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Sample updates for release v0.1.10 (#816)

This commit is contained in:
hongye-sun 2019-02-12 15:24:15 -08:00 committed by Kubernetes Prow Robot
parent 5df2cdc1ed
commit 7c633a97d7
7 changed files with 35 additions and 35 deletions
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2
components/kubeflow/launcher/kubeflow_tfjob_launcher_op.py
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@ -17,7 +17,7 @@ from kfp import dsl
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'): 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( return dsl.ContainerOp(
name = step_name, name = step_name,
image = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf:6ad2601ec7d04e842c212c50d5c78e548e12ddea', image = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f',
arguments = [ arguments = [
'--workers', number_of_workers, '--workers', number_of_workers,
'--pss', number_of_parameter_servers, '--pss', number_of_parameter_servers,

6
components/kubeflow/launcher/src/train.template.yaml
View File

@ -26,7 +26,7 @@ spec:
spec: spec:
containers: containers:
- name: tensorflow - name: tensorflow
image: gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:6ad2601ec7d04e842c212c50d5c78e548e12ddea image: gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f
command: command:
- python - python
- -m - -m
@ -49,7 +49,7 @@ spec:
spec: spec:
containers: containers:
- name: tensorflow - name: tensorflow
image: gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:6ad2601ec7d04e842c212c50d5c78e548e12ddea image: gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f
command: command:
- python - python
- -m - -m
@ -72,7 +72,7 @@ spec:
spec: spec:
containers: containers:
- name: tensorflow - name: tensorflow
image: gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:6ad2601ec7d04e842c212c50d5c78e548e12ddea image: gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f
command: command:
- python - python
- -m - -m

10
samples/kubeflow-tf/kubeflow-training-classification.py
View File

@ -21,7 +21,7 @@ import datetime
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'): 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( return dsl.ContainerOp(
name = step_name, name = step_name,
image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tft:6ad2601ec7d04e842c212c50d5c78e548e12ddea', image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tft:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f',
arguments = [ arguments = [
'--train', train_data, '--train', train_data,
'--eval', evaluation_data, '--eval', evaluation_data,
@ -38,7 +38,7 @@ 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', use_gpu=False): 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', use_gpu=False):
kubeflow_tf_training_op = dsl.ContainerOp( kubeflow_tf_training_op = dsl.ContainerOp(
name = step_name, name = step_name,
image = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:6ad2601ec7d04e842c212c50d5c78e548e12ddea', image = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f',
arguments = [ arguments = [
'--transformed-data-dir', transformed_data_dir, '--transformed-data-dir', transformed_data_dir,
'--schema', schema, '--schema', schema,
@ -52,7 +52,7 @@ def kubeflow_tf_training_op(transformed_data_dir, schema: 'GcsUri[text/json]', l
file_outputs = {'train': '/output.txt'} file_outputs = {'train': '/output.txt'}
) )
if use_gpu: if use_gpu:
kubeflow_tf_training_op.image = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer-gpu:6ad2601ec7d04e842c212c50d5c78e548e12ddea' kubeflow_tf_training_op.image = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer-gpu:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f'
kubeflow_tf_training_op.set_gpu_limit(1) kubeflow_tf_training_op.set_gpu_limit(1)
return kubeflow_tf_training_op return kubeflow_tf_training_op
@ -60,7 +60,7 @@ 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'): 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( return dsl.ContainerOp(
name = step_name, name = step_name,
image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tf-predict:6ad2601ec7d04e842c212c50d5c78e548e12ddea', image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tf-predict:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f',
arguments = [ arguments = [
'--data', evaluation_data, '--data', evaluation_data,
'--schema', schema, '--schema', schema,
@ -76,7 +76,7 @@ def dataflow_tf_predict_op(evaluation_data: 'GcsUri', schema: 'GcsUri[text/json]
def confusion_matrix_op(predictions, output, step_name='confusionmatrix'): def confusion_matrix_op(predictions, output, step_name='confusionmatrix'):
return dsl.ContainerOp( return dsl.ContainerOp(
name = step_name, name = step_name,
image = 'gcr.io/ml-pipeline/ml-pipeline-local-confusion-matrix:6ad2601ec7d04e842c212c50d5c78e548e12ddea', image = 'gcr.io/ml-pipeline/ml-pipeline-local-confusion-matrix:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f',
arguments = [ arguments = [
'--predictions', predictions, '--predictions', predictions,
'--output', output, '--output', output,

14
samples/notebooks/KubeFlow Pipeline Using TFX OSS Components.ipynb
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@ -43,13 +43,13 @@
"EVAL_DATA = 'gs://ml-pipeline-playground/tfx/taxi-cab-classification/eval.csv'\n", "EVAL_DATA = 'gs://ml-pipeline-playground/tfx/taxi-cab-classification/eval.csv'\n",
"HIDDEN_LAYER_SIZE = '1500'\n", "HIDDEN_LAYER_SIZE = '1500'\n",
"STEPS = 3000\n", "STEPS = 3000\n",
"DATAFLOW_TFDV_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tfdv:6ad2601ec7d04e842c212c50d5c78e548e12ddea'\n", "DATAFLOW_TFDV_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tfdv:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f'\n",
"DATAFLOW_TFT_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tft:6ad2601ec7d04e842c212c50d5c78e548e12ddea'\n", "DATAFLOW_TFT_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tft:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f'\n",
"DATAFLOW_TFMA_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tfma:6ad2601ec7d04e842c212c50d5c78e548e12ddea'\n", "DATAFLOW_TFMA_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tfma:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f'\n",
"DATAFLOW_TF_PREDICT_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tf-predict:6ad2601ec7d04e842c212c50d5c78e548e12ddea'\n", "DATAFLOW_TF_PREDICT_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tf-predict:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f'\n",
"KUBEFLOW_TF_TRAINER_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:6ad2601ec7d04e842c212c50d5c78e548e12ddea'\n", "KUBEFLOW_TF_TRAINER_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f'\n",
"KUBEFLOW_TF_TRAINER_GPU_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer-gpu:6ad2601ec7d04e842c212c50d5c78e548e12ddea'\n", "KUBEFLOW_TF_TRAINER_GPU_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer-gpu:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f'\n",
"KUBEFLOW_DEPLOYER_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-deployer:6ad2601ec7d04e842c212c50d5c78e548e12ddea'\n", "KUBEFLOW_DEPLOYER_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-deployer:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f'\n",
"DEPLOYER_MODEL = 'notebook_tfx_taxi'\n", "DEPLOYER_MODEL = 'notebook_tfx_taxi'\n",
"DEPLOYER_VERSION_DEV = 'dev'\n", "DEPLOYER_VERSION_DEV = 'dev'\n",
"DEPLOYER_VERSION_PROD = 'prod'" "DEPLOYER_VERSION_PROD = 'prod'"

6
samples/resnet-cmle/resnet-train-pipeline.py
View File

@ -22,7 +22,7 @@ def resnet_preprocess_op(project_id: 'GcpProject', output: 'GcsUri', train_csv:
validation_csv: 'GcsUri[text/csv]', labels, step_name='preprocess'): validation_csv: 'GcsUri[text/csv]', labels, step_name='preprocess'):
return dsl.ContainerOp( return dsl.ContainerOp(
name = step_name, name = step_name,
image = 'gcr.io/ml-pipeline/resnet-preprocess:6ad2601ec7d04e842c212c50d5c78e548e12ddea', image = 'gcr.io/ml-pipeline/resnet-preprocess:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f',
arguments = [ arguments = [
'--project_id', project_id, '--project_id', project_id,
'--output', output, '--output', output,
@ -38,7 +38,7 @@ def resnet_train_op(data_dir, output: 'GcsUri', region: 'GcpRegion', depth: int,
num_eval_images: int, num_label_classes: int, tf_version, step_name='train'): num_eval_images: int, num_label_classes: int, tf_version, step_name='train'):
return dsl.ContainerOp( return dsl.ContainerOp(
name = step_name, name = step_name,
image = 'gcr.io/ml-pipeline/resnet-train:6ad2601ec7d04e842c212c50d5c78e548e12ddea', image = 'gcr.io/ml-pipeline/resnet-train:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f',
arguments = [ arguments = [
'--data_dir', data_dir, '--data_dir', data_dir,
'--output', output, '--output', output,
@ -60,7 +60,7 @@ def resnet_deploy_op(model_dir, model, version, project_id: 'GcpProject', region
tf_version, step_name='deploy'): tf_version, step_name='deploy'):
return dsl.ContainerOp( return dsl.ContainerOp(
name = step_name, name = step_name,
image = 'gcr.io/ml-pipeline/resnet-deploy:6ad2601ec7d04e842c212c50d5c78e548e12ddea', image = 'gcr.io/ml-pipeline/resnet-deploy:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f',
arguments = [ arguments = [
'--model', model, '--model', model,
'--version', version, '--version', version,

16
samples/tfx/taxi-cab-classification-pipeline.py
View File

@ -21,7 +21,7 @@ import datetime
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'): 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( return dsl.ContainerOp(
name = step_name, name = step_name,
image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tfdv:6ad2601ec7d04e842c212c50d5c78e548e12ddea', image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tfdv:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f',
arguments = [ arguments = [
'--csv-data-for-inference', inference_data, '--csv-data-for-inference', inference_data,
'--csv-data-to-validate', validation_data, '--csv-data-to-validate', validation_data,
@ -40,7 +40,7 @@ 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'): 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( return dsl.ContainerOp(
name = step_name, name = step_name,
image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tft:6ad2601ec7d04e842c212c50d5c78e548e12ddea', image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tft:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f',
arguments = [ arguments = [
'--train', train_data, '--train', train_data,
'--eval', evaluation_data, '--eval', evaluation_data,
@ -57,7 +57,7 @@ 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'): 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( return dsl.ContainerOp(
name = step_name, name = step_name,
image = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:6ad2601ec7d04e842c212c50d5c78e548e12ddea', image = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f',
arguments = [ arguments = [
'--transformed-data-dir', transformed_data_dir, '--transformed-data-dir', transformed_data_dir,
'--schema', schema, '--schema', schema,
@ -74,7 +74,7 @@ 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'): 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( return dsl.ContainerOp(
name = step_name, name = step_name,
image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tfma:6ad2601ec7d04e842c212c50d5c78e548e12ddea', image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tfma:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f',
arguments = [ arguments = [
'--model', model, '--model', model,
'--eval', evaluation_data, '--eval', evaluation_data,
@ -91,7 +91,7 @@ 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'): 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( return dsl.ContainerOp(
name = step_name, name = step_name,
image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tf-predict:6ad2601ec7d04e842c212c50d5c78e548e12ddea', image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tf-predict:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f',
arguments = [ arguments = [
'--data', evaluation_data, '--data', evaluation_data,
'--schema', schema, '--schema', schema,
@ -108,7 +108,7 @@ def dataflow_tf_predict_op(evaluation_data: 'GcsUri', schema: 'GcsUri[text/json]
def confusion_matrix_op(predictions: 'GcsUri', output: 'GcsUri', step_name='confusion_matrix'): def confusion_matrix_op(predictions: 'GcsUri', output: 'GcsUri', step_name='confusion_matrix'):
return dsl.ContainerOp( return dsl.ContainerOp(
name=step_name, name=step_name,
image='gcr.io/ml-pipeline/ml-pipeline-local-confusion-matrix:6ad2601ec7d04e842c212c50d5c78e548e12ddea', image='gcr.io/ml-pipeline/ml-pipeline-local-confusion-matrix:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f',
arguments=[ arguments=[
'--output', '%s/{{workflow.name}}/confusionmatrix' % output, '--output', '%s/{{workflow.name}}/confusionmatrix' % output,
'--predictions', predictions, '--predictions', predictions,
@ -119,7 +119,7 @@ def confusion_matrix_op(predictions: 'GcsUri', output: 'GcsUri', step_name='conf
def roc_op(predictions: 'GcsUri', output: 'GcsUri', step_name='roc'): def roc_op(predictions: 'GcsUri', output: 'GcsUri', step_name='roc'):
return dsl.ContainerOp( return dsl.ContainerOp(
name=step_name, name=step_name,
image='gcr.io/ml-pipeline/ml-pipeline-local-roc:6ad2601ec7d04e842c212c50d5c78e548e12ddea', image='gcr.io/ml-pipeline/ml-pipeline-local-roc:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f',
arguments=[ arguments=[
'--output', '%s/{{workflow.name}}/roc' % output, '--output', '%s/{{workflow.name}}/roc' % output,
'--predictions', predictions, '--predictions', predictions,
@ -130,7 +130,7 @@ def roc_op(predictions: 'GcsUri', output: 'GcsUri', step_name='roc'):
def kubeflow_deploy_op(model: 'TensorFlow model', tf_server_name, step_name='deploy'): def kubeflow_deploy_op(model: 'TensorFlow model', tf_server_name, step_name='deploy'):
return dsl.ContainerOp( return dsl.ContainerOp(
name = step_name, name = step_name,
image = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-deployer:6ad2601ec7d04e842c212c50d5c78e548e12ddea', image = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-deployer:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f',
arguments = [ arguments = [
'--model-path', model, '--model-path', model,
'--server-name', tf_server_name '--server-name', tf_server_name

16
samples/xgboost-spark/xgboost-training-cm.py
View File

@ -26,7 +26,7 @@ class CreateClusterOp(dsl.ContainerOp):
def __init__(self, name, project, region, staging): def __init__(self, name, project, region, staging):
super(CreateClusterOp, self).__init__( super(CreateClusterOp, self).__init__(
name=name, name=name,
image='gcr.io/ml-pipeline/ml-pipeline-dataproc-create-cluster:6ad2601ec7d04e842c212c50d5c78e548e12ddea', image='gcr.io/ml-pipeline/ml-pipeline-dataproc-create-cluster:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f',
arguments=[ arguments=[
'--project', project, '--project', project,
'--region', region, '--region', region,
@ -41,7 +41,7 @@ class DeleteClusterOp(dsl.ContainerOp):
def __init__(self, name, project, region): def __init__(self, name, project, region):
super(DeleteClusterOp, self).__init__( super(DeleteClusterOp, self).__init__(
name=name, name=name,
image='gcr.io/ml-pipeline/ml-pipeline-dataproc-delete-cluster:6ad2601ec7d04e842c212c50d5c78e548e12ddea', image='gcr.io/ml-pipeline/ml-pipeline-dataproc-delete-cluster:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f',
arguments=[ arguments=[
'--project', project, '--project', project,
'--region', region, '--region', region,
@ -55,7 +55,7 @@ class AnalyzeOp(dsl.ContainerOp):
def __init__(self, name, project, region, cluster_name, schema, train_data, output): def __init__(self, name, project, region, cluster_name, schema, train_data, output):
super(AnalyzeOp, self).__init__( super(AnalyzeOp, self).__init__(
name=name, name=name,
image='gcr.io/ml-pipeline/ml-pipeline-dataproc-analyze:6ad2601ec7d04e842c212c50d5c78e548e12ddea', image='gcr.io/ml-pipeline/ml-pipeline-dataproc-analyze:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f',
arguments=[ arguments=[
'--project', project, '--project', project,
'--region', region, '--region', region,
@ -73,7 +73,7 @@ class TransformOp(dsl.ContainerOp):
target, analysis, output): target, analysis, output):
super(TransformOp, self).__init__( super(TransformOp, self).__init__(
name=name, name=name,
image='gcr.io/ml-pipeline/ml-pipeline-dataproc-transform:6ad2601ec7d04e842c212c50d5c78e548e12ddea', image='gcr.io/ml-pipeline/ml-pipeline-dataproc-transform:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f',
arguments=[ arguments=[
'--project', project, '--project', project,
'--region', region, '--region', region,
@ -98,7 +98,7 @@ class TrainerOp(dsl.ContainerOp):
super(TrainerOp, self).__init__( super(TrainerOp, self).__init__(
name=name, name=name,
image='gcr.io/ml-pipeline/ml-pipeline-dataproc-train:6ad2601ec7d04e842c212c50d5c78e548e12ddea', image='gcr.io/ml-pipeline/ml-pipeline-dataproc-train:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f',
arguments=[ arguments=[
'--project', project, '--project', project,
'--region', region, '--region', region,
@ -121,7 +121,7 @@ class PredictOp(dsl.ContainerOp):
def __init__(self, name, project, region, cluster_name, data, model, target, analysis, output): def __init__(self, name, project, region, cluster_name, data, model, target, analysis, output):
super(PredictOp, self).__init__( super(PredictOp, self).__init__(
name=name, name=name,
image='gcr.io/ml-pipeline/ml-pipeline-dataproc-predict:6ad2601ec7d04e842c212c50d5c78e548e12ddea', image='gcr.io/ml-pipeline/ml-pipeline-dataproc-predict:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f',
arguments=[ arguments=[
'--project', project, '--project', project,
'--region', region, '--region', region,
@ -141,7 +141,7 @@ class ConfusionMatrixOp(dsl.ContainerOp):
def __init__(self, name, predictions, output): def __init__(self, name, predictions, output):
super(ConfusionMatrixOp, self).__init__( super(ConfusionMatrixOp, self).__init__(
name=name, name=name,
image='gcr.io/ml-pipeline/ml-pipeline-local-confusion-matrix:6ad2601ec7d04e842c212c50d5c78e548e12ddea', image='gcr.io/ml-pipeline/ml-pipeline-local-confusion-matrix:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f',
arguments=[ arguments=[
'--output', output, '--output', output,
'--predictions', predictions '--predictions', predictions
@ -153,7 +153,7 @@ class RocOp(dsl.ContainerOp):
def __init__(self, name, predictions, trueclass, output): def __init__(self, name, predictions, trueclass, output):
super(RocOp, self).__init__( super(RocOp, self).__init__(
name=name, name=name,
image='gcr.io/ml-pipeline/ml-pipeline-local-roc:6ad2601ec7d04e842c212c50d5c78e548e12ddea', image='gcr.io/ml-pipeline/ml-pipeline-local-roc:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f',
arguments=[ arguments=[
'--output', output, '--output', output,
'--predictions', predictions, '--predictions', predictions,