Ejemplos¶
Un chefe.toml real es el recorrido más convincente. Este es el manifest detrás de un
monorepo de ML multilenguaje, recortado para mayor legibilidad pero con cada función a
la vista: conda + Python (torch desde un Ãndice personalizado) + Node.js + Rust, un mÃnimo de CUDA
del sistema, variables de entorno, superposiciones por plataforma, un entorno de serving
aislado y tareas.
[workspace]
name = "life"
platforms = ["osx-arm64", "linux-64", "linux-aarch64"]
channels = ["conda-forge", "nvidia"]
dotenv = true # auto-load .env
[system] # the conda virtual-package floor
cuda = "13.0"
[env] # build vars; secrets stay in .env
CUDA_DEVICE_ORDER = "PCI_BUS_ID"
CUDA_VISIBLE_DEVICES = "0"
[deps] # bare table is conda, the default resolver
python = ">=3.14"
nodejs = ">=25"
rust = "*"
ripgrep-all = ">=0.10"
pandoc = ">=3.9"
# … runtimes, libs, and CLIs all live here
[python] # settings for Python packages
index-strategy = "unsafe-best-match"
extra-index-urls = ["https://pypi.nvidia.com"]
[python.indexes] # named indexes a dep can pin
pytorch = "https://download.pytorch.org/whl/cu132"
[python.deps]
pydantic = ">=2.13"
polars = ">=1.40"
torch = { version = ">=2.11", index = "pytorch" } # pinned to the named index
# … the rest of the Python stack
[nodejs.dev.deps]
"@tobilu/qmd" = ">=0.1"
prettier = ">=3"
[rust.deps]
bookokrat = { version = ">=0.1", locked = true }
[on.linux.deps] # platform overlay → pixi [target.linux]
cuda-nvcc = ">=13.3"
cupy = ">=14"
[envs.serving] # a named, isolated environment
no-default = true
[envs.serving.deps]
python = "*"
[envs.serving.python.deps]
sglang = ">=0.5"
sglang-kernel = { url = "https://github.com/sgl-project/whl/releases/download/v0.4.2/sglang_kernel-0.4.2+cu130-cp310-abi3-manylinux2014_aarch64.whl" }
[envs.serving.on.linux-64.deps] # overlay nested inside an env
vllm = { version = ">=0.19", build = "cuda129*" }
[tasks] # run inside the activated env
test = "python -m pytest"
lint = "pre-commit run --all-files"
Cómo configurarlo¶
chefe sync # compile chefe.toml → .chefe/{pixi.toml, package.json}
chefe install # provision every language/toolchain at once
chefe shell # an activated shell with all the binaries on PATH
chefe run test # run a task inside the environment
chefe tree # declared vs installed, each checked in its own language/toolchain
El entorno serving aislado se aprovisiona e inspecciona de la misma manera:
chefe install serving # solve + provision just the serving env (sglang, vllm, …)
chefe tree serving
Y para herramientas de un solo uso, sin necesidad de manifest: