Parens for Python
summary
Library demonstrates that libpython-clj dynamically initialize binds to the system Python (supports 3.6–3.8) by using JNA loads Python shared library to call interpreter symbols, and it represents Python values as objects are native pointers marshaled as longs which the REPL converts via Python tostring. Concurrency hits the GIL so parallelism needs workarounds (advanced: copy and rename shared libs to load multiple interpreters). For complex environments the practical pattern is Docker+Conda recommended for complex envs; examples include Jupyter integration and a facial-rec demo.
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chapters (4)
- 0:00Start
- 11:47How Do You Handle Dependency Management for Python Libraries Especially in Non-Local Host Environments and Packaging Up via Docker
- 14:06Python Gui
- 31:28What Is a Good Resource for Papers on Deep Learning Machine Learning and Data Science
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