Development/Python: Difference between revisions
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Python versions which are offered via <b> |
Python versions which are offered via <b>software modules</b> can be identified via: |
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<source lang="bash"> |
<source lang="bash"> |
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module avail devel/python |
$ module avail devel/python |
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</source> |
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A specific version of Python can then be chosen e.g. as follows: |
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<source lang="bash"> |
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$ module load devel/python/3.12.3_gnu_13.3 |
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$ python --version |
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Python 3.12.3 |
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Lmod modules |
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== Installation == |
== Installation == |
Revision as of 10:42, 5 November 2024
Introduction
Python is a versatile, easy-to-learn, usually interpreted programming language. It offers a wide range of libraries for scientific tasks and visualization. Python is the de facto standard interface for applications of machine learning. Python can be used in particular as an open source alternative for tasks that have usually been used for Matlab.
Installation and Usage
Available versions
Python is available on all systems. Both as system Python, which comes bundled with the operating system, and via (Lmod) software modules.
System Python is available in different versions, typically the default system Python is too old for most users and applications. Newer versions are therefore installed alongside the standard version. You can access a specific Python version by specifying the version in the Python command. E.g. on bwUniCluster, the following versions are currently available. Please note, that these exact versions will change over time and differ from site to site!
$ python --version
Python 3.6.8
$ python3.8 --version
Python 3.8.16
$ python3.9 --version
Python 3.9.16
$ python3.11 --version
Python 3.11.2
$ python2 --version
Python 2.7.18
Python versions which are offered via software modules can be identified via:
$ module avail devel/python
A specific version of Python can then be chosen e.g. as follows:
$ module load devel/python/3.12.3_gnu_13.3
$ python --version
Python 3.12.3
Installation
Not required.
Usage
Interactively, prompt
Scripting
Via Jupyter
Package Manager
pip
Virtual Environments
Short description
Benefit
Creation
Usage
Best Practice
Always use virtual environments
Do's and don'ts