def _create_pipeline(self, pipeline_name: Text,
components: List[BaseComponent]):
"""Creates a pipeline given name and list of components."""
return tfx_pipeline.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=self._pipeline_root(pipeline_name),
metadata_connection_config=metadata_store_pb2.ConnectionConfig(),
components=components,
log_root='/var/tmp/tfx/logs',
additional_pipeline_args={
# Use a fixed WORKFLOW_ID (which is used as run id) for testing,
# for the purpose of making debugging easier.
'WORKFLOW_ID': pipeline_name,
},
)
def setUp(self):
super().setUp()
self._connection_config = metadata_store_pb2.ConnectionConfig()
self._connection_config.sqlite.SetInParent()
self._metadata = self.enter_context(
metadata.Metadata(connection_config=self._connection_config))
self._store = self._metadata.store
self._pipeline_info = data_types.PipelineInfo(
pipeline_name='my_pipeline',
pipeline_root='/tmp',
run_id='my_run_id')
self._component_info = data_types.ComponentInfo(
component_type='a.b.c',
component_id='my_component',
pipeline_info=self._pipeline_info)
def setUp(self):
super(ResolverDriverTest, self).setUp()
self.connection_config = metadata_store_pb2.ConnectionConfig()
self.connection_config.sqlite.SetInParent()
self.component_info = data_types.ComponentInfo(component_type='c_type',
component_id='c_id')
self.pipeline_info = data_types.PipelineInfo(pipeline_name='p_name',
pipeline_root='p_root',
run_id='run_id')
self.driver_args = data_types.DriverArgs(enable_cache=True)
self.source_channel_key = 'source_channel'
self.source_channels = {
self.source_channel_key:
types.Channel(type=standard_artifacts.Examples)
}
def _create_pipeline():
pipeline_name = _PIPELINE_NAME
pipeline_root = os.path.join(_get_test_output_dir(), pipeline_name)
components = test_utils.create_e2e_components(_get_csv_input_location())
return tfx_pipeline.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
metadata_connection_config=metadata_store_pb2.ConnectionConfig(),
components=components[:
2], # Run two components only to reduce overhead.
log_root='/var/tmp/tfx/logs',
additional_pipeline_args={
'WORKFLOW_ID': pipeline_name,
},
)
def testRun(self, mock_publisher):
mock_publisher.return_value.publish_execution.return_value = {}
test_dir = os.path.join(
os.environ.get('TEST_UNDECLARED_OUTPUTS_DIR', self.get_temp_dir()),
self._testMethodName)
connection_config = metadata_store_pb2.ConnectionConfig()
connection_config.sqlite.SetInParent()
metadata_connection = metadata.Metadata(connection_config)
pipeline_root = os.path.join(test_dir, 'Test')
input_path = os.path.join(test_dir, 'input')
fileio.makedirs(os.path.dirname(input_path))
file_io.write_string_to_file(input_path, 'test')
input_artifact = test_utils._InputArtifact()
input_artifact.uri = input_path
component = test_utils._FakeComponent(
name='FakeComponent',
input_channel=channel_utils.as_channel([input_artifact]))
pipeline_info = data_types.PipelineInfo(pipeline_name='Test',
pipeline_root=pipeline_root,
run_id='123')
driver_args = data_types.DriverArgs(enable_cache=True)
# We use InProcessComponentLauncher to test BaseComponentLauncher logics.
launcher = in_process_component_launcher.InProcessComponentLauncher.create(
component=component,
pipeline_info=pipeline_info,
driver_args=driver_args,
metadata_connection=metadata_connection,
beam_pipeline_args=[],
additional_pipeline_args={})
self.assertEqual(
launcher._component_info.component_type, '.'.join([
test_utils._FakeComponent.__module__,
test_utils._FakeComponent.__name__
]))
launcher.launch()
output_path = component.outputs['output'].get()[0].uri
self.assertTrue(fileio.exists(output_path))
contents = file_io.read_file_to_string(output_path)
self.assertEqual('test', contents)
def testRun(self):
component_a = _FakeComponent(
_FakeComponentSpecA(output=types.Channel(type=_ArtifactTypeA)),
enable_cache=True)
component_b = _FakeComponent(_FakeComponentSpecB(
a=component_a.outputs['output'],
output=types.Channel(type=_ArtifactTypeB)),
enable_cache=False)
component_c = _FakeComponent(
_FakeComponentSpecC(a=component_a.outputs['output'],
output=types.Channel(type=_ArtifactTypeC)),
True)
component_d = _FakeComponent(
_FakeComponentSpecD(b=component_b.outputs['output'],
c=component_c.outputs['output'],
output=types.Channel(type=_ArtifactTypeD)),
False)
component_e = _FakeComponent(
_FakeComponentSpecE(a=component_a.outputs['output'],
b=component_b.outputs['output'],
d=component_d.outputs['output'],
output=types.Channel(type=_ArtifactTypeE)))
test_pipeline = pipeline.Pipeline(
pipeline_name='x',
pipeline_root='y',
metadata_connection_config=metadata_store_pb2.ConnectionConfig(),
components=[
component_d, component_c, component_a, component_b, component_e
])
beam_dag_runner.BeamDagRunner().run(test_pipeline)
self.assertCountEqual(_executed_components, [
'_FakeComponent.a', '_FakeComponent.b', '_FakeComponent.c',
'_FakeComponent.d', '_FakeComponent.e'
])
self.assertEqual(_executed_components[0], '_FakeComponent.a')
self.assertEqual(_executed_components[3], '_FakeComponent.d')
self.assertEqual(_executed_components[4], '_FakeComponent.e')
self.assertDictEqual(
{
'_FakeComponent.a': True,
'_FakeComponent.b': False,
'_FakeComponent.c': True,
'_FakeComponent.d': False,
'_FakeComponent.e': False,
}, _executed_components_cached)
def sqlite_metadata_connection_config(
metadata_db_uri: Text) -> metadata_store_pb2.ConnectionConfig:
"""Convenience function to create file based metadata connection config.
Args:
metadata_db_uri: uri to metadata db.
Returns:
A metadata_store_pb2.ConnectionConfig based on given metadata db uri.
"""
tf.io.gfile.makedirs(os.path.dirname(metadata_db_uri))
connection_config = metadata_store_pb2.ConnectionConfig()
connection_config.sqlite.filename_uri = metadata_db_uri
connection_config.sqlite.connection_mode = \
metadata_store_pb2.SqliteMetadataSourceConfig.READWRITE_OPENCREATE
return connection_config
def get_default_kubernetes_metadata_config(
) -> metadata_store_pb2.ConnectionConfig:
"""Returns the default metadata connection config for a kubernetes cluster.
Returns:
A config proto that will be serialized as JSON and passed to the running
container so the TFX component driver is able to communicate with MLMD in
a kubernetes cluster.
"""
connection_config = metadata_store_pb2.ConnectionConfig()
connection_config.mysql.host = 'mysql'
connection_config.mysql.port = 3306
connection_config.mysql.database = 'mysql'
connection_config.mysql.user = '******'
connection_config.mysql.password = ''
return connection_config
def setUp(self):
super(KubernetesRemoteRunnerTest, self).setUp()
self.component_a = _FakeComponent(
_FakeComponentSpecA(output=types.Channel(type=_ArtifactTypeA)))
self.component_b = _FakeComponent(
_FakeComponentSpecB(a=self.component_a.outputs['output'],
output=types.Channel(type=_ArtifactTypeB)))
self.component_c = _FakeComponent(
_FakeComponentSpecC(a=self.component_a.outputs['output'],
b=self.component_b.outputs['output'],
output=types.Channel(type=_ArtifactTypeC)))
self.test_pipeline = tfx_pipeline.Pipeline(
pipeline_name='x',
pipeline_root='y',
metadata_connection_config=metadata_store_pb2.ConnectionConfig(),
components=[self.component_c, self.component_a, self.component_b])
def test_before_generating_trial_model(self):
connection_config = metadata_store_pb2.ConnectionConfig()
connection_config.sqlite.filename_uri = os.path.join(
FLAGS.test_tmpdir, "1")
connection_config.sqlite.connection_mode = 3
handler = ml_metadata_db.MLMetaData(
None, None, None, connection_config=connection_config)
handler.before_generating_trial_model(trial_id=1, model_dir="/tmp/1")
output = handler._store.get_executions_by_type("Trial")
self.assertLen(output, 1)
output = output[0]
self.assertEqual(output.properties["id"].int_value, 1)
self.assertEqual(output.properties["state"].string_value, "RUNNING")
self.assertEqual(output.properties["serialized_data"].string_value, "")
self.assertEqual(output.properties["model_dir"].string_value, "/tmp/1")
self.assertEqual(output.properties["evaluation"].string_value, "")
def test_report(self):
connection_config = metadata_store_pb2.ConnectionConfig()
connection_config.sqlite.filename_uri = os.path.join(
FLAGS.test_tmpdir, "2")
connection_config.sqlite.connection_mode = 3
handler = ml_metadata_db.MLMetaData(
None, None, None, connection_config=connection_config)
handler.before_generating_trial_model(trial_id=1, model_dir="/tmp/1")
handler.report(eval_dictionary={"loss": 0.5}, model_dir="/tmp/1")
output = handler.get_completed_trials()
self.assertLen(output, 1)
output = output[0]
self.assertEqual(output.id, 1)
self.assertEqual(output.status, "COMPLETED")
self.assertEqual(output.model_dir, "/tmp/1")
self.assertEqual(output.final_measurement.objective_value, 0.5)
def setUp(self):
super(MetadataTest, self).setUp()
self._connection_config = metadata_store_pb2.ConnectionConfig()
self._connection_config.sqlite.SetInParent()
self._component_info = data_types.ComponentInfo(
component_type='a.b.c', component_id='my_component')
self._component_info2 = data_types.ComponentInfo(
component_type='a.b.d', component_id='my_component_2')
self._pipeline_info = data_types.PipelineInfo(
pipeline_name='my_pipeline',
pipeline_root='/tmp',
run_id='my_run_id')
self._pipeline_info2 = data_types.PipelineInfo(
pipeline_name='my_pipeline',
pipeline_root='/tmp',
run_id='my_run_id2')
def test_run(self, mock_publisher):
mock_publisher.return_value.publish_execution.return_value = {}
test_dir = os.path.join(
os.environ.get('TEST_UNDECLARED_OUTPUTS_DIR', self.get_temp_dir()),
self._testMethodName)
connection_config = metadata_store_pb2.ConnectionConfig()
connection_config.sqlite.SetInParent()
pipeline_root = os.path.join(test_dir, 'Test')
input_path = os.path.join(test_dir, 'input')
tf.gfile.MakeDirs(os.path.dirname(input_path))
file_io.write_string_to_file(input_path, 'test')
input_artifact = types.TfxArtifact(type_name='InputPath')
input_artifact.uri = input_path
component = _FakeComponent(name='FakeComponent',
input_channel=channel.as_channel(
[input_artifact]))
pipeline_info = data_types.PipelineInfo(pipeline_name='Test',
pipeline_root=pipeline_root,
run_id='123')
driver_args = data_types.DriverArgs(worker_name=component.component_id,
base_output_dir=os.path.join(
pipeline_root,
component.component_id),
enable_cache=True)
launcher = component_launcher.ComponentLauncher(
component=component,
pipeline_info=pipeline_info,
driver_args=driver_args,
metadata_connection_config=connection_config,
additional_pipeline_args={})
self.assertEqual(
launcher._component_info.component_type,
'.'.join([_FakeComponent.__module__, _FakeComponent.__name__]))
launcher.launch()
output_path = os.path.join(pipeline_root, 'output')
self.assertTrue(tf.gfile.Exists(output_path))
contents = file_io.read_file_to_string(output_path)
self.assertEqual('test', contents)
def testResolverWithResolverPolicy(self):
pipeline = pipeline_pb2.Pipeline()
self.load_proto_from_text(
os.path.join(self._testdata_dir,
'pipeline_for_input_resolver_test.pbtxt'), pipeline)
my_example_gen = pipeline.nodes[0].pipeline_node
my_transform = pipeline.nodes[2].pipeline_node
connection_config = metadata_store_pb2.ConnectionConfig()
connection_config.sqlite.SetInParent()
with metadata.Metadata(connection_config=connection_config) as m:
# Publishes first ExampleGen with two output channels. `output_examples`
# will be consumed by downstream Transform.
output_example_1 = types.Artifact(
my_example_gen.outputs.outputs['output_examples'].
artifact_spec.type)
output_example_1.uri = 'my_examples_uri_1'
output_example_2 = types.Artifact(
my_example_gen.outputs.outputs['output_examples'].
artifact_spec.type)
output_example_2.uri = 'my_examples_uri_2'
contexts = context_lib.register_contexts_if_not_exists(
m, my_example_gen.contexts)
execution = execution_publish_utils.register_execution(
m, my_example_gen.node_info.type, contexts)
execution_publish_utils.publish_succeeded_execution(
m, execution.id, contexts, {
'output_examples': [output_example_1, output_example_2],
})
my_transform.inputs.resolver_config.resolver_policy = (
pipeline_pb2.ResolverConfig.LATEST_ARTIFACT)
# Gets inputs for transform. Should get back what the first ExampleGen
# published in the `output_examples` channel.
transform_inputs = inputs_utils.resolve_input_artifacts(
m, my_transform.inputs)
self.assertEqual(len(transform_inputs), 1)
self.assertEqual(len(transform_inputs['examples']), 1)
self.assertProtoPartiallyEquals(
transform_inputs['examples'][0].mlmd_artifact,
output_example_2.mlmd_artifact,
ignored_fields=[
'create_time_since_epoch', 'last_update_time_since_epoch'
])
def _get_metadata_store(grpc_max_receive_message_length=None):
if FLAGS.use_grpc_backend:
grpc_connection_config = metadata_store_pb2.MetadataStoreClientConfig()
if grpc_max_receive_message_length:
(grpc_connection_config.channel_arguments.max_receive_message_length
) = grpc_max_receive_message_length
if FLAGS.grpc_host is None:
raise ValueError("grpc_host argument not set.")
grpc_connection_config.host = FLAGS.grpc_host
if not FLAGS.grpc_port:
raise ValueError("grpc_port argument not set.")
grpc_connection_config.port = FLAGS.grpc_port
return metadata_store.MetadataStore(grpc_connection_config)
connection_config = metadata_store_pb2.ConnectionConfig()
connection_config.sqlite.SetInParent()
return metadata_store.MetadataStore(connection_config)
def _create_pipeline():
pipeline_name = _PIPELINE_NAME
test_output_dir = 'gs://{}/test_output'.format(test_utils.BUCKET_NAME)
pipeline_root = os.path.join(test_output_dir, pipeline_name)
components = test_utils.create_e2e_components(pipeline_root,
test_utils.DATA_ROOT,
test_utils.TAXI_MODULE_FILE)
return tfx_pipeline.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
metadata_connection_config=metadata_store_pb2.ConnectionConfig(),
components=components[:2],
log_root='/var/tmp/tfx/logs',
additional_pipeline_args={
'WORKFLOW_ID': pipeline_name,
},
)
def setUp(self):
super().setUp()
self._connection_config = metadata_store_pb2.ConnectionConfig()
self._connection_config.sqlite.SetInParent()
self._module_file_path = os.path.join(self.tmp_dir, 'module_file')
self._input_artifacts = {'input_examples': [standard_artifacts.Examples()]}
self._output_artifacts = {'output_models': [standard_artifacts.Model()]}
self._parameters = {'module_file': self._module_file_path}
self._module_file_content = 'module content'
self._pipeline_node = text_format.Parse(
"""
executor {
python_class_executor_spec {class_path: 'a.b.c'}
}
""", pipeline_pb2.PipelineNode())
self._executor_class_path = 'a.b.c'
self._pipeline_info = pipeline_pb2.PipelineInfo(id='pipeline_id')
def setUp(self):
super().setUp()
example_gen_output_config = data_types.RuntimeParameter(
name='example-gen-output-config', ptype=str)
example_gen = csv_example_gen_component.CsvExampleGen(
input_base='data_root', output_config=example_gen_output_config)
statistics_gen = statistics_gen_component.StatisticsGen(
examples=example_gen.outputs['examples']).with_id('foo')
test_pipeline_root = dsl.PipelineParam(name='pipeline-root-param')
pipeline = tfx_pipeline.Pipeline(
pipeline_name=self._test_pipeline_name,
pipeline_root='test_pipeline_root',
metadata_connection_config=metadata_store_pb2.ConnectionConfig(),
components=[example_gen, statistics_gen],
)
self._metadata_config = kubeflow_pb2.KubeflowMetadataConfig()
self._metadata_config.mysql_db_service_host.environment_variable = 'MYSQL_SERVICE_HOST'
self._tfx_ir = pipeline_pb2.Pipeline()
with dsl.Pipeline('test_pipeline'):
self.example_gen = base_component.BaseComponent(
component=example_gen,
depends_on=set(),
pipeline=pipeline,
pipeline_root=test_pipeline_root,
tfx_image='container_image',
kubeflow_metadata_config=self._metadata_config,
tfx_ir=self._tfx_ir,
pod_labels_to_attach={},
runtime_parameters=[example_gen_output_config])
self.statistics_gen = base_component.BaseComponent(
component=statistics_gen,
depends_on=set(),
pipeline=pipeline,
pipeline_root=test_pipeline_root,
tfx_image='container_image',
kubeflow_metadata_config=self._metadata_config,
tfx_ir=self._tfx_ir,
pod_labels_to_attach={},
runtime_parameters=[])
self.tfx_example_gen = example_gen
self.tfx_statistics_gen = statistics_gen
def setUp(self):
super(ImporterDriverTest, self).setUp()
self.connection_config = metadata_store_pb2.ConnectionConfig()
self.connection_config.sqlite.SetInParent()
self.artifact_type = 'Examples'
self.output_dict = {
importer_node.IMPORT_RESULT_KEY:
types.Channel(type_name=self.artifact_type)
}
self.source_uri = 'm/y/u/r/i'
self.existing_artifact = types.Artifact(type_name=self.artifact_type)
self.existing_artifact.uri = self.source_uri
self.component_info = data_types.ComponentInfo(
component_type='c_type', component_id='c_id')
self.pipeline_info = data_types.PipelineInfo(
pipeline_name='p_name', pipeline_root='p_root', run_id='run_id')
self.driver_args = data_types.DriverArgs(enable_cache=True)
def __init__(self,
components_to_always_add: List[BaseComponent],
benchmark_subpipelines: List[BenchmarkSubpipeline],
pipeline_name: Optional[str],
pipeline_root: Optional[str],
metadata_connection_config: Optional[
metadata_store_pb2.ConnectionConfig] = None,
beam_pipeline_args: Optional[List[str]] = None,
**kwargs):
if not benchmark_subpipelines and not components_to_always_add:
raise ValueError(
"Requires at least one benchmark subpipeline or component to run. "
"You may want to call `self.add(..., always=True) in order "
"to run Components, Subpipelines, or Pipeline even without requiring "
"a call to `self.evaluate(...)`.")
# Set defaults.
if not pipeline_name:
pipeline_name = "nitroml"
if not pipeline_root:
tmp_root_dir = os.path.join("/tmp", pipeline_name)
tf.io.gfile.makedirs(tmp_root_dir)
pipeline_root = tempfile.mkdtemp(dir=tmp_root_dir)
logging.info("Creating tmp pipeline_root at %s", pipeline_root)
if not metadata_connection_config:
metadata_connection_config = metadata_store_pb2.ConnectionConfig(
sqlite=metadata_store_pb2.SqliteMetadataSourceConfig(
filename_uri=os.path.join(pipeline_root, "mlmd.sqlite")))
# Ensure that pipeline dirs are created.
_make_pipeline_dirs(pipeline_root, metadata_connection_config)
components = set(components_to_always_add)
for benchmark_subpipeline in benchmark_subpipelines:
for component in benchmark_subpipeline.components:
components.add(component)
super().__init__(pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
metadata_connection_config=metadata_connection_config,
components=list(components),
beam_pipeline_args=beam_pipeline_args,
**kwargs)
self._subpipelines = benchmark_subpipelines
def testRun(self, mock_publisher):
mock_publisher.return_value.publish_execution.return_value = {}
example_gen = FileBasedExampleGen(
custom_executor_spec=executor_spec.ExecutorClassSpec(
avro_executor.Executor),
input_base=self.avro_dir_path,
input_config=self.input_config,
output_config=self.output_config,
instance_name='AvroExampleGen')
output_data_dir = os.path.join(
os.environ.get('TEST_UNDECLARED_OUTPUTS_DIR', self.get_temp_dir()),
self._testMethodName)
pipeline_root = os.path.join(output_data_dir, 'Test')
fileio.makedirs(pipeline_root)
pipeline_info = data_types.PipelineInfo(pipeline_name='Test',
pipeline_root=pipeline_root,
run_id='123')
driver_args = data_types.DriverArgs(enable_cache=True)
connection_config = metadata_store_pb2.ConnectionConfig()
connection_config.sqlite.SetInParent()
metadata_connection = metadata.Metadata(connection_config)
launcher = in_process_component_launcher.InProcessComponentLauncher.create(
component=example_gen,
pipeline_info=pipeline_info,
driver_args=driver_args,
metadata_connection=metadata_connection,
beam_pipeline_args=[],
additional_pipeline_args={})
self.assertEqual(
launcher._component_info.component_type, '.'.join(
[FileBasedExampleGen.__module__,
FileBasedExampleGen.__name__]))
launcher.launch()
mock_publisher.return_value.publish_execution.assert_called_once()
# Check output paths.
self.assertTrue(
fileio.exists(os.path.join(pipeline_root, example_gen.id)))
def _input_artifacts(self, pipeline_name: Text,
input_artifacts: List[Artifact]) -> Channel:
"""Publish input artifacts for test to MLMD and return channel to them."""
connection_config = metadata_store_pb2.ConnectionConfig(
mysql=metadata_store_pb2.MySQLDatabaseConfig(
host='127.0.0.1',
port=3306,
database=self._get_mlmd_db_name(pipeline_name),
user='******',
password=''))
dummy_artifact = (input_artifacts[0].type_name, self._random_id())
output_key = 'dummy_output_%s_%s' % dummy_artifact
producer_component_id = 'dummy_producer_id_%s_%s' % dummy_artifact
producer_component_type = 'dummy_producer_type_%s_%s' % dummy_artifact
# Input artifacts must have a unique name and producer in MLMD.
for artifact in input_artifacts:
artifact.name = output_key
artifact.pipeline_name = pipeline_name
artifact.producer_component = producer_component_id
with metadata.Metadata(connection_config=connection_config) as m:
# Register a dummy execution to metadata store as producer execution.
execution_id = m.register_execution(
exec_properties={},
pipeline_info=data_types.PipelineInfo(
pipeline_name=pipeline_name,
pipeline_root='/dummy_pipeline_root',
# test_utils uses pipeline_name as fixed WORKFLOW_ID.
run_id=pipeline_name,
),
component_info=data_types.ComponentInfo(
component_type=producer_component_type,
component_id=producer_component_id))
# Publish the test input artifact from the dummy execution.
published_artifacts = m.publish_execution(
execution_id=execution_id,
input_dict={},
output_dict={output_key: input_artifacts})
return channel_utils.as_channel(published_artifacts[output_key])
def _create_pipeline():
pipeline_name = _PIPELINE_NAME
pipeline_root = os.path.join(test_utils.get_test_output_dir(),
pipeline_name)
components = test_utils.create_e2e_components(
pipeline_root,
test_utils.get_csv_input_location(),
test_utils.get_transform_module(),
test_utils.get_trainer_module(),
)
return tfx_pipeline.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
metadata_connection_config=metadata_store_pb2.ConnectionConfig(),
components=components[:4],
log_root='/var/tmp/tfx/logs',
additional_pipeline_args={
'WORKFLOW_ID': pipeline_name,
},
)
def test_get_completed_trials(self):
connection_config = metadata_store_pb2.ConnectionConfig()
connection_config.sqlite.filename_uri = os.path.join(
FLAGS.test_tmpdir, "3")
connection_config.sqlite.connection_mode = 3
handler = ml_metadata_db.MLMetaData(
None, None, None, connection_config=connection_config)
handler.before_generating_trial_model(trial_id=1, model_dir="/tmp/1")
handler.before_generating_trial_model(trial_id=2, model_dir="/tmp/2")
handler.report(eval_dictionary={"loss": 0.1}, model_dir="/tmp/1")
handler.before_generating_trial_model(trial_id=3, model_dir="/tmp/3")
handler.report(eval_dictionary={"loss": 0.3}, model_dir="/tmp/3")
handler.report(eval_dictionary={"loss": 0.2}, model_dir="/tmp/2")
output = handler.get_completed_trials()
self.assertLen(output, 3)
for i in range(3):
self.assertEqual(output[i].status, "COMPLETED")
self.assertEqual(output[i].model_dir, "/tmp/" + str(output[i].id))
self.assertEqual(output[i].final_measurement.objective_value,
float(output[i].id) / 10)
def setUp(self):
super(KubeflowMetadataAdapterTest, self).setUp()
self._connection_config = metadata_store_pb2.ConnectionConfig()
self._connection_config.sqlite.SetInParent()
self._pipeline_info = data_types.PipelineInfo(
pipeline_name='fake_pipeline_name',
pipeline_root='/fake_pipeline_root',
run_id='fake_run_id')
self._pipeline_info2 = data_types.PipelineInfo(
pipeline_name='fake_pipeline_name',
pipeline_root='/fake_pipeline_root',
run_id='fake_run_id2')
self._component_info = data_types.ComponentInfo(
component_type='fake.component.type',
component_id='fake_component_id',
pipeline_info=self._pipeline_info)
self._component_info2 = data_types.ComponentInfo(
component_type='fake.component.type',
component_id='fake_component_id',
pipeline_info=self._pipeline_info2)
def testRunWithSameSpec(self, mock_kube_utils):
_initialize_executed_components()
mock_kube_utils.is_inside_cluster.return_value = True
component_a = _FakeComponent(spec=_FakeComponentSpecA(
output=types.Channel(type=_ArtifactTypeA)))
component_f1 = _FakeComponent(spec=_FakeComponentSpecF(
a=component_a.outputs['output'])).with_id('f1')
component_f2 = _FakeComponent(spec=_FakeComponentSpecF(
a=component_a.outputs['output'])).with_id('f2')
component_f2.add_upstream_node(component_f1)
test_pipeline = pipeline.Pipeline(
pipeline_name='x',
pipeline_root='y',
metadata_connection_config=metadata_store_pb2.ConnectionConfig(),
components=[component_f1, component_f2, component_a])
kubernetes_dag_runner.KubernetesDagRunner().run(test_pipeline)
self.assertEqual(_executed_components,
['a.Wrapper', 'f1.Wrapper', 'f2.Wrapper'])
def _two_step_pipeline() -> tfx_pipeline.Pipeline:
default_input_config = json.dumps({
'splits': [{
'name': 'single_split',
'pattern': 'SELECT * FROM default-table'
}]
})
input_config = data_types.RuntimeParameter(name='input_config',
ptype=str,
default=default_input_config)
example_gen = big_query_example_gen_component.BigQueryExampleGen(
input_config=input_config, output_config=example_gen_pb2.Output())
statistics_gen = statistics_gen_component.StatisticsGen(
examples=example_gen.outputs['examples'])
return tfx_pipeline.Pipeline(
pipeline_name='two_step_pipeline',
pipeline_root='pipeline_root',
metadata_connection_config=metadata_store_pb2.ConnectionConfig(),
components=[example_gen, statistics_gen],
)
def testRun(self, mock_kube_utils):
_initialize_executed_components()
mock_kube_utils.is_inside_cluster.return_value = True
component_a = _FakeComponent(
spec=_FakeComponentSpecA(output=types.Channel(type=_ArtifactTypeA)))
component_b = _FakeComponent(
spec=_FakeComponentSpecB(
a=component_a.outputs['output'],
output=types.Channel(type=_ArtifactTypeB)))
component_c = _FakeComponent(
spec=_FakeComponentSpecC(
a=component_a.outputs['output'],
output=types.Channel(type=_ArtifactTypeC)))
component_c.add_upstream_node(component_b)
component_d = _FakeComponent(
spec=_FakeComponentSpecD(
b=component_b.outputs['output'],
c=component_c.outputs['output'],
output=types.Channel(type=_ArtifactTypeD)))
component_e = _FakeComponent(
spec=_FakeComponentSpecE(
a=component_a.outputs['output'],
b=component_b.outputs['output'],
d=component_d.outputs['output'],
output=types.Channel(type=_ArtifactTypeE)))
test_pipeline = pipeline.Pipeline(
pipeline_name='x',
pipeline_root='y',
metadata_connection_config=metadata_store_pb2.ConnectionConfig(),
components=[
component_d, component_c, component_a, component_b, component_e
])
kubernetes_dag_runner.KubernetesDagRunner().run(test_pipeline)
self.assertEqual(_executed_components, [
'_FakeComponent.a.Wrapper', '_FakeComponent.b.Wrapper',
'_FakeComponent.c.Wrapper', '_FakeComponent.d.Wrapper',
'_FakeComponent.e.Wrapper'
])
def mysql_metadata_connection_config(
host: Text, port: int, database: Text, username: Text,
password: Text) -> metadata_store_pb2.ConnectionConfig:
"""Convenience function to create mysql-based metadata connection config.
Args:
host: The name or network address of the instance of MySQL to connect to.
port: The port MySQL is using to listen for connections.
database: The name of the database to use.
username: The MySQL login account being used.
password: The password for the MySQL account being used.
Returns:
A metadata_store_pb2.ConnectionConfig based on given metadata db uri.
"""
return metadata_store_pb2.ConnectionConfig(
mysql=metadata_store_pb2.MySQLDatabaseConfig(host=host,
port=port,
database=database,
user=username,
password=password))
def testRun(self):
component_a = _FakeComponent(
_FakeComponentSpecA(output=types.Channel(type_name='a')))
component_b = _FakeComponent(
_FakeComponentSpecB(
a=component_a.outputs['output'],
output=types.Channel(type_name='b')))
component_c = _FakeComponent(
_FakeComponentSpecC(
a=component_a.outputs['output'],
output=types.Channel(type_name='c')))
component_d = _FakeComponent(
_FakeComponentSpecD(
b=component_b.outputs['output'],
c=component_c.outputs['output'],
output=types.Channel(type_name='d')))
component_e = _FakeComponent(
_FakeComponentSpecE(
a=component_a.outputs['output'],
b=component_b.outputs['output'],
d=component_d.outputs['output'],
output=types.Channel(type_name='e')))
test_pipeline = pipeline.Pipeline(
pipeline_name='x',
pipeline_root='y',
metadata_connection_config=metadata_store_pb2.ConnectionConfig(),
components=[
component_d, component_c, component_a, component_b, component_e
])
beam_dag_runner.BeamDagRunner().run(test_pipeline)
self.assertItemsEqual(_executed_components, [
'_FakeComponent.a', '_FakeComponent.b', '_FakeComponent.c',
'_FakeComponent.d', '_FakeComponent.e'
])
self.assertEqual(_executed_components[0], '_FakeComponent.a')
self.assertEqual(_executed_components[3], '_FakeComponent.d')
self.assertEqual(_executed_components[4], '_FakeComponent.e')
