Check PyTorch, Transformers, and CUDA compatibility. Detect GPU, driver mismatches, and version conflicts in ML environments. Use when the user sets up ML/AI tools, installs torch or transformers, hits dependency errors, or asks about compatible versions.
Harmonia detects GPU, CUDA, driver, OS, Python, and installed ML packages — then reports exactly what's compatible with what. Zero dependencies, works offline.
pip install harmonia-ml
Full environment scan — use when diagnosing issues:
harmonia check
This scans OS, Python, GPU, CUDA driver chain, torch, transformers, and known conflicts all at once.
Deep system diagnostics — use when the user asks specifically about GPU, CUDA, or driver:
harmonia doctor
Shows GPU model, VRAM, driver version, CUDA (nvidia-smi vs nvcc vs torch), glibc, virtualenv status.
Suggest compatible versions — use when the user wants to know what works together:
# What works with a specific torch version?
harmonia suggest torch==2.5.1
# What works with a specific transformers version?
harmonia suggest transformers==4.44.2
# Best stack for specific Python + CUDA?
harmonia suggest transformers --python 3.11 --cuda 12.1
Show compatibility matrix — use when the user wants to see all options:
harmonia matrix pytorch
harmonia matrix transformers
List known conflicts — use when the user hit a specific error:
harmonia conflicts
Shows known bug patterns with exact error messages and fixes.
JSON output — use for programmatic processing:
harmonia check --json
❌ are errors that must be fixed⚠️ are warnings worth noting✅ mean everything is fine📦 Recommended compatible set section gives the exact versions to installInstall command at the bottom can be copied and run directlyWhen harmonia reports errors, help the user fix them by running the suggested commands. Common fixes:
pip install torchaudio==2.5.1 (use the version harmonia suggests)pip install torch>=2.4.0python -m venv .venv && source .venv/bin/activateharmonia check FIRST when a user reports any ML dependency issue — do not guesspip install harmonia-ml before running commandsPyTorch深度学习模式与最佳实践，用于构建稳健、高效且可复现的训练流程、模型架构和数据加载。