forked from code-ql-testing/kfserving_2
-
Notifications
You must be signed in to change notification settings - Fork 1
/
sample-tf-pipeline.py
54 lines (49 loc) · 2.17 KB
/
sample-tf-pipeline.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
# Copyright 2019 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.dsl as dsl
from kfp import components
import json
kfserving_op = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/master/components/kubeflow/kfserving/component.yaml')
@dsl.pipeline(
name='KFServing pipeline',
description='A pipeline for KFServing.'
)
def kfservingPipeline(
action = 'create',
model_name='tensorflow-sample',
default_model_uri='gs://kfserving-samples/models/tensorflow/flowers',
canary_model_uri='gs://kfserving-samples/models/tensorflow/flowers-2',
canary_model_traffic_percentage='10',
namespace='kubeflow',
framework='tensorflow',
default_custom_model_spec='{}',
canary_custom_model_spec='{}',
autoscaling_target=0,
kfserving_endpoint=''
):
# define workflow
kfserving = kfserving_op(action = action,
model_name=model_name,
default_model_uri=default_model_uri,
canary_model_uri=canary_model_uri,
canary_model_traffic_percentage=canary_model_traffic_percentage,
namespace=namespace,
framework=framework,
default_custom_model_spec=default_custom_model_spec,
canary_custom_model_spec=canary_custom_model_spec,
autoscaling_target=autoscaling_target,
kfserving_endpoint=kfserving_endpoint)
if __name__ == '__main__':
import kfp.compiler as compiler
compiler.Compiler().compile(kfservingPipeline, __file__ + '.tar.gz')