How to handle CUDA memory allocation issue

### Checklist

- [X] I have searched for [similar issues](https://github.com/isl-org/Open3D-ML/issues).
- [X] I have tested with the [latest development wheel](http://www.open3d.org/docs/latest/getting_started.html#development-version-pip).
- [X] I have checked the [release documentation](http://www.open3d.org/docs/release/) and the [latest documentation](http://www.open3d.org/docs/latest/) (for `master` branch).

### My Question

- Description
Dataset: Toronto3D
An error occurred while executing the following:
`python3 scripts/run_pipeline.py torch -c ml3d/configs/randlanet_toronto3d.yml --dataset.dataset_path /home/kim/Open3D-ML/data/Toronto_3D --pipeline SemanticSegmentation --dataset.use_cache True`
The error is related:
`RuntimeError: CUDA out of memory. Tried to allocate 16.00 MiB (GPU 0; 5.80 GiB total capacity; 4.30 GiB already allocated; 3.94 MiB free; 4.31 GiB reserved in total by PyTorch)`

Actually, the example, **semantickitti** in https://github.com/isl-org/Open3D-ML/tree/master/scripts is too big for my HDD, I choose Toronto3D. Is there a possible way to handle the memory allocation issue or Is there a smaller size dataset than Toronto3D?