80e5340dddd58b061b3582f980fc3bc7fc4650b6,google/datalab/contrib/mlworkbench/commands/_ml.py,,_transform,#Any#Any#,645

Before Change

  training_data = args["training_data"]
  if isinstance(training_data, dict):
    if "csv" in training_data:
      cmd_args.append("--csv=" + _abs_path(training_data["csv"]))
    elif "bigquery_table" in training_data:
      cmd_args.extend(["--bigquery", training_data["bigquery_table"]])
    elif "bigquery_sql" in training_data:
        // see https://cloud.google.com/bigquery/querying-data//temporary_and_permanent_tables
        print("Creating temporary table that will be deleted in 24 hours")
        r = bq.Query(training_data["bigquery_sql"]).execute().result()
        cmd_args.extend(["--bigquery", r.full_name])
    else:
      raise ValueError("Invalid training_data dict. "
                       "Requires either "csv", or "bigquery_talbe", or "
                       ""bigquery_sql".")
  elif isinstance(training_data, google.datalab.ml.CsvDataSet):
    for file_name in training_data.input_files:
      cmd_args.append("--csv=" + _abs_path(file_name))
  elif isinstance(training_data, google.datalab.ml.BigQueryDataSet):

After Change

    cmd_args.extend(["--project-id", google.datalab.Context.default().project_id])

  training_data = get_dataset_from_arg(args["training_data"])
  data_names = ("train", "eval")
  for name in data_names:
    cmd_args_copy = list(cmd_args)
    if isinstance(getattr(training_data, name), datalab_ml.CsvDataSet):
      for file_name in getattr(training_data, name).input_files:
        cmd_args_copy.append("--csv=" + _abs_path(file_name))
    elif isinstance(getattr(training_data, name), datalab_ml.BigQueryDataSet):
      cmd_args_copy.extend(["--bigquery", getattr(training_data, name).table])
    else:
      raise ValueError("Unexpected training data type. Only csv or bigquery are supported.")

    cmd_args_copy.extend(["--prefix", name])
    try:
      tmpdir = None
      if args["package"]:
        tmpdir = tempfile.mkdtemp()
        code_path = os.path.join(tmpdir, "package")
        _archive.extract_archive(args["package"], code_path)
      else:
        code_path = MLTOOLBOX_CODE_PATH
      _shell_process.run_and_monitor(cmd_args_copy, os.getpid(), cwd=code_path)
    finally:
      if tmpdir:
        shutil.rmtree(tmpdir)


def _train(args, cell):
  if args["cloud_config"] and not args["cloud"]:
    raise ValueError(""cloud_config" is provided but no "--cloud". "
                     "Do you want local run or cloud run?")

Instances