e1cfdfa129aa4b346ae6a8b29d1664dac3dc3759,solutionbox/image_classification/mltoolbox/image/classification/_cloud.py,Cloud,batch_predict,#Any#Any#Any#Any#Any#,147

Before Change

        "staging_location": os.path.join(pipeline_option["temp_location"], "staging"),
        "job_name": job_name,
        "project": _util.default_project(),
        "extra_packages": [staging_package_url],
        "teardown_policy": "TEARDOWN_ALWAYS",
        "no_save_main_session": True
    }

After Change

    opts = beam.pipeline.PipelineOptions(flags=[], **options)
    p = beam.Pipeline("DataflowRunner", options=opts)
    _predictor.configure_pipeline(p, dataset, model_dir, output_csv, output_bq_table)
    logger = logging.getLogger()		
    logger.setLevel(logging.ERROR)
    original_level = logger.getEffectiveLevel()
    try:
      job_results = p.run()
    finally:

Instances