AutoGluon TextPredictor.fit gives BrokenPipeError: [Errno 32] Broken pipe - possible solution - python-3.x

Question

I'm posting this as another potential solution to the AutoGluon TextPredictor.fit() "BrokenPipeError: [Errno 32] Broken pipe" and get feedback. I searched broadly, looked at related responses on SO, Github, PyTorch discussion, etc. and don't see option 2 below for AutoGluon which is why I'd like to propose it.
For the AutoGluon Text Prediction quick start tutorial I used an AWS EC2 P3.2xlarge EC2 instance with 8 vCPUs, 61GB ram, 1 NVIDIA v100 running Windows Server 2016.
Running the tutorial, TextPredictor.fit() gave a "BrokenPipeError: [Errno 32] Broken pipe" error.
Option 1 - One way to fix it is to wrap your code in a block as mentioned here:
def run():
# TextPredictor.fit() code goes here
if __name__ == '__main__':
run()
Option 2 - Earlier in that same SO post there is another resolution to set PyTorch 'num_workers' to 0, however in my case PyTorch is running via AutoGluon. I looked through the Autogluon docs here and noticed I could set both env.num_workers and env.num_workers_evaluation. In TextPredictor.fit() I set both to zero and it worked great on my EC2 instance.
predictor.fit(train_data,
time_limit = TRAIN_TIME_LIMIT,
hyperparameters = {"env.num_workers": 0, "env.num_workers_evaluation": 0})
Option 2 only takes ~3 minutes longer to run vs option 1 (17 mins vs 14 mins).
My full code is below. As mentioned earlier most of it was borrowed from the AutoGluon site.
# -*- coding: utf-8 -*-
# -----
# Libs
from datetime import datetime
import torch
from autogluon.text import TextPredictor
import autogluon as ag
from autogluon.core.utils.loaders import load_pd
import pandas as pd
# -----
# Constants.
# This is in seconds. Ex: 5 mins * 60 secs per minute.
TRAIN_TIME_LIMIT = 1*60
# Data source
TRAIN_DATA = '.\\data\\train.parquet'
TEST_DATA = '.\\data\\train.parquet'
TRAIN_SAMPLE_SIZE = 67300
PRED_LABEL_COL = 'label'
# -----
# Main
print("\nStart time: ", datetime.now())
# Sanity test pytorch:
print("Is Cuda available? ", torch.cuda.is_available()) # Should be True
print("Is Cuda device count > 0? ", torch.cuda.device_count()) # Should be > 0
# My debug stuff
from autogluon.text.version import __version__
print("autogluon.text.version: ", __version__)
train_data = load_pd.load(TRAIN_DATA)
test_data = load_pd.load(TEST_DATA)
train_data = train_data.sample(n = TRAIN_SAMPLE_SIZE, random_state = 42)
predictor = TextPredictor(label = PRED_LABEL_COL, eval_metric = 'acc', path = '.\\ag_sst')
# Set num workers to Zero.
predictor.fit(train_data,
time_limit = TRAIN_TIME_LIMIT,
hyperparameters = {"env.num_workers": 0, "env.num_workers_evaluation": 0})
test_score = predictor.evaluate(test_data)
print("\n\nTest score:", test_score)
test_score = predictor.evaluate(test_data, metrics=['acc'])
print(test_score)
print("\nEnd time: ", datetime.now())
Question - I'm not familiar writing multi-processor code. Is there anything wrong or inefficient setting the fit() num_workers hyperparameters above within the context of the full code?