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Using uv==0.5.16 I created a workspace dependency runtimes with conflicting extras cow5 and cow6 and specify the conflict in that package's pyproject.toml. I want to include runtimes[cow6] in the main dependencies group of an app package. However, it's unclear to me how to specify the conflict in app/pyproject.toml, as uv sync complains about an unconditional reference to the conflicting extra. I suspect this is potentially related to #10238?
Below is a simple bash script that should create the packages and add the attempted conflict resolution.
What I'm actually trying to do is utilize uv workspaces in a monorepo context where many common dependencies are pushed to dummy packages. Think pinned dependencies for machine learning runtimes, where individual projects (an app / inference endpoint) target a specific runtime. Originally I had thought to make a singular runtimes dummy package like the above, where runtime specific dependency sets are specified as optional dependencies. An obvious workaround for this particular situation is to simply make the various runtime targets separate dummy packages, but from a book-keeping perspective this seems sub-optimal (albeit very minor).
The text was updated successfully, but these errors were encountered:
# Description of Problem
Using
uv==0.5.16
I created a workspace dependencyruntimes
with conflicting extrascow5
andcow6
and specify the conflict in that package'spyproject.toml
. I want to includeruntimes[cow6]
in the maindependencies
group of anapp
package. However, it's unclear to me how to specify the conflict inapp/pyproject.toml
, asuv sync
complains about an unconditional reference to the conflicting extra. I suspect this is potentially related to #10238?Below is a simple bash script that should create the packages and add the attempted conflict resolution.
The "library"
pyproject.toml
is below.The "app"
pyproject.toml
is below.Context and workaround
What I'm actually trying to do is utilize
uv
workspaces in a monorepo context where many common dependencies are pushed to dummy packages. Think pinned dependencies for machine learning runtimes, where individual projects (an app / inference endpoint) target a specific runtime. Originally I had thought to make a singularruntimes
dummy package like the above, where runtime specific dependency sets are specified as optional dependencies. An obvious workaround for this particular situation is to simply make the various runtime targets separate dummy packages, but from a book-keeping perspective this seems sub-optimal (albeit very minor).The text was updated successfully, but these errors were encountered: