It has no advantages over virtual envs if you do not use docker for deployment.
Well, and see, that's why I said that if it was merely a replacement for virtualenvs, it was overkill. A virtual environment only replicates the bare minimum necessary, in terms of the Python interpreter and libraries you're using.
If you're actually deploying with Docker, that's another story altogether.
First, not everyone has Docker configured on their machine. Second, resources like disk space are not always "cheap" on any system. (Macs aren't the only machines to suffer from low disk space.) Internet may be limited, slow, or inexpensive, making a Docker build prohibitive. Time may be of the essence, RAM might be limited, or CPU might be in demand. There are so many scenarios in which Docker is just not going to be work.
Virtual environments are the one canonical "works everywhere" solution for sandboxing Python. Anyone and everyone who can run Python 3 (and most versions of 2) can create a virtual environment. They require very little overhead, generally minimal network time, and nothing in the way of extra processing power. Ironically, they're one of the fastest and most reliable ways to deploy a Python project in a Docker!
To this aim, your project should be configured properly so that it can be run in a virtual environment. (It doesn't take that much work; certainly less work than a Dockerfile does.)
The third problem is, if the Python project in question has anything to do with GUI or the like, you're going to be beyond the "just start a Docker container" situation. You'll have to configure and work with VNC, which for testing a local application is generally impractical. The same could be said of working with local filesystems (you have to plan what to access and mount it), system integration features, and the list goes on.
Docker certainly has a purpose in some Python development situations, but it should be considered a separate tool altogether, and never as a "replacement" for virtual environments.
Hey Jason, thanks for the great write-up! It certainly solves a lot of questions for me. But one is still there – what to do when I have databases and I don't want to have pq, mysql and mongo installed on my system in various versions?
Assuming SQLite is not an option, this is exactly the sort of scenario where Docker is a good solution: you aren't just needing the sandbox the Python environment itself (virtual environment), but the actual system environment.
Virtual environments really become extra helpful here, too, because it simplifies your Dockerfiles. Assuming your Python project has a requirements.txt file, you'd only need the following:
RUN python3 -m venv venv &&\
venv/bin/pip install-r requirements.txt
You'll note, I had no need to "activate" the virtual environment. You can just use the venv's binaries directly; they know to use the virtual environment.
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Well, and see, that's why I said that if it was merely a replacement for virtualenvs, it was overkill. A virtual environment only replicates the bare minimum necessary, in terms of the Python interpreter and libraries you're using.
If you're actually deploying with Docker, that's another story altogether.
First, not everyone has Docker configured on their machine. Second, resources like disk space are not always "cheap" on any system. (Macs aren't the only machines to suffer from low disk space.) Internet may be limited, slow, or inexpensive, making a Docker build prohibitive. Time may be of the essence, RAM might be limited, or CPU might be in demand. There are so many scenarios in which Docker is just not going to be work.
Virtual environments are the one canonical "works everywhere" solution for sandboxing Python. Anyone and everyone who can run Python 3 (and most versions of 2) can create a virtual environment. They require very little overhead, generally minimal network time, and nothing in the way of extra processing power. Ironically, they're one of the fastest and most reliable ways to deploy a Python project in a Docker!
To this aim, your project should be configured properly so that it can be run in a virtual environment. (It doesn't take that much work; certainly less work than a Dockerfile does.)
The third problem is, if the Python project in question has anything to do with GUI or the like, you're going to be beyond the "just start a Docker container" situation. You'll have to configure and work with VNC, which for testing a local application is generally impractical. The same could be said of working with local filesystems (you have to plan what to access and mount it), system integration features, and the list goes on.
Docker certainly has a purpose in some Python development situations, but it should be considered a separate tool altogether, and never as a "replacement" for virtual environments.
Hey Jason, thanks for the great write-up! It certainly solves a lot of questions for me. But one is still there – what to do when I have databases and I don't want to have pq, mysql and mongo installed on my system in various versions?
Assuming SQLite is not an option, this is exactly the sort of scenario where Docker is a good solution: you aren't just needing the sandbox the Python environment itself (virtual environment), but the actual system environment.
Virtual environments really become extra helpful here, too, because it simplifies your Dockerfiles. Assuming your Python project has a
requirements.txtfile, you'd only need the following:You'll note, I had no need to "activate" the virtual environment. You can just use the venv's binaries directly; they know to use the virtual environment.