Fix gluonts.mx.trainer.Trainer in case of empty data loader (#2228)

src/gluonts/mx/trainer/_base.py

@@ -320,9 +320,12 @@ def loop( # todo call run epoch
  )
 
  batch_iter = itertools.islice(batch_iter, num_batches_to_use)
-
  it = tqdm(batch_iter, total=num_batches_to_use)
+ any_batches = False
+
  for batch_no, batch in enumerate(it, start=1):
+ any_batches = True
+
  # `batch` here is expected to be a dictionary whose fields
  # should correspond 1-to-1 with the network inputs
  # see below how `batch.values()` is fed into the network
@@ -406,6 +409,13 @@ def loop( # todo call run epoch
  )
  it.close()
 
+ if not any_batches:
+ raise GluonTSDataError(
+ "No training data batch could be constructed; "
+ "this usually indicates that the training dataset "
+ "is empty, or consists of too short series."
+ )
+
  # mark epoch end time and log time cost of current epoch
  toc = time.time()
  logger.info(

test/mx/test_no_batches.py

@@ -0,0 +1,30 @@
+# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License").
+# You may not use this file except in compliance with the License.
+# A copy of the License is located at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# or in the "license" file accompanying this file. This file is distributed
+# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
+# express or implied. See the License for the specific language governing
+# permissions and limitations under the License.
+
+import pytest
+
+from gluonts.exceptions import GluonTSDataError
+from gluonts.mx.model.deepar import DeepAREstimator
+from gluonts.mx.trainer import Trainer
+
+
+@pytest.mark.parametrize("dataset", [[]])
+def test_deepar_no_batches(dataset):
+ estimator = DeepAREstimator(
+ prediction_length=10,
+ freq="H",
+ trainer=Trainer(epochs=1, num_batches_per_epoch=1),
+ )
+
+ with pytest.raises(GluonTSDataError):
+ estimator.train(dataset)