application. The application should be used on both Windows and Mac OS platforms. I see there are two ways of doing it.
Either create a executable using py2exe / py2app etc
Distribute the application using pip.
I would like to understand the use-cases for both the solutions.
A few things. As Sam Chats mentioned, one possible use case for creating the executable is if you don't want to share your source code, while if you distribute the application using pip, you will be able to. Additionally, if you distribute using pip vs. creating an application, more users may be able to easily access your application. As an example, check out this StackOverflow answer as a reason for using pip over Anaconda
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Some programming languages provide capability t create a self contained packages that can run on any machine.
For example, dotnet core can self-contained apps per below:
https://learn.microsoft.com/en-us/dotnet/core/deploying/#publish-self-contained
The C# self-contained apps can be single large file, or a directory of all files required to run the application. The package can target Linux, mac or Windows.
In Python, what is the closest feature to self-contained app packages described above?
PyInstaller seems to be the current go to, and it works well in my experience. However, some people have reported that it has very large file sizes, but I've personally never found that to be a major issue.
If you use that, you would also probably need some kind of UI, but that's a separate issue in itself.
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I want to install conda python packages on all cluster machines
Check out conda-pack:
conda-pack can be used to distribute conda environments to be used with Apache Spark jobs when deploying on Apache YARN. By bundling your environment for use with Spark, you can make use of all the libraries provided by conda, and ensure that they’re consistently provided on every node. This makes use of YARN’s resource localization by distributing environments as archives, which are then automatically unarchived on every node. In this case either the tar.gz or zip formats must be used.
Source: https://conda.github.io/conda-pack/spark.html
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I am a newbie to Web2py and Python and I am trying to add requests
(http://docs.python-requests.org) however I am unsure how to add
this to my current Web2py environment.
I am running OSX with Web2Py 2.12.3
If you are running the Mac binary version of web2py, it includes its own Python interpreter (so if you have Python installed on your system, it will ignore any libraries available in your system Python installation). To make requests available to your web2py app, you can either put the requests package in the app's /modules folder, or you can put it in the web2py /site-packages folder, which on the Mac is inside /Contents/Resources.
However, a better option is probably just to install Python on your machine and then use the source version of web2py instead of the Mac binary. Then you can use pip to install any external libraries and they will be available when running web2py.
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I have implemented a new algorithm in python, using sources which use the numpy, scipy libraries and deal with file I/O. I now need to build a Windows GUI app (python might not be installed in any form on a Windows OS) which will implement my existing python source, and take inputs such as file names and some other parameters. Please suggest some possible methods to make this happen.
You may create a GUI for your app, using different options like as below:
wxPython
TkInter
PyQt
This question may help you too.
Regarding creating the final executable you have some options like as below:
py2exe
GUI2EXE
cx_freeze
PyInstaller
There are other issues regarding the creation of an executable discussed here on the SO you may refer to them here if you are interested.
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I'm using Blender and Python and want to give the .py file to other users to use my script... My script uses external modules (like NumPy) which will rise the errors (like can't find module xxxx).
Not all people can install NumPy (or even Python :D) as many Blender users are just artists.
Another note is that NumPy doesn't work with Blender (I install it in the system's Python, then delete Blender Python so it relies on the system Python).
If you want to distribute your code with external dependencies then you should build a Python egg. The .egg format was created to solve the issue you are dealing with. It is a self-contained release of your code with dependencies and meta-data. Here is some information on how create Python eggs.