How do I set num_workers in DataLoader for an ASR model?
Actually I think I figured it out, it goes in the YAML under `train_ds` and `validation_ds` per `NeMo/examples/tts/conf/glow_tts.yaml`.
**Describe your question**

I am starting up training for an ASR model like this:

```
from ruamel.yaml import YAML
from omegaconf import DictConfig
config_path = 'amharic_16000.yaml'
yaml = YAML(typ='safe')
with open(config_path) as f:
    params = yaml.load(f)
train_manifest=f'{C.build_dir}/train_manifest.json'
test_manifest=f'{C.build_dir}/test_manifest.json'
params['model']['train_ds']['manifest_filepath'] = train_manifest
params['model']['validation_ds']['manifest_filepath'] = test_manifest
model.set_trainer(trainer)
model.setup_training_data(train_data_config=params['model']['train_ds'])
model.setup_validation_data(val_data_config=params['model']['validation_ds'])
model.setup_optimization(optim_config=DictConfig(params['model']['optim']))
trainer.fit(model)
```
Then `trainer.fit` gives me the message

```
[NeMo W 2020-10-15 21:33:23 nemo_logging:349] /home/catskills/anaconda3/envs/nemo/lib/python3.7/site-packages/pytorch_lightning/utilities/distributed.py:37:
 UserWarning: The dataloader, train dataloader, does not have many workers which may be a bottleneck. 
Consider increasing the value of the `num_workers` argument` 
(try 16 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.
```
Where/how do I set `num_workers`?

**Environment overview (please complete the following information)**

 - Environment location: [Bare-metal, Docker, Cloud(specify cloud provider - AWS, Azure, GCP, Collab)]
 - Method of NeMo install: [pip install or from source]. Please specify exact commands you used to install.
 - If method of install is [Docker], provide `docker pull` & `docker run` commands used

**Environment overview (please complete the following information)**

 - Environment location: Ubuntu 20.04, Anaconda Python 3.7
 - Method of NeMo install: pip install

**Environment details**

- OS version: Ubuntu 20.04LTS
- PyTorch version: 1.6.0
- Python version: 3.7.4
- GPU model: NVidia GeForce RTX 2080TI
- CUDA: 10.1.243
- cuDNN: 7.6.5
Add any other context about the problem here.
Example: GPU model
