Change target ci-runner to gh-hosted runner for unittests

[yunchu]

Summary

To distribute CI workloads across all available resources, unit test for the v2 would be targeted to run on the GH-hosted runner.
For the integration tests, it required larger GPU memory (>15G) so it would keep using the dedicated ci-runner (3090) and it will be re-targeted to the runner on AWS whenever there are the GPU instances that have suitable memory size.

How to test

Checklist

• I have added unit tests to cover my changes.​
• I have added integration tests to cover my changes.​
• I have added e2e tests for validation.
• I have added the description of my changes into CHANGELOG in my target branch (e.g., CHANGELOG in develop).​
• I have updated the documentation in my target branch accordingly (e.g., documentation in develop).
• I have linked related issues.

License

• I submit my code changes under the same Apache License that covers the project.
  Feel free to contact the maintainers if that's a concern.
• I have updated the license header for each file (see an example below).

# Copyright (C) 2023 Intel Corporation
# SPDX-License-Identifier: Apache-2.0

[vinnamkim]

AFAIK, there is no unit test which needs a GPU device and I think there will not be as well in the future. So, do we need to run our unit test on the T4 node?

[yunchu]

AFAIK, there is no unit test which needs a GPU device and I think there will not be as well in the future. So, do we need to run our unit test on the T4 node?

If you can confirm that all unit tests are not required GPUs, it would be targeted to the Github hosted runner.

[vinnamkim]

AFAIK, there is no unit test which needs a GPU device and I think there will not be as well in the future. So, do we need to run our unit test on the T4 node?

If you can confirm that all unit tests are not required GPUs, it would be targeted to the Github hosted runner.

======================================================================== short test summary info ========================================================================
FAILED tests/unit/algo/segmentation/heads/test_class_incremental_mixin.py::TestClassIncrementalMixin::test_ignore_label - RuntimeError: Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False. If you are running on a CPU-only machine, please use torch.l...
FAILED tests/unit/algo/visual_prompting/encoders/test_sam_prompt_encoder.py::TestSAMPromptEncoder::test_get_device[cuda] - RuntimeError: No CUDA GPUs are available
======================================================== 2 failed, 370 passed, 1 skipped, 53 warnings in 38.90s =========================================================
(otx-v2) vinnamki@vinnamki:~/otx/training_extensions$ 

I found two cases. I'll open a PR for them. I'll stack commits on this PR.