In Python machine learning, how do I manage dependencies?

Managing dependencies in Python machine learning projects is crucial for ensuring that your code runs smoothly across different environments. Here are some common practices:

  • Use Virtual Environments: Create isolated environments for each project to prevent package conflicts.
  • Requirements Files: Use a `requirements.txt` file to specify package versions and dependencies.
  • Dependency Management Tools: Utilize tools like pip, conda, or poetry to manage and install packages.
  • Docker: Use Docker containers to encapsulate your environment and ensure consistency across different systems.

Python Machine Learning Dependencies Virtual Environments Requirements Files Dependency Management Docker

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