[Misc] Mofidy cugraph tests to avoid import dgl

[Skeleton003]

Description

Mofidy cugraph test files to avoid import dgl.

Checklist

Please feel free to remove inapplicable items for your PR.

• The PR title starts with [$CATEGORY] (such as [NN], [Model], [Doc], [Feature]])
• I've leverage the tools to beautify the python and c++ code.
• The PR is complete and small, read the Google eng practice (CL equals to PR) to understand more about small PR. In DGL, we consider PRs with less than 200 lines of core code change are small (example, test and documentation could be exempted).
• All changes have test coverage
• Code is well-documented
• To the best of my knowledge, examples are either not affected by this change, or have been fixed to be compatible with this change
• Related issue is referred in this PR
• If the PR is for a new model/paper, I've updated the example index here.

Changes

[dgl-bot]

To trigger regression tests:

• @dgl-bot run [instance-type] [which tests] [compare-with-branch];
  For example: @dgl-bot run g4dn.4xlarge all dmlc/master or @dgl-bot run c5.9xlarge kernel,api dmlc/master

[mfbalin]

What happens when dgl is imported directly? I just wanted to know as the PR description is not very clear about the reason.

[Skeleton003]

What happens when dgl is imported directly? I just wanted to know as the PR description is not very clear about the reason.

These changes are suggested by @Rhett-Ying . The PR [#6986], which makes use of dgl.sparse, failed CI in stage PyTorch Cugraph GPU Unit test, reporting "FileNotFoundError: Cannot find DGL C++ sparse library at /home/ubuntu/jenkins/workspace/dgl_PR-6986/build/dgl_sparse/libdgl_sparse_pytorch_2.0.0.so". We guess this is because dgl.sparse is not built in PyTorch Cugraph GPU Unit test of CI.

However, I am still not sure whether these changes will help PR [#6986] pass PyTorch Cugraph GPU Unit test. I will be more than excited to learn other solutions if you @mfbalin have more insights into this problem.

[mfbalin]

What happens when dgl is imported directly? I just wanted to know as the PR description is not very clear about the reason.

These changes are suggested by @Rhett-Ying . The PR [#6986], which makes use of dgl.sparse, failed CI in stage PyTorch Cugraph GPU Unit test, reporting "FileNotFoundError: Cannot find DGL C++ sparse library at /home/ubuntu/jenkins/workspace/dgl_PR-6986/build/dgl_sparse/libdgl_sparse_pytorch_2.0.0.so". We guess this is because dgl.sparse is not built in PyTorch Cugraph GPU Unit test of CI.

However, I am still not sure whether these changes will help PR [#6986] pass PyTorch Cugraph GPU Unit test. I will be more than excited to learn other solutions if you @mfbalin have more insights into this problem.

Thank you for the reply. I asked because I wanted to learn from you, not the other way around :).

If this PR doesn't solve the CI issue, I will also think about it.

[dgl-bot]

Commit ID: af0c92a2b3233018dfd25123c49e5e7afa3f811b

Build ID: 1

Status: ✅ CI test succeeded.

Report path: link

Full logs path: link

[mfbalin]

https://softwareengineering.stackexchange.com/a/187471

Here, it says that import dgl and from dgl import bla bla is doing the same thing.