specify output file with a parameter

undo tf-launcher

.cloudbuild.yaml

@@ -97,12 +97,8 @@ steps:
   id: 'buildDeployer'
 - name: 'gcr.io/cloud-builders/docker'
   entrypoint: '/bin/bash'
-  args: ['-c', 'cd /workspace/components/kubeflow/tf-launcher && ./build_image.sh -p $PROJECT_ID -t $COMMIT_SHA']
+  args: ['-c', 'cd /workspace/components/kubeflow/launcher && ./build_image.sh -p $PROJECT_ID -t $COMMIT_SHA']
   id: 'buildLauncher'
-- name: 'gcr.io/cloud-builders/docker'
-  entrypoint: '/bin/bash'
-  args: ['-c', 'cd /workspace/components/kubeflow/katib-launcher && ./build_image.sh -p $PROJECT_ID -t $COMMIT_SHA']
-  id: 'buildKatibLauncher'
 - name: 'gcr.io/cloud-builders/docker'
   entrypoint: '/bin/bash'
   args: ['-c', 'cd /workspace/components/kubeflow/dnntrainer && ./build_image.sh -p $PROJECT_ID -t $COMMIT_SHA -l ml-pipeline-kubeflow-tf-trainer-gpu -b 1.6.0-gpu']

components/kubeflow/katib-launcher/kubeflow_katib_launcher_op.py

@@ -16,7 +16,7 @@
 
 def kubeflow_studyjob_launcher_op(name, namespace, optimizationtype, objectivevaluename, optimizationgoal, requestcount, metricsnames,
                                   parameterconfigs, nasConfig, workertemplatepath, mcollectortemplatepath, suggestionspec,
-                                  studyjob_timeout_minutes, output_dir=None, step_name='StudyJob-Launcher'):
+                                  studyjob_timeout_minutes, output_file='/output.txt', step_name='StudyJob-Launcher'):
     return dsl.ContainerOp(
         name = step_name,
         image = 'liuhougangxa/ml-pipeline-kubeflow-studyjob:latest',
@@ -33,7 +33,8 @@ def kubeflow_studyjob_launcher_op(name, namespace, optimizationtype, objectiveva
             "--workertemplatepath", workertemplatepath,
             "--mcollectortemplatepath", mcollectortemplatepath,
             "--suggestionspec", suggestionspec,
+            "--outputfile", output_file,
             '--studyjobtimeoutminutes', studyjob_timeout_minutes,
         ],
-        file_outputs = {'hyperparameter': '/output.txt'}
+        file_outputs = {'hyperparameter': output_file}
     )

components/kubeflow/katib-launcher/src/launch_study_job.py

@@ -118,6 +118,9 @@ def main(argv=None):
   parser.add_argument('--suggestionspec', type=yamlOrJsonStr,
                       default={},
                       help='StudyJob suggestion spec.')
+  parser.add_argument('--outputfile', type=str,
+                      default='/output.txt',
+                      help='The file which stores the best trial of the studyJob.')
   parser.add_argument('--studyjobtimeoutminutes', type=int,
                       default=10,
                       help='Time in minutes to wait for the StudyJob to complete')
@@ -147,7 +150,7 @@ def main(argv=None):
   if wait_response.get("status", {}).get("condition") == "Completed":
     succ = True
     trial = get_best_trial(wait_response["status"]["bestTrialId"])
-    with open('/output.txt', 'w') as f:
+    with open(args.outputfile, 'w') as f:
       ps_dict = {}
       for ps in trial.parameter_set:
           ps_dict[ps.name] = ps.value

components/kubeflow/tf-launcher/test/test_launcher.py → components/kubeflow/test/test_launcher.py

@@ -14,6 +14,7 @@
 
 
 import launcher
+from launcher import train
 import os
 import shutil
 import subprocess
@@ -35,7 +36,7 @@ def test_yaml_generation_basic(self):
     pss = 1
     args_list = []
     args_list.append('--learning-rate=0.1')
-    generated_yaml = _generate_train_yaml(train_template_file, tfjob_ns, worker, pss, args_list)
+    generated_yaml = train._generate_train_yaml(train_template_file, tfjob_ns, worker, pss, args_list)
     with open(os.path.join(test_data_dir, 'train_basic.yaml'), 'r') as f:
       golden = yaml.load(f)
     self.assertEqual(golden, generated_yaml)
@@ -50,7 +51,7 @@ def test_yaml_generation_advanced(self):
     args_list = []
     tfjob_ns = 'kubeflow'
     args_list.append('--learning-rate=0.1')
-    generated_yaml = _generate_train_yaml(train_template_file, tfjob_ns, worker, pss, args_list)
+    generated_yaml = train._generate_train_yaml(train_template_file, tfjob_ns, worker, pss, args_list)
     with open(os.path.join(test_data_dir, 'train_zero_worker.yaml'), 'r') as f:
       golden = yaml.load(f)
     self.assertEqual(golden, generated_yaml)

samples/kubeflow-tf/README.md

@@ -35,8 +35,8 @@ Preprocessing:
   [container](https://github.com/kubeflow/pipelines/tree/master/components/dataflow/tft)
 
 Training:
-  [source code](https://github.com/kubeflow/pipelines/tree/master/components/kubeflow/tf-launcher/src),
-  [container](https://github.com/kubeflow/pipelines/tree/master/components/kubeflow/tf-launcher)
+  [source code](https://github.com/kubeflow/pipelines/tree/master/components/kubeflow/launcher/src), 
+  [container](https://github.com/kubeflow/pipelines/tree/master/components/kubeflow/launcher)
 
 Prediction:
   [source code](https://github.com/kubeflow/pipelines/tree/master/components/dataflow/predict/src), 

samples/tfx/README.md

@@ -51,8 +51,8 @@ Preprocessing:
   [container](https://github.com/kubeflow/pipelines/tree/master/components/dataflow/tft)
 
 Training:
-  [source code](https://github.com/kubeflow/pipelines/tree/master/components/kubeflow/tf-launcher/src)
-  [container](https://github.com/kubeflow/pipelines/tree/master/components/kubeflow/tf-launcher)
+  [source code](https://github.com/kubeflow/pipelines/tree/master/components/kubeflow/launcher/src) 
+  [container](https://github.com/kubeflow/pipelines/tree/master/components/kubeflow/launcher)
 
 Analysis:
   [source code](https://github.com/kubeflow/pipelines/tree/master/components/dataflow/tfma/src)