def a_op(
field_m: {'GCSPath': {
'path_type': 'file',
'file_type': 'tsv'
}}, field_o: Integer()):
return ContainerOp(
name='operator a',
image='gcr.io/ml-pipeline/component-b',
arguments=[
'--field-l',
field_m,
'--field-o',
field_o,
],
)
def b_op(field_x: {'customized_type': {'openapi_schema_validator': '{"type": "string", "pattern": "^gcs://.*$"}'}},
field_y: Integer(),
field_z: GCSPath()) -> {'output_model_uri': 'GcsUri'}:
return ContainerOp(
name = 'operator b',
image = 'gcr.io/ml-pipeline/component-a',
command = [
'python3',
field_x,
],
arguments = [
'--field-y', field_y,
'--field-z', field_z,
],
file_outputs = {
'output_model_uri': '/schema.txt',
}
)
def b_op(field_x: {'customized_type_a': {'property_a': 'value_a', 'property_b': 'value_b'}},
field_y: Integer(),
field_z: {'ArtifactB': {'path_type': 'file', 'file_type':'tsv'}}) -> {'output_model_uri': 'GcsUri'}:
return ContainerOp(
name = 'operator b',
image = 'gcr.io/ml-pipeline/component-a',
command = [
'python3',
field_x,
],
arguments = [
'--field-y', field_y,
'--field-z', field_z,
],
file_outputs = {
'output_model_uri': '/schema.txt',
}
)
def a_op(field_l: Integer()) -> {
'field_m': 'GCSPath',
'field_n': {
'customized_type': {
'openapi_schema_validator':
'{"type": "string", "pattern": "^gs://.*$"}'
}
},
'field_o': 'Integer'
}:
return ContainerOp(name='operator a',
image='gcr.io/ml-pipeline/component-b',
arguments=[
'--field-l',
field_l,
],
file_outputs={
'field_m': '/schema.txt',
'field_n': '/feature.txt',
'field_o': '/output.txt'
})
def a_op(field_l: Integer()) -> {
'field_m': 'GCSPath',
'field_n': {
'customized_type': {
'property_a': 'value_a',
'property_b': 'value_b'
}
},
'field_o': 'Integer'
}:
return ContainerOp(name='operator a',
image='gcr.io/ml-pipeline/component-b',
arguments=[
'--field-l',
field_l,
],
file_outputs={
'field_m': '/schema.txt',
'field_n': '/feature.txt',
'field_o': '/output.txt'
})
def dhealth_inference_sl_segmentation_op(python_train_path, input_dataset_yaml,
model, output_path,
num_batch_size: Integer(),
gpu_boolean):
if gpu_boolean == 'yes':
return dsl.ContainerOp(
name='DeepHealth - Inference Skin Lesion Segmentation',
image='dhealth/pylibs:latest',
command=["python3", python_train_path],
arguments=[
input_dataset_yaml, model, '--out-dir', output_path,
'--batch-size', num_batch_size, '--gpu'
]).set_gpu_limit(1)
else:
return dsl.ContainerOp(
name='DeepHealth - Inference Skin Lesion Segmentation',
image='dhealth/pylibs:latest',
command=["python3", python_train_path],
arguments=[
input_dataset_yaml, model, '--out-dir', output_path,
'--batch-size', num_batch_size
])
def my_pipeline(a: {'GCSPath': {'path_type':'file', 'file_type': 'csv'}}='good', b: Integer()=12):
a_op(field_m=a, field_o=b)
def my_pipeline1(a: {'Schema': {'file_type': 'csv'}}='good', b: Integer()=12):
pass
def componentA(a: {'ArtifactA': {'file_type': 'csv'}}, b: Integer() = 12, c: {'ArtifactB': {'path_type': 'file', 'file_type':'tsv'}} = 'gs://hello/world') -> {'model': Integer()}:
return MockContainerOp()
