tfx/experimental/templates/penguin/local_runner.py [28:80]:
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OUTPUT_DIR = '.'

# TFX produces two types of outputs, files and metadata.
# - Files will be created under PIPELINE_ROOT directory.
# - Metadata will be written to SQLite database in METADATA_PATH.
PIPELINE_ROOT = os.path.join(OUTPUT_DIR, 'tfx_pipeline_output',
                             configs.PIPELINE_NAME)
METADATA_PATH = os.path.join(OUTPUT_DIR, 'tfx_metadata', configs.PIPELINE_NAME,
                             'metadata.db')

# The last component of the pipeline, "Pusher" will produce serving model under
# SERVING_MODEL_DIR.
SERVING_MODEL_DIR = os.path.join(PIPELINE_ROOT, 'serving_model')

# Specifies data file directory. DATA_PATH should be a directory containing CSV
# files for CsvExampleGen in this example. By default, data files are in the
# `data` directory.
# NOTE: If you upload data files to GCS(which is recommended if you use
#       Kubeflow), you can use a path starting "gs://YOUR_BUCKET_NAME/path" for
#       DATA_PATH. For example,
#       DATA_PATH = 'gs://bucket/penguin/csv/'.
# TODO(step 4): Specify the path for your data.
DATA_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'data')


def run():
  """Define a pipeline."""

  tfx.orchestration.LocalDagRunner().run(
      pipeline.create_pipeline(
          pipeline_name=configs.PIPELINE_NAME,
          pipeline_root=PIPELINE_ROOT,
          data_path=DATA_PATH,
          # NOTE: Use `query` instead of `data_path` to use BigQueryExampleGen.
          # query=configs.BIG_QUERY_QUERY,
          # NOTE: Set the path of the customized schema if any.
          # schema_path=generated_schema_path,
          preprocessing_fn=configs.PREPROCESSING_FN,
          run_fn=configs.RUN_FN,
          train_args=tfx.proto.TrainArgs(num_steps=configs.TRAIN_NUM_STEPS),
          eval_args=tfx.proto.EvalArgs(num_steps=configs.EVAL_NUM_STEPS),
          eval_accuracy_threshold=configs.EVAL_ACCURACY_THRESHOLD,
          serving_model_dir=SERVING_MODEL_DIR,
          # NOTE: Provide GCP configs to use BigQuery with Beam DirectRunner.
          # beam_pipeline_args=configs.
          # BIG_QUERY_WITH_DIRECT_RUNNER_BEAM_PIPELINE_ARGS,
          metadata_connection_config=tfx.orchestration.metadata
          .sqlite_metadata_connection_config(METADATA_PATH)))


if __name__ == '__main__':
  logging.set_verbosity(logging.INFO)
  run()
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tfx/experimental/templates/taxi/local_runner.py [28:80]:
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OUTPUT_DIR = '.'

# TFX produces two types of outputs, files and metadata.
# - Files will be created under PIPELINE_ROOT directory.
# - Metadata will be written to SQLite database in METADATA_PATH.
PIPELINE_ROOT = os.path.join(OUTPUT_DIR, 'tfx_pipeline_output',
                             configs.PIPELINE_NAME)
METADATA_PATH = os.path.join(OUTPUT_DIR, 'tfx_metadata', configs.PIPELINE_NAME,
                             'metadata.db')

# The last component of the pipeline, "Pusher" will produce serving model under
# SERVING_MODEL_DIR.
SERVING_MODEL_DIR = os.path.join(PIPELINE_ROOT, 'serving_model')

# Specifies data file directory. DATA_PATH should be a directory containing CSV
# files for CsvExampleGen in this example. By default, data files are in the
# `data` directory.
# NOTE: If you upload data files to GCS(which is recommended if you use
#       Kubeflow), you can use a path starting "gs://YOUR_BUCKET_NAME/path" for
#       DATA_PATH. For example,
#       DATA_PATH = 'gs://bucket/chicago_taxi_trips/csv/'.
DATA_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'data')


def run():
  """Define a local pipeline."""

  tfx.orchestration.LocalDagRunner().run(
      pipeline.create_pipeline(
          pipeline_name=configs.PIPELINE_NAME,
          pipeline_root=PIPELINE_ROOT,
          data_path=DATA_PATH,
          # TODO(step 7): (Optional) Uncomment here to use BigQueryExampleGen.
          # query=configs.BIG_QUERY_QUERY,
          # TODO(step 5): (Optional) Set the path of the customized schema.
          # schema_path=generated_schema_path,
          preprocessing_fn=configs.PREPROCESSING_FN,
          run_fn=configs.RUN_FN,
          train_args=tfx.proto.TrainArgs(num_steps=configs.TRAIN_NUM_STEPS),
          eval_args=tfx.proto.EvalArgs(num_steps=configs.EVAL_NUM_STEPS),
          eval_accuracy_threshold=configs.EVAL_ACCURACY_THRESHOLD,
          serving_model_dir=SERVING_MODEL_DIR,
          # TODO(step 7): (Optional) Uncomment here to use provide GCP related
          #               config for BigQuery with Beam DirectRunner.
          # beam_pipeline_args=configs.
          # BIG_QUERY_WITH_DIRECT_RUNNER_BEAM_PIPELINE_ARGS,
          metadata_connection_config=tfx.orchestration.metadata
          .sqlite_metadata_connection_config(METADATA_PATH)))


if __name__ == '__main__':
  logging.set_verbosity(logging.INFO)
  run()
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