Build with numpy 2.0

[oleksandr-pavlyk]

Build conda packages with NumPy 2.0 in the build environment. The meta.yaml file for dpctl was changed to not use pin_compatible('numpy', min='x.x', max='x') per migration guidelines from conda-forge.

Testing should still pick NumPy 1.26.4 from Intel channel.

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[github-actions]

Deleted rendered PR docs from intelpython.github.com/dpctl, latest should be updated shortly. 🤞

[coveralls]

coverage: 87.883%. remained the same
when pulling 318d55c on build-with-numpy-2.0
into 52edb6d on master.

[github-actions]

Array API standard conformance tests for dpctl=0.18.0dev0=py310hdf72452_166 ran successfully.
Passed: 894
Failed: 1
Skipped: 119

[github-actions]

Array API standard conformance tests for dpctl=0.18.0dev0=py310hdf72452_167 ran successfully.
Passed: 895
Failed: 0
Skipped: 119

[github-actions]

Array API standard conformance tests for dpctl=0.18.0dev0=py310hdf72452_173 ran successfully.
Passed: 894
Failed: 1
Skipped: 119

[github-actions]

Array API standard conformance tests for dpctl=0.18.0dev0=py310hdf72452_173 ran successfully.
Passed: 894
Failed: 1
Skipped: 119

[github-actions]

Array API standard conformance tests for dpctl=0.18.0dev0=py310hdf72452_177 ran successfully.
Passed: 894
Failed: 1
Skipped: 119

[ekomarova]

In order for this PR to be built in the internal CI with 2.0, I need to change the mamba build flag to --numpy 2.0.0. I can try to do this and restart the checks

[ekomarova]

For some reason, the environment still resolved in favor of 1.26.4, even despite --numpy 2.0.0 flag and the absence of any numpy pinning in recipe.. I believe the reason for this is 486637a that deleted numpy from host. For this to work, we need to return numpy to host

[oleksandr-pavlyk]

@ekomarova This PR modified conda-packages.yml workflow file to remove use of Intel channel from the conda build command and only use conda-forge. I just recheck that for both platforms and all version of Python (3.9, 3.10, 3.11, and 3.12) installed numpy 2.0.1 into the build environment.

The test environment continues to use Intel channel and thus 3.9, 3.10, and 3.11 install numpy 1.26.4 into test environment from there, while for Python 3.12 the latest numpy gets installed from conda-forge channel.

[ekomarova]

@oleksandr-pavlyk
Update: The pinning in the recipe https://github.com/IntelPython/dpctl/blob/master/pyproject.toml#L12 is always a priority compared to --numpy <version>. For --numpy <version> to work as expected, the pinning should not be anywhere in host, as it was previously here 486637a#diff-330610924437ff65867549da646165faa154ae5201567339b33169f521c084b2L33. Current situation in internal CI:

• strict channel priority -c gold -c conda-forge
• pinning numpy >= 1.23 in the recipe
• using --numpy 2.0.0

In the current situation, conda can only solve the environment so that it satisfies the first two conditions. Since numpy 1.26 is available on gold and it satisfies the strict channel priority and pinning in the recipe numpy >=1.23, and --numpy 2.0.0 is no longer important in this case. We had a similar thing when we needed to build with 1.24. It was the situation when there was a pinning in the recipe, and option --numpy was ignored
Example: IntelPython/dpnp#1771

[ekomarova]

It looks like in order to make this work in internal CI, we need to rebuild numpy from conda-forge with 2025 mkl, otherwise we will get conflicts. We can plan this activity.
Details can be found here: #1770 (comment)

[ekomarova]

Just for information: building numpy 2.0 with conda-forge recipe with 2025 mkl is WIP

[ekomarova]

Update: We expect a built numpy 2.0 package in the next few days

[ekomarova]

We managed to fix the windows problem. Next, I plan to test this PR with built numpy 2.0, and then add the status

[github-actions]

Array API standard conformance tests for dpctl=0.18.0dev0=py310hdf72452_280 ran successfully.
Passed: 895
Failed: 0
Skipped: 119

[ekomarova]

The package is hosted on a separate channel https://af01p-igk.devtools.intel.com/ui/repos/tree/General/idp-conda-pkgserver-igk-local/numpy, but I can't test this PR yet due to channel problems. As soon as we fix them, I'll restart the checks

[github-actions]

Array API standard conformance tests for dpctl=0.18.0dev0=py310hdf72452_285 ran successfully.
Passed: 894
Failed: 1
Skipped: 119

[ekomarova]

We need to fix something in CI logic in order for the conda build to pass, but in general this PR is now being built with numpy 2.0.1, and tested with 1.26.4 in internal CI

[github-actions]

Array API standard conformance tests for dpctl=0.18.0dev0=py310hdf72452_308 ran successfully.
Passed: 894
Failed: 1
Skipped: 119

[ndgrigorian]

LGTM, I think we can merge this.