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infer.py file from forecasting-pipelines sample folder

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Avaliação

Dificuldade
4/5
Tempo estimado
3-5 dias
Facilidade para iniciantes
25/100
Tipo de issue
Bug
Clareza
Precisa de esclarecimento
Status de atividade
Estagnada
Stack de tecnologia
azure, python

Direção de pesquisa

Comece comparando infer.py do exemplo forecasting-pipelines com as localizações do traceback nas linhas 39 e 154 e, em seguida, reproduza o pipeline de inferência usando os dados de vendas e os argumentos informados. Investigue a falha no registro do conjunto de dados do Azure ML e confirme que o pipeline é concluído com sucesso sem o erro invalid-file.

Escrita pelo modelo de indexação a partir do texto da issue.

Descrição

Hi
Im using the forecasting-pipelines sample, to build a training and inference pipeline. The training pipeline is running without error (I am using some public sales data) but the inferene pipeline is giving me this error:

/azureml-envs/azureml_d1809f029ef68d0db142d99e1cd4a92c/lib/python3.7/site-packages/sklearn/externals/joblib/init.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.
warnings.warn(msg, category=FutureWarning)
args passed are:
ojmodel
dominicks_OJ_test_pipeline
oj-output
Sales
model.pkl
azureml-models/ojmodel/36/outputs/model.pkl
Importing plotly failed. Interactive plots will not work.
/bin/bash: /azureml-envs/azureml_d1809f029ef68d0db142d99e1cd4a92c/lib/libtinfo.so.6: no version information available (required by /bin/bash)
/bin/bash: /azureml-envs/azureml_d1809f029ef68d0db142d99e1cd4a92c/lib/libtinfo.so.6: no version information available (required by /bin/bash)
/bin/bash: /azureml-envs/azureml_d1809f029ef68d0db142d99e1cd4a92c/lib/libtinfo.so.6: no version information available (required by /bin/bash)
/bin/bash: /azureml-envs/azureml_d1809f029ef68d0db142d99e1cd4a92c/lib/libtinfo.so.6: no version information available (required by /bin/bash)
/bin/bash: /azureml-envs/azureml_d1809f029ef68d0db142d99e1cd4a92c/lib/libtinfo.so.6: no version information available (required by /bin/bash)
/bin/bash: /azureml-envs/azureml_d1809f029ef68d0db142d99e1cd4a92c/lib/libtinfo.so.6: no version information available (required by /bin/bash)
/bin/bash: /azureml-envs/azureml_d1809f029ef68d0db142d99e1cd4a92c/lib/libtinfo.so.6: no version information available (required by /bin/bash)
/bin/bash: /azureml-envs/azureml_d1809f029ef68d0db142d99e1cd4a92c/lib/libtinfo.so.6: no version information available (required by /bin/bash)
/bin/bash: /azureml-envs/azureml_d1809f029ef68d0db142d99e1cd4a92c/lib/libtinfo.so.6: no version information available (required by /bin/bash)
/bin/bash: /azureml-envs/azureml_d1809f029ef68d0db142d99e1cd4a92c/lib/libtinfo.so.6: no version information available (required by /bin/bash)
/bin/bash: /azureml-envs/azureml_d1809f029ef68d0db142d99e1cd4a92c/lib/libtinfo.so.6: no version information available (required by /bin/bash)
/bin/bash: /azureml-envs/azureml_d1809f029ef68d0db142d99e1cd4a92c/lib/libtinfo.so.6: no version information available (required by /bin/bash)
/bin/bash: /azureml-envs/azureml_d1809f029ef68d0db142d99e1cd4a92c/lib/libtinfo.so.6: no version information available (required by /bin/bash)
/bin/bash: /azureml-envs/azureml_d1809f029ef68d0db142d99e1cd4a92c/lib/libtinfo.so.6: no version information available (required by /bin/bash)
Validating arguments.
Arguments validated.
Successfully obtained datastore reference and path.
Uploading file to 2023-01-27-6571d9/7dc81504-f7db-43d2-b7f2-18f10c631e05/
Cleaning up all outstanding Run operations, waiting 300.0 seconds
1 items cleaning up...
Cleanup took 0.09214901924133301 seconds
Traceback (most recent call last):
File "infer.py", line 154, in
X_test_df, y_test, fitted_model, args.output_path, ouput_dataset_name
File "infer.py", line 39, in infer_forecasting_dataset_tcn
name=output_dataset_name,
File "/azureml-envs/azureml_d1809f029ef68d0db142d99e1cd4a92c/lib/python3.7/site-packages/azureml/data/_loggerfactory.py", line 132, in wrapper
return func(*args, **kwargs)
File "/azureml-envs/azureml_d1809f029ef68d0db142d99e1cd4a92c/lib/python3.7/site-packages/azureml/data/dataset_factory.py", line 655, in register_pandas_dataframe
dflow.write_to_parquet(directory_path=target_directory_path).run_local()
File "/azureml-envs/azureml_d1809f029ef68d0db142d99e1cd4a92c/lib/python3.7/site-packages/azureml/dataprep/api/_loggerfactory.py", line 273, in wrapper
return func(*args, **kwargs)
File "/azureml-envs/azureml_d1809f029ef68d0db142d99e1cd4a92c/lib/python3.7/site-packages/azureml/dataprep/api/dataflow.py", line 636, in run_local
_execute('Dataflow.run_local', self, span_context=to_dprep_span_context(span.get_context()))
File "/azureml-envs/azureml_d1809f029ef68d0db142d99e1cd4a92c/lib/python3.7/site-packages/azureml/dataprep/api/_dataframereader.py", line 608, in _execute
return clex_execute()
File "/azureml-envs/azureml_d1809f029ef68d0db142d99e1cd4a92c/lib/python3.7/site-packages/azureml/dataprep/api/_dataframereader.py", line 440, in clex_execute
anonymous_activity=activity_data, span_context=span_context
File "/azureml-envs/azureml_d1809f029ef68d0db142d99e1cd4a92c/lib/python3.7/site-packages/azureml/dataprep/api/_aml_helper.py", line 44, in wrapper
return send_message_func(op_code, message, cancellation_token)
File "/azureml-envs/azureml_d1809f029ef68d0db142d99e1cd4a92c/lib/python3.7/site-packages/azureml/dataprep/api/engineapi/api.py", line 159, in execute_anonymous_activity
response = self._message_channel.send_message('Engine.ExecuteActivity', message_args, cancellation_token)
File "/azureml-envs/azureml_d1809f029ef68d0db142d99e1cd4a92c/lib/python3.7/site-packages/azureml/dataprep/api/engineapi/engine.py", line 291, in send_message
raise_engine_error(response['error'])
File "/azureml-envs/azureml_d1809f029ef68d0db142d99e1cd4a92c/lib/python3.7/site-packages/azureml/dataprep/api/errorhandlers.py", line 10, in raise_engine_error
raise ExecutionError(error_response)
azureml.dataprep.api.errorhandlers.ExecutionError:
Error Code: ScriptExecution.ReadDataFrame.StreamAccess.Validation
Validation Error Code: Invalid
Validation Target: PreppyFile
Failed Step: 9cf6aeee-adcf-4f1e-acbd-b5529e4402ad
Error Message: ScriptExecutionException was caused by ReadDataFrameException.
Failed to read Pandas DataFrame form Python host. Make sure Dataflow is created directly from the source Pandas DataFrame.
StreamAccessException was caused by ValidationException.
Trying to read an invalid file. Missing sentinel value in the beginning
| session_id=b6a0e361-a061-4fb9-ba08-9567b9f3994b

I am using the infor.py copied directly from the sample "forecasting-pipelines" file - does it have to be change in some way? or is it something else that is causing the error?

´´´
inference_pipeline = Pipeline(ws, [inference_step])
inference_run = experiment.submit(inference_pipeline)
´´´

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