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Complete Outline: Python for ML & LLM Inference

Part 1: Fundamentals (4 guides - COMPLETE)

01-Fundamentals/

  • Type System & Annotations.md - Dynamic typing, type hints, runtime introspection
  • Data Structures Essentials.md - Lists, dicts, sets, tuples for ML
  • Iterator & Generator Protocol.md - Lazy evaluation, DataLoaders
  • Context Managers & Resource Management.md - GPU memory, file handling

Part 2: Object-Oriented Patterns (5 guides - 2 complete, 3 planned)

02-Object-Oriented Patterns/

  • Classes & Inheritance.md - Module hierarchies, mixins, MRO
  • Decorators.md - @property, @staticmethod, custom decorators
  • Descriptors & Properties.md - Lazy loading, parameter binding
  • Metaclasses.md - Framework magic, model registration
  • Magic Methods.md - __init__, __call__, __getitem__, operators

Planned Content: - Descriptor protocol for lazy loading (HuggingFace patterns) - Property caching and invalidation - Using descriptors in custom layers - Metaclasses for tensor creation - Custom metaclasses for framework registration - __call__ for model inference - __getitem__ for batch access - Arithmetic operators (+, -, *, @) overloading - __repr__ and __str__ for debugging

Part 3: Functional Programming (5 guides)

03-Functional Programming/

  • First-class Functions.md - Functions as values, higher-order functions
  • Closures & Scope.md - Captured state, functional composition
  • Lambda Functions & Partial Application.md - Inline transformations
  • Comprehensions.md - List, dict, set, generator expressions
  • Advanced Decorators.md - Functional wrapping patterns

Planned Content: - Map, filter, reduce patterns - Function composition and currying - Partial application for callbacks - List/dict/set comprehensions performance - Generator expressions for memory efficiency - Comprehensions with conditions - Decorator factories and stacking - Functional composition libraries

Part 4: C Extensions & FFI (5 guides)

04-C Extensions & FFI/

  • ctypes for FFI.md - Direct C library calls
  • Python C API.md - CPython internals, extending with C
  • PyBind11.md - Modern C++/Python bindings
  • cffi & SWIG.md - Alternative FFI approaches
  • Building Custom CUDA Kernels.md - GPU acceleration

Planned Content: - ctypes function signatures and data types - Calling LAPACK/BLAS from Python - Python C API reference counting - Building C extensions from scratch - PyBind11 class bindings - PyTorch CUDA extension compilation - Custom kernel implementation patterns - Performance characteristics of FFI

Part 5: Memory & Performance (6 guides)

05-Memory & Performance/

  • Reference Counting & Garbage Collection.md - Memory semantics
  • Memory Layout & Cache Efficiency.md - Contiguous arrays
  • Global Interpreter Lock (GIL).md - Threading limitations
  • Multithreading vs Multiprocessing.md - Concurrency patterns
  • Async/Await.md - Asynchronous I/O
  • Memory Profiling & Optimization.md - Bottleneck identification

Planned Content: - Reference counting cycle detection - WeakRef usage in frameworks - Memory layout and cache lines - NUMA-aware memory placement - GIL contention and release patterns - Thread pools for I/O - Process pools for CPU-bound - Async inference servers - Memory profilers: tracemalloc, memory_profiler - Memory allocation patterns in PyTorch

Part 6: Dynamic Features (5 guides)

06-Dynamic Features/

  • getattr, setattr, delattr.md - Dynamic attribute access
  • __getattr__ & __setattr__.md - Lazy loading, hooking
  • Dynamic Module Composition.md - Building at runtime
  • Monkey Patching.md - Debugging and profiling
  • Import System & Dynamic Imports.md - Plugin systems

Planned Content: - Attribute access patterns - Lazy loading for large models - Parameter hooking for profiling - HuggingFace pretrained model loading - Runtime model construction - Feature flags and conditional layers - Monkey patching for testing - sys.modules manipulation - importlib for dynamic imports - Plugin architectures

Part 7: Advanced Typing (5 guides)

07-Advanced Typing/

  • Type Hints & Annotations.md - Function signatures, annotations
  • Protocol Types.md - Structural subtyping
  • Generic Types & TypeVar.md - Parameterized types
  • Runtime Type Checking.md - Validation
  • Pydantic & Data Validation.md - Config management

Planned Content: - Type hint syntax and semantics - Overload for multiple signatures - TypeGuard and isinstance narrowing - Protocol definition and checking - Contravariance and covariance - Generic classes and methods - TypeVar constraints and bounds - Runtime validators - Pydantic models for config - Serialization/deserialization

Part 8: Module System (5 guides)

08-Module System/

  • Import Mechanisms.md - import vs from, relative imports
  • Module Caching & Reloading.md - sys.modules, importlib
  • Package Structures.md - __init__.py, namespace packages
  • Virtual Environments.md - Isolation, reproducibility
  • Dependency Management.md - pip, Poetry, Conda

Planned Content: - Import system architecture - Module initialization order - Import hooks for custom loading - Circular import resolution - Module reloading and gotchas - Namespace packages for plugins - Package configuration - Virtual environment management - Dependency resolution - Lock files and reproducibility

Part 9: Bytecode & Execution (5 guides)

09-Bytecode & Execution/

  • Python Bytecode.md - Compilation, disassembly
  • CPython Internals.md - Frame objects, code objects
  • JIT Compilation.md - PyPy, Numba, Mojo
  • Profiling & Optimization.md - cProfile, line_profiler
  • Tracing & Debugging.md - sys.settrace, debuggers

Planned Content: - Bytecode instructions - dis module for disassembly - Code object structure - Frame objects and locals/globals - Stack frames and tracebacks - PyPy JIT optimization - Numba for numerical code - Mojo for ML acceleration - cProfile for function profiling - line_profiler for line-by-line - Memory profiler for allocations - Flamegraph visualization - Breakpoint debugging

Part 10: ML-Specific Patterns (6 guides)

10-ML-Specific Patterns/

  • Tensor Abstractions.md - NumPy, PyTorch, JAX protocols
  • Autograd Implementation.md - Forward/backward passes
  • Distributed Training.md - DataParallel, DistributedDataParallel
  • Model Serialization.md - pickle, torch.save, safetensors
  • Custom Operators.md - CUDA kernels, backward passes
  • Inference Optimization Patterns.md - Batching, caching

Planned Content: - NumPy array protocol - PyTorch tensor creation and conversion - JAX array handling - Computation graphs - Forward/backward implementation - Custom autograd functions - DataParallel mechanics - DistributedDataParallel setup - Gradient accumulation across devices - Model checkpointing - State dict structure - Pickle vs SafeTensors - CUDA kernel compilation - Backward pass implementation - Batch processing patterns - KV cache management - Inference serving

Summary Statistics

Section Guides Status
Fundamentals 4 ✅ Complete
OOP Patterns 5 🔄 40% (2/5)
Functional 5 ⏳ Planned
C Extensions 5 ⏳ Planned
Memory & Performance 6 ⏳ Planned
Dynamic Features 5 ⏳ Planned
Advanced Typing 5 ⏳ Planned
Module System 5 ⏳ Planned
Bytecode & Execution 5 ⏳ Planned
ML-Specific 6 ⏳ Planned
TOTAL 52 19% (2/52)

Priority Order for Expansion

  1. High Priority (Core to understanding PyTorch/JAX)
  2. Descriptors & Properties
  3. Metaclasses
  4. Magic Methods
  5. Memory & Performance (all)
  6. Autograd Implementation

  7. Custom Operators

  8. Medium Priority (Important but less critical)

  9. Functional Programming (all)
  10. Advanced Typing (all)

  11. C Extensions & FFI (all except PyBind11)

  12. Lower Priority (Useful but less core)

  13. Bytecode & Execution
  14. Module System (except dependencies)
  15. Dynamic Features (except imports)
  • Readme - LLM Optimization techniques (55 guides)
  • Readme - Agent design and frameworks

Last Updated: 2026-08-08