Comments (12)
Yeah I think its actually still a bug. It looks like mkl 2023.*.*
is parsed as mkl[version="2023.*", build=".*"]
. I think it should become mkl[version="2023.*"]
.
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You're welcome, I appreciate the thorough tests! I just set in motion the release of py-rattler 0.6.1 which should include these fixes.
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Just did a little test, and this line also fails:
asyncio.run(rattler.solve([rattler.Channel("main")], ["binutils_impl_linux-64=2.38.0.*"], platforms=["linux-64", "noarch"], virtual_packages=virtual_packages))
I'll do some more testing.
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So it seems like you might have found a bug 🎉 ! - micromamba (and probably conda?) do select 2.38
when asking for 2.38.0
which makes sense.
We should fix that in rattler
.
Thanks!
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Thanks for the thorough tests!
I think I have a fix for this in #686
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I'm having a similar problem with pytorch, more precisely
pytorch=2.2.0=cpu_py38hdc00b08_0
pytorch=2.2.0=cpu_py312hb9e5694_0
pytorch=2.2.0=cpu_py310hdc00b08_0
pytorch=2.2.0=cpu_py311h6d93b4c_0
pytorch=2.2.0=cpu_py39hdc00b08_0
where I get
Failed to resolve pytorch=2.2.0=cpu_py38hdc00b08_0 (0.0691 secs): Cannot solve the request because of: pytorch ==2.2.0 cpu_py38hdc00b08_0 cannot be installed because there are no viable options:
└─ pytorch 2.2.0 would require
└─ mkl 2023.* .*, for which no candidates were found.
Could this be the same problem?
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For the MKL issue I found the line. It says: mkl 2023.*.*
in the dependencies.
I think that's not really valid - if you would want to express the constraint like that, IMO it should be a regex.
Maybe we should strip everything after the first starts_with
operator.
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![Screenshot 2024-05-28 at 07 35 23](https://private-user-images.githubusercontent.com/885054/334280043-73ac0781-a49e-4a13-94cb-6fa8ece93b8d.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJnaXRodWIuY29tIiwiYXVkIjoicmF3LmdpdGh1YnVzZXJjb250ZW50LmNvbSIsImtleSI6ImtleTUiLCJleHAiOjE3MTc0NDI1ODgsIm5iZiI6MTcxNzQ0MjI4OCwicGF0aCI6Ii84ODUwNTQvMzM0MjgwMDQzLTczYWMwNzgxLWE0OWUtNGExMy05NGNiLTZmYThlY2U5M2I4ZC5wbmc_WC1BbXotQWxnb3JpdGhtPUFXUzQtSE1BQy1TSEEyNTYmWC1BbXotQ3JlZGVudGlhbD1BS0lBVkNPRFlMU0E1M1BRSzRaQSUyRjIwMjQwNjAzJTJGdXMtZWFzdC0xJTJGczMlMkZhd3M0X3JlcXVlc3QmWC1BbXotRGF0ZT0yMDI0MDYwM1QxOTE4MDhaJlgtQW16LUV4cGlyZXM9MzAwJlgtQW16LVNpZ25hdHVyZT02OGY1ZjZiMDJjMjhmMmNhM2JhYzI1OWNiOTcxZGRkYTk2NmNlNjQ4YWE2YjIwNWUwMDI4MGM5OWY4MGE4NWYyJlgtQW16LVNpZ25lZEhlYWRlcnM9aG9zdCZhY3Rvcl9pZD0wJmtleV9pZD0wJnJlcG9faWQ9MCJ9.x0lwZweLiGv_QLgJU6gVOfkNWIc7XQEX4qwX-Uwhl1U)
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#688 should fix the issue with mkl
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cargo run -- create --dry-run --platform linux-64 pytorch=2.2.0=cpu_py38hdc00b08_0 -c main --virtual-package __glibc=2.17=0 --virtual-package __archspec=1=x86_64 --virtual-package __unix=0=0 --virtual-package __linux=0=0
Shows that this is indeed solvable with #688
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Thank you for the fixes!
from rattler.
Just tested and it seems to be working for both cases! Thanks again!
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