def setUp(self):
super(BaseComponentWithPipelineParamTest, self).setUp()
test_pipeline_root = dsl.PipelineParam(name='pipeline-root-param')
example_gen_buckets = data_types.RuntimeParameter(
name='example-gen-buckets', ptype=int, default=10)
examples = standard_artifacts.ExternalArtifact()
example_gen = csv_example_gen_component.CsvExampleGen(
input=channel_utils.as_channel([examples]),
output_config={
'split_config': {
'splits': [{
'name': 'examples',
'hash_buckets': example_gen_buckets
}]
}
})
statistics_gen = statistics_gen_component.StatisticsGen(
examples=example_gen.outputs['examples'], instance_name='foo')
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,
component_launcher_class=in_process_component_launcher
.InProcessComponentLauncher,
depends_on=set(),
pipeline=pipeline,
pipeline_name=self._test_pipeline_name,
pipeline_root=test_pipeline_root,
tfx_image='container_image',
kubeflow_metadata_config=self._metadata_config,
component_config=None,
tfx_ir=self._tfx_ir)
self.statistics_gen = base_component.BaseComponent(
component=statistics_gen,
component_launcher_class=in_process_component_launcher
.InProcessComponentLauncher,
depends_on=set(),
pipeline=pipeline,
pipeline_name=self._test_pipeline_name,
pipeline_root=test_pipeline_root,
tfx_image='container_image',
kubeflow_metadata_config=self._metadata_config,
component_config=None,
tfx_ir=self._tfx_ir
)
self.tfx_example_gen = example_gen
self.tfx_statistics_gen = statistics_gen
def setUp(self):
super(BaseComponentWithPipelineParamTest, self).setUp()
test_pipeline_root = dsl.PipelineParam(name='pipeline-root-param')
example_gen_output_name = runtime_string_parameter.RuntimeStringParameter(
name='example-gen-output-name', default='default-to-be-discarded')
examples = standard_artifacts.ExternalArtifact()
example_gen = csv_example_gen_component.CsvExampleGen(
input=channel_utils.as_channel([examples]),
output_config=example_gen_pb2.Output(
split_config=example_gen_pb2.SplitConfig(splits=[
example_gen_pb2.SplitConfig.Split(
name=example_gen_output_name, hash_buckets=10)
])))
statistics_gen = statistics_gen_component.StatisticsGen(
examples=example_gen.outputs['examples'], instance_name='foo')
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'
with dsl.Pipeline('test_pipeline'):
self.example_gen = base_component.BaseComponent(
component=example_gen,
component_launcher_class=in_process_component_launcher.
InProcessComponentLauncher,
depends_on=set(),
pipeline=pipeline,
pipeline_name=self._test_pipeline_name,
pipeline_root=test_pipeline_root,
tfx_image='container_image',
kubeflow_metadata_config=self._metadata_config,
component_config=None)
self.statistics_gen = base_component.BaseComponent(
component=statistics_gen,
component_launcher_class=in_process_component_launcher.
InProcessComponentLauncher,
depends_on=set(),
pipeline=pipeline,
pipeline_name=self._test_pipeline_name,
pipeline_root=test_pipeline_root,
tfx_image='container_image',
kubeflow_metadata_config=self._metadata_config,
component_config=None,
)
self.tfx_example_gen = example_gen
self.tfx_statistics_gen = statistics_gen
def _construct_pipeline_graph(self, pipeline: tfx_pipeline.Pipeline):
"""Constructs a Kubeflow Pipeline graph.
Args:
pipeline: The logical TFX pipeline to base the construction on.
"""
component_to_kfp_op = {}
# Assumption: There is a partial ordering of components in the list, i.e.,
# if component A depends on component B and C, then A appears after B and C
# in the list.
for component in pipeline.components:
# Keep track of the set of upstream dsl.ContainerOps for this component.
depends_on = set()
for upstream_component in component.upstream_nodes:
depends_on.add(component_to_kfp_op[upstream_component])
kfp_component = base_component.BaseComponent(
component=component,
depends_on=depends_on,
pipeline=pipeline,
tfx_image=self._config.tfx_image,
kubeflow_metadata_config=self._config.kubeflow_metadata_config)
for operator in self._config.pipeline_operator_funcs:
kfp_component.container_op.apply(operator)
component_to_kfp_op[component] = kfp_component.container_op
def setUp(self):
super(BaseComponentTest, self).setUp()
examples = standard_artifacts.ExternalArtifact()
example_gen = csv_example_gen_component.CsvExampleGen(
input_base=channel_utils.as_channel([examples]))
statistics_gen = statistics_gen_component.StatisticsGen(
input_data=example_gen.outputs.examples, instance_name='foo')
pipeline = tfx_pipeline.Pipeline(
pipeline_name='test_pipeline',
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'
with dsl.Pipeline('test_pipeline'):
self.component = base_component.BaseComponent(
component=statistics_gen,
depends_on=set(),
pipeline=pipeline,
tfx_image='container_image',
kubeflow_metadata_config=self._metadata_config,
)
self.tfx_component = statistics_gen
def setUp(self):
super(BaseComponentTest, self).setUp()
examples = standard_artifacts.ExternalArtifact()
example_gen = csv_example_gen_component.CsvExampleGen(
input=channel_utils.as_channel([examples]))
statistics_gen = statistics_gen_component.StatisticsGen(
examples=example_gen.outputs['examples'], instance_name='foo')
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],
)
test_pipeline_root = dsl.PipelineParam(name='pipeline-root-param')
self._metadata_config = kubeflow_pb2.KubeflowMetadataConfig()
self._metadata_config.mysql_db_service_host.environment_variable = 'MYSQL_SERVICE_HOST'
with dsl.Pipeline('test_pipeline'):
self.component = base_component.BaseComponent(
component=statistics_gen,
component_launcher_class=in_process_component_launcher
.InProcessComponentLauncher,
depends_on=set(),
pipeline=pipeline,
pipeline_name=self._test_pipeline_name,
pipeline_root=test_pipeline_root,
tfx_image='container_image',
kubeflow_metadata_config=self._metadata_config,
component_config=None,
)
self.tfx_component = statistics_gen
def setUp(self):
super(BaseComponentTest, self).setUp()
example_gen = csv_example_gen_component.CsvExampleGen(
input_base='data_input')
statistics_gen = statistics_gen_component.StatisticsGen(
examples=example_gen.outputs['examples']).with_id('foo')
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],
)
test_pipeline_root = dsl.PipelineParam(name='pipeline-root-param')
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.component = 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,
)
self.tfx_component = statistics_gen
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):
output_dict = {
'output_name': [types.TfxType(type_name='ExamplesPath')]
}
with dsl.Pipeline('test_pipeline'):
self.component = base_component.BaseComponent(
component_name='TFXComponent',
input_dict={
'input_data': 'input-data-contents',
'train_steps': 300,
'accuracy_threshold': 0.3,
},
output_dict=output_dict,
exec_properties={'module_file': '/path/to/module.py'},
)
def _construct_pipeline_graph(self, pipeline: tfx_pipeline.Pipeline,
pipeline_root: dsl.PipelineParam):
"""Constructs a Kubeflow Pipeline graph.
Args:
pipeline: The logical TFX pipeline to base the construction on.
pipeline_root: dsl.PipelineParam representing the pipeline root.
"""
component_to_kfp_op = {}
# Assumption: There is a partial ordering of components in the list, i.e.,
# if component A depends on component B and C, then A appears after B and C
# in the list.
for component in pipeline.components:
# Keep track of the set of upstream dsl.ContainerOps for this component.
depends_on = set()
for upstream_component in component.upstream_nodes:
depends_on.add(component_to_kfp_op[upstream_component])
(component_launcher_class,
component_config) = config_utils.find_component_launch_info(
self._config, component)
kfp_component = base_component.BaseComponent(
component=component,
component_launcher_class=component_launcher_class,
depends_on=depends_on,
pipeline=pipeline,
pipeline_name=pipeline.pipeline_info.pipeline_name,
pipeline_root=pipeline_root,
tfx_image=self._config.tfx_image,
kubeflow_metadata_config=self._config.kubeflow_metadata_config,
component_config=component_config)
for operator in self._config.pipeline_operator_funcs:
kfp_component.container_op.apply(operator)
kfp_component.container_op.add_pod_label(SDK_ENV_LABEL,
self._sdk_env)
assert self._pipeline_id, 'Failed to generate pipeline ID.'
kfp_component.container_op.add_pod_label(PIPELINE_UUID_LABEL,
self._pipeline_id)
component_to_kfp_op[component] = kfp_component.container_op
def _construct_pipeline_graph(self, pipeline: tfx_pipeline.Pipeline,
pipeline_root: dsl.PipelineParam):
"""Constructs a Kubeflow Pipeline graph.
Args:
pipeline: The logical TFX pipeline to base the construction on.
pipeline_root: dsl.PipelineParam representing the pipeline root.
"""
component_to_kfp_op = {}
tfx_ir = self._generate_tfx_ir(pipeline)
# Assumption: There is a partial ordering of components in the list, i.e.,
# if component A depends on component B and C, then A appears after B and C
# in the list.
for component in pipeline.components:
# Keep track of the set of upstream dsl.ContainerOps for this component.
depends_on = set()
for upstream_component in component.upstream_nodes:
depends_on.add(component_to_kfp_op[upstream_component])
# remove the extra pipeline node information
tfx_node_ir = self._dehydrate_tfx_ir(tfx_ir, component.id)
kfp_component = base_component.BaseComponent(
component=component,
depends_on=depends_on,
pipeline=pipeline,
pipeline_root=pipeline_root,
tfx_image=self._config.tfx_image,
kubeflow_metadata_config=self._config.kubeflow_metadata_config,
pod_labels_to_attach=self._pod_labels_to_attach,
tfx_ir=tfx_node_ir,
metadata_ui_path=self._config.metadata_ui_path,
runtime_parameters=(
self._params_by_component_id[component.id] +
[tfx_pipeline.ROOT_PARAMETER]))
for operator in self._config.pipeline_operator_funcs:
kfp_component.container_op.apply(operator)
component_to_kfp_op[component] = kfp_component.container_op
def _construct_pipeline_graph(self, pipeline):
"""Constructs a Kubeflow Pipeline graph.
Args:
pipeline: The logical TFX pipeline to base the construction on.
"""
# producers is a map from an output Channel, to a Kubeflow component that
# is responsible for the named output represented by the Channel.
# Assumption: Channels are unique in a pipeline.
producers = {}
# Assumption: There is a partial ordering of components in the list, i.e.,
# if component A depends on component B and C, then A appears after B and C
# in the list.
for component in pipeline.components:
input_dict = {}
for input_name, input_channel in component.input_dict.items():
if input_channel in producers:
output = getattr(
producers[input_channel]['component'].outputs,
producers[input_channel]['channel_name'])
if not isinstance(output, dsl.PipelineParam):
raise ValueError(
'Component outputs should be of type dsl.PipelineParam.'
' Got type {} for output {}'.format(
type(output), output))
input_dict[input_name] = output
else:
input_dict[input_name] = json.dumps(
[x.json_dict() for x in input_channel.get()])
kfp_component = base_component.BaseComponent(
component_name=component.component_name,
input_dict=input_dict,
output_dict=self._prepare_output_dict(component.outputs),
exec_properties=component.exec_properties)
for channel_name, channel in component.outputs.get_all().items():
producers[channel] = {}
producers[channel]['component'] = kfp_component
producers[channel]['channel_name'] = channel_name
def setUp(self):
self._output_dict = {'output_name': [standard_artifacts.Examples()]}
self._pipeline_properties = base_component.PipelineProperties(
output_dir='output_dir',
log_root='log_root',
)
with dsl.Pipeline('test_pipeline'):
self.component = base_component.BaseComponent(
component_name='TFXComponent',
input_dict=collections.OrderedDict([
('input_data', 'input-data-contents'),
('train_steps', 300),
('accuracy_threshold', 0.3),
]),
output_dict=self._output_dict,
exec_properties=collections.OrderedDict([
('module_file', '/path/to/module.py')
]),
executor_class_path='some.executor.Class',
pipeline_properties=self._pipeline_properties,
)
def _construct_pipeline_graph(self, pipeline):
"""Constructs a Kubeflow Pipeline graph.
Args:
pipeline: The logical TFX pipeline to base the construction on.
"""
output_dir = os.path.join(pipeline.pipeline_args['pipeline_root'],
pipeline.pipeline_args['pipeline_name'])
beam_pipeline_args = []
tfx_image = None
if 'additional_pipeline_args' in pipeline.pipeline_args:
additional_pipeline_args = pipeline.pipeline_args[
'additional_pipeline_args']
beam_pipeline_args = additional_pipeline_args.get(
'beam_pipeline_args', [])
tfx_image = additional_pipeline_args.get('tfx_image')
pipeline_properties = base_component.PipelineProperties(
output_dir=output_dir,
log_root=pipeline.pipeline_args['log_root'],
beam_pipeline_args=beam_pipeline_args,
tfx_image=tfx_image,
)
# producers is a map from an output Channel, to a Kubeflow component that
# is responsible for the named output represented by the Channel.
# Assumption: Channels are unique in a pipeline.
producers = {}
# Assumption: There is a partial ordering of components in the list, i.e.,
# if component A depends on component B and C, then A appears after B and C
# in the list.
for component in pipeline.components:
input_dict = {}
for input_name, input_channel in component.input_dict.items():
if input_channel in producers:
output = getattr(
producers[input_channel]['component'].outputs,
producers[input_channel]['channel_name'])
if not isinstance(output, dsl.PipelineParam):
raise ValueError(
'Component outputs should be of type dsl.PipelineParam.'
' Got type {} for output {}'.format(
type(output), output))
input_dict[input_name] = output
else:
input_dict[input_name] = json.dumps(
[x.json_dict() for x in input_channel.get()])
executor_class_path = '.'.join(
[component.executor.__module__, component.executor.__name__])
kfp_component = base_component.BaseComponent(
component_name=component.component_name,
input_dict=input_dict,
output_dict=self._prepare_output_dict(component.outputs),
exec_properties=component.exec_properties,
executor_class_path=executor_class_path,
pipeline_properties=pipeline_properties)
for channel_name, channel in component.outputs.get_all().items():
producers[channel] = {}
producers[channel]['component'] = kfp_component
producers[channel]['channel_name'] = channel_name
