To create the project setup, all we need is to install these packages in whatever system we intend. In the requirements.txt file, we will store what package we have used in our project, and we also keep track of the versions we are using.
What is the requirements.txt file?įor now, just remembers using the requirements.txt file, we will keep track of each package or library we used in the project with a specific version details.īelow is the sample requirement.txt file with contents. For now, we will discuss this more in our upcoming sections of this article. Don’t worry about the requirements.txt file. That’s the reason you will find the requirements.txt file in Github projects. So It’s always recommended to use the same library versions in our projects.
#Xgboost install anaconda windows update#
Suppose we update the code and run our script without doing these modifications. Whereas in the new version, it’s changed to frequency_values. In this version, to get the frequency of the categorical values, we use the syntax get_frequency. If we update, few functions in the last version code will throw errors. Let’s say the packages we used got a few updations. The other thing we can do is, using the same machine for both the projects, cost optimization wise this approach is perfect.īut we will face issues with this approach as well. The ideal approach is having two various machines for these two projects so we won’t find any issues.īut this approach is not a feasible one, and it’s not a cost-effective way too. The data science packages and project setup is completely different for these two projects.
In the world of data science, at the same time, we usually work on various projects.įor example, we are building models to find the fraudulent activities of credit cards, and at the same time, we are testing the performance of the email spam classifier model we have already built. Let’s start the discussion with why we need to maintain environments?
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