Dependency Management - Revision history

Ajshen at 20:37, 28 June 2026

2026-06-28T20:37:55Z

← Older revision		Revision as of 20:37, 28 June 2026
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	{{ReproducibilitySidebar}}		{{ReproducibilitySidebar}}

	'''Dependencies''' are any software or ~~software libraries~~ that your research workflow requires to execute. For example, data analysis in Python often relies on the NumPy or Pandas ~~Python~~ packages, while R workflows might rely in the Tidyverse library. It is important to keep track of which dependencies and '''which versions of each dependency''' are being used in your workflow. Unclear or insufficient dependency management can lead to inconsistent results or failure of other researchers to reproduce your results.		'''Dependencies''' are any external software, libraries, or packages that your research workflow requires to execute. For example, data analysis in Python often relies on the NumPy or Pandas packages, while R workflows might rely heavily on the Tidyverse ecosystem.

	~~== Procedures~~ and ~~Tools ==~~		As your projects grow, it is critical to keep track of exactly which dependencies—and '''which specific versions''' of those dependencies—are being used. Unclear or insufficient dependency management is a leading cause of code breaking over time and is a major barrier to other researchers (or future you) reproducing your results.
	~~{\| class~~="~~wikitable~~"
	\|+		This page covers basic procedures, best practices, and tools for managing dependencies and encapsulating your computational environments.
	~~!Title~~
	~~!Description~~		<div style="background-color: #fff3cd; border-left: 5px solid #ffc107; padding: 15px; margin-bottom: 20px; margin-top: 15px; overflow: hidden;">
	~~!Technical Level~~		'''Important: Project Isolation'''<br>
	~~!Relevance~~		A common beginner mistake is installing all packages globally into your computer's base environment. You should strive to create a '''separate, isolated environment''' for every distinct research project. This prevents version conflicts (e.g., Project A needs Python 3.9 but Project B requires Python 3.11) and makes it much easier to document your work.
	\|-		</div>
	\|[[Virtual Environment Introduction]]
	\|Introduces virtual environments for dependency management		== 1. Virtual Environments ==
	\|		Virtual environments are lightweight, self-contained directories that contain a specific installation of a programming language and its packages. They are the first line of defense for reproducible code on your local machine.
	\|
	\|-		<div style="background-color: #f8f9fa; border-left: 4px solid #6c757d; padding: 15px; margin-bottom: 10px;">
	\|		'''[[Virtual Environment Introduction]]'''<br>
	*[~~https://info.stat.cmu.edu/index.php?title=Introduction_to_containers~~ Introduction to containers]		Introduces the core concepts of virtual environments for dependency management. Covers how to set up isolated workspaces using common tools like <code>venv</code> or <code>conda</code> for your analysis scripts.
	*[~~https://info.stat.cmu.edu/index.php?title=~~Docker,~~_and_why~~-you-should-use-it-for-your-research Docker, and why-~~you~~-~~should~~-~~use-it-for-your-research]~~		</div>
	*[~~https://info.stat.cmu.edu/index.php?title=~~Tutorials Basic ~~tutorial~~]
	*[https:/~~/info~~.~~stat.cmu.edu~~/~~index.php?title~~=~~Running_Docker_locally_and_on_Hydras~~ Running Docker locally and on Hydras]		== 2. Containerization ==
	~~\|need~~ to ~~organize~~		While virtual environments handle software packages, they do not capture the underlying operating system or system-level libraries. Containers solve this by packaging your code along with the ''entire'' system environment it needs to run, ensuring perfect reproducibility across different machines and servers.
	\|
	\|		<div style="background-color: #f8f9fa; border-left: 4px solid #005A9C; padding: 15px; margin-bottom: 10px;">
	\|-		'''[[Introduction to containers]] & [[Docker, and why-you-should-use-it-for-your-research\|Why Use Docker]]'''<br>
	\|		A conceptual overview of what containers are, how they differ from traditional virtual machines, and why Docker has become an essential tool for robust research workflows.
	\|		</div>
	\|
	\|		<div style="background-color: #f8f9fa; border-left: 4px solid #75AADB; padding: 15px; margin-bottom: 10px;">
	\|}		'''[[Tutorials\|Docker Basic Tutorial]]'''<br>
			A hands-on guide to building your first container. Learn how to write a basic <code>Dockerfile</code>, build an image, and execute your code inside an isolated container environment.
			</div>

			<div style="background-color: #f8f9fa; border-left: 4px solid #6f42c1; padding: 15px; margin-bottom: 10px;">
			'''[[Running Docker locally and on Hydras]]'''<br>
			Specific instructions for scaling up your containerized workflows. Covers how to test your Docker containers locally and subsequently deploy them on the department's Hydra computing clusters.
			</div>

Ajshen at 20:25, 28 June 2026

2026-06-28T20:25:33Z

← Older revision		Revision as of 20:25, 28 June 2026
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	[[Category:Beginner's_Guide_Reproducibility\|02]]		[[Category:Beginner's_Guide_Reproducibility\|02]]

			{{ReproducibilitySidebar}}

	'''Dependencies''' are any software or software libraries that your research workflow requires to execute. For example, data analysis in Python often relies on the NumPy or Pandas Python packages, while R workflows might rely in the Tidyverse library. It is important to keep track of which dependencies and '''which versions of each dependency''' are being used in your workflow. Unclear or insufficient dependency management can lead to inconsistent results or failure of other researchers to reproduce your results.		'''Dependencies''' are any software or software libraries that your research workflow requires to execute. For example, data analysis in Python often relies on the NumPy or Pandas Python packages, while R workflows might rely in the Tidyverse library. It is important to keep track of which dependencies and '''which versions of each dependency''' are being used in your workflow. Unclear or insufficient dependency management can lead to inconsistent results or failure of other researchers to reproduce your results.

Ajshen at 20:24, 28 June 2026

2026-06-28T20:24:16Z

← Older revision		Revision as of 20:24, 28 June 2026
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	'''Dependencies''' are any software or software libraries that your research workflow requires to execute. For example, data analysis in Python often relies on the NumPy or Pandas Python packages, while R workflows might rely in the Tidyverse library. It is important to keep track of which dependencies and '''which versions of each dependency''' are being used in your workflow. Unclear or insufficient dependency management can lead to inconsistent results or failure of other researchers to reproduce your results.		'''Dependencies''' are any software or software libraries that your research workflow requires to execute. For example, data analysis in Python often relies on the NumPy or Pandas Python packages, while R workflows might rely in the Tidyverse library. It is important to keep track of which dependencies and '''which versions of each dependency''' are being used in your workflow. Unclear or insufficient dependency management can lead to inconsistent results or failure of other researchers to reproduce your results.

	~~Back to [[Beginner's Guide to Computation in Statistical Research]]~~

	== Procedures and Tools ==		== Procedures and Tools ==

Ajshen at 20:24, 28 June 2026

2026-06-28T20:24:03Z

← Older revision		Revision as of 20:24, 28 June 2026
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			[[Category:Beginner's_Guide_Reproducibility\|02]]

	'''Dependencies''' are any software or software libraries that your research workflow requires to execute. For example, data analysis in Python often relies on the NumPy or Pandas Python packages, while R workflows might rely in the Tidyverse library. It is important to keep track of which dependencies and '''which versions of each dependency''' are being used in your workflow. Unclear or insufficient dependency management can lead to inconsistent results or failure of other researchers to reproduce your results.		'''Dependencies''' are any software or software libraries that your research workflow requires to execute. For example, data analysis in Python often relies on the NumPy or Pandas Python packages, while R workflows might rely in the Tidyverse library. It is important to keep track of which dependencies and '''which versions of each dependency''' are being used in your workflow. Unclear or insufficient dependency management can lead to inconsistent results or failure of other researchers to reproduce your results.

Ajshen: /* Procedures and Tools */

2026-06-27T06:32:54Z

Procedures and Tools

← Older revision		Revision as of 06:32, 27 June 2026
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	\|		\|
	*[https://info.stat.cmu.edu/index.php&title=Introduction_to_containers Introduction to containers]		*[https://info.stat.cmu.edu/index.php?title=Introduction_to_containers Introduction to containers]
	*[https://info.stat.cmu.edu/index.php~~%3Ftitle~~=Docker,_and_why-you-should-use-it-for-your-research Docker, and why-you-should-use-it-for-your-research]		*[https://info.stat.cmu.edu/index.php?title=Docker,_and_why-you-should-use-it-for-your-research Docker, and why-you-should-use-it-for-your-research]
	*[https://info.stat.cmu.edu/index.php~~%3Ftitle~~=Tutorials Basic tutorial]		*[https://info.stat.cmu.edu/index.php?title=Tutorials Basic tutorial]
	*[https://info.stat.cmu.edu/index.php~~%3Ftitle~~=Running_Docker_locally_and_on_Hydras Running Docker locally and on Hydras]		*[https://info.stat.cmu.edu/index.php?title=Running_Docker_locally_and_on_Hydras Running Docker locally and on Hydras]
	\|need to organize		\|need to organize
	\|		\|

Ajshen: /* Procedures and Tools */

2026-06-27T06:32:37Z

Procedures and Tools

← Older revision		Revision as of 06:32, 27 June 2026
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	\|		\|
	*[https://info.stat.cmu.edu/index.php~~%3Ftitle~~=Introduction_to_containers Introduction to containers]		*[https://info.stat.cmu.edu/index.php&title=Introduction_to_containers Introduction to containers]
	*[https://info.stat.cmu.edu/index.php%3Ftitle=Docker,_and_why-you-should-use-it-for-your-research Docker, and why-you-should-use-it-for-your-research]		*[https://info.stat.cmu.edu/index.php%3Ftitle=Docker,_and_why-you-should-use-it-for-your-research Docker, and why-you-should-use-it-for-your-research]
	*[https://info.stat.cmu.edu/index.php%3Ftitle=Tutorials Basic tutorial]		*[https://info.stat.cmu.edu/index.php%3Ftitle=Tutorials Basic tutorial]

Ajshen at 07:51, 10 April 2026

2026-04-10T07:51:50Z

← Older revision		Revision as of 07:51, 10 April 2026
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	'''Dependencies''' are any software or software libraries that your research workflow requires to execute. For example, data analysis in Python often relies on the NumPy or Pandas Python packages, while R workflows might rely in the Tidyverse library. It is important to keep track of which dependencies and '''which versions of each dependency''' are being used in your workflow. Unclear or insufficient dependency management can lead to inconsistent results or failure of other researchers to reproduce your results.		'''Dependencies''' are any software or software libraries that your research workflow requires to execute. For example, data analysis in Python often relies on the NumPy or Pandas Python packages, while R workflows might rely in the Tidyverse library. It is important to keep track of which dependencies and '''which versions of each dependency''' are being used in your workflow. Unclear or insufficient dependency management can lead to inconsistent results or failure of other researchers to reproduce your results.

			Back to [[Beginner's Guide to Computation in Statistical Research]]

	== Procedures and Tools ==		== Procedures and Tools ==
			{\| class="wikitable"
			\|+
			!Title
			!Description
			!Technical Level
			!Relevance
			\|-
			\|[[Virtual Environment Introduction]]
			\|Introduces virtual environments for dependency management
			\|
			\|
			\|-
			\|
			*[https://info.stat.cmu.edu/index.php%3Ftitle=Introduction_to_containers Introduction to containers]
			*[https://info.stat.cmu.edu/index.php%3Ftitle=Docker,_and_why-you-should-use-it-for-your-research Docker, and why-you-should-use-it-for-your-research]
			*[https://info.stat.cmu.edu/index.php%3Ftitle=Tutorials Basic tutorial]
			*[https://info.stat.cmu.edu/index.php%3Ftitle=Running_Docker_locally_and_on_Hydras Running Docker locally and on Hydras]
			\|need to organize
			\|
			\|
			\|-
			\|
			\|
			\|
			\|
			\|}

Ajshen: Created page with "'''Dependencies''' are any software or software libraries that your research workflow requires to execute. For example, data analysis in Python often relies on the NumPy or Pa..."

2026-04-10T07:50:30Z

Created page with "'''Dependencies''' are any software or software libraries that your research workflow requires to execute. For example, data analysis in Python often relies on the NumPy or Pa..."

New page

'''Dependencies''' are any software or software libraries that your research workflow requires to execute. For example, data analysis in Python often relies on the NumPy or Pandas Python packages, while R workflows might rely in the Tidyverse library. It is important to keep track of which dependencies and '''which versions of each dependency''' are being used in your workflow. Unclear or insufficient dependency management can lead to inconsistent results or failure of other researchers to reproduce your results.

== Procedures and Tools ==