This PR replaces the original RABIT implementation with a new one, which has already been partially merged into XGBoost. The new one features:
- Federated learning for both CPU and GPU.
- NCCL.
- More data types.
- A unified interface for all the underlying implementations.
- Improved timeout handling for both tracker and workers.
- Exhausted tests with metrics (fixed a couple of bugs along the way).
- A reusable tracker for Python and JVM packages.
- Add a test for blocking calls.
- Do not require the queue to be empty after waking up; this frees up the thread to answer blocking calls.
- Handle EOF in read.
- Improve the error message in the result. Allow concatenation of multiple results.
This aligns dask with pyspark, users need to explicitly call:
```
from xgboost.dask import DaskXGBClassifier
from xgboost import dask as dxgb
```
In future releases, we might stop using the default import and remove the lazy loader.
* Handle the new `device` parameter in dask and demos.
- Check no ordinal is specified in the dask interface.
- Update demos.
- Update dask doc.
- Update the condition for QDM.
