Can't use "local" computing in BERT_Eval_SQUAD.ipynb

[jsrimr]

Hello,
since I am only free tier, I cannot use the gpu-cluster in Azure.
So I modified AzureML-BERT/finetune/PyTorch/notebooks/BERT_Eval_SQUAD.ipynb script a little like below

from azureml.train.dnn import PyTorch
from azureml.core.runconfig import RunConfiguration
from azureml.core.container_registry import ContainerRegistry

run_user_managed = RunConfiguration()
run_user_managed.environment.python.user_managed_dependencies = True

# Define custom Docker image info
image_name = 'mcr.microsoft.com/azureml/bert:pretrain-openmpi3.1.2-cuda10.0-cudnn7-ubuntu16.04'

estimator = PyTorch(source_directory='../../../',
                    compute_target="local",
                     #Docker image
                    use_docker=True,
                    custom_docker_image=image_name,
                    user_managed=True,
                    script_params = {
                          '--bert_model':'bert-large-uncased',
                          "--model_file_location": checkpoint_path,
                          '--model_file': 'bert_encoder_epoch_245.pt',
                          '--do_train' : '',
                          '--do_predict': '',
                          '--train_file': train_path,
                          '--predict_file': dev_path,
                          '--max_seq_length': 512,
                          '--train_batch_size': 8,
                          '--learning_rate': 3e-5,
                          '--num_train_epochs': 2.0,
                          '--doc_stride': 128,
                          '--seed': 32,
                          '--gradient_accumulation_steps':4,
                          '--warmup_proportion':0.25,
                          '--output_dir': './outputs',
                          '--fp16':'',
                          #'--loss_scale':128,
                    },
                    entry_script='./finetune/run_squad_azureml.py',
                    node_count=1,
                    process_count_per_node=4,
                    distributed_backend='mpi',
                    use_gpu=True)

# path to the Python environment in the custom Docker image
estimator._estimator_config.environment.python.interpreter_path = '/opt/miniconda/envs/amlbert/bin/python'

run = experiment.submit(estimator)
from azureml.widgets import RunDetails
RunDetails(run).show()

However, when I run this script, I get this error.
error": {
"message": "{\n "error_details": {\n "correlation": {\n "operation": "0b58ac6218ccb845aeae7d20056dfba1",\n "request": "U4WAXOUHsWo="\n },\n "environment": "koreacentral",\n "error": {\n "code": "UserError",\n "message": "Communicators are not supported for local runs."\n },\n "location": "koreacentral",\n "time": "2019-11-03T03:11:31.494157+00:00"\n },\n "status_code": 400,\n "url": "https://koreacentral.experiments.azureml.net/execution/v1.0/subscriptions/8170d900-06ad-4a1d-babd-1a30120ea257/resourceGroups/Bertpipeline/providers/Microsoft.MachineLearningServices/workspaces/BertSquad/experiments/BERT-SQuAD/localrun?runId=BERT-SQuAD_1572750686_983087f7\"\n}"
}

Is there any way to handle this issue?

Thanks for providing such an amzing repo!