pixi.toml

# pixi.toml — conda-based environment for GPUs that the pip `faiss-gpu-cu12`
# wheel does NOT support (Hopper / H100 = sm_90, and newer).
#
# Why this exists alongside uv:
#   * uv (see pyproject.toml) is the default dev/install path. Its `gpu` extra
#     pulls the PyPI `faiss-gpu-cu12` wheel, which only ships kernels for
#     and L40S nodes, but it errors on H100 with CUDA 209 "no kernel image".
#   * pixi installs the *conda* `faiss-gpu` build (pytorch channel), which is
#     compiled with a broad CUDA arch list that INCLUDES Hopper (sm_90).
#
# How the two stay conflict-free:
#   faiss is an OPTIONAL dependency in pyproject.toml (the `cpu` / `gpu`
#   extras). Below we install SAND editable WITHOUT selecting either extra, so
#   no pip faiss is installed — the only faiss in this env is the conda
#   `faiss-gpu`. pixi maps conda packages to their PyPI names, so SAND's base
#   `torch` requirement is satisfied by the conda `pytorch` (one consistent
#   CUDA stack) rather than a second PyPI torch.
#
# Usage:
#   pixi install            # resolve + create the env
#   pixi shell              # drop into the env
#   pixi run python ...     # run a one-off command
#
# Keep uv for CPU / L40S work; reach for pixi only when you need H100 GPU FAISS.

[workspace]
name = "sand"
# conda-forge MUST come first: under strict channel priority the general
# package stack (pillow/libjpeg-turbo → rdkit, numpy, …) has to resolve from
# conda-forge. `pytorch` is kept only for `faiss-gpu`, `nvidia` for CUDA libs.
channels = ["conda-forge", "pytorch", "nvidia"]
platforms = ["linux-64"]

# Declare that the target machine has a CUDA driver. Without this, solving on a
# GPU-less login node fails: conda can't find the `__cuda`
# virtual package and rejects every cuda* build. This lets you resolve/install
# on the login node and run on the GPU compute nodes.
[system-requirements]
cuda = "12"

[dependencies]
python = "3.12.*"
# Keep CUDA consistent with [system-requirements] above so the solve doesn't
# thrash trying CUDA-13 builds that get rejected against `__cuda 12`. If your
# driver supports CUDA 13 (check `nvidia-smi`), bump BOTH this and the
# system-requirements `cuda` to 13 together to get the newest faiss/torch.
cuda-version = "12.*"
# Force the CUDA build of torch. Without this, conda-forge happily resolves a
# `cpu_mkl_*` build and you get no GPU. `pytorch-gpu` is conda-forge's
# metapackage that pulls the matching cuda* build of pytorch.
pytorch = "*"
pytorch-gpu = "*"
# The whole point: conda faiss-gpu HAS Hopper (sm_90) kernels.
faiss-gpu = "*"
# faiss (pytorch channel) is built against Intel MKL. The CUDA torch build
# doesn't pull MKL in like the `cpu_mkl_*` build does, so add it explicitly —
# otherwise `import faiss` fails with `libmkl_intel_lp64.so.2: cannot open ...`.
# Pin <2026: mkl 2026 bumped the soname to libmkl_intel_lp64.so.3, but faiss
# 1.10 links .so.2, so the newest mkl breaks the import at runtime.
mkl = ">=2024,<2026"
# IMPORTANT: install the whole numpy-C-extension / compiled scientific stack
# from CONDA, not PyPI. If pip installs a second numpy/scipy alongside conda's
# torch, you get "numpy._core.multiarray failed to import" ABI crashes. Pixi
# maps these conda packages to their PyPI names, so SAND's requirements (see
# pyproject.toml) are marked satisfied and pip does NOT reinstall them.
numpy = "*"
scipy = "*"
pandas = "*"
polars = "*"
pyarrow = "*"
h5py = "*"
hdf5plugin = "*"
pytables = "*"
zarr = "*"
matplotlib = "*"
rdkit = "*"
torchmetrics = "*"
lightning = "*"
pytorch_geometric = "*"
tqdm = "*"
py3dmol = "*"
universal_pathlib = "*"

[pypi-dependencies]
# Editable SAND WITHOUT the cpu/gpu extras → no pip faiss is pulled in. Every
# numpy-ABI-sensitive dep is provided by conda above; pip only needs to install
# SAND itself plus the few packages that aren't on conda channels.
sand = { path = ".", editable = true }
jsonargparse = { version = ">=4.18.0", extras = ["signatures"] }
pylance = "*"

[feature.dev.pypi-dependencies]
pytest = ">=6"
pytest-cov = "*"
mypy = "*"
ruff = "*"

[environments]
default = { solve-group = "default" }
dev = { features = ["dev"], solve-group = "default" }