[project]
name = "gpu-server-test-suite"
version = "0.2.0"
description = "GPU training server diagnostics & benchmarks (A100/A800/H100/H200/B200/B300)."
readme = "README.md"
requires-python = ">=3.10,<3.13"

dependencies = [
    "rich>=13.0",
    "pyyaml>=6.0",
    "numpy>=1.24",
]

[project.optional-dependencies]
# Install ONE matching your system CUDA version:
#   uv sync --extra torch-cu118
#   uv sync --extra torch-cu121
#   uv sync --extra torch-cu124   (recommended for CUDA 12.x + Driver 535+)
#   uv sync --extra torch-cu128   (for CUDA 12.8+ / Driver 570+)
torch-cu118 = ["torch>=2.1.0"]
torch-cu121 = ["torch>=2.1.0"]
torch-cu124 = ["torch>=2.1.0"]
torch-cu128 = ["torch>=2.1.0"]

[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"

# PyTorch wheel indexes — one per CUDA version
[[tool.uv.index]]
name = "pytorch-cu118"
url = "https://download.pytorch.org/whl/cu118"
explicit = true

[[tool.uv.index]]
name = "pytorch-cu121"
url = "https://download.pytorch.org/whl/cu121"
explicit = true

[[tool.uv.index]]
name = "pytorch-cu124"
url = "https://download.pytorch.org/whl/cu124"
explicit = true

[[tool.uv.index]]
name = "pytorch-cu128"
url = "https://download.pytorch.org/whl/cu128"
explicit = true

# Default: resolve torch from cu124 index (most broadly compatible with 535+ drivers)
# To use a different CUDA version, override:
#   uv pip install torch --index-url https://download.pytorch.org/whl/cu128
[tool.uv.sources]
torch = { index = "pytorch-cu124" }

[tool.hatch.build.targets.wheel]
packages = ["modules"]