Python Environments
It covers everything from venv·uv·conda in Ubuntu·Rocky Linux to environment configuration for web·data·AI·OCR projects.
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Install Python Packages Offline: Prepare Wheels and Dependencies
Build a wheel collection for the target Python and platform, include dependencies and test installation without online package access.
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[Python Environments 06] Structure OCR Tools, Language Data, APIs and Workers
Separate OCR executables, language packs, Python dependencies and worker processes when building an OCR application.
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[Python Environments 05] Plan pandas, Jupyter, CPU and GPU Environments
Separate data analysis, notebooks and GPU workloads into reproducible environments with explicit system and Python dependencies.
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[Python Environments 04] Structure a Web Project with src, uv and Tests
Organize a Python web project, manage its environment and tests, and distinguish development settings from deployment requirements.
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[Python Environments 03] Use conda, Miniforge and Jupyter Kernels
Create a Miniforge environment, install analysis tools and connect the correct Python environment to a Jupyter kernel.
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[Python Environments 02] Use uv for Python, Dependencies and Lockfiles
Select Python with uv, manage project dependencies and use a lockfile to recreate the environment.
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[Python Environments 01] Choose venv, uv or conda on Linux
Prepare a Python development environment on Ubuntu or Rocky Linux and choose venv, uv or conda for the project’s requirements.