# Buggy code
def __iter__(self):
    self.cycle_num += 1
    if self.num_workers == 0:
        generator = sequential_sample_generator(
            self.dataset, self.transformation, self.is_train, self.cyclic
        )

        def same_process_iter():
            while True:
                # take the next batch size elements
                sample_batch = list(itertools.islice(generator, self.batch_size))

                # terminate if no more batches to be dealt with
                if len(sample_batch) == 0:
                    return

                # make them into a single batch
                batch = self.batchify_fn(
                    data=sample_batch,
                    parallel_processing=False,
                    dtype=self.dtype,
                )

                # either pin to cpu memory, or return with the right context straight away
                batch = {
                    k: v.as_in_context(self.ctx)
                    if isinstance(
                        v, nd.NDArray
                    )  # context.cpu_pinned(self.pin_device_id)
                    else v
                    for k, v in batch.items()
                }
                yield batch

        return same_process_iter()
    else:
        # multi-worker takes care of asynchronously preparing batches
        # only cache multi_worker for cyclic datasets
        if self.multi_worker_cache is None:
            multi_worker = _MultiWorkerIter(
                worker_pool=self.worker_pool,
                num_workers=self.num_workers,
                batch_size=self.batch_size,
                shuffle=self.shuffle,
                batchify_fn=self.batchify_fn,
                dtype=self.dtype,
                ctx=self.ctx,
                is_train=self.is_train,
                cyclic=self.cyclic,
                worker_fn=_worker_fn,
                num_prefetch=self.num_prefetch,
                dataset_len=self.dataset_len,
                cycle_num=self.cycle_num,
            )
            if self.cyclic:
                self.multi_worker_cache = iter(multi_worker)
            return iter(multi_worker)
        else:
            # This way we can recycle the unused pre-fetched batches for the next epoch
            # (cycle num is irrelevant for cyclic datasets, and rest of the arguments stays same between epochs)
            return self.multi_worker_cache


# Traceback (mxnet.base.MXNetError)
Traceback (most recent call last):
File "/usr/local/lib/python3.6/runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "/usr/local/lib/python3.6/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/opt/ml/code/train.py", line 137, in <module>
forecasts, tss, agg_metrics, item_metrics = evaluate(predictor, dataset.test)
File "/opt/ml/code/utils.py", line 30, in evaluate
forecasts = list(forecast_it)
File "/usr/local/lib/python3.6/site-packages/gluonts/model/predictor.py", line 317, in predict
num_samples=num_samples,
File "/usr/local/lib/python3.6/site-packages/gluonts/model/forecast_generator.py", line 195, in __call__
for batch in inference_data_loader:
File "/usr/local/lib/python3.6/site-packages/gluonts/dataset/loader.py", line 101, in __iter__
yield from self.parallel_data_loader
File "/usr/local/lib/python3.6/site-packages/gluonts/dataset/parallelized_loader.py", line 572, in same_process_iter
for k, v in batch.items()
File "/usr/local/lib/python3.6/site-packages/gluonts/dataset/parallelized_loader.py", line 572, in <dictcomp>
for k, v in batch.items()
File "/usr/local/lib/python3.6/site-packages/mxnet/ndarray/ndarray.py", line 2765, in as_in_context
return self.copyto(context)
File "/usr/local/lib/python3.6/site-packages/mxnet/ndarray/ndarray.py", line 2632, in copyto
return _internal._copyto(self, out=hret)
File "<string>", line 27, in _copyto
File "/usr/local/lib/python3.6/site-packages/mxnet/_ctypes/ndarray.py", line 107, in _imperative_invoke
ctypes.byref(out_stypes)))
File "/usr/local/lib/python3.6/site-packages/mxnet/base.py", line 255, in check_call
raise MXNetError(py_str(_LIB.MXGetLastError()))
mxnet.base.MXNetError: [18:26:25] src/imperative/./imperative_utils.h:146: Operator _copyto inferring shapes failed.
input shapes:
[1,321,-1]
output shapes:
[1,321,-1]
operator attributes:

Stack trace:
[bt] (0) /usr/local/lib/python3.6/site-packages/mxnet/libmxnet.so(+0x6dfb0b) [0x7f99a9e28b0b]
[bt] (1) /usr/local/lib/python3.6/site-packages/mxnet/libmxnet.so(mxnet::imperative::SetShapeType(mxnet::Context const&amp;, nnvm::NodeAttrs const&amp;, std::vector<mxnet::NDArray*, std::allocator<mxnet::NDArray*> > const&amp;, std::vector<mxnet::NDArray*, std::allocator<mxnet::NDArray*> > const&amp;, mxnet::DispatchMode*)+0x363b) [0x7f99ad0da57b]
[bt] (2) /usr/local/lib/python3.6/site-packages/mxnet/libmxnet.so(mxnet::Imperative::Invoke(mxnet::Context const&amp;, nnvm::NodeAttrs const&amp;, std::vector<mxnet::NDArray*, std::allocator<mxnet::NDArray*> > const&amp;, std::vector<mxnet::NDArray*, std::allocator<mxnet::NDArray*> > const&amp;)+0x1db) [0x7f99ad0e24bb]
[bt] (3) /usr/local/lib/python3.6/site-packages/mxnet/libmxnet.so(+0x3847f3f) [0x7f99acf90f3f]
[bt] (4) /usr/local/lib/python3.6/site-packages/mxnet/libmxnet.so(MXImperativeInvokeEx+0x62) [0x7f99acf91502]
[bt] (5) /usr/local/lib/python3.6/lib-dynload/_ctypes.cpython-36m-x86_64-linux-gnu.so(ffi_call_unix64+0x4c) [0x7f9a01ccf4e6]
[bt] (6) /usr/local/lib/python3.6/lib-dynload/_ctypes.cpython-36m-x86_64-linux-gnu.so(ffi_call+0x3f1) [0x7f9a01cce241]
[bt] (7) /usr/local/lib/python3.6/lib-dynload/_ctypes.cpython-36m-x86_64-linux-gnu.so(_ctypes_callproc+0x2cf) [0x7f9a01cc58ff]
[2020-04-02 18:26:25.315 ip-10-0-168-31.us-west-2.compute.internal:213 INFO utils.py:25] The end of training job file will not be written for jobs running under SageMaker.
[bt] (8) /usr/local/lib/python3.6/lib-dynload/_ctypes.cpython-36m-x86_64-linux-gnu.so(+0x9829) [0x7f9a01cbc829]

# Fixed code
def __iter__(self):
    self.cycle_num += 1
    if self.num_workers == 0:
        generator = _sequential_sample_generator(
            self.dataset, self.transformation, self.is_train, self.cyclic
        )

        def same_process_iter():
            while True:
                # take the next batch size elements
                sample_batch = list(itertools.islice(generator, self.batch_size))

                # terminate if no more batches to be dealt with
                if len(sample_batch) == 0:
                    return

                # make them into a single batch
                batch = self.batchify_fn(
                    data=sample_batch,
                    multi_processing=False,
                    dtype=self.dtype,
                    single_process_ctx=self.ctx,
                )

                yield batch

        return same_process_iter()
    else:
        # multi-worker takes care of asynchronously preparing batches
        # only cache multi_worker for cyclic datasets
        if self.multi_worker_cache is None:
            multi_worker = _MultiWorkerIter(
                worker_pool=self.worker_pool,
                num_workers=self.num_workers,
                batch_size=self.batch_size,
                shuffle=self.shuffle,
                batchify_fn=self.batchify_fn,
                dtype=self.dtype,
                ctx=self.ctx,
                is_train=self.is_train,
                cyclic=self.cyclic,
                worker_fn=_worker_fn,
                num_prefetch=self.num_prefetch,
                dataset_len=self.dataset_len,
                cycle_num=self.cycle_num,
            )
            if self.cyclic:
                self.multi_worker_cache = iter(multi_worker)
            return iter(multi_worker)
        else:
            # This way we can recycle the unused pre-fetched batches for the next epoch
            # (cycle num is irrelevant for cyclic datasets, and rest of the arguments stays same between epochs)
            return self.multi_worker_cache