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We are aware of the fact that Anaconda is widely used in several domains, such as data science, AI, bioinformatics etc. Anaconda is a useful solution for simplifying the management of Python and scientific libraries on a personal computer. However, on a cluster like those supported by the Alliance, the management of these libraries and dependencies should be done by our staff, in order to ensure compatibility and optimal performance. Here is a list of reasons: | We are aware of the fact that Anaconda is widely used in several domains, such as data science, AI, bioinformatics etc. Anaconda is a useful solution for simplifying the management of Python and scientific libraries on a personal computer. However, on a cluster like those supported by the Alliance, the management of these libraries and dependencies should be done by our staff, in order to ensure compatibility and optimal performance. Here is a list of reasons: | ||
* Anaconda very often installs software (compilers, scientific libraries etc.) which already exist on our clusters as modules, with a configuration that is not optimal. | |||
* Anaconda very often installs software (compilers, scientific libraries etc.) which already exist on | |||
* It installs binaries which are not optimized for the processor architecture on our clusters. | * It installs binaries which are not optimized for the processor architecture on our clusters. | ||
* It makes incorrect assumptions about the location of various system libraries. | * It makes incorrect assumptions about the location of various system libraries. | ||
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* Anaconda is slower than the installation of packages via Python wheels. | * Anaconda is slower than the installation of packages via Python wheels. | ||
* Anaconda modifies the <tt>$HOME/.bashrc</tt> file, which can easily cause conflicts. | * Anaconda modifies the <tt>$HOME/.bashrc</tt> file, which can easily cause conflicts. | ||
==How to transition from Conda to Virtualenv == | ==How to transition from Conda to Virtualenv == | ||