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Describe the bug Does not resolve dependency 👍 Show error when I run; pip install auto-tensorflow I got this message: Could not find a version that matches keras-nightly~=2.5.0.dev

To Reproduce Steps to reproduce the behavior: pip install auto-tensorflow Expected behavior Install auto-tensorflow

Versions:

• Auto-Tensorflow:1.2.0
• Tensorflow:
• Tensorflow-Extended:

Additional context Add any other context about the problem here.

Trying to run the Colab Regression notebook. All dependencies get installed, I Restart and Run All to start the code. It errors out here:

##Step 1
##Run Data setup -> Infer Schema, find anomalies, create profile and show viz
tfa.step_data_explore(viz=False)

Data: Pipeline execution started...
WARNING:apache_beam.runners.interactive.interactive_environment: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.
WARNING:apache_beam.io.tfrecordio:Couldn't find python-snappy so the implementation of _TFRecordUtil._masked_crc32c is not as fast as it could be.
WARNING:apache_beam.io.tfrecordio:Couldn't find python-snappy so the implementation of _TFRecordUtil._masked_crc32c is not as fast as it could be.
ERROR:absl:Execution 2 failed.
---------------------------------------------------------------------------
TypeCheckError                            Traceback (most recent call last)
[<ipython-input-6-7e17a616f197>](https://localhost:8080/#) in <module>
      1 ##Step 1
      2 ##Run Data setup -> Infer Schema, find anomalies, create profile and show viz
----> 3 tfa.step_data_explore(viz=False)

14 frames
[/usr/local/lib/python3.7/dist-packages/auto_tensorflow/tfa.py](https://localhost:8080/#) in step_data_explore(self, viz)
   1216     Viz: (False) Is data visualization required ?
   1217     '''
-> 1218     self.pipeline = self.tfadata.run_initial(self._train_data_path, self._test_data_path, self._tfx_root, self._metadata_db_root, self.tfautils, viz)
   1219     self.generate_config_json()
   1220 

[/usr/local/lib/python3.7/dist-packages/auto_tensorflow/tfa.py](https://localhost:8080/#) in run_initial(self, _train_data_path, _test_data_path, _tfx_root, _metadata_db_root, tfautils, viz)
    211     #Run data pipeline
    212     print("Data: Pipeline execution started...")
--> 213     LocalDagRunner().run(self.pipeline)
    214     self._run = True
    215 

[/usr/local/lib/python3.7/dist-packages/tfx/orchestration/portable/tfx_runner.py](https://localhost:8080/#) in run(self, pipeline)
     76     c = compiler.Compiler()
     77     pipeline_pb = c.compile(pipeline)
---> 78     return self.run_with_ir(pipeline_pb)

[/usr/local/lib/python3.7/dist-packages/tfx/orchestration/local/local_dag_runner.py](https://localhost:8080/#) in run_with_ir(self, pipeline)
     85           with metadata.Metadata(connection_config) as mlmd_handle:
     86             partial_run_utils.snapshot(mlmd_handle, pipeline)
---> 87         component_launcher.launch()
     88         logging.info('Component %s is finished.', node_id)

[/usr/local/lib/python3.7/dist-packages/tfx/orchestration/portable/launcher.py](https://localhost:8080/#) in launch(self)
    543               executor_watcher.address)
    544           executor_watcher.start()
--> 545         executor_output = self._run_executor(execution_info)
    546       except Exception as e:  # pylint: disable=broad-except
    547         execution_output = (

[/usr/local/lib/python3.7/dist-packages/tfx/orchestration/portable/launcher.py](https://localhost:8080/#) in _run_executor(self, execution_info)
    418     outputs_utils.make_output_dirs(execution_info.output_dict)
    419     try:
--> 420       executor_output = self._executor_operator.run_executor(execution_info)
    421       code = executor_output.execution_result.code
    422       if code != 0:

[/usr/local/lib/python3.7/dist-packages/tfx/orchestration/portable/beam_executor_operator.py](https://localhost:8080/#) in run_executor(self, execution_info, make_beam_pipeline_fn)
     96         make_beam_pipeline_fn=make_beam_pipeline_fn)
     97     executor = self._executor_cls(context=context)
---> 98     return python_executor_operator.run_with_executor(execution_info, executor)

[/usr/local/lib/python3.7/dist-packages/tfx/orchestration/portable/python_executor_operator.py](https://localhost:8080/#) in run_with_executor(execution_info, executor)
     57   output_dict = copy.deepcopy(execution_info.output_dict)
     58   result = executor.Do(execution_info.input_dict, output_dict,
---> 59                        execution_info.exec_properties)
     60   if not result:
     61     # If result is not returned from the Do function, then try to

[/usr/local/lib/python3.7/dist-packages/tfx/components/statistics_gen/executor.py](https://localhost:8080/#) in Do(self, input_dict, output_dict, exec_properties)
    138             stats_api.GenerateStatistics(stats_options)
    139             | 'WriteStatsOutput[%s]' % split >>
--> 140             stats_api.WriteStatisticsToBinaryFile(output_path))
    141         logging.info('Statistics for split %s written to %s.', split,
    142                      output_uri)

[/usr/local/lib/python3.7/dist-packages/apache_beam/pvalue.py](https://localhost:8080/#) in __or__(self, ptransform)
    135 
    136   def __or__(self, ptransform):
--> 137     return self.pipeline.apply(ptransform, self)
    138 
    139 

[/usr/local/lib/python3.7/dist-packages/apache_beam/pipeline.py](https://localhost:8080/#) in apply(self, transform, pvalueish, label)
    651     if isinstance(transform, ptransform._NamedPTransform):
    652       return self.apply(
--> 653           transform.transform, pvalueish, label or transform.label)
    654 
    655     if not isinstance(transform, ptransform.PTransform):

[/usr/local/lib/python3.7/dist-packages/apache_beam/pipeline.py](https://localhost:8080/#) in apply(self, transform, pvalueish, label)
    661       old_label, transform.label = transform.label, label
    662       try:
--> 663         return self.apply(transform, pvalueish)
    664       finally:
    665         transform.label = old_label

[/usr/local/lib/python3.7/dist-packages/apache_beam/pipeline.py](https://localhost:8080/#) in apply(self, transform, pvalueish, label)
    710 
    711       if type_options is not None and type_options.pipeline_type_check:
--> 712         transform.type_check_outputs(pvalueish_result)
    713 
    714       for tag, result in ptransform.get_named_nested_pvalues(pvalueish_result):

[/usr/local/lib/python3.7/dist-packages/apache_beam/transforms/ptransform.py](https://localhost:8080/#) in type_check_outputs(self, pvalueish)
    464 
    465   def type_check_outputs(self, pvalueish):
--> 466     self.type_check_inputs_or_outputs(pvalueish, 'output')
    467 
    468   def type_check_inputs_or_outputs(self, pvalueish, input_or_output):

[/usr/local/lib/python3.7/dist-packages/apache_beam/transforms/ptransform.py](https://localhost:8080/#) in type_check_inputs_or_outputs(self, pvalueish, input_or_output)
    495                 hint=hint,
    496                 actual_type=pvalue_.element_type,
--> 497                 debug_str=type_hints.debug_str()))
    498 
    499   def _infer_output_coder(self, input_type=None, input_coder=None):

TypeCheckError: Output type hint violation at WriteStatsOutput[train]: expected <class 'apache_beam.pvalue.PDone'>, got <class 'str'>
Full type hint:
IOTypeHints[inputs=((<class 'tensorflow_metadata.proto.v0.statistics_pb2.DatasetFeatureStatisticsList'>,), {}), outputs=((<class 'apache_beam.pvalue.PDone'>,), {})]
File "<frozen importlib._bootstrap>", line 677, in _load_unlocked
File "<frozen importlib._bootstrap_external>", line 728, in exec_module
File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed
File "/usr/local/lib/python3.7/dist-packages/tensorflow_data_validation/api/stats_api.py", line 113, in <module>
    class WriteStatisticsToBinaryFile(beam.PTransform):
File "/usr/local/lib/python3.7/dist-packages/apache_beam/typehints/decorators.py", line 776, in annotate_input_types
    *converted_positional_hints, **converted_keyword_hints)

based on:
  IOTypeHints[inputs=None, outputs=((<class 'apache_beam.pvalue.PDone'>,), {})]
  File "<frozen importlib._bootstrap>", line 677, in _load_unlocked
  File "<frozen importlib._bootstrap_external>", line 728, in exec_module
  File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed
  File "/usr/local/lib/python3.7/dist-packages/tensorflow_data_validation/api/stats_api.py", line 113, in <module>
      class WriteStatisticsToBinaryFile(beam.PTransform):
  File "/usr/local/lib/python3.7/dist-packages/apache_beam/typehints/decorators.py", line 863, in annotate_output_types
      f._type_hints = th.with_output_types(return_type_hint)  # pylint: disable=protected-access

Describe the bug The model dumps with error when training a model on GPU runtime

To Reproduce Train a model with Free text column on GPU device

Expected behavior Should not give any error

Versions:

• Auto-Tensorflow: 1.0.1
• Tensorflow: 2.5.0
• Tensorflow-Extended: 0.29.0

Additional context Add any other context about the problem here.

Is your feature request related to a problem? Please describe. Yes

Describe the solution you'd like Add more feature engineering options for automated consideration:

1. Squared
2. Square root
3. Min-Max scaling( Normalization is already there )
4. etc

Describe alternatives you've considered A clear and concise description of any alternative solutions or features you've considered.

Additional context Add any other context or screenshots about the feature request here.

There are a few limitations in the initial release but we are working day and night to resolve these and add them as future features.

1. Doesn't support Image / Audio data
2. Doesn't support - quote delimited CSVs( TFX doesn't support qCSV yet )
3. Classification only supports integer labels from 0 to N

Is your feature request related to a problem? Please describe. A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]

Describe the solution you'd like A clear and concise description of what you want to happen.

Describe alternatives you've considered A clear and concise description of any alternative solutions or features you've considered.

Additional context Add any other context or screenshots about the feature request here.