Verify AI library compatibility

When specifying AI/ML library dependencies, ensure version compatibility between related packages and verify that prebuilt binaries are available for the target environment. Many AI libraries like auto_gptq, autoawq, and flash_attn are compiled against specific versions of PyTorch and CUDA, and version mismatches can force compilation from source or cause...

copy reviewer prompt

Prompt

Reviewer Prompt
Deploy Agent

When specifying AI/ML library dependencies, ensure version compatibility between related packages and verify that prebuilt binaries are available for the target environment. Many AI libraries like auto_gptq, autoawq, and flash_attn are compiled against specific versions of PyTorch and CUDA, and version mismatches can force compilation from source or cause runtime failures.

Before finalizing requirements, check:

  • Library compatibility matrices (e.g., auto_gptq 0.7.1 requires torch 2.2.1, not 2.3.1)
  • Availability of prebuilt wheels for your CUDA version
  • Whether dependencies require CUDA compilers if building from source

Consider separating incompatible libraries into different requirements files when necessary.

Example of problematic dependencies:

torch==2.3.1
auto_gptq==0.7.1  # Compiled against torch 2.2.1, will cause issues

Better approach:

# requirements-gptq.txt
torch==2.2.1
auto_gptq==0.7.1

# requirements-awq.txt  
torch==2.4.1
autoawq==0.2.6

Source discussions