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Greetings uv team and congratulations on the release of 0.3!
I would like to present a rather vexing issue when it comes to project management.
As you may know, Anaconda has a significant presence in the realm of data science and scientific python usage. Currently, conda lags behind current PEP standards and does not support reading dependencies from pyproject.toml. It is typically used by orgs that deploy to airgapped environments since it comes bundled with Anaconda alongside a number of other popular packages such as pytorch.
These orgs vastly prefer to use modern packaging tools like uv in non-airgapped development environments, but this requires carefully maintaining project dependencies for compatibility with Anaconda/conda and avoidance of adding new runtime dependencies to the airgapped deployment environments.
One such solution to avoid duplication of dependencies between conda environment.yml files and those defined pyproject.toml is by utilizing requirements.txt in conjunction with dynamic project.dependencies (as seen in Drphoton’s answer here).
This allows users to have a project layout like so:
while also letting them use uv pip in non-airgapped environments:
uv pip install -e .
This approach places a dependency on setuptools to read the dynamic dependencies, and is admittedly just a stop-gap solution to make up for conda’s lack of pyproject.toml support.
It would be nice if uv supported reading dynamic dependencies such that commands like uv sync would work if dependencies were defined in other files (such as split being across numerous requirements.txt).
(Eagerly waiting to see if Astral ever offers a product similar to Anaconda that provides a number of high quality MIT-licensed python packages/python interpreter/uv/ruff/etc).
The text was updated successfully, but these errors were encountered:
Greetings
uv
team and congratulations on the release of0.3
!I would like to present a rather vexing issue when it comes to project management.
As you may know, Anaconda has a significant presence in the realm of data science and scientific python usage. Currently,
conda
lags behind current PEP standards and does not support reading dependencies frompyproject.toml
. It is typically used by orgs that deploy to airgapped environments since it comes bundled with Anaconda alongside a number of other popular packages such aspytorch
.These orgs vastly prefer to use modern packaging tools like
uv
in non-airgapped development environments, but this requires carefully maintaining project dependencies for compatibility with Anaconda/conda
and avoidance of adding new runtime dependencies to the airgapped deployment environments.One such solution to avoid duplication of dependencies between conda
environment.yml
files and those definedpyproject.toml
is by utilizingrequirements.txt
in conjunction with dynamicproject.dependencies
(as seen in Drphoton’s answer here).This allows users to have a project layout like so:
and a
pyproject.toml
with dynamic dependency fields:This setup allows users to run
conda
in airgapped environments like so:while also letting them use
uv pip
in non-airgapped environments:uv pip install -e .
This approach places a dependency on
setuptools
to read the dynamic dependencies, and is admittedly just a stop-gap solution to make up for conda’s lack ofpyproject.toml
support.It would be nice if
uv
supported reading dynamic dependencies such that commands likeuv sync
would work if dependencies were defined in other files (such as split being across numerousrequirements.txt
).(Eagerly waiting to see if Astral ever offers a product similar to Anaconda that provides a number of high quality MIT-licensed python packages/python interpreter/uv/ruff/etc).
The text was updated successfully, but these errors were encountered: