pipelines/components/gcp/ml_engine/deploy/component.yaml

119 lines
4.4 KiB
YAML

# Copyright 2018 Google LLC
#
# 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.
name: Deploying a trained model to Cloud Machine Learning Engine
description: |
A Kubeflow Pipeline component to deploy a trained model from a Cloud Storage
path to a Cloud Machine Learning Engine service.
metadata:
labels:
add-pod-env: 'true'
inputs:
- name: model_uri
description: >-
Required. The Cloud Storage URI which contains a model file. Commonly
used TF model search paths (export/exporter) will be used if they exist.
type: GCSPath
- name: project_id
description: 'Required.The ID of the parent project of the serving model.'
type: GCPProjectID
- name: model_id
description: >-
Optional. The user-specified name of the model. If it is not provided,
the operation uses a random name.
default: ''
type: String
- name: version_id
description: >-
Optional. The user-specified name of the version. If it is not provided,
the operation uses a random name.
default: ''
type: String
- name: runtime_version
description: >-
Optional. The [Cloud ML Engine runtime version](https://cloud.google.com/ml-engine/docs/tensorflow/runtime-version-list) to use for
this deployment. If it is not set, the Cloud ML Engine uses the default
stable version, 1.0.
default: ''
type: String
- name: python_version
description: >-
Optional. The version of Python used in the prediction. If it is not set,
the default version is `2.7`. Python `3.5` is available when the
runtime_version is set to `1.4` and above. Python `2.7` works with all
supported runtime versions.
default: ''
type: String
- name: model
description: >-
Optional. The JSON payload of the new
[Model](https://cloud.google.com/ml-engine/reference/rest/v1/projects.models), if it does not exist.
default: ''
type: Dict
- name: version
description: >-
Optional. The JSON payload of the new
[Version](https://cloud.google.com/ml-engine/reference/rest/v1/projects.models.versions).
default: ''
type: Dict
- name: replace_existing_version
description: >-
A Boolean flag that indicates whether to replace existing version in case of conflict.
default: 'Fasle'
type: Bool
- name: set_default
description: >-
A Boolean flag that indicates whether to set the new version as default version in the model.
default: 'False'
type: Bool
- name: wait_interval
description: 'A time-interval to wait for in case the operation has a long run time.'
default: '30'
type: Integer
outputs:
- name: model_uri
description: 'The Cloud Storage URI of the trained model.'
type: GCSPath
- name: model_name
description: 'The name of the deployed model.'
type: String
- name: version_name
description: 'The name of the deployed version.'
type: String
- name: MLPipeline UI metadata
type: UI metadata
implementation:
container:
image: gcr.io/ml-pipeline/ml-pipeline-gcp:1.4.0-rc.1
args: [
--ui_metadata_path, {outputPath: MLPipeline UI metadata},
kfp_component.google.ml_engine, deploy,
--model_uri, {inputValue: model_uri},
--project_id, {inputValue: project_id},
--model_id, {inputValue: model_id},
--version_id, {inputValue: version_id},
--runtime_version, {inputValue: runtime_version},
--python_version, {inputValue: python_version},
--model, {inputValue: model},
--version, {inputValue: version},
--replace_existing_version, {inputValue: replace_existing_version},
--set_default, {inputValue: set_default},
--wait_interval, {inputValue: wait_interval},
--model_uri_output_path, {outputPath: model_uri},
--model_name_output_path, {outputPath: model_name},
--version_name_output_path, {outputPath: version_name},
]
env:
KFP_POD_NAME: "{{pod.name}}"