def test_component_spec_with_placeholder_referencing_nonexisting_input_output(
self):
with self.assertRaisesRegex(
pydantic.ValidationError, 'Argument "input_name=\'input000\'" '
'references non-existing input.'):
structures.ComponentSpec(
name='component_1',
implementation=structures.Implementation(
container=structures.ContainerSpec(
image='alpine',
commands=[
'sh',
'-c',
'set -ex\necho "$0" > "$1"',
structures.InputValuePlaceholder(
input_name='input000'),
structures.OutputPathPlaceholder(
output_name='output1'),
],
)),
inputs={'input1': structures.InputSpec(type='String')},
outputs={'output1': structures.OutputSpec(type='String')},
)
with self.assertRaisesRegex(
pydantic.ValidationError,
'Argument "output_name=\'output000\'" '
'references non-existing output.'):
structures.ComponentSpec(
name='component_1',
implementation=structures.Implementation(
container=structures.ContainerSpec(
image='alpine',
commands=[
'sh',
'-c',
'set -ex\necho "$0" > "$1"',
structures.InputValuePlaceholder(
input_name='input1'),
structures.OutputPathPlaceholder(
output_name='output000'),
],
)),
inputs={'input1': structures.InputSpec(type='String')},
outputs={'output1': structures.OutputSpec(type='String')},
)
def test_simple_component_spec_save_to_component_yaml(self):
open_mock = mock.mock_open()
expected_yaml = textwrap.dedent("""\
name: component_1
inputs:
input1: {type: String}
outputs:
output1: {type: String}
implementation:
container:
image: alpine
commands:
- sh
- -c
- 'set -ex
echo "$0" > "$1"'
- {inputValue: input1}
- {outputPath: output1}
""")
with mock.patch("builtins.open", open_mock, create=True):
structures.ComponentSpec(
name='component_1',
implementation=structures.Implementation(
container=structures.ContainerSpec(
image='alpine',
commands=[
'sh',
'-c',
'set -ex\necho "$0" > "$1"',
structures.InputValuePlaceholder(
input_name='input1'),
structures.OutputPathPlaceholder(
output_name='output1'),
],
)),
inputs={
'input1': structures.InputSpec(type='String')
},
outputs={
'output1': structures.OutputSpec(type='String')
},
).save_to_component_yaml('test_save_file.txt')
open_mock.assert_called_with('test_save_file.txt', 'a')
open_mock.return_value.write.assert_called_once_with(expected_yaml)
def test_create_pipeline_task_valid(self):
expected_component_spec = structures.ComponentSpec(
name='component1',
implementation=structures.Implementation(
container=structures.ContainerSpec(
image='alpine',
commands=['sh', '-c', 'echo "$0" >> "$1"'],
arguments=[
structures.InputValuePlaceholder(input_name='input1'),
structures.OutputPathPlaceholder(
output_name='output1'),
],
)),
inputs={
'input1': structures.InputSpec(type='String'),
},
outputs={
'output1': structures.OutputSpec(type='Artifact'),
},
)
expected_task_spec = structures.TaskSpec(
name='component1',
inputs={'input1': 'value'},
dependent_tasks=[],
component_ref='component1',
)
expected_container_spec = structures.ContainerSpec(
image='alpine',
commands=['sh', '-c', 'echo "$0" >> "$1"'],
arguments=[
"{{$.inputs.parameters['input1']}}",
"{{$.outputs.artifacts['output1'].path}}",
],
)
task = pipeline_task.PipelineTask(
component_spec=structures.ComponentSpec.load_from_component_yaml(
V2_YAML),
arguments={'input1': 'value'},
)
self.assertEqual(task.task_spec, expected_task_spec)
self.assertEqual(task.component_spec, expected_component_spec)
self.assertEqual(task.container_spec, expected_container_spec)
def test_simple_component_spec_load_from_v2_component_yaml(self):
component_yaml_v2 = textwrap.dedent("""\
name: component_1
inputs:
input1:
type: String
outputs:
output1:
type: String
implementation:
container:
image: alpine
commands:
- sh
- -c
- 'set -ex
echo "$0" > "$1"'
- inputValue: input1
- outputPath: output1
""")
generated_spec = structures.ComponentSpec.load_from_component_yaml(
component_yaml_v2)
expected_spec = structures.ComponentSpec(
name='component_1',
implementation=structures.Implementation(
container=structures.ContainerSpec(
image='alpine',
commands=[
'sh',
'-c',
'set -ex\necho "$0" > "$1"',
structures.InputValuePlaceholder(input_name='input1'),
structures.OutputPathPlaceholder(
output_name='output1'),
],
)),
inputs={'input1': structures.InputSpec(type='String')},
outputs={'output1': structures.OutputSpec(type='String')})
self.assertEqual(generated_spec, expected_spec)
from kfp.v2.compiler.experimental import compiler
class TestComponent(base_component.BaseComponent):
def execute(self, *args, **kwargs):
pass
component_op = TestComponent(component_spec=structures.ComponentSpec(
name='component_1',
implementation=structures.Implementation(
container=structures.ContainerSpec(
image='alpine',
commands=[
'sh',
'-c',
'set -ex\necho "$0" > "$1"',
structures.InputValuePlaceholder(input_name='input1'),
structures.OutputPathPlaceholder(output_name='output1'),
],
)),
inputs={'input1': structures.InputSpec(type='String')},
outputs={'output1': structures.OutputSpec(type='String')},
))
@dsl.pipeline(name='experimental-v2-component', pipeline_root='dummy_root')
def my_pipeline(text: str = 'Hello world!'):
component_1 = component_op(input1=text)
component_2 = component_op(input1=component_1.outputs['output1'])
def create_component_from_func(func: Callable,
base_image: Optional[str] = None,
target_image: Optional[str] = None,
packages_to_install: List[str] = None,
output_component_file: Optional[str] = None,
install_kfp_package: bool = True,
kfp_package_path: Optional[str] = None):
"""Implementation for the @component decorator.
The decorator is defined under component_decorator.py. See the
decorator for the canonical documentation for this function.
"""
packages_to_install = packages_to_install or []
if install_kfp_package and target_image is None:
if kfp_package_path is None:
kfp_package_path = _get_default_kfp_package_path()
packages_to_install.append(kfp_package_path)
packages_to_install_command = _get_packages_to_install_command(
package_list=packages_to_install)
command = []
args = []
if base_image is None:
base_image = _DEFAULT_BASE_IMAGE
component_image = base_image
if target_image:
component_image = target_image
command, args = _get_command_and_args_for_containerized_component(
function_name=func.__name__, )
else:
command, args = _get_command_and_args_for_lightweight_component(
func=func)
component_spec = extract_component_interface(func)
component_spec.implementation = structures.Implementation(
container=structures.ContainerSpec(
image=component_image,
commands=packages_to_install_command + command,
arguments=args,
))
module_path = pathlib.Path(inspect.getsourcefile(func))
module_path.resolve()
component_name = _python_function_name_to_component_name(func.__name__)
component_info = ComponentInfo(name=component_name,
function_name=func.__name__,
func=func,
target_image=target_image,
module_path=module_path,
component_spec=component_spec,
output_component_file=output_component_file,
base_image=base_image)
if REGISTERED_MODULES is not None:
REGISTERED_MODULES[component_name] = component_info
if output_component_file:
component_spec.save(output_component_file)
return python_component.PythonComponent(component_spec=component_spec,
python_func=func)
def extract_component_interface(func: Callable) -> structures.ComponentSpec:
single_output_name_const = 'Output'
signature = inspect.signature(func)
parameters = list(signature.parameters.values())
parsed_docstring = docstring_parser.parse(inspect.getdoc(func))
doc_dict = {p.arg_name: p.description for p in parsed_docstring.params}
inputs = {}
outputs = {}
input_names = set()
output_names = set()
for parameter in parameters:
parameter_type = type_annotations.maybe_strip_optional_from_annotation(
parameter.annotation)
passing_style = None
io_name = parameter.name
if type_annotations.is_artifact_annotation(parameter_type):
# passing_style is either type_annotations.InputAnnotation or
# type_annotations.OutputAnnotation.
passing_style = type_annotations.get_io_artifact_annotation(
parameter_type)
# parameter_type is type_annotations.Artifact or one of its subclasses.
parameter_type = type_annotations.get_io_artifact_class(
parameter_type)
if not issubclass(parameter_type, artifact_types.Artifact):
raise ValueError(
'Input[T] and Output[T] are only supported when T is a '
'subclass of Artifact. Found `{} with type {}`'.format(
io_name, parameter_type))
if parameter.default is not inspect.Parameter.empty:
raise ValueError(
'Default values for Input/Output artifacts are not supported.'
)
elif isinstance(
parameter_type,
(type_annotations.InputPath, type_annotations.OutputPath)):
passing_style = type(parameter_type)
parameter_type = parameter_type.type
if parameter.default is not inspect.Parameter.empty and not (
passing_style == type_annotations.InputPath
and parameter.default is None):
raise ValueError(
'Path inputs only support default values of None. Default'
' values for outputs are not supported.')
type_struct = _annotation_to_type_struct(parameter_type)
if passing_style in [
type_annotations.OutputAnnotation, type_annotations.OutputPath
]:
io_name = _maybe_make_unique(io_name, output_names)
output_names.add(io_name)
output_spec = structures.OutputSpec(type=type_struct,
description=doc_dict.get(
parameter.name))
outputs[io_name] = output_spec
else:
io_name = _maybe_make_unique(io_name, input_names)
input_names.add(io_name)
input_spec = structures.InputSpec(type=type_struct,
description=doc_dict.get(
parameter.name))
if parameter.default is not inspect.Parameter.empty:
# input_spec.optional = True
if parameter.default is not None:
outer_type_name = list(
type_struct.keys())[0] if isinstance(
type_struct, dict) else type_struct
try:
input_spec.default = parameter.default
except Exception as ex:
warnings.warn(
'Could not serialize the default value of the'
' parameter "{}". {}'.format(parameter.name, ex))
inputs[io_name] = input_spec
#Analyzing the return type annotations.
return_ann = signature.return_annotation
if hasattr(return_ann, '_fields'): #NamedTuple
# Getting field type annotations.
# __annotations__ does not exist in python 3.5 and earlier
# _field_types does not exist in python 3.9 and later
field_annotations = getattr(return_ann,
'__annotations__', None) or getattr(
return_ann, '_field_types', None)
for field_name in return_ann._fields:
type_struct = None
if field_annotations:
type_struct = _annotation_to_type_struct(
field_annotations.get(field_name, None))
output_name = _maybe_make_unique(field_name, output_names)
output_names.add(output_name)
output_spec = structures.OutputSpec(type=type_struct)
outputs[output_name] = output_spec
# Deprecated dict-based way of declaring multiple outputs. Was only used by
# the @component decorator
elif isinstance(return_ann, dict):
warnings.warn(
"The ability to specify multiple outputs using the dict syntax"
" has been deprecated. It will be removed soon after release"
" 0.1.32. Please use typing.NamedTuple to declare multiple"
" outputs.")
for output_name, output_type_annotation in return_ann.items():
output_type_struct = _annotation_to_type_struct(
output_type_annotation)
output_spec = structures.OutputSpec(type=output_type_struct)
outputs[name] = output_spec
elif signature.return_annotation is not None and signature.return_annotation != inspect.Parameter.empty:
output_name = _maybe_make_unique(single_output_name_const,
output_names)
# Fixes exotic, but possible collision:
# `def func(output_path: OutputPath()) -> str: ...`
output_names.add(output_name)
type_struct = _annotation_to_type_struct(signature.return_annotation)
output_spec = structures.OutputSpec(type=type_struct)
outputs[output_name] = output_spec
# Component name and description are derived from the function's name and
# docstring. The name can be overridden by setting setting func.__name__
# attribute (of the legacy func._component_human_name attribute). The
# description can be overridden by setting the func.__doc__ attribute (or
# the legacy func._component_description attribute).
component_name = getattr(func, '_component_human_name',
None) or _python_function_name_to_component_name(
func.__name__)
description = getattr(func, '_component_description',
None) or parsed_docstring.short_description
if description:
description = description.strip()
component_spec = structures.ComponentSpec(
name=component_name,
description=description,
inputs=inputs if inputs else None,
outputs=outputs if outputs else None,
# Dummy implementation to bypass model validation.
implementation=structures.Implementation(),
)
return component_spec
def test_component_spec_load_from_v1_component_yaml(self):
component_yaml_v1 = textwrap.dedent("""\
name: Component with 2 inputs and 2 outputs
inputs:
- {name: Input parameter, type: String}
- {name: Input artifact}
outputs:
- {name: Output 1}
- {name: Output 2}
implementation:
container:
image: busybox
command: [sh, -c, '
mkdir -p $(dirname "$2")
mkdir -p $(dirname "$3")
echo "$0" > "$2"
cp "$1" "$3"
'
]
args:
- {inputValue: Input parameter}
- {inputPath: Input artifact}
- {outputPath: Output 1}
- {outputPath: Output 2}
""")
generated_spec = structures.ComponentSpec.load_from_component_yaml(
component_yaml_v1)
expected_spec = structures.ComponentSpec(
name='Component with 2 inputs and 2 outputs',
implementation=structures.Implementation(
container=structures.ContainerSpec(
image='busybox',
commands=[
'sh',
'-c',
(' mkdir -p $(dirname "$2") mkdir -p $(dirname "$3") '
'echo "$0" > "$2" cp "$1" "$3" '),
],
arguments=[
structures.InputValuePlaceholder(
input_name='Input parameter'),
structures.InputPathPlaceholder(
input_name='Input artifact'),
structures.OutputPathPlaceholder(
output_name='Output 1'),
structures.OutputPathPlaceholder(
output_name='Output 2'),
],
env={},
)),
inputs={
'Input parameter': structures.InputSpec(type='String'),
'Input artifact': structures.InputSpec(type='Artifact')
},
outputs={
'Output 1': structures.OutputSpec(type='Artifact'),
'Output 2': structures.OutputSpec(type='Artifact'),
})
self.assertEqual(generated_spec, expected_spec)
- --arg1
- {inputValue: optional_input_1}
otherwise: [--arg2, default]
""")
V2_COMPONENT_SPEC_IF_PLACEHOLDER = structures.ComponentSpec(
name='component_if',
implementation=structures.Implementation(
container=structures.ContainerSpec(
image='alpine',
arguments=[
structures.IfPresentPlaceholder(
if_structure=structures.IfPresentPlaceholderStructure(
input_name='optional_input_1',
then=[
'--arg1',
structures.InputValuePlaceholder(
input_name='optional_input_1'),
],
otherwise=[
'--arg2',
'default',
]))
])),
inputs={'optional_input_1': structures.InputSpec(type='String')},
)
V1_YAML_CONCAT_PLACEHOLDER = textwrap.dedent("""\
name: component_concat
inputs:
- {name: input_prefix, type: String}