Cleaning hyperopt logging

[arnavgarg1]

This PR aims to resolve two different logging messages seen generally during training and also during hyperopt. (See issue here)

• The scope global has keys logging messages during hyperopt show up because the verbosity for logging is set to 1 by default for HorovodConfig when using Ray 11.2. The easiest fix is to disable verbosity for the HorovodConfig in our 11.2 compatibility module.
• Instead of creating a Tensor from the input/target feature ndarrays directly, pytorch wants us to create Tensors from a copy of the numpy array during training.

[ShreyaR]

Awesome, thanks for adding these!

[github-actions]

Unit Test Results

       6 files  ±0         6 suites  ±0   2h 23m 23s ⏱️ + 3m 25s
2 827 tests ±0  2 793 ✔️ ±0    34 💤 ±0  0 ❌ ±0 
8 481 runs  ±0  8 375 ✔️ ±0  106 💤 ±0  0 ❌ ±0 

Results for commit c422797. ± Comparison against base commit bed01a5.

[justinxzhao]

• Instead of creating a Tensor from the input/target feature ndarrays directly, pytorch wants us to create Tensors from a copy of the numpy array during training.

Curious what the warning you are seeing from torch is. @geoffreyangus FYI

[arnavgarg1]

here

@justinxzhao @geoffreyangus here's the error message that we were seeing:

(BaseWorkerMixin pid=3115, ip=10.124.9.3) /home/ray/anaconda3/lib/python3.8/site-packages/ludwig/models/trainer.py:974: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  ../torch/csrc/utils/tensor_numpy.cpp:178.)

(BaseWorkerMixin pid=3115, ip=10.124.9.3)   i_feat.feature_name: torch.from_numpy(batch[i_feat.proc_column]).to(self.device)

[justinxzhao]

Nice! I've seen this one all over the place, thanks for getting rid of it!