def test_ignite_dataset_with_plain_client(self):
"""Test Ignite Dataset with plain client.
"""
self._clear_env()
ds = ignite_io.IgniteDataset(cache_name="SQL_PUBLIC_TEST_CACHE", port=10800)
self._check_dataset(ds)
def test_ignite_dataset_with_plain_client(self):
"""Test Ignite Dataset with plain client."""
tf.compat.v1.disable_eager_execution()
import tensorflow_io.ignite as ignite_io
self._clear_env()
ds = ignite_io.IgniteDataset(cache_name="SQL_PUBLIC_TEST_CACHE",
port=10800)
self._check_dataset(ds)
def test_ignite_dataset_with_plain_client_with_interleave(self):
"""Test Ignite Dataset with plain client with interleave.
"""
self._clear_env()
ds = data.Dataset.from_tensor_slices(["localhost"]).interleave(
lambda host: ignite_io.IgniteDataset(cache_name="SQL_PUBLIC_TEST_CACHE",
schema_host="localhost", host=host,
port=42300), cycle_length=4, block_length=16
)
self._check_dataset(ds)
def test_ignite_dataset_with_plain_client_with_interleave(self):
"""Test Ignite Dataset with plain client with interleave.
"""
self._clear_env()
igds_local = ignite_io.IgniteDataset(
cache_name="SQL_PUBLIC_TEST_CACHE",
schema_host="localhost",
host='localhost',
port=10800)
# TODO: this is a workaround due to failure to build a TypeSpec for
# IgniteDataset in non-eager mode
ds = data.Dataset.from_tensor_slices(["localhost"]).interleave(
lambda host: igds_local, cycle_length=4, block_length=16)
self._check_dataset(ds)