But I got the following error.
2021-07-12 12:19:13,646 — zenml.pipelines.training_pipeline — INFO — Datasource Pima Indians Diabetes Dataset has no commits. Creating the first one..
2021-07-12 12:19:13,648 — zenml.pipelines.base_pipeline — INFO — Pipeline 1626085153648 created.
2021-07-12 12:19:13,724 — apache_beam.options.pipeline_options — WARNING — Discarding unparseable args: ['-f', '/home/gs/.local/share/jupyter/runtime/kernel-20bd2f0f-6d09-43d9-9eec-d3553c030468.json']
2021-07-12 12:19:15,886 — zenml.datasources.csv_datasource — INFO — Matched 1: ['gs://zenml_quickstart/diabetes.csv']
2021-07-12 12:19:15,892 — zenml.datasources.csv_datasource — INFO — Using header from file: gs://zenml_quickstart/diabetes.csv.
2021-07-12 12:19:16,070 — zenml.datasources.csv_datasource — INFO — Header: ['times_pregnant', 'pgc', 'dbp', 'tst', 'insulin', 'bmi', 'pedigree', 'age', 'has_diabetes'].
2021-07-12 12:19:16,430 — apache_beam.runners.interactive.interactive_environment — WARNING — Dependencies required for Interactive Beam PCollection visualization are not available, please use: `pip install apache-beam[interactive]` to install necessary dependencies to enable all data visualization features.
2021-07-12 12:19:16,600 — apache_beam.options.pipeline_options — WARNING — Discarding unparseable args: ['-f', '/home/gs/.local/share/jupyter/runtime/kernel-20bd2f0f-6d09-43d9-9eec-d3553c030468.json']
2021-07-12 12:19:17,496 — apache_beam.io.tfrecordio — WARNING — Couldn't find python-snappy so the implementation of _TFRecordUtil._masked_crc32c is not as fast as it could be.
2021-07-12 12:19:18,762 — apache_beam.options.pipeline_options — WARNING — Discarding unparseable args: ['-f', '/home/gs/.local/share/jupyter/runtime/kernel-20bd2f0f-6d09-43d9-9eec-d3553c030468.json']
2021-07-12 12:19:19,850 — apache_beam.options.pipeline_options — WARNING — Discarding unparseable args: ['-f', '/home/gs/.local/share/jupyter/runtime/kernel-20bd2f0f-6d09-43d9-9eec-d3553c030468.json']
2021-07-12 12:19:20,900 — apache_beam.options.pipeline_options — WARNING — Discarding unparseable args: ['-f', '/home/gs/.local/share/jupyter/runtime/kernel-20bd2f0f-6d09-43d9-9eec-d3553c030468.json']
2021-07-12 12:19:24,609 — apache_beam.options.pipeline_options — WARNING — Discarding unparseable args: ['-f', '/home/gs/.local/share/jupyter/runtime/kernel-20bd2f0f-6d09-43d9-9eec-d3553c030468.json']
2021-07-12 12:19:26,134 — tensorflow — WARNING — From /home/gs/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/tensorflow_transform/tf_utils.py:266: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use ref() instead.
2021-07-12 12:19:26,600 — root — WARNING — This output type hint will be ignored and not used for type-checking purposes. Typically, output type hints for a PTransform are single (or nested) types wrapped by a PCollection, PDone, or None. Got: Tuple[Dict[str, Union[NoneType, _Dataset]], Union[Dict[str, Dict[str, PCollection]], NoneType]] instead.
2021-07-12 12:19:27,134 — root — WARNING — This output type hint will be ignored and not used for type-checking purposes. Typically, output type hints for a PTransform are single (or nested) types wrapped by a PCollection, PDone, or None. Got: Tuple[Dict[str, Union[NoneType, _Dataset]], Union[Dict[str, Dict[str, PCollection]], NoneType]] instead.
2021-07-12 12:19:27,168 — tensorflow — WARNING — Tensorflow version (2.4.1) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended.
2021-07-12 12:19:29,328 — tensorflow — WARNING — Tensorflow version (2.4.1) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended.
2021-07-12 12:19:29,393 — tensorflow — WARNING — Tensorflow version (2.4.1) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended.
2021-07-12 12:19:29,458 — tensorflow — WARNING — Tensorflow version (2.4.1) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended.
2021-07-12 12:19:29,495 — apache_beam.typehints.typehints — WARNING — Ignoring send_type hint: <class 'NoneType'>
2021-07-12 12:19:29,495 — apache_beam.typehints.typehints — WARNING — Ignoring return_type hint: <class 'NoneType'>
2021-07-12 12:19:29,496 — apache_beam.typehints.typehints — WARNING — Ignoring send_type hint: <class 'NoneType'>
2021-07-12 12:19:29,496 — apache_beam.typehints.typehints — WARNING — Ignoring return_type hint: <class 'NoneType'>
2021-07-12 12:19:29,497 — apache_beam.typehints.typehints — WARNING — Ignoring send_type hint: <class 'NoneType'>
2021-07-12 12:19:29,497 — apache_beam.typehints.typehints — WARNING — Ignoring return_type hint: <class 'NoneType'>
2021-07-12 12:19:29,539 — apache_beam.typehints.typehints — WARNING — Ignoring send_type hint: <class 'NoneType'>
2021-07-12 12:19:29,540 — apache_beam.typehints.typehints — WARNING — Ignoring return_type hint: <class 'NoneType'>
2021-07-12 12:19:29,540 — apache_beam.typehints.typehints — WARNING — Ignoring send_type hint: <class 'NoneType'>
2021-07-12 12:19:29,541 — apache_beam.typehints.typehints — WARNING — Ignoring return_type hint: <class 'NoneType'>
2021-07-12 12:19:29,541 — apache_beam.typehints.typehints — WARNING — Ignoring send_type hint: <class 'NoneType'>
2021-07-12 12:19:29,542 — apache_beam.typehints.typehints — WARNING — Ignoring return_type hint: <class 'NoneType'>
2021-07-12 12:19:29,584 — apache_beam.typehints.typehints — WARNING — Ignoring send_type hint: <class 'NoneType'>
2021-07-12 12:19:29,585 — apache_beam.typehints.typehints — WARNING — Ignoring return_type hint: <class 'NoneType'>
2021-07-12 12:19:29,585 — apache_beam.typehints.typehints — WARNING — Ignoring send_type hint: <class 'NoneType'>
2021-07-12 12:19:29,586 — apache_beam.typehints.typehints — WARNING — Ignoring return_type hint: <class 'NoneType'>
2021-07-12 12:19:29,587 — apache_beam.typehints.typehints — WARNING — Ignoring send_type hint: <class 'NoneType'>
2021-07-12 12:19:29,588 — apache_beam.typehints.typehints — WARNING — Ignoring return_type hint: <class 'NoneType'>
---------------------------------------------------------------------------
NotFoundError Traceback (most recent call last)
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/tensorflow_transform/beam/impl.py in _handle_batch(self, batch)
380 else:
--> 381 result = self._graph_state.callable_get_outputs(feed_dict)
382 assert len(self._graph_state.outputs_tensor_keys) == len(result)
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/tensorflow_transform/saved/saved_transform_io_v2.py in apply_transform_model(self, logical_input_map)
363 elif self._is_finalized:
--> 364 return self._apply_v2_transform_model_finalized(logical_input_map)
365 else:
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/tensorflow_transform/saved/saved_transform_io_v2.py in _apply_v2_transform_model_finalized(self, logical_input_map)
288 modified_inputs = self._format_input_map_as_tensors(logical_input_map)
--> 289 return self._wrapped_function_finalized(modified_inputs)
290
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/tensorflow/python/eager/function.py in __call__(self, *args, **kwargs)
1668 """
-> 1669 return self._call_impl(args, kwargs)
1670
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/tensorflow/python/eager/function.py in _call_impl(self, args, kwargs, cancellation_manager)
1678 return self._call_with_structured_signature(args, kwargs,
-> 1679 cancellation_manager)
1680 except TypeError as structured_err:
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/tensorflow/python/eager/function.py in _call_with_structured_signature(self, args, kwargs, cancellation_manager)
1761 captured_inputs=self.captured_inputs,
-> 1762 cancellation_manager=cancellation_manager)
1763
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/tensorflow/python/eager/function.py in _call_flat(self, args, captured_inputs, cancellation_manager)
1918 return self._build_call_outputs(self._inference_function.call(
-> 1919 ctx, args, cancellation_manager=cancellation_manager))
1920 forward_backward = self._select_forward_and_backward_functions(
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/tensorflow/python/eager/function.py in call(self, ctx, args, cancellation_manager)
559 attrs=attrs,
--> 560 ctx=ctx)
561 else:
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
59 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
---> 60 inputs, attrs, num_outputs)
61 except core._NotOkStatusException as e:
NotFoundError: No registered 'Min' OpKernel for 'GPU' devices compatible with node {{node StatefulPartitionedCall/max_6/min_and_max/Max_1}}
(OpKernel was found, but attributes didn't match) Requested Attributes: T=DT_INT64, Tidx=DT_INT32, _XlaHasReferenceVars=false, keep_dims=false, _device="/job:localhost/replica:0/task:0/device:GPU:0"
. Registered: device='XLA_CPU_JIT'; Tidx in [DT_INT32, DT_INT64]; T in [DT_FLOAT, DT_DOUBLE, DT_INT32, DT_UINT8, DT_INT16, DT_INT8, DT_INT64, DT_QINT8, DT_QUINT8, DT_QINT32, DT_BFLOAT16, DT_UINT16, DT_HALF, DT_UINT32, DT_UINT64]
device='XLA_GPU_JIT'; Tidx in [DT_INT32, DT_INT64]; T in [DT_FLOAT, DT_DOUBLE, DT_INT32, DT_UINT8, DT_INT16, DT_INT8, DT_INT64, DT_QINT8, DT_QUINT8, DT_QINT32, DT_BFLOAT16, DT_UINT16, DT_HALF, DT_UINT32, DT_UINT64]
device='GPU'; T in [DT_INT32]; Tidx in [DT_INT64]
device='GPU'; T in [DT_INT32]; Tidx in [DT_INT32]
device='GPU'; T in [DT_DOUBLE]; Tidx in [DT_INT64]
device='GPU'; T in [DT_DOUBLE]; Tidx in [DT_INT32]
device='GPU'; T in [DT_FLOAT]; Tidx in [DT_INT64]
device='GPU'; T in [DT_FLOAT]; Tidx in [DT_INT32]
device='GPU'; T in [DT_HALF]; Tidx in [DT_INT64]
device='GPU'; T in [DT_HALF]; Tidx in [DT_INT32]
device='CPU'; T in [DT_DOUBLE]; Tidx in [DT_INT64]
device='CPU'; T in [DT_DOUBLE]; Tidx in [DT_INT32]
device='CPU'; T in [DT_FLOAT]; Tidx in [DT_INT64]
device='CPU'; T in [DT_FLOAT]; Tidx in [DT_INT32]
device='CPU'; T in [DT_BFLOAT16]; Tidx in [DT_INT64]
device='CPU'; T in [DT_BFLOAT16]; Tidx in [DT_INT32]
device='CPU'; T in [DT_HALF]; Tidx in [DT_INT64]
device='CPU'; T in [DT_HALF]; Tidx in [DT_INT32]
device='CPU'; T in [DT_INT32]; Tidx in [DT_INT64]
device='CPU'; T in [DT_INT32]; Tidx in [DT_INT32]
device='CPU'; T in [DT_INT8]; Tidx in [DT_INT64]
device='CPU'; T in [DT_INT8]; Tidx in [DT_INT32]
device='CPU'; T in [DT_UINT8]; Tidx in [DT_INT64]
device='CPU'; T in [DT_UINT8]; Tidx in [DT_INT32]
device='CPU'; T in [DT_INT16]; Tidx in [DT_INT64]
device='CPU'; T in [DT_INT16]; Tidx in [DT_INT32]
device='CPU'; T in [DT_UINT16]; Tidx in [DT_INT64]
device='CPU'; T in [DT_UINT16]; Tidx in [DT_INT32]
device='CPU'; T in [DT_UINT32]; Tidx in [DT_INT64]
device='CPU'; T in [DT_UINT32]; Tidx in [DT_INT32]
device='CPU'; T in [DT_INT64]; Tidx in [DT_INT64]
device='CPU'; T in [DT_INT64]; Tidx in [DT_INT32]
device='CPU'; T in [DT_UINT64]; Tidx in [DT_INT64]
device='CPU'; T in [DT_UINT64]; Tidx in [DT_INT32]
[[StatefulPartitionedCall/max_6/min_and_max/Max_1]] [Op:__inference_wrapped_finalized_5475]
Function call stack:
wrapped_finalized
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner.process()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.PerWindowInvoker.invoke_process()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common._OutputProcessor.process_outputs()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/tensorflow_transform/beam/impl.py in process(self, batch, saved_model_dir)
440
--> 441 yield self._handle_batch(batch)
442
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/tensorflow_transform/beam/impl.py in _handle_batch(self, batch)
387 Fetching the values for the following Tensor keys: {}.""".format(
--> 388 str(e), batch, self._graph_state.outputs_tensor_keys))
389
ValueError: An error occured while trying to apply the transformation: " No registered 'Min' OpKernel for 'GPU' devices compatible with node {{node StatefulPartitionedCall/max_6/min_and_max/Max_1}}
(OpKernel was found, but attributes didn't match) Requested Attributes: T=DT_INT64, Tidx=DT_INT32, _XlaHasReferenceVars=false, keep_dims=false, _device="/job:localhost/replica:0/task:0/device:GPU:0"
. Registered: device='XLA_CPU_JIT'; Tidx in [DT_INT32, DT_INT64]; T in [DT_FLOAT, DT_DOUBLE, DT_INT32, DT_UINT8, DT_INT16, DT_INT8, DT_INT64, DT_QINT8, DT_QUINT8, DT_QINT32, DT_BFLOAT16, DT_UINT16, DT_HALF, DT_UINT32, DT_UINT64]
device='XLA_GPU_JIT'; Tidx in [DT_INT32, DT_INT64]; T in [DT_FLOAT, DT_DOUBLE, DT_INT32, DT_UINT8, DT_INT16, DT_INT8, DT_INT64, DT_QINT8, DT_QUINT8, DT_QINT32, DT_BFLOAT16, DT_UINT16, DT_HALF, DT_UINT32, DT_UINT64]
device='GPU'; T in [DT_INT32]; Tidx in [DT_INT64]
device='GPU'; T in [DT_INT32]; Tidx in [DT_INT32]
device='GPU'; T in [DT_DOUBLE]; Tidx in [DT_INT64]
device='GPU'; T in [DT_DOUBLE]; Tidx in [DT_INT32]
device='GPU'; T in [DT_FLOAT]; Tidx in [DT_INT64]
device='GPU'; T in [DT_FLOAT]; Tidx in [DT_INT32]
device='GPU'; T in [DT_HALF]; Tidx in [DT_INT64]
device='GPU'; T in [DT_HALF]; Tidx in [DT_INT32]
device='CPU'; T in [DT_DOUBLE]; Tidx in [DT_INT64]
device='CPU'; T in [DT_DOUBLE]; Tidx in [DT_INT32]
device='CPU'; T in [DT_FLOAT]; Tidx in [DT_INT64]
device='CPU'; T in [DT_FLOAT]; Tidx in [DT_INT32]
device='CPU'; T in [DT_BFLOAT16]; Tidx in [DT_INT64]
device='CPU'; T in [DT_BFLOAT16]; Tidx in [DT_INT32]
device='CPU'; T in [DT_HALF]; Tidx in [DT_INT64]
device='CPU'; T in [DT_HALF]; Tidx in [DT_INT32]
device='CPU'; T in [DT_INT32]; Tidx in [DT_INT64]
device='CPU'; T in [DT_INT32]; Tidx in [DT_INT32]
device='CPU'; T in [DT_INT8]; Tidx in [DT_INT64]
device='CPU'; T in [DT_INT8]; Tidx in [DT_INT32]
device='CPU'; T in [DT_UINT8]; Tidx in [DT_INT64]
device='CPU'; T in [DT_UINT8]; Tidx in [DT_INT32]
device='CPU'; T in [DT_INT16]; Tidx in [DT_INT64]
device='CPU'; T in [DT_INT16]; Tidx in [DT_INT32]
device='CPU'; T in [DT_UINT16]; Tidx in [DT_INT64]
device='CPU'; T in [DT_UINT16]; Tidx in [DT_INT32]
device='CPU'; T in [DT_UINT32]; Tidx in [DT_INT64]
device='CPU'; T in [DT_UINT32]; Tidx in [DT_INT32]
device='CPU'; T in [DT_INT64]; Tidx in [DT_INT64]
device='CPU'; T in [DT_INT64]; Tidx in [DT_INT32]
device='CPU'; T in [DT_UINT64]; Tidx in [DT_INT64]
device='CPU'; T in [DT_UINT64]; Tidx in [DT_INT32]
[[StatefulPartitionedCall/max_6/min_and_max/Max_1]] [Op:__inference_wrapped_finalized_5475]
Function call stack:
wrapped_finalized
".
Batch instances: pyarrow.RecordBatch
age: large_list<item: int64>
child 0, item: int64
bmi: large_list<item: float>
child 0, item: float
dbp: large_list<item: int64>
child 0, item: int64
has_diabetes: large_list<item: int64>
child 0, item: int64
insulin: large_list<item: int64>
child 0, item: int64
pedigree: large_list<item: float>
child 0, item: float
pgc: large_list<item: int64>
child 0, item: int64
times_pregnant: large_list<item: int64>
child 0, item: int64
tst: large_list<item: int64>
child 0, item: int64,
Fetching the values for the following Tensor keys: {'max_2/min_and_max/Identity_1', 'max_6/min_and_max/Identity_1', 'max_4/min_and_max/Identity', 'max/min_and_max/Identity_1', 'max_6/min_and_max/Identity', 'max_4/min_and_max/Identity_1', 'max_1/min_and_max/Identity', 'max_7/min_and_max/Identity_1', 'max_8/min_and_max/Identity_1', 'max_3/min_and_max/Identity', 'max_1/min_and_max/Identity_1', 'max_3/min_and_max/Identity_1', 'max/min_and_max/Identity', 'max_5/min_and_max/Identity', 'max_2/min_and_max/Identity', 'max_7/min_and_max/Identity', 'max_5/min_and_max/Identity_1', 'max_8/min_and_max/Identity'}.
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
<ipython-input-16-43278ec7954a> in <module>
1 # Run the pipeline locally
----> 2 training_pipeline.run()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/zenml/utils/analytics_utils.py in inner_func(*args, **kwargs)
175 def inner_func(*args, **kwargs):
176 track_event(event, metadata=metadata)
--> 177 result = func(*args, **kwargs)
178 return result
179
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/zenml/pipelines/base_pipeline.py in run(self, backend, metadata_store, artifact_store)
455 self.register_pipeline(config)
456
--> 457 self.run_config(config)
458
459 # After running, pipeline is immutable
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/zenml/pipelines/base_pipeline.py in run_config(self, config)
376 """
377 assert issubclass(self.backend.__class__, OrchestratorBaseBackend)
--> 378 self.backend.run(config)
379
380 @track(event=RUN_PIPELINE)
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/zenml/backends/orchestrator/base/orchestrator_base_backend.py in run(self, config)
107 """
108 tfx_pipeline = self.get_tfx_pipeline(config)
--> 109 ZenMLLocalDagRunner().run(tfx_pipeline)
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/zenml/backends/orchestrator/base/zenml_local_orchestrator.py in run(self, pipeline)
95 custom_driver_spec=custom_driver_spec)
96 logging.info('Component %s is running.', node_id)
---> 97 component_launcher.launch()
98 logging.info('Component %s is finished.', node_id)
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/tfx/orchestration/portable/launcher.py in launch(self)
429 if is_execution_needed:
430 try:
--> 431 executor_output = self._run_executor(execution_info)
432 except Exception as e: # pylint: disable=broad-except
433 execution_output = (
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/tfx/orchestration/portable/launcher.py in _run_executor(self, execution_info)
323 outputs_utils.make_output_dirs(execution_info.output_dict)
324 try:
--> 325 executor_output = self._executor_operator.run_executor(execution_info)
326 code = executor_output.execution_result.code
327 if code != 0:
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/tfx/orchestration/portable/beam_executor_operator.py in run_executor(self, execution_info)
84 stateful_working_dir=execution_info.stateful_working_dir)
85 executor = self._executor_cls(context=context)
---> 86 return python_executor_operator.run_with_executor(execution_info, executor)
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/tfx/orchestration/portable/python_executor_operator.py in run_with_executor(execution_info, executor)
64 output_dict = copy.deepcopy(execution_info.output_dict)
65 result = executor.Do(execution_info.input_dict, output_dict,
---> 66 execution_info.exec_properties)
67 if not result:
68 # If result is not returned from the Do function, then try to
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/tfx/components/transform/executor.py in Do(self, input_dict, output_dict, exec_properties)
490 label_outputs[labels.CACHE_OUTPUT_PATH_LABEL] = cache_output
491 status_file = 'status_file' # Unused
--> 492 self.Transform(label_inputs, label_outputs, status_file)
493 absl.logging.debug('Cleaning up temp path %s on executor success',
494 temp_path)
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/tfx/components/transform/executor.py in Transform(***failed resolving arguments***)
1025 output_cache_dir, compute_statistics,
1026 per_set_stats_output_paths, materialization_format,
-> 1027 len(analyze_data_paths))
1028 # TODO(b/122478841): Writes status to status file.
1029
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/tfx/components/transform/executor.py in _RunBeamImpl(self, analyze_data_list, transform_data_list, preprocessing_fn, stats_options_updater_fn, force_tf_compat_v1, input_dataset_metadata, transform_output_path, raw_examples_data_format, temp_path, input_cache_dir, output_cache_dir, compute_statistics, per_set_stats_output_paths, materialization_format, analyze_paths_count)
1338 Executor._RecordBatchToExamples)
1339 | 'Materialize[{}]'.format(infix) >> self._WriteExamples(
-> 1340 materialization_format, dataset.materialize_output_path))
1341
1342 return _Status.OK()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/pipeline.py in __exit__(self, exc_type, exc_val, exc_tb)
578 try:
579 if not exc_type:
--> 580 self.result = self.run()
581 self.result.wait_until_finish()
582 finally:
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/pipeline.py in run(self, test_runner_api)
557 finally:
558 shutil.rmtree(tmpdir)
--> 559 return self.runner.run_pipeline(self, self._options)
560 finally:
561 shutil.rmtree(self.local_tempdir, ignore_errors=True)
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/direct/direct_runner.py in run_pipeline(self, pipeline, options)
131 runner = BundleBasedDirectRunner()
132
--> 133 return runner.run_pipeline(pipeline, options)
134
135
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py in run_pipeline(self, pipeline, options)
181
182 self._latest_run_result = self.run_via_runner_api(
--> 183 pipeline.to_runner_api(default_environment=self._default_environment))
184 return self._latest_run_result
185
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py in run_via_runner_api(self, pipeline_proto)
191 # TODO(pabloem, BEAM-7514): Create a watermark manager (that has access to
192 # the teststream (if any), and all the stages).
--> 193 return self.run_stages(stage_context, stages)
194
195 @contextlib.contextmanager
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py in run_stages(self, stage_context, stages)
357 stage_results = self._run_stage(
358 runner_execution_context,
--> 359 bundle_context_manager,
360 )
361 monitoring_infos_by_stage[stage.name] = (
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py in _run_stage(self, runner_execution_context, bundle_context_manager)
553 input_timers,
554 expected_timer_output,
--> 555 bundle_manager)
556
557 final_result = merge_results(last_result)
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py in _run_bundle(self, runner_execution_context, bundle_context_manager, data_input, data_output, input_timers, expected_timer_output, bundle_manager)
593
594 result, splits = bundle_manager.process_bundle(
--> 595 data_input, data_output, input_timers, expected_timer_output)
596 # Now we collect all the deferred inputs remaining from bundle execution.
597 # Deferred inputs can be:
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py in process_bundle(self, inputs, expected_outputs, fired_timers, expected_output_timers, dry_run)
894 process_bundle_descriptor.id,
895 cache_tokens=[next(self._cache_token_generator)]))
--> 896 result_future = self._worker_handler.control_conn.push(process_bundle_req)
897
898 split_results = [] # type: List[beam_fn_api_pb2.ProcessBundleSplitResponse]
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/portability/fn_api_runner/worker_handlers.py in push(self, request)
378 self._uid_counter += 1
379 request.instruction_id = 'control_%s' % self._uid_counter
--> 380 response = self.worker.do_instruction(request)
381 return ControlFuture(request.instruction_id, response)
382
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/worker/sdk_worker.py in do_instruction(self, request)
605 # E.g. if register is set, this will call self.register(request.register))
606 return getattr(self, request_type)(
--> 607 getattr(request, request_type), request.instruction_id)
608 else:
609 raise NotImplementedError
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/worker/sdk_worker.py in process_bundle(self, request, instruction_id)
642 with self.maybe_profile(instruction_id):
643 delayed_applications, requests_finalization = (
--> 644 bundle_processor.process_bundle(instruction_id))
645 monitoring_infos = bundle_processor.monitoring_infos()
646 monitoring_infos.extend(self.state_cache_metrics_fn())
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/worker/bundle_processor.py in process_bundle(self, instruction_id)
998 elif isinstance(element, beam_fn_api_pb2.Elements.Data):
999 input_op_by_transform_id[element.transform_id].process_encoded(
-> 1000 element.data)
1001
1002 # Finish all operations.
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/worker/bundle_processor.py in process_encoded(self, encoded_windowed_values)
226 decoded_value = self.windowed_coder_impl.decode_from_stream(
227 input_stream, True)
--> 228 self.output(decoded_value)
229
230 def monitoring_infos(self, transform_id, tag_to_pcollection_id):
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/worker/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.Operation.output()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/worker/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.Operation.output()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/worker/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.SingletonConsumerSet.receive()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/worker/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.SdfProcessSizedElements.process()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/worker/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.SdfProcessSizedElements.process()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner.process_with_sized_restriction()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.PerWindowInvoker.invoke_process()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common._OutputProcessor.process_outputs()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/worker/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.SingletonConsumerSet.receive()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/worker/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.FlattenOperation.process()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/worker/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.FlattenOperation.process()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/worker/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.Operation.output()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/worker/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.SingletonConsumerSet.receive()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/worker/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.DoOperation.process()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/worker/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.DoOperation.process()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner.process()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner._reraise_augmented()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner.process()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.SimpleInvoker.invoke_process()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common._OutputProcessor.process_outputs()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/worker/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.SingletonConsumerSet.receive()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/worker/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.DoOperation.process()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/worker/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.DoOperation.process()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner.process()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner._reraise_augmented()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner.process()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.SimpleInvoker.invoke_process()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common._OutputProcessor.process_outputs()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/worker/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.SingletonConsumerSet.receive()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/worker/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.DoOperation.process()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/worker/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.DoOperation.process()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner.process()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner._reraise_augmented()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner.process()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.SimpleInvoker.invoke_process()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common._OutputProcessor.process_outputs()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/worker/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.SingletonConsumerSet.receive()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/worker/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.DoOperation.process()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/worker/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.DoOperation.process()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner.process()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner._reraise_augmented()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner.process()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.SimpleInvoker.invoke_process()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common._OutputProcessor.process_outputs()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/worker/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.ConsumerSet.receive()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/worker/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.DoOperation.process()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/worker/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.DoOperation.process()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner.process()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner._reraise_augmented()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner.process()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.SimpleInvoker.invoke_process()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common._OutputProcessor.process_outputs()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/worker/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.SingletonConsumerSet.receive()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/worker/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.DoOperation.process()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/worker/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.DoOperation.process()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner.process()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner._reraise_augmented()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/future/utils/__init__.py in raise_with_traceback(exc, traceback)
444 if traceback == Ellipsis:
445 _, _, traceback = sys.exc_info()
--> 446 raise exc.with_traceback(traceback)
447
448 else:
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner.process()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.PerWindowInvoker.invoke_process()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common._OutputProcessor.process_outputs()
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/tensorflow_transform/beam/impl.py in process(self, batch, saved_model_dir)
439 assert self._graph_state.saved_model_dir == saved_model_dir
440
--> 441 yield self._handle_batch(batch)
442
443
~/miniconda3/envs/zenml-py36/lib/python3.6/site-packages/tensorflow_transform/beam/impl.py in _handle_batch(self, batch)
386 Batch instances: {},
387 Fetching the values for the following Tensor keys: {}.""".format(
--> 388 str(e), batch, self._graph_state.outputs_tensor_keys))
389
390 result.update(self._get_passthrough_data_from_recordbatch(batch))
ValueError: An error occured while trying to apply the transformation: " No registered 'Min' OpKernel for 'GPU' devices compatible with node {{node StatefulPartitionedCall/max_6/min_and_max/Max_1}}
(OpKernel was found, but attributes didn't match) Requested Attributes: T=DT_INT64, Tidx=DT_INT32, _XlaHasReferenceVars=false, keep_dims=false, _device="/job:localhost/replica:0/task:0/device:GPU:0"
. Registered: device='XLA_CPU_JIT'; Tidx in [DT_INT32, DT_INT64]; T in [DT_FLOAT, DT_DOUBLE, DT_INT32, DT_UINT8, DT_INT16, DT_INT8, DT_INT64, DT_QINT8, DT_QUINT8, DT_QINT32, DT_BFLOAT16, DT_UINT16, DT_HALF, DT_UINT32, DT_UINT64]
device='XLA_GPU_JIT'; Tidx in [DT_INT32, DT_INT64]; T in [DT_FLOAT, DT_DOUBLE, DT_INT32, DT_UINT8, DT_INT16, DT_INT8, DT_INT64, DT_QINT8, DT_QUINT8, DT_QINT32, DT_BFLOAT16, DT_UINT16, DT_HALF, DT_UINT32, DT_UINT64]
device='GPU'; T in [DT_INT32]; Tidx in [DT_INT64]
device='GPU'; T in [DT_INT32]; Tidx in [DT_INT32]
device='GPU'; T in [DT_DOUBLE]; Tidx in [DT_INT64]
device='GPU'; T in [DT_DOUBLE]; Tidx in [DT_INT32]
device='GPU'; T in [DT_FLOAT]; Tidx in [DT_INT64]
device='GPU'; T in [DT_FLOAT]; Tidx in [DT_INT32]
device='GPU'; T in [DT_HALF]; Tidx in [DT_INT64]
device='GPU'; T in [DT_HALF]; Tidx in [DT_INT32]
device='CPU'; T in [DT_DOUBLE]; Tidx in [DT_INT64]
device='CPU'; T in [DT_DOUBLE]; Tidx in [DT_INT32]
device='CPU'; T in [DT_FLOAT]; Tidx in [DT_INT64]
device='CPU'; T in [DT_FLOAT]; Tidx in [DT_INT32]
device='CPU'; T in [DT_BFLOAT16]; Tidx in [DT_INT64]
device='CPU'; T in [DT_BFLOAT16]; Tidx in [DT_INT32]
device='CPU'; T in [DT_HALF]; Tidx in [DT_INT64]
device='CPU'; T in [DT_HALF]; Tidx in [DT_INT32]
device='CPU'; T in [DT_INT32]; Tidx in [DT_INT64]
device='CPU'; T in [DT_INT32]; Tidx in [DT_INT32]
device='CPU'; T in [DT_INT8]; Tidx in [DT_INT64]
device='CPU'; T in [DT_INT8]; Tidx in [DT_INT32]
device='CPU'; T in [DT_UINT8]; Tidx in [DT_INT64]
device='CPU'; T in [DT_UINT8]; Tidx in [DT_INT32]
device='CPU'; T in [DT_INT16]; Tidx in [DT_INT64]
device='CPU'; T in [DT_INT16]; Tidx in [DT_INT32]
device='CPU'; T in [DT_UINT16]; Tidx in [DT_INT64]
device='CPU'; T in [DT_UINT16]; Tidx in [DT_INT32]
device='CPU'; T in [DT_UINT32]; Tidx in [DT_INT64]
device='CPU'; T in [DT_UINT32]; Tidx in [DT_INT32]
device='CPU'; T in [DT_INT64]; Tidx in [DT_INT64]
device='CPU'; T in [DT_INT64]; Tidx in [DT_INT32]
device='CPU'; T in [DT_UINT64]; Tidx in [DT_INT64]
device='CPU'; T in [DT_UINT64]; Tidx in [DT_INT32]
[[StatefulPartitionedCall/max_6/min_and_max/Max_1]] [Op:__inference_wrapped_finalized_5475]
Function call stack:
wrapped_finalized
".
Batch instances: pyarrow.RecordBatch
age: large_list<item: int64>
child 0, item: int64
bmi: large_list<item: float>
child 0, item: float
dbp: large_list<item: int64>
child 0, item: int64
has_diabetes: large_list<item: int64>
child 0, item: int64
insulin: large_list<item: int64>
child 0, item: int64
pedigree: large_list<item: float>
child 0, item: float
pgc: large_list<item: int64>
child 0, item: int64
times_pregnant: large_list<item: int64>
child 0, item: int64
tst: large_list<item: int64>
child 0, item: int64,
Fetching the values for the following Tensor keys: {'max_2/min_and_max/Identity_1', 'max_6/min_and_max/Identity_1', 'max_4/min_and_max/Identity', 'max/min_and_max/Identity_1', 'max_6/min_and_max/Identity', 'max_4/min_and_max/Identity_1', 'max_1/min_and_max/Identity', 'max_7/min_and_max/Identity_1', 'max_8/min_and_max/Identity_1', 'max_3/min_and_max/Identity', 'max_1/min_and_max/Identity_1', 'max_3/min_and_max/Identity_1', 'max/min_and_max/Identity', 'max_5/min_and_max/Identity', 'max_2/min_and_max/Identity', 'max_7/min_and_max/Identity', 'max_5/min_and_max/Identity_1', 'max_8/min_and_max/Identity'}. [while running 'Analyze/ApplySavedModel[Phase0][AnalysisIndex0]/ApplySavedModel']