Encountered an internal AutoML error- ClientException: Message: No objects to concatenate - azure-machine-learning-service

Question

I am trying to implement Hierarchical time series forecasting on azureautoml pipelines.
I followed this notebook for implementation
https://github.com/Azure/azureml-examples/blob/main/v1/python-sdk/tutorials/automl-with-azureml/forecasting-hierarchical-timeseries/auto-ml-forecasting-hierarchical-timeseries.ipynb
While I ran training pipeline on compute instance it worked, but when I am running the same on compute cluster it breaks at hts-proportion-calculation part.
This is the error I am getting,
system error:
Encountered an internal AutoML error. Error Message/Code: ClientException. Additional Info: ClientException:
      Message: No objects to concatenate
      InnerException: None
      ErrorResponse
{
"error": {
"message": "No objects to concatenate"
}
}
logs :
Loading arguments for scenario proportions-calculation
adding argument --input-medatadata
adding argument --hts-graph
adding argument --enable-event-logger
Input arguments dict is {'--input-medatadata': '/mnt/azureml/cr/j/85509be625484b6caa3c1d97b7ab2e33/cap/data-capability/wd/INPUT_automl_training_workspaceblobstore/azureml/17ca5ae7-7269-4246-888f-e781071e3f5c/automl_training', '--hts-graph': '/mnt/azureml/cr/j/85509be625484b6caa3c1d97b7ab2e33/cap/data-capability/wd/INPUT_hts_graph_workspaceblobstore/azureml/a2c1b15a-c895-41e8-b6a6-1ca37ebe9e77/hts_graph', '--enable-event-logger': None}
Unknown file to proceed outputs.txt
processing: outputs.txt with type None.
Cleaning up all outstanding Run operations, waiting 300.0 seconds
3 items cleaning up...
Cleanup took 0.001676321029663086 seconds
Traceback (most recent call last):
File "proportions_calculation_wrapper.py", line 47, in <module>
runtime_wrapper.run()
File "/azureml-envs/azureml_e34d0633ffc4cb2fa25d91e3da5f59be/lib/python3.7/site-packages/azureml/train/automl/runtime/_many_models/automl_pipeline_step_wrapper.py", line 63, in run
self._run()
File "/azureml-envs/azureml_e34d0633ffc4cb2fa25d91e3da5f59be/lib/python3.7/site-packages/azureml/train/automl/runtime/_hts/proportions_calculation.py", line 44, in _run
proportions_calculation(self.arguments_dict, self.event_logger, script_run=self.step_run)
File "/azureml-envs/azureml_e34d0633ffc4cb2fa25d91e3da5f59be/lib/python3.7/site-packages/azureml/train/automl/runtime/_hts/proportions_calculation.py", line 173, in proportions_calculation
proportion_files_list, forecasting_parameters.time_column_name, graph.label_column_name
File "/azureml-envs/azureml_e34d0633ffc4cb2fa25d91e3da5f59be/lib/python3.7/site-packages/azureml/train/automl/runtime/_hts/proportions_calculation.py", line 92, in calculate_time_agg_sum_for_all_files
df = pd.concat(pool.map(concat_func, files_batches), ignore_index=True)
File "/azureml-envs/azureml_e34d0633ffc4cb2fa25d91e3da5f59be/lib/python3.7/site-packages/pandas/util/_decorators.py", line 311, in wrapper
return func(*args, **kwargs)
File "/azureml-envs/azureml_e34d0633ffc4cb2fa25d91e3da5f59be/lib/python3.7/site-packages/pandas/core/reshape/concat.py", line 304, in concat
sort=sort,
File "/azureml-envs/azureml_e34d0633ffc4cb2fa25d91e3da5f59be/lib/python3.7/site-packages/pandas/core/reshape/concat.py", line 351, in __init__
raise ValueError("No objects to concatenate")
ValueError: No objects to concatenate
Please let me know how can I resolve this issue ?

Answer 1

This error was incurred as Iteration timeout was not less than experiment timeout , but the system error & logs are a kind of misleading.
df = pd.concat(pool.map(concat_func, files_batches), ignore_index=True)
logs was pointing to pandas "No objects to concatenate"
This error can be overcome by setting iterationtimeout value less than experimenttime out value.
I had set iteration_timeout_minutes=60 which caused the error.
automl_settings = AutoMLConfig(
task="forecasting",
primary_metric="normalized_root_mean_squared_error",
experiment_timeout_hours=1,
label_column_name=label_column_name,
track_child_runs=False,
forecasting_parameters=forecasting_parameters,
pipeline_fetch_max_batch_size=15,
model_explainability=model_explainability,
n_cross_validations="auto", # Feel free to set to a small integer (>=2) if runtime is an issue.
cv_step_size="auto",
# The following settings are specific to this sample and should be adjusted according to your own needs.
iteration_timeout_minutes=10,
iterations=15,
)

Answer 2

We are able to run the sample successfully using the compute cluster as given below.
from azureml.core.compute import ComputeTarget, AmlCompute
# Name your cluster
compute_name = "hts-compute"
if compute_name in ws.compute_targets:
compute_target = ws.compute_targets[compute_name]
if compute_target and type(compute_target) is AmlCompute:
print("Found compute target: " + compute_name)
else:
print("Creating a new compute target...")
provisioning_config = AmlCompute.provisioning_configuration(
vm_size="STANDARD_D16S_V3", max_nodes=20
)
# Create the compute target
compute_target = ComputeTarget.create(ws, compute_name, provisioning_config)
# Can poll for a minimum number of nodes and for a specific timeout.
# If no min node count is provided it will use the scale settings for the cluster
compute_target.wait_for_completion(
show_output=True, min_node_count=None, timeout_in_minutes=20
)
# For a more detailed view of current cluster status, use the 'status' property
print(compute_target.status.serialize())