Run training, scraping and data work on hardware sized for it, with conda managing the packages and the Python version.
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Miniconda is the small installer for conda. You get Python, the conda package manager and almost nothing else, then you add what a project needs. Full Anaconda arrives with hundreds of packages already in place, which is convenient on a desktop and wasteful on a server where you know what you're building. Conda handles the Python version itself as well as the libraries, which is the part pip cannot do, and it settles the compiled dependencies that scientific packages tend to drag behind them.
One thing worth knowing before you install it anywhere commercial: Conda is BSD licensed and free, but Miniconda points by default at Anaconda's own package repository, and Anaconda's terms ask organisations above roughly two hundred people to pay for access to it. The community channel conda-forge has no such condition at any size, and switching your default channel to it is a single line of configuration. Miniforge is the same tool shipped pointing there already. None of that is a reason to avoid conda. It is a reason to know which channel you're pulling from.
Pin the interpreter version per project, which pip and venv leave to you.
Export to environment.yml and rebuild it identically on another machine.
CUDA builds, BLAS and the rest arrive working instead of needing a compiler.
Training a model, working through a large dataset or crawling a site are all tasks that want a machine which stays awake. Start it over SSH inside tmux, close the laptop and come back to a finished run rather than a cancelled one.
An environment.yml file turns "it works on my machine" into a checked-in fact. Build the environment once on the server, export it, and every teammate and every rebuild starts from the same interpreter and the same package versions.
Anaconda installs several gigabytes of packages you may never open. Miniconda installs conda and lets you decide. On a server that difference shows up as disk, as image build time and as a shorter list of things to keep patched. Start small and add what the project asks for.
Anaconda's repository carries commercial terms above roughly two hundred employees, while conda-forge carries none at any size. Setting conda-forge as the default channel takes one command and removes the question entirely. Miniforge is the same tool with that choice already made, if you would rather not think about it again.
Conda is how most people install a working CUDA toolchain without building anything by hand. That gets you a ready environment on CPU, and the same environment file moves onto a GPU VPS when the job needs real hardware behind it.
A pipeline that runs nightly needs somewhere that is awake at night. Conda activates inside a cron entry the same way it does in your shell, so a scheduled job gets the exact interpreter and packages you tested against.
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Scientific packages tend to want system libraries alongside the Python ones, and a container slice with no real root turns that into an afternoon. KVM gives you a whole machine, so apt and conda both behave the way the documentation says they will.
Conda environments are not small, and a machine with four projects on it ends up with four copies of a lot of things. NVMe storage keeps solving that quick, and you can resize when the package cache grows past what you planned for.
This is the whole argument for putting Python work on a server. The job runs while you commute, sleep or do something else, and you reconnect to a result rather than restarting from the beginning.
Prototype the environment on EPYC cores, then move the same environment.yml onto a GPU VPS when the model needs VRAM. Same account, same dashboard, so there is no migration to plan.

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