image tag update for release (#114)

* update image tag for new releases * add more * delete the accidentaly added sample
kubeflow · Nov 7, 2018 · f25d32b · f25d32b
1 parent 5862940
commit f25d32b
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Showing 9 changed files with 29 additions and 29 deletions.
diff --git a/bootstrapper.yaml b/bootstrapper.yaml
@@ -65,7 +65,7 @@ spec:
     spec:
       containers:
       - name: deploy
-        image: gcr.io/ml-pipeline/bootstrapper:0.0.42
+        image: gcr.io/ml-pipeline/bootstrapper:0.1.0
         imagePullPolicy: 'Always'
         # Additional parameter available:
         args: [

diff --git a/components/kubeflow/launcher/kubeflow_tfjob_launcher_op.py b/components/kubeflow/launcher/kubeflow_tfjob_launcher_op.py
@@ -17,7 +17,7 @@
 def kubeflow_tfjob_launcher_op(container_image, command, number_of_workers: int, number_of_parameter_servers: int, tfjob_timeout_minutes: int, output_dir=None, step_name='TFJob-launcher'):
     return dsl.ContainerOp(
         name = step_name,
-        image = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf:0.0.42', #TODO: Update the name in next release.
+        image = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf:0.1.0',
         arguments = [
             '--workers', number_of_workers,
             '--pss', number_of_parameter_servers,

diff --git a/components/kubeflow/launcher/train.template.yaml b/components/kubeflow/launcher/train.template.yaml
@@ -26,7 +26,7 @@ spec:
         spec:
           containers:
           - name: tensorflow
-            image: gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:0.0.42
+            image: gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:0.1.0
             command:
               - python
               - -m
@@ -38,7 +38,7 @@ spec:
         spec:
           containers:
           - name: tensorflow
-            image: gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:0.0.42
+            image: gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:0.1.0
             command:
               - python
               - -m
@@ -50,7 +50,7 @@ spec:
             spec:
               containers:
               - name: tensorflow
-                image: gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:0.0.42
+                image: gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:0.1.0
                 command:
                   - python
                   - -m

diff --git a/ml-pipeline/ml-pipeline/prototypes/ml-pipeline.jsonnet b/ml-pipeline/ml-pipeline/prototypes/ml-pipeline.jsonnet
@@ -4,10 +4,10 @@
 // @shortDescription ML pipeline
 // @param name string Name to give to each of the components
 // @optionalParam namespace string default Namespace
-// @optionalParam api_image string gcr.io/ml-pipeline/api-server:0.0.42 API docker image
-// @optionalParam scheduledworkflow_image string gcr.io/ml-pipeline/scheduledworkflow:0.0.42 schedule workflow docker image
-// @optionalParam persistenceagent_image string gcr.io/ml-pipeline/persistenceagent:0.0.42 persistence agent docker image
-// @optionalParam ui_image string gcr.io/ml-pipeline/frontend:0.0.42 UI docker image
+// @optionalParam api_image string gcr.io/ml-pipeline/api-server:0.1.0 API docker image
+// @optionalParam scheduledworkflow_image string gcr.io/ml-pipeline/scheduledworkflow:0.1.0 schedule workflow docker image
+// @optionalParam persistenceagent_image string gcr.io/ml-pipeline/persistenceagent:0.1.0 persistence agent docker image
+// @optionalParam ui_image string gcr.io/ml-pipeline/frontend:0.1.0 UI docker image
 // @optionalParam deploy_argo string false flag to deploy argo
 // @optionalParam report_usage string false flag to report usage
 

diff --git a/samples/kubeflow-tf/kubeflow-training-classification.py b/samples/kubeflow-tf/kubeflow-training-classification.py
@@ -20,7 +20,7 @@
 def dataflow_tf_transform_op(train_data: 'GcsUri', evaluation_data: 'GcsUri', schema: 'GcsUri[text/json]', project: 'GcpProject', preprocess_mode, preprocess_module: 'GcsUri[text/code/python]', transform_output: 'GcsUri[Directory]', step_name='preprocess'):
     return dsl.ContainerOp(
         name = step_name,
-        image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tft:0.0.42',
+        image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tft:0.1.0',
         arguments = [
             '--train', train_data,
             '--eval', evaluation_data,
@@ -37,7 +37,7 @@ def dataflow_tf_transform_op(train_data: 'GcsUri', evaluation_data: 'GcsUri', sc
 def kubeflow_tf_training_op(transformed_data_dir, schema: 'GcsUri[text/json]', learning_rate: float, hidden_layer_size: int, steps: int, target, preprocess_module: 'GcsUri[text/code/python]', training_output: 'GcsUri[Directory]', step_name='training'):
     return dsl.ContainerOp(
         name = step_name,
-        image = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:0.0.42',
+        image = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:0.1.0',
         arguments = [
             '--transformed-data-dir', transformed_data_dir,
             '--schema', schema,
@@ -54,7 +54,7 @@ def kubeflow_tf_training_op(transformed_data_dir, schema: 'GcsUri[text/json]', l
 def dataflow_tf_predict_op(evaluation_data: 'GcsUri', schema: 'GcsUri[text/json]', target: str, model: 'TensorFlow model', predict_mode, project: 'GcpProject', prediction_output: 'GcsUri', step_name='prediction'):
     return dsl.ContainerOp(
         name = step_name,
-        image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tf-predict:0.0.42',
+        image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tf-predict:0.1.0',
         arguments = [
             '--data', evaluation_data,
             '--schema', schema,
@@ -70,7 +70,7 @@ def dataflow_tf_predict_op(evaluation_data: 'GcsUri', schema: 'GcsUri[text/json]
 def confusion_matrix_op(predictions, output, step_name='confusionmatrix'):
     return dsl.ContainerOp(
         name = step_name,
-        image = 'gcr.io/ml-pipeline/ml-pipeline-local-confusion-matrix:0.0.42',
+        image = 'gcr.io/ml-pipeline/ml-pipeline-local-confusion-matrix:0.1.0',
         arguments = [
           '--predictions', predictions,
           '--output', output,

diff --git a/samples/tfx/taxi-cab-classification-pipeline.py b/samples/tfx/taxi-cab-classification-pipeline.py
@@ -20,7 +20,7 @@
 def dataflow_tf_data_validation_op(inference_data: 'GcsUri', validation_data: 'GcsUri', column_names: 'GcsUri[text/json]', key_columns, project: 'GcpProject', mode, validation_output: 'GcsUri[Directory]', step_name='validation'):
     return dsl.ContainerOp(
         name = step_name,
-        image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tfdv:dev',
+        image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tfdv:0.1.0',
         arguments = [
             '--csv-data-for-inference', inference_data,
             '--csv-data-to-validate', validation_data,
@@ -39,7 +39,7 @@ def dataflow_tf_data_validation_op(inference_data: 'GcsUri', validation_data: 'G
 def dataflow_tf_transform_op(train_data: 'GcsUri', evaluation_data: 'GcsUri', schema: 'GcsUri[text/json]', project: 'GcpProject', preprocess_mode, preprocess_module: 'GcsUri[text/code/python]', transform_output: 'GcsUri[Directory]', step_name='preprocess'):
     return dsl.ContainerOp(
         name = step_name,
-        image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tft:0.0.42',
+        image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tft:0.1.0',
         arguments = [
             '--train', train_data,
             '--eval', evaluation_data,
@@ -56,7 +56,7 @@ def dataflow_tf_transform_op(train_data: 'GcsUri', evaluation_data: 'GcsUri', sc
 def tf_train_op(transformed_data_dir, schema: 'GcsUri[text/json]', learning_rate: float, hidden_layer_size: int, steps: int, target: str, preprocess_module: 'GcsUri[text/code/python]', training_output: 'GcsUri[Directory]', step_name='training'):
     return dsl.ContainerOp(
         name = step_name,
-        image = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:0.0.42',
+        image = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:0.1.0',
         arguments = [
             '--transformed-data-dir', transformed_data_dir,
             '--schema', schema,
@@ -73,7 +73,7 @@ def tf_train_op(transformed_data_dir, schema: 'GcsUri[text/json]', learning_rate
 def dataflow_tf_model_analyze_op(model: 'TensorFlow model', evaluation_data: 'GcsUri', schema: 'GcsUri[text/json]', project: 'GcpProject', analyze_mode, analyze_slice_column, analysis_output: 'GcsUri', step_name='analysis'):
     return dsl.ContainerOp(
         name = step_name,
-        image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tfma:0.0.42',
+        image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tfma:0.1.0',
         arguments = [
             '--model', model,
             '--eval', evaluation_data,
@@ -90,7 +90,7 @@ def dataflow_tf_model_analyze_op(model: 'TensorFlow model', evaluation_data: 'Gc
 def dataflow_tf_predict_op(evaluation_data: 'GcsUri', schema: 'GcsUri[text/json]', target: str, model: 'TensorFlow model', predict_mode, project: 'GcpProject', prediction_output: 'GcsUri', step_name='prediction'):
     return dsl.ContainerOp(
         name = step_name,
-        image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tf-predict:0.0.42',
+        image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tf-predict:0.1.0',
         arguments = [
             '--data', evaluation_data,
             '--schema', schema,
@@ -106,7 +106,7 @@ def dataflow_tf_predict_op(evaluation_data: 'GcsUri', schema: 'GcsUri[text/json]
 def kubeflow_deploy_op(model: 'TensorFlow model', tf_server_name, step_name='deploy'):
     return dsl.ContainerOp(
         name = step_name,
-        image = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-deployer:dev', #TODO: change the tag to the release versions when new releases are built with the updated image
+        image = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-deployer:0.1.0',
         arguments = [
             '--model-path', model,
             '--server-name', tf_server_name

diff --git a/samples/xgboost-spark/xgboost-training-cm.py b/samples/xgboost-spark/xgboost-training-cm.py
@@ -25,7 +25,7 @@ class CreateClusterOp(dsl.ContainerOp):
   def __init__(self, name, project, region, staging):
     super(CreateClusterOp, self).__init__(
       name=name,
-      image='gcr.io/ml-pipeline/ml-pipeline-dataproc-create-cluster:0.0.42',
+      image='gcr.io/ml-pipeline/ml-pipeline-dataproc-create-cluster:0.1.0',
       arguments=[
           '--project', project,
           '--region', region,
@@ -40,7 +40,7 @@ class DeleteClusterOp(dsl.ContainerOp):
   def __init__(self, name, project, region):
     super(DeleteClusterOp, self).__init__(
       name=name,
-      image='gcr.io/ml-pipeline/ml-pipeline-dataproc-delete-cluster:0.0.42',
+      image='gcr.io/ml-pipeline/ml-pipeline-dataproc-delete-cluster:0.1.0',
       arguments=[
           '--project', project,
           '--region', region,
@@ -54,7 +54,7 @@ class AnalyzeOp(dsl.ContainerOp):
   def __init__(self, name, project, region, cluster_name, schema, train_data, output):
     super(AnalyzeOp, self).__init__(
       name=name,
-      image='gcr.io/ml-pipeline/ml-pipeline-dataproc-analyze:0.0.42',
+      image='gcr.io/ml-pipeline/ml-pipeline-dataproc-analyze:0.1.0',
       arguments=[
           '--project', project,
           '--region', region,
@@ -72,7 +72,7 @@ def __init__(self, name, project, region, cluster_name, train_data, eval_data,
                target, analysis, output):
     super(TransformOp, self).__init__(
       name=name,
-      image='gcr.io/ml-pipeline/ml-pipeline-dataproc-transform:0.0.42',
+      image='gcr.io/ml-pipeline/ml-pipeline-dataproc-transform:0.1.0',
       arguments=[
           '--project', project,
           '--region', region,
@@ -97,7 +97,7 @@ def __init__(self, name, project, region, cluster_name, train_data, eval_data,
 
     super(TrainerOp, self).__init__(
       name=name,
-      image='gcr.io/ml-pipeline/ml-pipeline-dataproc-train:0.0.42',
+      image='gcr.io/ml-pipeline/ml-pipeline-dataproc-train:0.1.0',
       arguments=[
           '--project', project,
           '--region', region,
@@ -120,7 +120,7 @@ class PredictOp(dsl.ContainerOp):
   def __init__(self, name, project, region, cluster_name, data, model, target, analysis, output):
     super(PredictOp, self).__init__(
       name=name,
-      image='gcr.io/ml-pipeline/ml-pipeline-dataproc-predict:0.0.42',
+      image='gcr.io/ml-pipeline/ml-pipeline-dataproc-predict:0.1.0',
       arguments=[
           '--project', project,
           '--region', region,
@@ -140,7 +140,7 @@ class ConfusionMatrixOp(dsl.ContainerOp):
   def __init__(self, name, predictions, output):
     super(ConfusionMatrixOp, self).__init__(
       name=name,
-      image='gcr.io/ml-pipeline/ml-pipeline-local-confusion-matrix:0.0.42',
+      image='gcr.io/ml-pipeline/ml-pipeline-local-confusion-matrix:0.1.0',
       arguments=[
         '--output', output,
         '--predictions', predictions
@@ -152,7 +152,7 @@ class RocOp(dsl.ContainerOp):
   def __init__(self, name, predictions, trueclass, output):
     super(RocOp, self).__init__(
       name=name,
-      image='gcr.io/ml-pipeline/ml-pipeline-local-roc:0.0.42',
+      image='gcr.io/ml-pipeline/ml-pipeline-local-roc:0.1.0',
       arguments=[
         '--output', output,
         '--predictions', predictions,

diff --git a/sdk/python/kfp/dsl/components/kubeflow_tfjob_launcher_op.py b/sdk/python/kfp/dsl/components/kubeflow_tfjob_launcher_op.py
@@ -17,7 +17,7 @@
 def kubeflow_tfjob_launcher_op(container_image, command, number_of_workers: int, number_of_parameter_servers: int, tfjob_timeout_minutes: int, output_dir=None, step_name='TFJob-launcher'):
     return dsl.ContainerOp(
         name = step_name,
-        image = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf:0.0.42', #TODO: Update the name in next release.
+        image = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf:0.1.0', 
         arguments = [
             '--workers', number_of_workers,
             '--pss', number_of_parameter_servers,

diff --git a/uninstaller.yaml b/uninstaller.yaml
@@ -24,7 +24,7 @@ spec:
     spec:
       containers:
       - name: uninstaller
-        image: gcr.io/ml-pipeline/bootstrapper:0.0.42
+        image: gcr.io/ml-pipeline/bootstrapper:0.1.0
         imagePullPolicy: 'Always'
         # Additional parameter available:
         args: [