Conda (package manager)

Conda is an open-source,[2] cross-platform,[3] language-agnostic package manager and environment management system. It was originally developed to solve difficult package management challenges faced by Python data scientists, and today is a popular package manager for Python and R.[4][5] At first part of Anaconda Python distribution developed by Anaconda Inc., it ended up being useful on its own and for things other than Python, so it was spun out as a separate package,[6] released under the BSD license.[2][7][8][9][10][11] The conda package and environment manager is included in all versions of Anaconda, Miniconda,[12] and Anaconda Repository.[13]

Conda
Stable release
4.9.2[1] / 12 November 2020 (12 November 2020)
Repository
LicenseBSD
Websiteconda.io 

Conda allows users to easily install different versions of binary software packages and any required libraries appropriate for their computing platform. Also, it allows users to switch between package versions and download and install updates from a software repository. Conda is written in the Python programming language, but can manage projects containing code written in any language (e.g., R), including multi-language projects. Conda can install Python,[14] while similar Python-based cross-platform package managers (such as wheel or pip) cannot.

The big difference between conda and the pip package manager is in how package dependencies are managed, which is a significant challenge for Python data science and the reason conda was created. Pip installs all Python package dependencies required, whether or not those conflict with other packages previously installed. So a working installation of, for example, Google TensorFlow can suddenly stop working when a user pip-installs a new package that needs a different version of the NumPy library. More insidiously, everything might still appear to work, but the user gets different results, or is unable to reproduce the same results elsewhere because the user did not pip-install in the same order. Conda analyzes the user's current environment, everything that has been installed, any version limitations that the user specifies (e.g. if the user only wants tensorflow >= 2.0) and figures out how to install compatible dependencies. Otherwise, it will tell the user that what he or she wants can't be done. Pip, by contrast, will just install the package the user specified and any dependencies, even if that breaks other packages.

A popular conda channel for bioinformatics software is Bioconda, which provides multiple software distributions for computational biology.[15][16]

See also

References
  1. "Release 4.9.2". 12 November 2020. Retrieved 9 December 2020.
  2. "Conda". conda.io. Retrieved 11 June 2019.
  3. "Building Conda Packages for Multiple Operating Systems". Pydannt. 29 January 2015. Retrieved 11 June 2019.
  4. Gorelick (Author), Micha; Ozsvald, Ian (September 2014). High Performance Python: Practical Performant Programming for Humans (1st ed.). O'Reilly Media. p. 370. ISBN 978-1449361594.
  5. Jackson, Joab (5 February 2013). "Python gets a big data boost from DARPA". networkworld. Retrieved October 30, 2014.
  6. "What's the difference between Anaconda, conda, and Miniconda?". FAQ - Bioconda documentation. Retrieved 22 April 2020.
  7. "State of Conda, Oct. 2014". Pen and Pants. Retrieved 9 April 2015.
  8. Tony Ojeda; Sean Patrick Murphy; Benjamin Bengfort; Abhijit Dasgupta (25 September 2014). Practical Data Science Cookbook. Packt Publishing Ltd. ISBN 978-1783980253. Retrieved 19 March 2015.
  9. Langtangen, Hans Petter (2014). A Primer on Scientific Programming with Python. Springer. ISBN 978-3642549595. Retrieved 19 March 2015.
  10. Yves Hilpisch (11 December 2014). Python for Finance: Analyze Big Financial Data. O'Reilly Media. ISBN 9781491945391. Retrieved 19 March 2015.
  11. "Continuum Analytics Launches Anaconda Server for Enterprise Package Management". Yahoo Finance. 30 January 2014. Retrieved 19 March 2015.
  12. "Miniconda". conda.io. Archived from the original on 2018-09-10. Retrieved 2018-07-11.
  13. "Anaconda repository". anaconda.org.
  14. "3. Managing Python". 2015. Archived from the original on 16 June 2015. Retrieved 16 Jun 2015. So now let’s say you need Python 3 to learn programming, but you don’t want to overwrite your Python 2.7 environment by updating Python. You can create and activate a new environment named snakes, and install the latest version of Python 3 as follows...
  15. Bioconda official website.
  16. Grüning, Bjorn; the Bioconda Team (27 October 2017). "Bioconda: A sustainable and comprehensive software distribution for the life sciences". bioRxiv 10.1101/207092.


This article is issued from Wikipedia. The text is licensed under Creative Commons - Attribution - Sharealike. Additional terms may apply for the media files.