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def make_remote_dataset(endpoint):
  """Make a TensorFlow Arrow Dataset that reads from a remote Arrow stream."""

  # Create the Arrow Dataset from a remote host serving a stream
  ds = arrow_io.ArrowStreamDataset(
      [endpoint],
      columns=(0, 1, 2),
      output_types=(tf.int64, tf.float64, tf.float64),
      batch_mode='auto')

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def serve_csv_data(ip_addr, port_num, directory):
  """
  Create a socket and serve Arrow record batches as a stream read from the
  given directory containing CVS files.
  """

  # Create the socket
  sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
  sock.bind((ip_addr, port_num))
  sock.listen(1)

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def read_and_process_dir(directory):
  """Read a directory of CSV files and yield processed Arrow batches."""

  for f in os.listdir(directory):
    if f.endswith(".csv"):
      filename = os.path.join(directory, f)
      for batch in read_and_process(filename):
        yield batch

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ds = make_local_dataset(filename)
model = model_fit(ds)

print("Fit model with weights: {}".format(model.get_weights()))
# Fit model with weights:
# [array([[0.7793554 ], [0.61216295]], dtype=float32),
#  array([0.03328196], dtype=float32)]

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def make_local_dataset(filename):
  """Make a TensorFlow Arrow Dataset that reads from a local CSV file."""
 
  # Read the local file and get a record batch iterator
  batch_iter = read_and_process(filename)
  
  # Create the Arrow Dataset as a stream from local iterator of record batches
  ds = arrow_io.ArrowStreamDataset.from_record_batches(
    batch_iter,
    output_types=(tf.int64, tf.float64, tf.float64),

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def read_and_process(filename):
  """Read the given CSV file and yield processed Arrow batches."""

  # Read a CSV file into an Arrow Table with threading enabled and
  # set block_size in bytes to break the file into chunks for granularity,
  # which determines the number of batches in the resulting pyarrow.Table
  opts = pyarrow.csv.ReadOptions(use_threads=True, block_size=4096)
  table = pyarrow.csv.read_csv(filename, opts)

  # Fit the feature transform

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def model_fit(ds):
  """Create and fit a Keras logistic regression model."""
  
  # Build the Keras model
  model = tf.keras.Sequential()
  model.add(tf.keras.layers.Dense(1, input_shape=(2,),
            activation='sigmoid'))
  model.compile(optimizer='sgd', loss='mean_squared_error',
                metrics=['accuracy'])

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import tensorflow_io.arrow as arrow_io

ds = arrow_io.ArrowStreamDataset.from_pandas(
    df,
    batch_size=2,
    preserve_index=False)

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import tensorflow_io.arrow as arrow_io
from pyarrow.feather import write_feather

# Write the Pandas DataFrame to a Feather file
write_feather(df, '/path/to/df.feather')

# Create the dataset with one or more filenames
ds = arrow_io.ArrowFeatherDataset(
    ['/path/to/df.feather'],
    columns=(0, 1, 2),

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import tensorflow_io.arrow as arrow_io

ds = arrow_io.ArrowDataset.from_pandas(
    df,
    batch_size=2,
    preserve_index=False)

# Make an iterator to the dataset
ds_iter = iter(ds)