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Packaging in Python has always been polarizing. From complicated beginnings where you couldn't uninstall libraries, to virtualenv that allows you to work in a self-contained environment that can be thrown away. In this video, I'll present an introduction to Conda, and to a chanel-specific...
| Autores principales: | , |
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| Autor Corporativo: | |
| Formato: | Video |
| Idioma: | Inglés |
| Publicado: |
[Erscheinungsort nicht ermittelbar] :
Pragmatic AI Solutions,
2021
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| Edición: | 1st edition. |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
| Sumario: | Packaging in Python has always been polarizing. From complicated beginnings where you couldn't uninstall libraries, to virtualenv that allows you to work in a self-contained environment that can be thrown away. In this video, I'll present an introduction to Conda, and to a chanel-specific variation of it that is maintained by the community called miniforge. If you are looking for a way to get scientific packages for the new Apple M1 silicon, this video will also help you out get everything sorted, including installing versions of Python that are pre-packaged and ready for data science tasks. Topics include: * Install Conda by using the community version called miniforge. * Use conda to install a specific version of Python, without system installs * Find more about the differences between virtualenv, pip, and conda * Install data science libraries in the new Apple M1 silicon Useful links: * Conda documentation * Conda Forge community documentation * Conda miniforge project. |
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| Notas: | Online resource; Title from title screen (viewed August 18, 2021). |
| Descripción Física: | 1 online resource (1 video file, circa 27 min.) |