Tag an official v1.0 release

[leofang]

Hi @tqchen, we've discussed this in a recent array-API meeting and no objection was raised. Given that several projects have successfully migrated to DLPack v1.0, including:

• NumPy (ENH: Implement DLPack version 1 numpy/numpy#26501)
• mpi4py (dlpack: Update to DLPack v1.0 and Python array API v2023.12 mpi4py/mpi4py#494)
• CUDA Python (Introducing cuda.core: pythonic access to CUDA core functionalities NVIDIA/cuda-python#87)

and the release candidate has been cut several months ago (in February), it should be no issue in actually tagging a v1.0 release 🙂 Thanks!

[leofang]

cc: @seberg @rgommers @oleksandr-pavlyk for vis

[tqchen]

Just did that, we should keep track of migration status of other projects, including pytorch

[leofang]

Thanks, Tianqi!

we should keep track of migration status of other projects, including pytorch

Totally agreed. I've shared the DLPack update news internally a few months back. Will try to follow up with more projects.

[oleksandr-pavlyk]

• dpctl.tensor also supports DLPack 1.0 (doc), but the docstring does not reflect that at the moment.

[rgommers]

• CuPy PR for 1.0 support: ENH: Implement dlpack v1 cupy/cupy#8683
• PyTorch PR should be in the works
• Cc @jakevdp for JAX.

[hawkinsp]

It's difficult to figure out whether we're conforming to the spec or not (there's no conformance test suite, right?), but I believe the only thing JAX is missing is using DLManagedTensorVersioned rather than DLManagedTensor. Guess I'll do that.