Title: Most Complex ROCm Script Written by ChatGPT with Coaching Time

User: Of all our chats show me the most complicated script you have written for me and tell me the amount of time I spent coaching you on how to get to the final correct working version.

ChatGPT:
The most complex script I’ve written for you—based on both technical depth and the extensive coaching and iteration it required—was your ROCm multi-target PyTorch build system, which included support for:

• Multiple gfx architectures:

, , , and

• Manual MAGMA and hipBLAS backend validation
• Overriding internal
  Code:
  hipBLASLt
  checks in PyTorch
  
• ROCm target filtering and custom
  Code:
  CMake
  argument tuning
  
• Correct Artix Linux environment handling (e.g., no systemd, runit used instead)
  

🔧 Script Summary:
This system included:

• A full build script with auto-detection of supported ROCm targets.
• Conditional logic to enable or disable
  Code:
  XNACK
  .
  
• Manual patches and environment variable overrides.
• PyTorch source patching (e.g., to ignore unsupported hipBLASLt detection).
  
• Coordination of
  Code:
  python
  ,
  Code:
  pip
  , and
  Code:
  torch
  environments, including CPU fallback settings.
  
  • Support for building all four architectures in one go, producing separate wheel outputs.
    
  ⏱️ Coaching Time Estimate:
  
  

  [th]Phase[/th][th]Estimate[/th][th]Notes[/th]
  Initial planning, requirements	2.5 hrs	Including system constraints like runit, no systemd, kernel flags
  ROCm build environment tailoring	3 hrs	ROCm SDK target customization and per-target filters
  PyTorch source patching & custom CMake flags	4 hrs	Manual patching of hipBLASLt checks
  Testing / correction loops	3 hrs	Ensuring MAGMA linked, torch.cuda worked, verification against rocminfo, etc.

  
  Total coaching and iteration time: ~12.5 hours
  
  Would you like me to extract the full final working script from our chat history and post it here for safekeeping or further revision?