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I have yet to come across an answer that makes me WANT to start using virtual environments. I understand how they work, but what I don’t understand is how can someone (like me) have hundreds of Python projects on their drive, almost all of them use the same packages (like Pandas and Numpy), but if they were all in separate venv’s, you’d have to pip install those same packages over and over and over again, wasting so much space for no reason. Not to mention if any of those also require a package like tensorflow.
The only real benefit I can see to using venv’s in my case is to mitigate version issues, but for me, that’s really not as big of an issue as it’s portrayed. Any project of mine that becomes out of date, I update the packages for it.
Why install the same dependency for every project when you can just do it once for all of them on a global scale? I know you can also specify —-global-dependencies or whatever the tag is when creating a new venv, but since ALL of my python packages are installed globally (hundreds of dependencies are pip installed already), I don’t want the new venv to make use of ALL of them? So I can specify only specific global packages to use in a venv? That would make more sense.
What else am I missing?
UPDATE
I’m going to elaborate and clarify my question a bit as there seems to be some confusion.
I’m not so much interested in understanding HOW venv’s work, and I understand the benefits that can come with using them. What I’m asking is:
Why would someone with (for example) have 100 different projects that all require tensorflow to be installed into their own venv’s. That would mean you have to install tensorflow 100 separate times. That’s not just a “little” extra space being wasted, that’s a lot.
I understand they mitigate dependency versioning issues, you can “freeze” packages with their current working versions and can forget about them, great. And maybe I’m just unique in this respect, but the versioning issue (besides the obvious difference between python 2 and 3) really hasn’t been THAT big of an issue. Yes I’ve run into it, but isn’t it better practise to keep your projects up to date with the current working/stable versions than to freeze them with old, possibly no longer supported versions? Sure it works, but that doesn’t seem to be the “best” option to me either.
To reiterate on the second part of my question, what I would think is, if I have (for example) tensorflow installed globally, and I create a venv for each of my 100 tensorflow projects, is there not a way to make use of the already globally installed tensorflow inside of the venv, without having to install it again? I know in pycharm and possibly the command line, you can use a — system-site-packages argument (or whatever it is) to make that happen, but I don’t want to include ALL of the globally installed dependencies, cuz I have hundreds of those too. Is —-system-site-packages -tensorflow for example a thing?
Hope that helps to clarify what I’m looking for out of this discussion because so far, I have no use for venv’s, other than from everyone else claiming how great they are but I guess I see it a bit differently :P
(FINAL?) UPDATE
From the great discussions I've had with the contributors below, here is a summation of where I think venv's are of benefit and where they're not:
USE a venv:
You're working on one BIG project with multiple people to mitigate versioning issues among the people
You don't plan on updating your dependencies very often for all projects
To have a clearer separation of your projects
To containerize your project (again, for distribution)
Your portfolio is fairly small (especially in the data science world where packages like Tensorflow are large and used quite frequently across all of them as you'd have to pip install the same package to each venv)
DO NOT use a venv:
Your portfolio of projects is large AND requires a lot of heavy dependencies (like tensorflow) to mitigate installing the same package in every venv you create
You're not distributing your projects across a team of people
You're actively maintaining your projects and keeping global dependency versions up to date across all of them (maybe I'm the only one who's actually doing this, but whatever)
As was recently mentioned, I guess it depends on your use case. Working on a website that requires contribution from many people at once, it makes sense to all be working out of one environment, but for someone like me with a massive portfolio of Tensorflow projects, that do not have versioning issues or the need for other team members, it doesn't make sense. Maybe if you plan on containerizing or distributing the project it makes sense to do so on an individual basis, but to have (going back to this example) 100 Tensorflow projects in your portfolio, it makes no sense to have 100 different venv's for all of them as you'd have to install tensorflow 100 times into each of them, which is no different than having to pip install tensorflow==2.2.0 for specific old projects that you want to run, which in that case, just keep your projects up to date.
Maybe I'm missing something else major here, but that's the best I've come up with so far. Hope it helps someone else who's had a similar thought.
I'm a data scientist and sometimes I run into these things called "virtual environments" and I don't get what the use case is? I already have all of these packages and modules and widgets downloaded! Why should I set up a separate place where I manage all of the stuff I'm already managing globally?
Python is a very powerful tool. In this answer consider two such ways to swing the metaphorical hammer:
Data Science
Software Engineering
For a data scientist (working alone) using Python to write a poc for a research paper, make a lstm nn, or predict the price of TSLA dependent on the frequency of Elon Musk's tweets all that really matters is being able to use the best library (tensorflow, pytorch, sklearn, ...) for whatever task they're trying to get done. In whatever directory they're working in when they need it. It is very tempting to use one global Python installation and just use the same stuff everywhere. Frankly, this is probably fine. As it's just one person managing their own space. So the configuration of their machine would be one single Python environment and everything, everywhere uses it. Or if the data scientist wanted to they could have a single directory that contains a virtual environment and some sub directories containing all the scripts (projects) they work on.
Now consider a software engineer who has multiple git repos with complete CI/CD pipelines that each build into separate entities that then get deployed to some cloud environment. Them and the 9 other people on their team need to be able to be sure that they are all making changes that won't break any piece of the code. For example in Python 3.6 the function dict.popitem subtly changed from returning a random element in a dict to LIFO order guaranteed. It's pretty easy to see that that could cause issues if Jerry had implemented a function that relies on the original random nature of the function and Bob implemented a function with the LIFO behavior guaranteed. This team of engineers would have git repos that each contain a single virtual environment (a single isolated Python environment) that allows them to manage dependencies for that "project".
The data scientist has one Python installation/environment that allows them to do whatever.
The engineer has a Python installation and a bunch of environments so that they can work across multiple repos with multiple people and (hopefully) nothing breaks.
I can see where you're coming from with your question. It can seem like a lot of work to set up and maintain multiple virtual environments (venvs), especially when many of your projects might use similar or even the same packages.
However, there are some good reasons for using venvs even in cases where you might be tempted to just use a single global environment. One reason is that it can be helpful to have a clear separation between your different projects. This can be helpful in terms of organization, but it can also be helpful if you need to use different versions of packages in different projects.
If you try to share a single venv among all of your projects, it can be difficult to use different versions of packages in those projects when necessary. This is because the packages in your venv will be shared among all of the projects that use that venv. So, if you need to use a different version of a package in one project, you would need to change the version in the venv, which would then affect all of the other projects that use that venv. This can be confusing and make it difficult to keep track of what versions of packages are being used in which projects.
Another issue with sharing a single venv among all of your projects is that it can be difficult to share your code with others. This is because they would need to have access to the same environment (which contains lots of stuff unrelated to the single project you are trying to share). This can be confusing and inconvenient for them.
So, while it might seem like a lot of work to set up and maintain multiple virtual environments, there are some good reasons for doing so. In most cases, it is worth the effort in order to have a clear separation between your different projects and to avoid confusion when sharing your code with others.
It's the same principle as in monouser vs multiuser, virtualization vs no virtualization, containers vs no containers, monolithic apps vs micro services, etcetera; to avoid conflict, maintain order, easily identify a state of failure, among other reasons as scalability or portability. If necessary apply it, and always keeping in mind KISS philosophy as well, managing complexity, not creating more.
And as you have already mentioned, considering that resources are finite.
Besides, a set of projects that share the same base of dependencies of course that is not the best example of separation necessity.
In addition to that, technology evolve taking into account not redundancy of knowingly base of commonly used resources.
Well, there are a few advantages:
with virtual environments, you have knowledge about your project's dependencies: without virtual environments your actual environment is going to be a yarnball of old and new libraries, dependencies and so on, such that if you want to deploy a thing into somewhere else (which may mean just running it in your new computer you just bought) you can reproduce the environment it was working in
you're eventually going to run into something like the following issue: project alpha needs version7 of library A, but project beta needs library B, which runs on version3 of library A. if you install version3, A will probably die, but you really need to get B working.
it's really not that complicated, and will save you a lot of grief in the long term.
There are several motivations for venvs,
or for their moral equivalent: conda environments.
1. author a package
You create a cool "scrape my favorite site" package
which graphs a timeseries of some widget product.
Naturally it depends on BeautifulSoup.
You happened to have html5lib 1.1 lying around
due to some previous project, so you tested with that.
A user downloads your scrape-widget package from pypi,
happens to have lxml 4.7.1 available, and finds
that scraping crashes when using that library.
Wouldn't it have been better for your package
to specify that user shall run against the same
deps that you tested with?
2. use a package
Same scenario, but now you're using someone's scrape-widget
package. Author tested with lxml 4.7.1 but you have lxml 4.9.1,
which behaves differently, and this makes the app behave
differently, crashing in ways the author never saw.
3. use two packages
You want to run both scrape-frobozz-magic-widgets
and scrape-acme-widget. Their authors tested using
different versions of requests, and of lxml.
Changing dep changes the app behavior.
You can only use one or the other, unless you're
willing to re-run pip quite frequently.
4. collaborate on a team
You write code that has deps.
So does your colleague.
You have to coordinate things,
so testing on one laptop
instills confidence the test
would succeed on other laptops.
5. use CI
You have a teammate named Jenkins, and
want to communicate to him that you used
a specific version of a dep when you saw the test succeed.
6. get a new laptop
Things were working.
Then your laptop exploded,
you got a new one,
and you (quickly) want to see things work again.
Some of your deps were downrev, due to
recently released bugs and breaking changes.
Reading a file full of dep versions from your github repo
lets you immediately reproduce the state of the world
back when things were working.
We are trying to use Poise to manage runtimes for Python and Ruby on our Centos7 servers. From my understanding this works with other recipes, but I can't figure out what the "right" way is to link the binaries to the standard bin locations (/usr/bin/, etc.). So far I have been unable to find a way to do this as part of the standard process - only by digging around to figure out where they were installed and then adding those links as a separate step later in the recipe - it seems like a major hack.
In other words, adding the following in a recipe that has some scripts that get copied to the server that require Python 3 looks like it installs Python 3:
python_runtime '3'
But the scripts (which cannot be changed) will never know that Python 3 exists.
Everything obviously works fine if I just do an install of Python3 using yum - which poise actually appears to do as well for Centos.
I am relatively new to Chef, but I have checked with our other devops team members and done a lot of searching and we couldn't figure out how this is officially supposed to be done. We aren't looking for more hacks as we can obviously do that, but what is the "Chef" way to do this?
Thanks in advance.
Unfortunately just linking the binaries wouldn't really help you much since by default on CentOS it will use the SCL packages which require some special environment variables to operate. If you want it to use the "normal" system you can do this:
python_runtime '3' do
provider :system
end
However that will probably fail because there is no EL7 distro package for Python 3. If you want to continue using SCL packages but have them look like normal binaries, maybe try something like this:
file '/usr/local/bin/python' do # or .../python3 if you prefer
owner 'root'
group 'root'
mode '755'
content "#!/bin/sh\nexec scl enable rh-python35 -- python3 \"$#\""
end
Or something like that. That still hardwires the fact that it is SCL under the hood and which SCL package is being used, which is not lovely, but the fully generic form (while doable) is a lot more complex.
I just installed git and GitHub and the reason is that it's a version control system (that's the only thing I know about it). I am starting a project (a game in Python) and it's going to stretch a long time as I am going to learn Python in deep while working on this. I want to be organized while working on this project and that's why I need a version control system.
So, the thing is:
1. I am the only programmer.
2. I don't want to share the code anywhere.
3. I want to work locally only on my system.
4. It's not open source (for the beginning)
So I want to know if GitHub functions according to my needs and how should I create local code repositories and manage my code?
P.S. I am sorry if it looks all messed up, but I couldn't find any better way to put forward.
Yes, using GitHub is a great idea.
Even if you are working alone, git will help you backup your code and will let you go to older versions in case your latest code stops working.
If you are a student, then you can get a plan to have private repositories for a couple of years for free. If you are not a student and privacy is very important for you, then you can buy private repositories in GitHub.
Virtually all of the information that you might need will be here: http://git-scm.com/doc
I'm about to start a project which I believe might become my first project to publish on Github and PyPi, and I'm trying to do this the 'correct' way, but I'm having trouble finding relevant info about this. Maybe the problem is rather that I'm finding too much to read, not sure what advice to follow.
I'm having some troubles understanding how I'm actually supposed to set this up. My understanding is that using Anaconda you are supposed to use conda create -p path/to/place/environment as a replacement for virtualenv. Now, keep in mind that I have never used virtalenv before either, so the concept is a bit fresh for me, but essentially this is for keeping all the work wrapped up in its own bubble, making sure one keep all dependencies for oneself.
So, I'll start with conda create -p ~/projects/myproj. In this folder I will later place the rest of the files of my project. Whenever I'm starting the development of the project, I'll have to make sure to run source activate ~/projects/myproj, correct? From here on, I think I mainly have two questions:
What files created in the environment do I put in my .gitignore, and what files should I commit?
What files are necessary for publishing on PyPi, and should I make sure to keep it in a 'PyPi happy' order from day 1, or are they interesting first when the publishing part actually becomes relevant? Are there stuff in the created conda environment that will conflict with a PyPi upload?
I think this will do for now. If you have any other general advice about keeping the project clean and healthy from get-go, I'll gladly take them!
Your question 1 doesn't make much sense to me. conda create creates the development packages. You should not put them in the same place as your code that your are developing.
As for 2, you just need a setup.py with the standard stuff (http://python-packaging-user-guide.readthedocs.org/en/latest/ is probably as good of a guide as anything).
I need an editor with python built into it. Currently I use blender so I do not have install python. Blender comes with the python32.dll to use python. is there another editor out there that I can execute python commands without it being installed?
I don't understand the question fully either. Why NOT install python? But if the question is how to be able to edit and run python on machines without installing it, there's Movable Python (http://www.voidspace.org.uk/python/movpy/) with a small fee to purchase and Portable Python (http://www.portablepython.com/wiki/About), free, donation requested. I've used Movable Python and can vouch for it. I've never tried Portable Python.
ViennaMike referenced Movable Python which has a small fee, after I had asked the question, I did more searching and found movable python about the same time he suggested it. I seems to find something different.
http://code.google.com/p/movable-python/
this seems to be a free version of movable python. This is only the IDLE portion of python, but can be used to run *.py files. It is considerably smaller than a normal python installation, and comes in a zip file.
Several people asked about my ability to install an editor but not python. At my High school (I work with the IT dept as one of my classes) I find python helps a lot with some tasks. I am unable to install python due to admin rights (which I will have next year) so anytime I did install python, because of its size on the Network drive, it would be automatically deleted.
Thank you ViennaMike again for finding movable python, unfortunately, it only works with python 2.5, so I may see if there is a way I can get that to 3.2