Rules for creating conda environments in Bazel 💚
For more info see the docs or the example.
rules_conda
don't have any strict requirements by themselves.
Just make sure you are able to use conda
.
Add this to your WORKSPACE
file:
load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive")
http_archive(
name = "rules_conda",
sha256 = "9793f86162ec5cfb32a1f1f13f5bf776e2c06b243c4f1ee314b9ec870144220d",
url = "https://github.com/spietras/rules_conda/releases/download/0.1.0/rules_conda-0.1.0.zip"
)
load("@rules_conda//:defs.bzl", "conda_create", "load_conda", "register_toolchain")
load_conda(quiet = False)
conda_create(
name = "py3_env",
environment = "@//:environment.yml",
quiet = False,
)
register_toolchain(py3_env = "py3_env")
After that, all Python targets will use the environment specified in register_toolchain
.
See below for more advanced example.
This example shows all possibilities of rules_conda
:
load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive")
http_archive(
name = "rules_conda",
sha256 = "9793f86162ec5cfb32a1f1f13f5bf776e2c06b243c4f1ee314b9ec870144220d",
url = "https://github.com/spietras/rules_conda/releases/download/0.1.0/rules_conda-0.1.0.zip",
)
load("@rules_conda//:defs.bzl", "conda_create", "load_conda", "register_toolchain")
load_conda(
conda_version = "4.10.3", # version of conda to download, default is 4.10.3
installer = "miniforge", # which conda installer to download, either miniconda or miniforge, default is miniconda
install_mamba = True, # whether to install mamba, which is a faster drop-in replacement for conda, default is False
mamba_version = "0.17.0", # version of mamba to install, default is 0.17.0
quiet = False, # True if conda output should be hidden, default is True
timeout = 600, # how many seconds each execute action can take, default is 3600
)
conda_create(
name = "py3_env", # name of the environment
environment = "@//:py3_environment.yml", # label pointing to environment configuration file
use_mamba = True, # Whether to use mamba to create the conda environment. If this is True, install_mamba must also be True False
clean = False, # True if conda cache should be cleaned (less space taken, but slower subsequent builds), default is False
quiet = False, # True if conda output should be hidden True, default is True
timeout = 600, # how many seconds each execute action can take, default is 3600
)
conda_create(
name = "py2_env", # name of the environment
environment = "@//:py2_environment.yml", # label pointing to environment configuration file
)
register_toolchain(
py2_env = "py2_env", # python2 is optional
py3_env = "py3_env",
)
These rules allow you to download and install conda
, create conda
environments and register Python toolchain from environments.
This means you can achieve truly reproducible and hermetic local Python environments.
Pros:
- easy to use
- no existing
conda
installation necessary - no global
conda
installation, no globalPATH
modifications - virtually impossible to corrupt your environment by mistake as it always reflects your
environment.yml
- all Python targets will implicitly have access to the whole environment (the one registered in toolchain)
Cons:
- every time you update your environment configuration in
environment.yml
, the whole environment will be recreated from scratch (but cached package data can be reused) - on Windows you need to add environment location to
PATH
or setCONDA_DLL_SEARCH_MODIFICATION_ENABLE=1
during runtime, so DLLs can be loaded properly (more on that here)
So I think these rules suit you if:
- you want to use Bazel (e.g. you fell into Python monorepo trap)
- you want to use
conda
for Python environment management - you don't want to set up your Python environment manually or want your Python targets to just work on clean systems
- you are okay with environments being recreated every time something changes