I was trying to understand the purpose of use_develop and from the docs, I found this:
Install the current package in development mode with develop mode. For pip this uses -e option, so should be avoided if you’ve specified a custom install_command that does not support -e.
I don't understand what "development mode" means. Is this a python concept or it's specific to tox? Either way what does it mean?
development mode or editable installs is a Python concept, or even more specific a Python packaging concept.
Usually, when you package a Python application or library, the source files are packaged into a "container", either a wheel or a source distribution.
This is a good thing to distribute a package, but not for developing, as then the source files are no longer accessible.
editable installs is a concept, that instead of "moving/copying" the files into the package container, the files are just symlinked (at least this is one way).
So when you edit the source files, also the package is updated immediately.
For tox this also means that the files in the root of the source tree are importable by Python, not only the one in the package.
This might be comfortable, but there is one huge caveat. If you misconfigure the packaging setup, maybe the tests ran by tox are green, but it is entirely possible that you forget to include the source files in the package you deliver to your users.
Related
I maintain a Python utility that allows bpy to be installable as a Python module. Due to the hugeness of the spurce code, and the length of time it takes to download the libraries, I have chosen to provide this module as a wheel.
Unfortunately, platform differences and Blender runtime expectations makes support for this tricky at times.
Currently, one of my big goals is to get the Blender addon scripts directory to install into the correct location. The directory (simply named after the version of Blender API) has to exist in the same directory as the Python executable.
Unfortunately the way that setuptools works (or at least the way that I have it configured) the 2.79 directory is not always placed as a sibling to the Python executable. It fails on Windows platforms outside of virtual environments.
However, I noticed in setuptools documentation that you can specify eager_resources that supposedly guarantees the location of extracted files.
https://setuptools.readthedocs.io/en/latest/setuptools.html#automatic-resource-extraction
https://setuptools.readthedocs.io/en/latest/pkg_resources.html#resource-extraction
There was a lot of hand waving and jargon in the documentation, and 0 examples. I'm really confused as to how to structure my setup.py file in order to guarantee the resource extraction. Currently, I just label the whole 2.79 directory as "scripts" in my setuptools Extension and ship it.
Is there a way to write my setup.py and package my module so as to guarantee the 2.79 directory's location is the same as the currently running python executable when someone runs
py -3.6.8-32 -m pip install bpy
Besides simply "hacking it in"? I was considering writing a install_requires module that would simply move it if possible but that is mangling with the user's file system and kind of hacky. However it's the route I am going to go if this proves impossible.
Here is the original issue for anyone interested.
https://github.com/TylerGubala/blenderpy/issues/13
My build process is identical to the process descsribed in my answer here
https://stackoverflow.com/a/51575996/6767685
Maybe try the data_files option of distutils/setuptools.
You could start by adding data_files=[('mydata', ['setup.py'],)], to your setuptools.setup function call. Build a wheel, then install it and see if you can find mydata/setup.py somewhere in your sys.prefix.
In your case the difficult part will be to compute the actual target directory (mydata in this example). It will depend on the platform (Linux, Windows, etc.), if it's in a virtual environment or not, if it's a global or local install (not actually feasible with wheels currently, see update below) and so on.
Finally of course, check that everything gets removed cleanly on uninstall. It's a bit unnecessary when working with virtual environments, but very important in case of a global installation.
Update
Looks like your use case requires a custom step at install time of your package (since the location of the binary for the Python interpreter relative to sys.prefix can not be known in advance). This can not be done currently with wheels. You have seen it yourself in this discussion.
Knowing this, my recommendation would be to follow the advice from Jan Vlcinsky in his comment for his answer to this question:
Post install script after installing a wheel.
Add an extra setuptools console entry point to your package (let's call it bpyconfigure).
Instruct the users of your package to run it immediately after installing your package (pip install bpy && bpyconfigure).
The purpose of bpyconfigure should be clearly stated (in the documentation and maybe also as a notice shown in the console right after starting bpyconfigure) since it would write into locations of the file system where pip install does not usually write.
bpyconfigure should figure out where is the Python interpreter, and where to write the extra data.
The extra data to write should be packaged as package_data, so that it can be found with pkg_resources.
Of course bpyconfigure --uninstall should be available as well!
I really don't get it. When i run npm install on the main folder, why does it have to download all the dependencies in the node_modules and why does this need to be done for each single project? In Sinatra(Ruby microframework), I never had to do this and it is easy to use the gems that are installed globally without having to download and save each one of them into the project folder again.
I read somewhere that it is done to avoid version mismatch issues but if its working by installing it globally and simply 'require'ing it in many other languages like Python(uses virtualenv to tackle version issues), Ruby etc., why can't it be the same for node.js?
Whatever happened to DRY?
You can do npm install --global (or -g for short) to install globally. But that creates a problem when 2 projects reference to different version of the same dependency. You will have conflicts that is hard to trace. Also install it locally makes it more portable. You can reference to this document
This problem does not only exist in node.js world. Just different language approach this problem differently. I don't know ruby well, but:
In python people use virtualenv to separate dependencies, which needs more effort.
Maven of Java will cache artifacts in $HOME/.m2 folder, but when compile the projects, they will copy those bytecode from .m2 folder to a local folder target.
There is time you want to do npm install --global though for those tools such as grunt.js/gulp.js.
I just think it has nothing to do with DRY, because you didn't write code twice. It's just downloaded twice.
That being said, you still can install everything globlally.
Using from setuptools.command.install import install, I can easily run a custom post-install script if I run python setup.py install. This is fairly trivial to do.
Currently, the script does nothing but print some text but I want it to deal with system changes that need to happen when a new package is installed -- for example, back up the database that the package is using.
I want to generate the a Python wheel for my package and then copy that and install it on a a set of deployment machines. However, my custom install script is no longer run on the deployment machine.
What am I doing wrong? Is that even possible?
Do not mix package installation and system deployment
Installation of Python packages (using any sort of packaging tools or formats) shall be focused on making that package usable from Python code.
Deployment, what might include database modifications etc. is definitely out of scope and shall be handled by other tools like fab, salt-stack etc.
The fact, that something seems fairly trivial does not mean, one shall do it.
The risk is, you will make your package installation difficult to reuse, as it will be spoiled by others things, which are not related to pure package installation.
The option to hook into installation process and modify environment is by some people even considered flaw in design, causing big mess in Python packaging situation - see Armin Roacher in Python Packaging: Hate, Hate, Hate Everywhere, chapter "PTH: The failed Design that enabled it all"
PEP 427 which specifies the wheel package format does not leave any provisions for custom pre or post installation scripts.
Therefore running a custom script is not possible during wheel package installation.
You'll have to add the custom script to a place in your package where you expect the developer to execute first.
My goal is to distribute a Python package that has several other widely used Python packages as dependencies. My package depends on well written, Pypi-indexed packages like pandas, scipy and numpy, and specifies in the setup.py that certain versions or higher of these are needed, e.g. "numpy >= 1.5".
I found that it's immensely frustrating and nearly impossible for Unix savvy users who are not experts in Python packaging (even if they know how to write Python) to install a package like mine, even when using what are supposed to be easy to use package managers. I am wondering if there is an alternative to this painful process that someone can offer, or if my experience just reflects the very difficult current state of Python packaging and distribution.
Suppose users download your package onto their system. Most will try to install it "naively", using something like:
$ python setup.py install
Since if you google instructions on installing Python packages, this is usually what comes up. This will fail for the vast majority of users, since most do not have root access on their Unix/Linux servers. With more searching, they will discover the "--prefix" option and try:
$ python setup.py install --prefix=/some/local/dir
Since the users are not aware of the intricacies of Python packaging, they will pick an arbitrary directory as an argument to --prefix, e.g. "~/software/mypackage/". It will not be a cleanly curated directory where all other Python packages reside, because again, most users are not aware of these details. If they install another package "myotherpackage", they might pass it "~/software/myotherpackage", and you can imagine how down the road this will lead to frustrating hacking of PYTHONPATH and other complications.
Continuing with the installation process, the call to "setup.py install" with "--prefix" will also fail once users try to use the package, even though it appeared to have been installed correctly, since one of the dependencies might be missing (e.g. pandas, scipy or numpy) and a package manager is not used. They will try to install these packages individually. Even if successful, the packages will inevitably not be in the PYTHONPATH due to the non-standard directories given to "--prefix" and patient users will dabble with modifications of their PYTHONPATH to get the dependencies to be visible.
At this stage, users might be told by a Python savvy friend that they should use a package manager like "easy_install", the mainstream manager, to install the software and have dependencies taken care of. After installing "easy_install", which might be difficult, they will try:
$ easy_install setup.py
This too will fail, since users again do not typically have permission to install software globally on production Unix servers. With more reading, they will learn about the "--user" option, and try:
$ easy_install setup.py --user
They will get the error:
usage: easy_install [options] requirement_or_url ...
or: easy_install --help
error: option --user not recognized
They will be extremely puzzled why their easy_install does not have the --user option where there are clearly pages online describing the option. They might try to upgrade their easy_install to the latest version and find that it still fails.
If they continue and consult a Python packaging expert, they will discover that there are two versions of easy_install, both named "easy_install" so as to maximize confusion, but one part of "distribute" and the other part of "setuptools". It happens to be that only the "easy_install" of "distribute" supports "--user" and the vast majority of servers/sys admins install "setuptools"'s easy_install and so local installation will not be possible. Keep in mind that these distinctions between "distribute" and "setuptools" are meaningless and hard to understand for people who are not experts in Python package management.
At this point, I would have lost 90% of even the most determined, savvy and patient users who try to install my software package -- and rightfully so! They wanted to install a piece of software that happened to be written in Python, not to become experts in state of the art Python package distribution, and this is far too confusing and complex. They will give up and be frustrated at the time wasted.
The tiny minority of users who continue on and ask more Python experts will be told that they ought to use pip/virtualenv instead of easy_install. Installing pip and virtualenv and figuring out how these tools work and how they are different from the conventional "python setup.py" or "easy_install" calls is in itself time consuming and difficult, and again too much to ask from users who just wanted to install a simple piece of Python software and use it. Even those who pursue this path will be confused as to whether whatever dependencies they installed with easy_install or setup.py install --prefix are still usable with pip/virtualenv or if everything needs to be reinstalled from scratch.
This problem is exacerbated if one or more of the packages in question depends on installing a different version of Python than the one that is the default. The difficulty of ensuring that your Python package manger is using the Python version you want it to, and that the required dependencies are installed in the relevant Python 2.x directory and not Python 2.y, will be so endlessly frustrating to users that they will certainly give up at that stage.
Is there a simpler way to install Python software that doesn't require users to delve into all of these technical details of Python packages, paths and locations? For example, I am not a big Java user, but I do use some Java tools occasionally, and don't recall ever having to worry about X and Y dependencies of the Java software I was installing, and I have no clue how Java package managing works (and I'm happy that I don't -- I just wanted to use a tool that happened to be written in Java.) My recollection is that if you download a Jar, you just get it and it tends to work.
Is there an equivalent for Python? A way to distribute software in a way that doesn't depend on users having to chase down all these dependencies and versions? A way to perhaps compile all the relevant packages into something self-contained that can just be downloaded and used as a binary?
I would like to emphasize that this frustration happens even with the narrow goal of distributing a package to savvy Unix users, which makes the problem simpler by not worrying about cross platform issues, etc. I assume that the users are Unix savvy, and might even know Python, but just aren't aware (and don't want to be made aware) about the ins and outs of Python packaging and the myriad of internal complications/rivalries of different package managers. A disturbing feature of this issue is that it happens even when all of your Python package dependencies are well-known, well-written and well-maintained Pypi-available packages like Pandas, Scipy and Numpy. It's not like I was relying on some obscure dependencies that are not properly formed packages: rather, I was using the most mainstream packages that many might rely on.
Any help or advice on this will be greatly appreciated. I think Python is a great language with great libraries, but I find it virtually impossible to distribute the software I write in it (once it has dependencies) in a way that is easy for people to install locally and just run. I would like to clarify that the software I'm writing is not a Python library for programmatic use, but software that has executable scripts that users run as individual programs. Thanks.
We also develop software projects that depend on numpy, scipy and other PyPI packages. Hands down, the best tool currently available out there for managing remote installations is zc.buildout. It is very easy to use. You download a bootstrapping script from their website and distribute that with your package. You write a "local deployment" file, called normally buildout.cfg, that explains how to install the package locally. You ship both the bootstrap.py file and buildout.cfg with your package - we use the MANIFEST.in file in our python packages to force the embedding of these two files with the zip or tar balls distributed by PyPI. When the user unpackages it, it should execute two commands:
$ python bootstrap.py # this will download zc.buildout and setuptools
$ ./bin/buildout # this will build and **locally** install your package + deps
The package is compiled and all dependencies are installed locally, which means that the user installing your package doesn't even need root privileges, which is an added feature. The scripts are (normally) placed under ./bin, so the user can just execute them after that. zc.buildout uses setuptools for interaction with PyPI so everything you expect works out of the box.
You can extend zc.buildout quite easily if all that power is not enough - you create the so-called "recipes" that can help the user to create extra configuration files, download other stuff from the net or instantiate custom programs. zc.buildout website contains a video tutorial that explains in details how to use buildout and how to extend it. Our project Bob makes extensive use of buildout for distributing packages for scientific usage. If you would like, please visit the following page that contains detailed instructions for our developers on how they can setup their python packages so other people can build and install them locally using zc.buildout.
We're currently working to make it easier for users to get started installing Python software in a platform independent manner (in particular see https://python-packaging-user-guide.readthedocs.org/en/latest/future.html and http://www.python.org/dev/peps/pep-0453/)
For right now, the problem with two competing versions of easy_install has been resolved, with the competing fork "distribute" being merged backing into the setuptools main line of development.
The best currently available advice on cross-platform distribution and installation of Python software is captured here: https://packaging.python.org/
I want to distribute some python code, with a few external dependencies, to machines with only core python installed (and users that unfamiliar with easy_install etc.).
I was wondering if perhaps virtualenv can be used for this purpose? I should be able to write some bash scripts that trigger the virtualenv (with the suitable packages) and then run my code.. but this seems somewhat messy, and I'm wondering if I'm re-inventing the wheel?
Are there any simple solutions to distributing python code with dependencies, that ideally doesn't require sudo on client machines?
Buildout - http://pypi.python.org/pypi/zc.buildout
As sample look at my clean project: http://hg.jackleo.info/hyde-0.5.3-buildout-enviroment/src its only 2 files that do the magic, more over Makefile is optional but then you'll need bootstrap.py (Make file downloads it, but it runs only on Linux). buildout.cfg is the main file where you write dependency's and configuration how project is laid down.
To get bootstrap.py just download from http://svn.zope.org/repos/main/zc.buildout/trunk/bootstrap/bootstrap.py
Then run python bootstap.py and bin/buildout. I do not recommend to install buildout locally although it is possible, just use the one bootstrap downloads.
I must admit that buildout is not the easiest solution but its really powerful. So learning is worth time.
UPDATE 2014-05-30
Since It was recently up-voted and used as an answer (probably), I wan to notify of few changes.
First of - buildout is now downloaded from github https://raw.githubusercontent.com/buildout/buildout/master/bootstrap/bootstrap.py
That hyde project would probably fail due to buildout 2 breaking changes.
Here you can find better samples http://www.buildout.org/en/latest/docs/index.html also I want to suggest to look at "collection of links related to Buildout" part, it might contain info for your project.
Secondly I am personally more in favor of setup.py script that can be installed using python. More about the egg structure can be found here http://peak.telecommunity.com/DevCenter/PythonEggs and if that looks too scary - look up google (query for python egg). It's actually more simple in my opinion than buildout (definitely easier to debug) as well as it is probably more useful since it can be distributed more easily and installed anywhere with a help of virtualenv or globally where with buildout you have to provide all of the building scripts with the source all of the time.
You can use a tool like PyInstaller for this purpose. Your application will appear as a single executable on all platforms, and include dependencies. The user doesn't even need Python installed!
See as an example my logview package, which has dependencies on PyQt4 and ZeroMQ and includes distributions for Linux, Mac OSX and Windows all created using PyInstaller.
You don't want to distribute your virtualenv, if that's what you're asking. But you can use pip to create a requirements file - typically called requirements.txt - and tell your users to create a virtualenv then run pip install -r requirements.txt, which will install all the dependencies for them.
See the pip docs for a description of the requirements file format, and the Pinax project for an example of a project that does this very well.