[memo] High memory consumption and the places of doubts

I write down the current memory usage as a memo just in case when we encounter memory leak issues in the future.
This post is based on [the current implementation](https://github.com/automl/Auto-PyTorch/tree/a4e08e297dc298b53d5962f0f4b4e55b5e9ded8c).

When we run a dataset with the size of 300B, AutoPytorch consumes ~1.5GB and the followings are the major source of the memory consumptions:
|Source| Consumption [GB] |
|-| :-:|
|Import modules| 0.35 |
|Dask Client | 0.35 |
|Logger (Thread safe) | 0.4 |
| Running of context.Process in multiprocessing module | 0.4 |
| Model | 0 ~ inf |
|**Total**| 1.5 ~ inf |

When we run a dataset with the size of 300MB (400,000 instances x 80 features) such as Albert, AutoPytorch consumes ~2.5GB and the followings are the major source of the memory consumptions:
|Source| Consumption [GB] |
|-| :-:|
|Import modules| 0.35|
|Dask Client | 0.35 |
|Logger (Thread safe) | 0.4 |
| Dataset itself | 0.3 |
|self.categories in InputValidator| 0.3 |
| Running of context.Process in multiprocessing module | 0.4 |
| Model (e.g. LightGBM) | 0.4 ~ inf |
|**Total**| 2.5 ~ inf |

All the information was obtained by:
```
$ mprof run --include-children python -m examples.tabular.20_basics.example_tabular_classification
```
and the logger which I set for the debugging. Note that I also added `time.sleep(0.5)` before and after the line of interest to eliminate the possibilities of the influences from other elements and checked each line in detail.