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Support for current dev version of qutip #46

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@dstrbad dstrbad commented Mar 30, 2023

As reported in issue #45 there were changes in QuTiPv5 that affected qutip-cupy

Pull requests introduces 2 changes:

  1. locked version of packaging (<=21.3.) due to te deprecation of LegacyVersion attribute used in qutip-cupy in newer versions of packaging library
  2. change to support QuTiPv5

Test was conducted on AWS GPU instance and basic check looks good but due to dated packages and qutip-cupy there is possibility for other issues to exist.

dstrbad and others added 6 commits March 30, 2023 15:41
Use packaging version 21.3 to avoid getting no attribute "LegacyVersion" since it was deprecated in newer versions
Use packaging version 21.3 to avoid getting no attribute "LegacyVersion" since it was deprecated in newer versions
machine_image update, trying to fix test jobs
Remove deprecated workflow parameter, adjust labels to contain cirun
Match runners by label
@@ -1,7 +1,7 @@
[build-system]
requires = [
"setuptools>=36.6",
"packaging",
"packaging<=21.3",
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We can just replace the code in setup.py as was done in qutip/qutip@2fc35ba8c6 instead of pinning packaging.

@@ -7,22 +7,20 @@ runners:
instance_type: g4dn.xlarge # Cheapest VM on AWS
# Amazon AMI: Deep Learning AMI GPU CUDA 11.2.1 (Ubuntu 20.04) 20210625
# for eu-west-1
machine_image: ami-07b138161bedd526d
machine_image: ami-00ac0c28c01352e53
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Could you find the name and region for the new AMI and update the comment above? Note that we need the AMI for eu-west-1 (I think).

count: 1
labels:
- gpu
- "cirun-gpu-runner"
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Why did the label name change?

@@ -80,14 +80,14 @@ def is_cupydense(data):
data.norm.max.add_specialisations([(CuPyDense, cdf.max_cupydense)])
data.norm.one.add_specialisations([(CuPyDense, cdf.one_cupydense)])

data.inv.add_specialisations([(CuPyDense, linalg.inv_cupydense)])
data.inv.add_specialisations([(CuPyDense, CuPyDense, linalg.inv_cupydense)])
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Thanks!

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Thank you for taking a look. I left some questions and requests for changes, but the important fixes to the specializations look good.

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2 participants