# Buggy code
def __next__(self):
    # Try to get a batch, sometimes its possible that an iterator was
    # exhausted and thus we don't get a new batch
    success = False
    while not success:
        try:
            self._push_next()
            if self._rcvd_idx == self._sent_idx:
                assert not self._data_buffer, (
                    "Data buffer should be empty at this moment"
                )
                raise StopIteration

            assert self._rcvd_idx < self._sent_idx, (
                "rcvd_idx must be smaller than sent_idx"
            )
            assert self._rcvd_idx in self._data_buffer, (
                "fatal error with _push_next, rcvd_idx missing"
            )
            ret = self._data_buffer.pop(self._rcvd_idx)

            got = ret.get(self._timeout)
            self._rcvd_idx += 1

            success, dataset_id, batch = pickle.loads(got)

            # If iterator exhausted/empty
            if not success:
                self._exhausted_iterators.add(dataset_id)
                if self.num_workers == len(self._exhausted_iterators):
                    # No more batches to be generated
                    return []
                else:
                    self._push_next()
            else:
                # 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()
                }
                return batch
        except multiprocessing.context.TimeoutError:
            print(
                f"Worker timed out after {self._timeout} seconds. This might be caused by "
                "\n - Slow transform. Please increase timeout to allow slower data loading in each worker. "
                "\n - Insufficient shared_memory if `timeout` is large enough. "
                "\n Please consider to reduce `num_workers` or increase shared_memory in system."
            )
            raise
        except Exception:
            self._worker_pool.terminate()
            raise


# 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 __next__(self):
    # Try to get a batch, sometimes its possible that an iterator was
    # exhausted and thus we don't get a new batch
    success = False
    while not success:
        try:
            self._push_next()
            if self._rcvd_idx == self._sent_idx:
                assert not self._data_buffer, (
                    "Data buffer should be empty at this moment"
                )
                raise StopIteration

            assert self._rcvd_idx < self._sent_idx, (
                "rcvd_idx must be smaller than sent_idx"
            )
            assert self._rcvd_idx in self._data_buffer, (
                "fatal error with _push_next, rcvd_idx missing"
            )
            ret = self._data_buffer.pop(self._rcvd_idx)

            got = ret.get(self._timeout)
            self._rcvd_idx += 1

            success, dataset_id, batch = pickle.loads(got)

            # If iterator exhausted/empty
            if not success:
                self._exhausted_iterators.add(dataset_id)
                if self.num_workers == len(self._exhausted_iterators):
                    # No more batches to be generated
                    return []
                else:
                    self._push_next()
            else:
                # either pin to cpu memory (with ctx=context.cpu_pinned(self.pin_device_id)),
                # or return with the right context straight away
                return _as_in_context(batch, self.ctx)
        except multiprocessing.context.TimeoutError:
            print(
                f"Worker timed out after {self._timeout} seconds. This might be caused by "
                "\n - Slow transform. Please increase timeout to allow slower data loading in each worker. "
                "\n - Insufficient shared_memory if `timeout` is large enough. "
                "\n Please consider to reduce `num_workers` or increase shared_memory in system."
            )
            raise
        except Exception:
            self._worker_pool.terminate()
            raise