A pixi-build pixi.toml for building deepspeed

pixi.toml

[workspace]
name = "deepspeed"
platforms = ["linux-64"]
channels = ["conda-forge", "bioconda"]
preview = ["pixi-build"]

[system-requirements]
cuda = "13"

[dependencies]
deepspeed = { path = "." }

[package] 
name = "deepspeed"
version = "0.18.6"

[package.build]
source.git = "https://github.com/deepspeedai/DeepSpeed.git"
backend = { name = "pixi-build-python", version = "0.4.*" }

[package.build.config]
ignore-pyproject-manifest = true
compilers = ["c", "cxx"]
# TODO: add support for the cuda compiler

# Trick deepspeed - we could also point to $CONDA_PREFIX
env.CUTLASS_PATH = "DS_IGNORE_CUTLASS_DETECTION"
# --- Isolating completely the environment, including built extensions
# Note that this might not play well with advanced users or people who do not want to recompile
# So we likely do not want to enforce it or document it well

# Change triton cache to be isolated
env.TRITON_CACHE_DIR = "$CONDA_PREFIX/caches/triton"
# Change torch extensions cache, used by deepspeed, to be isolated
env.TORCH_EXTENSIONS_DIR = "$CONDA_PREFIX/caches/torch_extensions"

[package.build-dependencies]
cuda-profiler-api = "*"
libcublas-dev = "*"
libcurand-dev = "*"
libcusolver-dev = "*"
libcusparse-dev = "*"

[package.host-dependencies]
setuptools = "*"
pytorch = "*"

[package.run-dependencies]
einops = "*"
hjson-py = "*"
msgpack-python = "*"
numpy = "*"
packaging = ">=20.0"
psutil = "*"
py-cpuinfo = "*"
pydantic = ">=2.0.0"
python = "*"
pytorch = "*"
tqdm = "*"
nvidia-ml-py = "*"