A set of automation scripts work with no issue from a local machine, but an experiment needs to take place with a new package found online.
How is this new package isolated from the main code base?
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A. B. C. D.D.
When working with automation scripts, it is important to manage dependencies and package versions to ensure the reliability and stability of the code. If you need to experiment with a new package found online, it is best practice to isolate it from the main code base to avoid any potential conflicts or issues.
Option A: Add the new package to your requirements.txt file. Adding the new package to the requirements.txt file would not isolate it from the main code base. The requirements.txt file is typically used to list all the required packages and their versions, and to install them using a package manager like pip. Adding the new package to this file would make it a part of the main code base, which may not be desirable in this scenario.
Option B: Create a new virtual machine and perform a pip install of the new package. Creating a new virtual machine is a good way to isolate the new package from the main code base. A virtual machine provides a separate environment with its own operating system, software, and dependencies. By installing the new package on a virtual machine, you can experiment with it without affecting the main code base. You can also delete the virtual machine once the experiment is complete, keeping the main code base intact.
Option C: Perform a pip install of the new package when logged into your local machine as root. Performing a pip install of the new package when logged into your local machine as root would not isolate it from the main code base. Installing the package as root would make it a part of the system-wide packages, which may cause conflicts with other packages or affect the stability of the system.
Option D: Create a new virtual environment and perform a pip install of the new package. Creating a new virtual environment is another good way to isolate the new package from the main code base. A virtual environment provides a separate Python environment with its own packages and dependencies. By installing the new package in a virtual environment, you can experiment with it without affecting the main code base. You can also delete the virtual environment once the experiment is complete, keeping the main code base intact.
Therefore, the best option to isolate the new package from the main code base would be either to create a new virtual machine or a new virtual environment and perform a pip install of the new package.