-
Notifications
You must be signed in to change notification settings - Fork 123
/
parallelfor_item_argument_resolving.py
77 lines (55 loc) · 2.32 KB
/
parallelfor_item_argument_resolving.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
# Copyright 2021 kubeflow.org
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import kfp
from kfp.components import func_to_container_op
from kfp_tekton.compiler import TektonCompiler
class Coder:
def empty(self):
return ""
TektonCompiler._get_unique_id_code = Coder.empty
@func_to_container_op
def produce_str() -> str:
return "Hello"
@func_to_container_op
def produce_list_of_dicts() -> list:
return [{"aaa": "aaa1", "bbb": "bbb1"}, {"aaa": "aaa2", "bbb": "bbb2"}]
@func_to_container_op
def produce_list_of_strings() -> list:
return ["a", "z"]
@func_to_container_op
def produce_list_of_ints() -> list:
return [1234567890, 987654321]
@func_to_container_op
def consume(param1: str):
print(param1)
@kfp.dsl.pipeline(name='parallelfor-item-argument-resolving')
def parallelfor_item_argument_resolving():
produce_str_task = produce_str()
produce_list_of_strings_task = produce_list_of_strings()
produce_list_of_ints_task = produce_list_of_ints()
produce_list_of_dicts_task = produce_list_of_dicts()
with kfp.dsl.ParallelFor(produce_list_of_strings_task.output) as loop_item:
consume(str(produce_list_of_strings_task.output))
consume(str(loop_item))
consume(str(produce_str_task.output))
with kfp.dsl.ParallelFor(produce_list_of_ints_task.output) as loop_item:
consume(str(produce_list_of_ints_task.output))
consume(str(loop_item))
with kfp.dsl.ParallelFor(produce_list_of_dicts_task.output) as loop_item:
consume(str(produce_list_of_dicts_task.output))
# consume(loop_item) # Cannot use the full loop item when it's a dict
consume(str(loop_item.aaa))
if __name__ == '__main__':
from kfp_tekton.compiler import TektonCompiler
TektonCompiler().compile(parallelfor_item_argument_resolving, __file__.replace('.py', '.yaml'))