def testMNISTPipelineNativeKeras(self):
if not tf.executing_eagerly():
self.skipTest('The test requires TF2.')
BeamDagRunner().run(
mnist_pipeline_native_keras._create_pipeline(
pipeline_name=self._pipeline_name,
data_root=self._data_root,
module_file=self._module_file,
module_file_lite=self._module_file_lite,
serving_model_dir=self._serving_model_dir,
serving_model_dir_lite=self._serving_model_dir_lite,
pipeline_root=self._pipeline_root,
metadata_path=self._metadata_path,
beam_pipeline_args=[]))
self.assertTrue(fileio.exists(self._serving_model_dir))
self.assertTrue(fileio.exists(self._serving_model_dir_lite))
self.assertTrue(fileio.exists(self._metadata_path))
metadata_config = metadata.sqlite_metadata_connection_config(
self._metadata_path)
expected_execution_count = 11
with metadata.Metadata(metadata_config) as m:
artifact_count = len(m.store.get_artifacts())
execution_count = len(m.store.get_executions())
self.assertGreaterEqual(artifact_count, execution_count)
self.assertEqual(execution_count, expected_execution_count)
self.assertPipelineExecution()
# Runs pipeline the second time.
BeamDagRunner().run(
mnist_pipeline_native_keras._create_pipeline(
pipeline_name=self._pipeline_name,
data_root=self._data_root,
module_file=self._module_file,
module_file_lite=self._module_file_lite,
serving_model_dir=self._serving_model_dir,
serving_model_dir_lite=self._serving_model_dir_lite,
pipeline_root=self._pipeline_root,
metadata_path=self._metadata_path,
beam_pipeline_args=[]))
# Asserts cache execution.
with metadata.Metadata(metadata_config) as m:
# Artifact count is unchanged.
self.assertLen(m.store.get_artifacts(), artifact_count)
self.assertLen(m.store.get_executions(),
expected_execution_count * 2)
def testMNISTPipelineNativeKeras(self):
BeamDagRunner().run(
mnist_pipeline_native_keras._create_pipeline(
pipeline_name=self._pipeline_name,
data_root=self._data_root,
module_file=self._module_file,
pipeline_root=self._pipeline_root,
metadata_path=self._metadata_path))
self.assertTrue(tf.io.gfile.exists(self._metadata_path))
metadata_config = metadata.sqlite_metadata_connection_config(
self._metadata_path)
with metadata.Metadata(metadata_config) as m:
artifact_count = len(m.store.get_artifacts())
execution_count = len(m.store.get_executions())
self.assertGreaterEqual(artifact_count, execution_count)
self.assertEqual(7, execution_count)
self.assertPipelineExecution()
def testMNISTPipelineNativeKeras(self):
BeamDagRunner().run(
mnist_pipeline_native_keras._create_pipeline(
pipeline_name=self._pipeline_name,
data_root=self._data_root,
module_file=self._module_file,
serving_model_dir=self._serving_model_dir,
pipeline_root=self._pipeline_root,
metadata_path=self._metadata_path,
direct_num_workers=1))
self.assertTrue(tf.io.gfile.exists(self._serving_model_dir))
self.assertTrue(tf.io.gfile.exists(self._metadata_path))
metadata_config = metadata.sqlite_metadata_connection_config(
self._metadata_path)
expected_execution_count = 9 # 8 components + 1 resolver
with metadata.Metadata(metadata_config) as m:
artifact_count = len(m.store.get_artifacts())
execution_count = len(m.store.get_executions())
self.assertGreaterEqual(artifact_count, execution_count)
self.assertEqual(expected_execution_count, execution_count)
self.assertPipelineExecution()