Add 'serving + webapp only' pipeline; + only training step requires GPUs now (amygdala#28)

Author: amygdala

ml/kubeflow-pipelines/components/t2t/webapp-launcher/t2tapp-template.yaml

@@ -9,7 +9,7 @@ metadata:
       name: webapp-MODEL_NAME
       prefix: /webapp/
       rewrite: /
-      timeout_ms: 300000
+      timeout_ms: 1200000
       service: MODEL_NAME-webappsvc.KUBEFLOW_NAMESPACE:80
   name: MODEL_NAME-webappsvc
   labels:
@@ -41,9 +41,9 @@ spec:
       containers:
       - name: MODEL_NAME-webapp
         image: gcr.io/google-samples/ml-pipeline-t2tapp
-        resources:
-          limits:
-            nvidia.com/gpu: 1
+        # resources:
+        #   limits:
+        #     nvidia.com/gpu: 1
         imagePullPolicy: Always
         env:
         - name: TFSERVING_HOST

ml/kubeflow-pipelines/samples/kubeflow-tf/gh_summ_serve.py

@@ -0,0 +1,49 @@
+# 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.
+
+
+import kfp.dsl as dsl
+
+@dsl.pipeline(
+  name='Github issue summarization',
+  description='Demonstrate Tensor2Tensor-based training and TF-Serving'
+)
+def gh_summ(
+  project: dsl.PipelineParam=dsl.PipelineParam(name='project', value='YOUR_PROJECT_HERE'),
+  github_token: dsl.PipelineParam=dsl.PipelineParam(name='github-token', value='YOUR_GITHUB_TOKEN_HERE'),
+  working_dir: dsl.PipelineParam=dsl.PipelineParam(name='working-dir', value='YOUR_GCS_DIR_HERE'),
+  ):
+
+
+  serve = dsl.ContainerOp(
+      name = 'serve',
+      image = 'gcr.io/google-samples/ml-pipeline-kubeflow-tfserve',
+      arguments = ["--model_name", 'ghsumm-%s' % ('{{workflow.name}}',),
+          "--model_path", 'gs://aju-dev-demos-codelabs/kubecon/example_t2t_model/model_output/export'
+          ]
+      )
+
+  webapp = dsl.ContainerOp(
+      name = 'webapp',
+      image = 'gcr.io/google-samples/ml-pipeline-webapp-launcher',
+      arguments = ["--model_name", 'ghsumm-%s' % ('{{workflow.name}}',),
+          "--github_token", github_token]
+
+      )
+  webapp.after(serve)
+
+
+if __name__ == '__main__':
+  import kfp.compiler as compiler
+  compiler.Compiler().compile(gh_summ, __file__ + '.tar.gz')