3.6 KiB
3.6 KiB
Recommended Tech Stack for Apple M4 (ARM) + 16GB RAM with Python & uv
1. Hardware-Specific Considerations
- Apple M4 (ARM, Apple Silicon): Prioritize native ARM support and pre-built wheels for Python packages.
- 16GB RAM: Comfortable for moderate-sized AI inference (text/image) and routine vision tasks, but be mindful with large downloadable models.
2. Python Environment Setup
- Continue using uv for environment and dependency management for isolation and reproducible builds.
- Python Version: 3.11 or higher, with native ARM build (recommended: managed via pyenv or uv’s Python installer).
3. Core Packages (Optimized for Apple Silicon)
| Function | Recommended Packages | Notes & Tips |
|---|---|---|
| Image Processing | pillow, opencv-python | These support ARM64; avoid opencv-contrib-python (heavier) unless needed. |
| OCR | easyocr | PyTesseract possible, but EasyOCR runs on CPU and ARM better. |
| AI Inpainting | diffusers, torch (Apple Silicon support), transformers | Use PyTorch 2.1+ with Metal support for Apple Silicon. |
| GUI / Interactive Layer | streamlit, gradio, dearpygui, tkinter | All run natively on macOS; choose per GUI style. |
| Numeric, utility | numpy, requests | Well-supported, native builds. |
4. Special Notes for Apple M4 (ARM, Apple Silicon)
- PyTorch: As of 2025, install via pip pre-built wheels for Apple Silicon, which leverage Apple's Metal backend for GPU acceleration.
- Example:
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/metal.html
- Example:
- diffusers: Works well on CPU, but with torch+Metal, certain inpainting models will benefit from hardware acceleration.
- opencv-python: Install via pip (
uv add opencv-python); all core features work on ARM/macOS. - easyocr: Uses PyTorch, so installs and runs fine on Apple Silicon.
- No CUDA: Apple M4 does not support CUDA; all GPU acceleration uses Metal, not NVIDIA CUDA.
5. uv Commands Example
uv add pillow opencv-python easyocr diffusers torch torchvision transformers streamlit gradio dearpygui numpy requests
If you want a lighter GUI, you can skip DearPyGui and use only streamlit or gradio.
6. Tesseract OCR (Optional)
If using pytesseract:
- You must install the Tesseract binary for macOS first (e.g., via Homebrew:
brew install tesseract). - Not as highly optimized as EasyOCR for Apple Silicon; use for simple OCR needs.
7. Additional Optimization Tips
- Large Models: Download models (e.g., Stable Diffusion weights) with care; 16GB RAM is sufficient for single-image, but avoid very large batch inference.
- Environment Isolation: Manage project strictly in your uv environment to prevent C dependency conflicts.
- Metal Acceleration: Make sure torch and all AI dependencies are ARM64 builds; avoid x86-64 wheels/emulation.
Summary:
This tech stack is optimized for Apple M4 and macOS, ensures all AI/vision code runs natively, and enables you to use uv for clean, reproducible Python development.
For best performance and compatibility, use EasyOCR over PyTesseract and leverage PyTorch's Metal backend for AI computations.