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