def _create_task_object(name: str,
container_image: str,
command=None,
arguments=None,
file_inputs=None,
file_outputs=None):
import kfp.dsl as dsl
global _dummy_pipeline
need_dummy = dsl.Pipeline._default_pipeline is None
if need_dummy:
if _dummy_pipeline == None:
_dummy_pipeline = dsl.Pipeline('dummy pipeline')
_dummy_pipeline.__enter__()
task = dsl.ContainerOp(
name=name,
image=container_image,
command=command,
arguments=arguments,
file_inputs=file_inputs,
file_outputs=file_outputs,
)
if need_dummy:
_dummy_pipeline.__exit__()
return task
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(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(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(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 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 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):
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 _compile(self, pipeline_func):
"""Compile the given pipeline function into workflow."""
argspec = inspect.getfullargspec(pipeline_func)
self._validate_args(argspec)
registered_pipeline_functions = dsl.Pipeline.get_pipeline_functions()
if pipeline_func not in registered_pipeline_functions:
raise ValueError(
'Please use a function with @dsl.pipeline decorator.')
pipeline_name, _ = dsl.Pipeline.get_pipeline_functions()[pipeline_func]
pipeline_name = self._sanitize_name(pipeline_name)
# Create the arg list with no default values and call pipeline function.
args_list = [
dsl.PipelineParam(self._sanitize_name(arg_name))
for arg_name in argspec.args
]
with dsl.Pipeline(pipeline_name) as p:
pipeline_func(*args_list)
# Remove when argo supports local exit handler.
self._validate_exit_handler(p)
# Fill in the default values.
args_list_with_defaults = [
dsl.PipelineParam(self._sanitize_name(arg_name))
for arg_name in argspec.args
]
if argspec.defaults:
for arg, default in zip(reversed(args_list_with_defaults),
reversed(argspec.defaults)):
arg.value = default.value
workflow = self._create_pipeline_workflow(args_list_with_defaults, p)
return workflow
def test_operator_to_template(self):
"""Test converting operator to template"""
with dsl.Pipeline('somename') as p:
msg1 = dsl.PipelineParam('msg1')
msg2 = dsl.PipelineParam('msg2', value='value2')
op = dsl.ContainerOp(
name='echo',
image='image',
command=['sh', '-c'],
arguments=['echo %s %s | tee /tmp/message.txt' % (msg1, msg2)],
file_outputs={'merged': '/tmp/message.txt'})
golden_output = {
'container': {
'image':
'image',
'args': [
'echo {{inputs.parameters.msg1}} {{inputs.parameters.msg2}} | tee /tmp/message.txt'
],
'command': ['sh', '-c'],
},
'inputs': {
'parameters': [
{
'name': 'msg1'
},
{
'name': 'msg2',
'value': 'value2'
},
]
},
'name': 'echo',
'outputs': {
'parameters': [{
'name': 'echo-merged',
'valueFrom': {
'path': '/tmp/message.txt'
}
}],
'artifacts': [{
'name': 'mlpipeline-ui-metadata',
'path': '/mlpipeline-ui-metadata.json',
's3': {
'accessKeySecret': {
'key': 'accesskey',
'name': 'mlpipeline-minio-artifact',
},
'bucket': 'mlpipeline',
'endpoint': 'minio-service.kubeflow:9000',
'insecure': True,
'key':
'runs/{{workflow.uid}}/{{pod.name}}/mlpipeline-ui-metadata.tgz',
'secretKeySecret': {
'key': 'secretkey',
'name': 'mlpipeline-minio-artifact',
}
}
}, {
'name': 'mlpipeline-metrics',
'path': '/mlpipeline-metrics.json',
's3': {
'accessKeySecret': {
'key': 'accesskey',
'name': 'mlpipeline-minio-artifact',
},
'bucket': 'mlpipeline',
'endpoint': 'minio-service.kubeflow:9000',
'insecure': True,
'key':
'runs/{{workflow.uid}}/{{pod.name}}/mlpipeline-metrics.tgz',
'secretKeySecret': {
'key': 'secretkey',
'name': 'mlpipeline-minio-artifact',
}
}
}]
}
}
self.maxDiff = None
self.assertEqual(golden_output,
compiler.Compiler()._op_to_template(op))
def test_operator_to_template(self):
"""Test converting operator to template"""
from kubernetes import client as k8s_client
with dsl.Pipeline('somename') as p:
msg1 = dsl.PipelineParam('msg1')
msg2 = dsl.PipelineParam('msg2', value='value2')
op = dsl.ContainerOp(name='echo', image='image', command=['sh', '-c'],
arguments=['echo %s %s | tee /tmp/message.txt' % (msg1, msg2)],
file_outputs={'merged': '/tmp/message.txt'}) \
.add_volume_mount(k8s_client.V1VolumeMount(
mount_path='/secret/gcp-credentials',
name='gcp-credentials')) \
.add_env_variable(k8s_client.V1EnvVar(
name='GOOGLE_APPLICATION_CREDENTIALS',
value='/secret/gcp-credentials/user-gcp-sa.json'))
golden_output = {
'container': {
'image': 'image',
'args': [
'echo {{inputs.parameters.msg1}} {{inputs.parameters.msg2}} | tee /tmp/message.txt'
],
'command': ['sh', '-c'],
'env': [
{
'name': 'GOOGLE_APPLICATION_CREDENTIALS',
'value': '/secret/gcp-credentials/user-gcp-sa.json'
}
],
'volumeMounts':[
{
'mountPath': '/secret/gcp-credentials',
'name': 'gcp-credentials',
}
]
},
'inputs': {'parameters':
[
{'name': 'msg1'},
{'name': 'msg2', 'value': 'value2'},
]},
'name': 'echo',
'outputs': {
'parameters': [
{'name': 'echo-merged',
'valueFrom': {'path': '/tmp/message.txt'}
}],
'artifacts': [{
'name': 'mlpipeline-ui-metadata',
'path': '/mlpipeline-ui-metadata.json',
's3': {
'accessKeySecret': {
'key': 'accesskey',
'name': 'mlpipeline-minio-artifact',
},
'bucket': 'mlpipeline',
'endpoint': 'minio-service.kubeflow:9000',
'insecure': True,
'key': 'runs/{{workflow.uid}}/{{pod.name}}/mlpipeline-ui-metadata.tgz',
'secretKeySecret': {
'key': 'secretkey',
'name': 'mlpipeline-minio-artifact',
}
}
},{
'name': 'mlpipeline-metrics',
'path': '/mlpipeline-metrics.json',
's3': {
'accessKeySecret': {
'key': 'accesskey',
'name': 'mlpipeline-minio-artifact',
},
'bucket': 'mlpipeline',
'endpoint': 'minio-service.kubeflow:9000',
'insecure': True,
'key': 'runs/{{workflow.uid}}/{{pod.name}}/mlpipeline-metrics.tgz',
'secretKeySecret': {
'key': 'secretkey',
'name': 'mlpipeline-minio-artifact',
}
}
}]
}
}
self.maxDiff = None
self.assertEqual(golden_output, compiler.Compiler()._op_to_template(op))
def test_operator_to_template(self):
"""Test converting operator to template"""
from kubernetes import client as k8s_client
with dsl.Pipeline('somename') as p:
msg1 = dsl.PipelineParam('msg1')
msg2 = dsl.PipelineParam('msg2', value='value2')
json = dsl.PipelineParam('json')
kind = dsl.PipelineParam('kind')
op = dsl.ContainerOp(name='echo', image='image', command=['sh', '-c'],
arguments=['echo %s %s | tee /tmp/message.txt' % (msg1, msg2)],
file_outputs={'merged': '/tmp/message.txt'}) \
.add_volume_mount(k8s_client.V1VolumeMount(
mount_path='/secret/gcp-credentials',
name='gcp-credentials')) \
.add_env_variable(k8s_client.V1EnvVar(
name='GOOGLE_APPLICATION_CREDENTIALS',
value='/secret/gcp-credentials/user-gcp-sa.json'))
res = dsl.ResourceOp(
name="test-resource",
k8s_resource=k8s_client.V1PersistentVolumeClaim(
api_version="v1",
kind=kind,
metadata=k8s_client.V1ObjectMeta(name="resource")),
attribute_outputs={"out": json})
golden_output = {
'container': {
'image':
'image',
'args': [
'echo {{inputs.parameters.msg1}} {{inputs.parameters.msg2}} | tee /tmp/message.txt'
],
'command': ['sh', '-c'],
'env': [{
'name': 'GOOGLE_APPLICATION_CREDENTIALS',
'value': '/secret/gcp-credentials/user-gcp-sa.json'
}],
'volumeMounts': [{
'mountPath': '/secret/gcp-credentials',
'name': 'gcp-credentials',
}]
},
'inputs': {
'parameters': [
{
'name': 'msg1'
},
{
'name': 'msg2',
'value': 'value2'
},
]
},
'name': 'echo',
'outputs': {
'parameters': [{
'name': 'echo-merged',
'valueFrom': {
'path': '/tmp/message.txt'
}
}],
'artifacts': [{
'name': 'mlpipeline-ui-metadata',
'path': '/mlpipeline-ui-metadata.json',
's3': {
'accessKeySecret': {
'key': 'accesskey',
'name': 'mlpipeline-minio-artifact',
},
'bucket': 'mlpipeline',
'endpoint': 'minio-service.kubeflow:9000',
'insecure': True,
'key':
'runs/{{workflow.uid}}/{{pod.name}}/mlpipeline-ui-metadata.tgz',
'secretKeySecret': {
'key': 'secretkey',
'name': 'mlpipeline-minio-artifact',
}
}
}, {
'name': 'mlpipeline-metrics',
'path': '/mlpipeline-metrics.json',
's3': {
'accessKeySecret': {
'key': 'accesskey',
'name': 'mlpipeline-minio-artifact',
},
'bucket': 'mlpipeline',
'endpoint': 'minio-service.kubeflow:9000',
'insecure': True,
'key':
'runs/{{workflow.uid}}/{{pod.name}}/mlpipeline-metrics.tgz',
'secretKeySecret': {
'key': 'secretkey',
'name': 'mlpipeline-minio-artifact',
}
}
}]
}
}
res_output = {
'inputs': {
'parameters': [{
'name': 'json'
}, {
'name': 'kind'
}]
},
'name': 'test-resource',
'outputs': {
'parameters': [{
'name': 'test-resource-manifest',
'valueFrom': {
'jsonPath': '{}'
}
}, {
'name': 'test-resource-name',
'valueFrom': {
'jsonPath': '{.metadata.name}'
}
}, {
'name': 'test-resource-out',
'valueFrom': {
'jsonPath': '{{inputs.parameters.json}}'
}
}]
},
'resource': {
'action':
'create',
'manifest': ("apiVersion: v1\n"
"kind: '{{inputs.parameters.kind}}'\n"
"metadata:\n"
" name: resource\n")
}
}
self.maxDiff = None
self.assertEqual(golden_output,
compiler.Compiler()._op_to_template(op))
self.assertEqual(res_output, compiler.Compiler()._op_to_template(res))
def _create_workflow(self,
pipeline_func: Callable,
pipeline_name: Text=None,
pipeline_description: Text=None,
params_list: List[dsl.PipelineParam]=None,
pipeline_conf: dsl.PipelineConf = None,
) -> List[Dict[Text, Any]]: # Tekton change, signature
""" Internal implementation of create_workflow."""
params_list = params_list or []
argspec = inspect.getfullargspec(pipeline_func)
# Create the arg list with no default values and call pipeline function.
# Assign type information to the PipelineParam
pipeline_meta = _extract_pipeline_metadata(pipeline_func)
pipeline_meta.name = pipeline_name or pipeline_meta.name
pipeline_meta.description = pipeline_description or pipeline_meta.description
pipeline_name = sanitize_k8s_name(pipeline_meta.name)
# Need to first clear the default value of dsl.PipelineParams. Otherwise, it
# will be resolved immediately in place when being to each component.
default_param_values = {}
for param in params_list:
default_param_values[param.name] = param.value
param.value = None
# Currently only allow specifying pipeline params at one place.
if params_list and pipeline_meta.inputs:
raise ValueError('Either specify pipeline params in the pipeline function, or in "params_list", but not both.')
args_list = []
for arg_name in argspec.args:
arg_type = None
for input in pipeline_meta.inputs or []:
if arg_name == input.name:
arg_type = input.type
break
args_list.append(dsl.PipelineParam(sanitize_k8s_name(arg_name, True), param_type=arg_type))
with dsl.Pipeline(pipeline_name) as dsl_pipeline:
pipeline_func(*args_list)
pipeline_conf = pipeline_conf or dsl_pipeline.conf # Configuration passed to the compiler is overriding. Unfortunately, it's not trivial to detect whether the dsl_pipeline.conf was ever modified.
self._validate_exit_handler(dsl_pipeline)
self._sanitize_and_inject_artifact(dsl_pipeline, pipeline_conf)
# Fill in the default values.
args_list_with_defaults = []
if pipeline_meta.inputs:
args_list_with_defaults = [dsl.PipelineParam(sanitize_k8s_name(arg_name, True))
for arg_name in argspec.args]
if argspec.defaults:
for arg, default in zip(reversed(args_list_with_defaults), reversed(argspec.defaults)):
arg.value = default.value if isinstance(default, dsl.PipelineParam) else default
elif params_list:
# Or, if args are provided by params_list, fill in pipeline_meta.
for param in params_list:
param.value = default_param_values[param.name]
args_list_with_defaults = params_list
pipeline_meta.inputs = [
InputSpec(
name=param.name,
type=param.param_type,
default=param.value) for param in params_list]
op_transformers = [add_pod_env]
op_transformers.extend(pipeline_conf.op_transformers)
workflow = self._create_pipeline_workflow(
args_list_with_defaults,
dsl_pipeline,
op_transformers,
pipeline_conf,
)
from ._data_passing_rewriter import fix_big_data_passing
workflow = fix_big_data_passing(workflow)
import json
pipeline = [item for item in workflow if item["kind"] == "Pipeline"][0] # Tekton change
pipeline.setdefault('metadata', {}).setdefault('annotations', {})['pipelines.kubeflow.org/pipeline_spec'] = json.dumps(pipeline_meta.to_dict(), sort_keys=True)
return workflow
def _create_pipeline_v2(
self,
pipeline_func: Callable[..., Any],
pipeline_root: Optional[str] = None,
pipeline_name: Optional[str] = None,
pipeline_parameters_override: Optional[Mapping[str, Any]] = None,
) -> pipeline_spec_pb2.PipelineJob:
"""Creates a pipeline instance and constructs the pipeline spec from it.
Args:
pipeline_func: Pipeline function with @dsl.pipeline decorator.
pipeline_root: The root of the pipeline outputs. Optional.
pipeline_name: The name of the pipeline. Optional.
pipeline_parameters_override: The mapping from parameter names to values.
Optional.
Returns:
A PipelineJob proto representing the compiled pipeline.
"""
# Create the arg list with no default values and call pipeline function.
# Assign type information to the PipelineParam
pipeline_meta = _python_op._extract_component_interface(pipeline_func)
pipeline_name = pipeline_name or pipeline_meta.name
pipeline_root = pipeline_root or getattr(pipeline_func, 'output_directory',
None)
if not pipeline_root:
warnings.warn('pipeline_root is None or empty. A valid pipeline_root '
'must be provided at job submission.')
args_list = []
signature = inspect.signature(pipeline_func)
for arg_name in signature.parameters:
arg_type = None
for pipeline_input in pipeline_meta.inputs or []:
if arg_name == pipeline_input.name:
arg_type = pipeline_input.type
break
args_list.append(
dsl.PipelineParam(
sanitize_k8s_name(arg_name, True), param_type=arg_type))
with dsl.Pipeline(pipeline_name) as dsl_pipeline:
pipeline_func(*args_list)
self._sanitize_and_inject_artifact(dsl_pipeline)
# Fill in the default values.
args_list_with_defaults = []
if pipeline_meta.inputs:
args_list_with_defaults = [
dsl.PipelineParam(
sanitize_k8s_name(input_spec.name, True),
param_type=input_spec.type,
value=input_spec.default) for input_spec in pipeline_meta.inputs
]
# Making the pipeline group name unique to prevent name clashes with templates
pipeline_group = dsl_pipeline.groups[0]
temp_pipeline_group_name = uuid.uuid4().hex
pipeline_group.name = temp_pipeline_group_name
pipeline_spec = self._create_pipeline_spec(
args_list_with_defaults,
dsl_pipeline,
)
pipeline_parameters = {
param.name: param for param in args_list_with_defaults
}
# Update pipeline parameters override if there were any.
pipeline_parameters_override = pipeline_parameters_override or {}
for k, v in pipeline_parameters_override.items():
if k not in pipeline_parameters:
raise ValueError('Pipeline parameter {} does not match any known '
'pipeline argument.'.format(k))
pipeline_parameters[k].value = v
runtime_config = compiler_utils.build_runtime_config_spec(
output_directory=pipeline_root, pipeline_parameters=pipeline_parameters)
pipeline_job = pipeline_spec_pb2.PipelineJob(runtime_config=runtime_config)
pipeline_job.pipeline_spec.update(json_format.MessageToDict(pipeline_spec))
return pipeline_job
def _create_workflow(
self,
pipeline_func: Callable,
pipeline_name: Text = None,
pipeline_description: Text = None,
params_list: List[dsl.PipelineParam] = None,
pipeline_conf: dsl.PipelineConf = None,
) -> Dict[Text, Any]:
""" Internal implementation of create_workflow."""
params_list = params_list or []
argspec = inspect.getfullargspec(pipeline_func)
# Create the arg list with no default values and call pipeline function.
# Assign type information to the PipelineParam
pipeline_meta = _extract_pipeline_metadata(pipeline_func)
pipeline_meta.name = pipeline_name or pipeline_meta.name
pipeline_meta.description = pipeline_description or pipeline_meta.description
pipeline_name = sanitize_k8s_name(pipeline_meta.name)
# Need to first clear the default value of dsl.PipelineParams. Otherwise, it
# will be resolved immediately in place when being to each component.
default_param_values = {}
for param in params_list:
default_param_values[param.name] = param.value
param.value = None
# Currently only allow specifying pipeline params at one place.
if params_list and pipeline_meta.inputs:
raise ValueError(
'Either specify pipeline params in the pipeline function, or in "params_list", but not both.'
)
args_list = []
for arg_name in argspec.args:
arg_type = None
for input in pipeline_meta.inputs or []:
if arg_name == input.name:
arg_type = input.type
break
args_list.append(
dsl.PipelineParam(sanitize_k8s_name(arg_name, True),
param_type=arg_type))
with dsl.Pipeline(pipeline_name) as dsl_pipeline:
pipeline_func(*args_list)
# Configuration passed to the compiler is overriding. Unfortunately, it is
# not trivial to detect whether the dsl_pipeline.conf was ever modified.
pipeline_conf = pipeline_conf or dsl_pipeline.conf
self._validate_exit_handler(dsl_pipeline)
self._sanitize_and_inject_artifact(dsl_pipeline, pipeline_conf)
# Fill in the default values.
args_list_with_defaults = []
if pipeline_meta.inputs:
args_list_with_defaults = [
dsl.PipelineParam(sanitize_k8s_name(arg_name, True))
for arg_name in argspec.args
]
if argspec.defaults:
for arg, default in zip(reversed(args_list_with_defaults),
reversed(argspec.defaults)):
arg.value = default.value if isinstance(
default, dsl.PipelineParam) else default
elif params_list:
# Or, if args are provided by params_list, fill in pipeline_meta.
for param in params_list:
param.value = default_param_values[param.name]
args_list_with_defaults = params_list
pipeline_meta.inputs = [
InputSpec(name=param.name,
type=param.param_type,
default=param.value) for param in params_list
]
op_transformers = [add_pod_env]
# # By default adds telemetry instruments. Users can opt out toggling
# # allow_telemetry.
# # Also, TFX pipelines will be bypassed for pipeline compiled by tfx>0.21.4.
# if allow_telemetry:
# pod_labels = get_default_telemetry_labels()
# op_transformers.append(add_pod_labels(pod_labels))
op_transformers.extend(pipeline_conf.op_transformers)
workflow = self._create_pipeline_workflow(
args_list_with_defaults,
dsl_pipeline,
op_transformers,
pipeline_conf,
)
workflow = fix_big_data_passing(workflow)
workflow.setdefault('metadata', {}).setdefault('annotations', {})['pipelines.kubeflow.org/pipeline_spec'] = \
json.dumps(pipeline_meta.to_dict(), sort_keys=True)
# recursively strip empty structures, DANGER: this may remove necessary empty elements ?!
def remove_empty_elements(obj) -> dict:
if not isinstance(obj, (dict, list)):
return obj
if isinstance(obj, list):
return [remove_empty_elements(o) for o in obj if o != []]
return {
k: remove_empty_elements(v)
for k, v in obj.items() if v != []
}
workflow = remove_empty_elements(workflow)
return workflow
def _create_workflow(
self,
pipeline_func: Callable,
pipeline_name: Optional[Text] = None,
pipeline_description: Optional[Text] = None,
params_list: Optional[List[dsl.PipelineParam]] = None,
pipeline_conf: Optional[dsl.PipelineConf] = None,
) -> Dict[Text, Any]:
""" Internal implementation of create_workflow."""
params_list = params_list or []
# Create the arg list with no default values and call pipeline function.
# Assign type information to the PipelineParam
pipeline_meta = _extract_pipeline_metadata(pipeline_func)
pipeline_meta.name = pipeline_name or pipeline_meta.name
pipeline_meta.description = pipeline_description or pipeline_meta.description
pipeline_name = sanitize_k8s_name(pipeline_meta.name)
# Need to first clear the default value of dsl.PipelineParams. Otherwise, it
# will be resolved immediately in place when being to each component.
default_param_values = OrderedDict()
if self._pipeline_root_param:
params_list.append(self._pipeline_root_param)
if self._pipeline_name_param:
params_list.append(self._pipeline_name_param)
for param in params_list:
default_param_values[param.name] = param.value
param.value = None
args_list = []
kwargs_dict = dict()
signature = inspect.signature(pipeline_func)
for arg_name, arg in signature.parameters.items():
arg_type = None
for input in pipeline_meta.inputs or []:
if arg_name == input.name:
arg_type = input.type
break
param = dsl.PipelineParam(sanitize_k8s_name(arg_name, True), param_type=arg_type)
if arg.kind == inspect.Parameter.KEYWORD_ONLY:
kwargs_dict[arg_name] = param
else:
args_list.append(param)
with dsl.Pipeline(pipeline_name) as dsl_pipeline:
pipeline_func(*args_list, **kwargs_dict)
pipeline_conf = pipeline_conf or dsl_pipeline.conf # Configuration passed to the compiler is overriding. Unfortunately, it's not trivial to detect whether the dsl_pipeline.conf was ever modified.
self._validate_exit_handler(dsl_pipeline)
self._sanitize_and_inject_artifact(dsl_pipeline, pipeline_conf)
# Fill in the default values by merging two param lists.
args_list_with_defaults = OrderedDict()
if pipeline_meta.inputs:
args_list_with_defaults = OrderedDict([
(sanitize_k8s_name(input_spec.name, True), input_spec.default)
for input_spec in pipeline_meta.inputs
])
if params_list:
# Or, if args are provided by params_list, fill in pipeline_meta.
for k, v in default_param_values.items():
args_list_with_defaults[k] = v
pipeline_meta.inputs = pipeline_meta.inputs or []
for param in params_list:
pipeline_meta.inputs.append(
InputSpec(
name=param.name,
type=param.param_type,
default=default_param_values[param.name]))
op_transformers = [add_pod_env]
pod_labels = {_SDK_VERSION_LABEL: kfp.__version__, _SDK_ENV_LABEL:_SDK_ENV_DEFAULT}
op_transformers.append(add_pod_labels(pod_labels))
op_transformers.extend(pipeline_conf.op_transformers)
if self._mode == dsl.PipelineExecutionMode.V2_COMPATIBLE:
# Add self._pipeline_name_param and self._pipeline_root_param to ops inputs
# if they don't exist already.
for op in dsl_pipeline.ops.values():
insert_pipeline_name_param = True
insert_pipeline_root_param = True
for param in op.inputs:
if param.name == self._pipeline_name_param.name:
insert_pipeline_name_param = False
elif param.name == self._pipeline_root_param.name:
insert_pipeline_root_param = False
if insert_pipeline_name_param:
op.inputs.append(self._pipeline_name_param)
if insert_pipeline_root_param:
op.inputs.append(self._pipeline_root_param)
workflow = self._create_pipeline_workflow(
args_list_with_defaults,
dsl_pipeline,
op_transformers,
pipeline_conf,
)
from ._data_passing_rewriter import fix_big_data_passing
workflow = fix_big_data_passing(workflow)
workflow = _data_passing_rewriter.add_pod_name_passing(
workflow, str(self._pipeline_root_param or None))
if pipeline_conf and pipeline_conf.data_passing_method != None:
workflow = pipeline_conf.data_passing_method(workflow)
metadata = workflow.setdefault('metadata', {})
annotations = metadata.setdefault('annotations', {})
labels = metadata.setdefault('labels', {})
annotations[_SDK_VERSION_LABEL] = kfp.__version__
annotations['pipelines.kubeflow.org/pipeline_compilation_time'] = datetime.datetime.now().isoformat()
annotations['pipelines.kubeflow.org/pipeline_spec'] = json.dumps(pipeline_meta.to_dict(), sort_keys=True)
if self._mode == dsl.PipelineExecutionMode.V2_COMPATIBLE:
annotations['pipelines.kubeflow.org/v2_pipeline'] = "true"
labels['pipelines.kubeflow.org/v2_pipeline'] = "true"
# Labels might be logged better than annotations so adding some information here as well
labels[_SDK_VERSION_LABEL] = kfp.__version__
return workflow