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update (#1021)
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‎components/gcp/bigquery/query/component.yaml

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type: GCSPath
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implementation:
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container:
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image: gcr.io/ml-pipeline/ml-pipeline-gcp:latest
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image: gcr.io/ml-pipeline/ml-pipeline-gcp:f98ec68488cfbac022893a87c5bca083f03f2065
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args: [
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kfp_component.google.bigquery, query,
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--query, {inputValue: query},

‎components/gcp/dataflow/launch_python/component.yaml

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type: String
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implementation:
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container:
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image: gcr.io/ml-pipeline/ml-pipeline-gcp:latest
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image: gcr.io/ml-pipeline/ml-pipeline-gcp:f98ec68488cfbac022893a87c5bca083f03f2065
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args: [
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kfp_component.google.dataflow, launch_python,
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--python_file_path, {inputValue: python_file_path},

‎components/gcp/dataflow/launch_template/component.yaml

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type: String
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implementation:
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container:
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image: gcr.io/ml-pipeline/ml-pipeline-gcp:latest
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image: gcr.io/ml-pipeline/ml-pipeline-gcp:f98ec68488cfbac022893a87c5bca083f03f2065
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args: [
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kfp_component.google.dataflow, launch_template,
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--project_id, {inputValue: project_id},

‎components/gcp/dataproc/create_cluster/component.yaml

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type: String
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implementation:
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container:
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image: gcr.io/ml-pipeline/ml-pipeline-gcp:latest
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image: gcr.io/ml-pipeline/ml-pipeline-gcp:f98ec68488cfbac022893a87c5bca083f03f2065
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args: [
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kfp_component.google.dataproc, create_cluster,
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--project_id, {inputValue: project_id},

‎components/gcp/dataproc/delete_cluster/component.yaml

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@@ -33,7 +33,7 @@ inputs:
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type: Integer
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implementation:
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container:
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image: gcr.io/ml-pipeline/ml-pipeline-gcp:latest
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image: gcr.io/ml-pipeline/ml-pipeline-gcp:f98ec68488cfbac022893a87c5bca083f03f2065
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args: [
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kfp_component.google.dataproc, delete_cluster,
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--project_id, {inputValue: project_id},

‎components/gcp/dataproc/submit_hadoop_job/component.yaml

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type: String
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implementation:
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container:
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image: gcr.io/ml-pipeline/ml-pipeline-gcp:latest
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image: gcr.io/ml-pipeline/ml-pipeline-gcp:f98ec68488cfbac022893a87c5bca083f03f2065
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args: [
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kfp_component.google.dataproc, submit_hadoop_job,
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--project_id, {inputValue: project_id},

‎components/gcp/dataproc/submit_hive_job/component.yaml

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@@ -70,7 +70,7 @@ outputs:
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type: String
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implementation:
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container:
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image: gcr.io/ml-pipeline/ml-pipeline-gcp:latest
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image: gcr.io/ml-pipeline/ml-pipeline-gcp:f98ec68488cfbac022893a87c5bca083f03f2065
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args: [
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kfp_component.google.dataproc, submit_hive_job,
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--project_id, {inputValue: project_id},

‎components/gcp/dataproc/submit_pig_job/component.yaml

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type: String
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implementation:
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container:
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image: gcr.io/ml-pipeline/ml-pipeline-gcp:latest
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image: gcr.io/ml-pipeline/ml-pipeline-gcp:f98ec68488cfbac022893a87c5bca083f03f2065
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args: [
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kfp_component.google.dataproc, submit_pig_job,
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--project_id, {inputValue: project_id},

‎components/gcp/dataproc/submit_pyspark_job/component.yaml

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type: String
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implementation:
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container:
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image: gcr.io/ml-pipeline/ml-pipeline-gcp:latest
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image: gcr.io/ml-pipeline/ml-pipeline-gcp:f98ec68488cfbac022893a87c5bca083f03f2065
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args: [
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kfp_component.google.dataproc, submit_pyspark_job,
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--project_id, {inputValue: project_id},

‎components/gcp/dataproc/submit_spark_job/component.yaml

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@@ -71,7 +71,7 @@ outputs:
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type: String
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implementation:
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container:
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image: gcr.io/ml-pipeline/ml-pipeline-gcp:latest
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image: gcr.io/ml-pipeline/ml-pipeline-gcp:f98ec68488cfbac022893a87c5bca083f03f2065
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args: [
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kfp_component.google.dataproc, submit_spark_job,
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--project_id, {inputValue: project_id},

‎components/gcp/dataproc/submit_sparksql_job/component.yaml

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type: String
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implementation:
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container:
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image: gcr.io/ml-pipeline/ml-pipeline-gcp:latest
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image: gcr.io/ml-pipeline/ml-pipeline-gcp:f98ec68488cfbac022893a87c5bca083f03f2065
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args: [
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kfp_component.google.dataproc, submit_sparksql_job,
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--project_id, {inputValue: project_id},

‎components/gcp/ml_engine/batch_predict/component.yaml

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type: String
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implementation:
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container:
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image: gcr.io/ml-pipeline/ml-pipeline-gcp:latest
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image: gcr.io/ml-pipeline/ml-pipeline-gcp:f98ec68488cfbac022893a87c5bca083f03f2065
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args: [
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kfp_component.google.ml_engine, batch_predict,
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--project_id, {inputValue: project_id},

‎components/gcp/ml_engine/deploy/component.yaml

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type: String
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implementation:
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container:
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image: gcr.io/ml-pipeline/ml-pipeline-gcp:latest
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image: gcr.io/ml-pipeline/ml-pipeline-gcp:f98ec68488cfbac022893a87c5bca083f03f2065
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args: [
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kfp_component.google.ml_engine, deploy,
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--model_uri, {inputValue: model_uri},

‎components/gcp/ml_engine/train/component.yaml

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@@ -91,7 +91,7 @@ outputs:
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type: GCSPath
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implementation:
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container:
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image: gcr.io/ml-pipeline/ml-pipeline-gcp:latest
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image: gcr.io/ml-pipeline/ml-pipeline-gcp:f98ec68488cfbac022893a87c5bca083f03f2065
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args: [
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kfp_component.google.ml_engine, train,
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--project_id, {inputValue: project_id},

‎components/kubeflow/launcher/kubeflow_tfjob_launcher_op.py

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@@ -17,7 +17,7 @@
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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'):
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return dsl.ContainerOp(
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name = step_name,
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image = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf:7775692adf28d6f79098e76e839986c9ee55dd61',
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image = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf:f98ec68488cfbac022893a87c5bca083f03f2065',
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arguments = [
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'--workers', number_of_workers,
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'--pss', number_of_parameter_servers,

‎components/kubeflow/launcher/src/train.template.yaml

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spec:
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containers:
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- name: tensorflow
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image: gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:7775692adf28d6f79098e76e839986c9ee55dd61
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image: gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:f98ec68488cfbac022893a87c5bca083f03f2065
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command:
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- python
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- -m
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spec:
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containers:
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- name: tensorflow
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image: gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:7775692adf28d6f79098e76e839986c9ee55dd61
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image: gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:f98ec68488cfbac022893a87c5bca083f03f2065
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command:
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- python
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- -m
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spec:
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containers:
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- name: tensorflow
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image: gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:7775692adf28d6f79098e76e839986c9ee55dd61
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image: gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:f98ec68488cfbac022893a87c5bca083f03f2065
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command:
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- python
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- -m

‎samples/kubeflow-tf/kubeflow-training-classification.py

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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'):
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return dsl.ContainerOp(
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name = step_name,
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image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tft:7775692adf28d6f79098e76e839986c9ee55dd61',
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image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tft:f98ec68488cfbac022893a87c5bca083f03f2065',
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arguments = [
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'--train', train_data,
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'--eval', evaluation_data,
@@ -38,7 +38,7 @@ def dataflow_tf_transform_op(train_data: 'GcsUri', evaluation_data: 'GcsUri', sc
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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):
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kubeflow_tf_training_op = dsl.ContainerOp(
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name = step_name,
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image = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:7775692adf28d6f79098e76e839986c9ee55dd61',
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image = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:f98ec68488cfbac022893a87c5bca083f03f2065',
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arguments = [
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'--transformed-data-dir', transformed_data_dir,
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'--schema', schema,
@@ -52,15 +52,15 @@ def kubeflow_tf_training_op(transformed_data_dir, schema: 'GcsUri[text/json]', l
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file_outputs = {'train': '/output.txt'}
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)
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if use_gpu:
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kubeflow_tf_training_op.image = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer-gpu:7775692adf28d6f79098e76e839986c9ee55dd61'
55+
kubeflow_tf_training_op.image = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer-gpu:f98ec68488cfbac022893a87c5bca083f03f2065'
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kubeflow_tf_training_op.set_gpu_limit(1)
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return kubeflow_tf_training_op
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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'):
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return dsl.ContainerOp(
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name = step_name,
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image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tf-predict:7775692adf28d6f79098e76e839986c9ee55dd61',
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image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tf-predict:f98ec68488cfbac022893a87c5bca083f03f2065',
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arguments = [
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'--data', evaluation_data,
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'--schema', schema,
@@ -76,7 +76,7 @@ def dataflow_tf_predict_op(evaluation_data: 'GcsUri', schema: 'GcsUri[text/json]
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def confusion_matrix_op(predictions, output, step_name='confusionmatrix'):
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return dsl.ContainerOp(
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name = step_name,
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image = 'gcr.io/ml-pipeline/ml-pipeline-local-confusion-matrix:7775692adf28d6f79098e76e839986c9ee55dd61',
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image = 'gcr.io/ml-pipeline/ml-pipeline-local-confusion-matrix:f98ec68488cfbac022893a87c5bca083f03f2065',
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arguments = [
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'--predictions', predictions,
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'--output', output,

‎samples/notebooks/KubeFlow Pipeline Using TFX OSS Components.ipynb

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"EVAL_DATA = 'gs://ml-pipeline-playground/tfx/taxi-cab-classification/eval.csv'\n",
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"HIDDEN_LAYER_SIZE = '1500'\n",
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"STEPS = 3000\n",
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"DATAFLOW_TFDV_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tfdv:7775692adf28d6f79098e76e839986c9ee55dd61'\n",
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"DATAFLOW_TFT_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tft:7775692adf28d6f79098e76e839986c9ee55dd61'\n",
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"DATAFLOW_TFMA_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tfma:7775692adf28d6f79098e76e839986c9ee55dd61'\n",
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"DATAFLOW_TF_PREDICT_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tf-predict:7775692adf28d6f79098e76e839986c9ee55dd61'\n",
51-
"KUBEFLOW_TF_TRAINER_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:7775692adf28d6f79098e76e839986c9ee55dd61'\n",
52-
"KUBEFLOW_TF_TRAINER_GPU_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer-gpu:7775692adf28d6f79098e76e839986c9ee55dd61'\n",
53-
"KUBEFLOW_DEPLOYER_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-deployer:7775692adf28d6f79098e76e839986c9ee55dd61'\n",
47+
"DATAFLOW_TFDV_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tfdv:f98ec68488cfbac022893a87c5bca083f03f2065'\n",
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"DATAFLOW_TFT_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tft:f98ec68488cfbac022893a87c5bca083f03f2065'\n",
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"DATAFLOW_TFMA_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tfma:f98ec68488cfbac022893a87c5bca083f03f2065'\n",
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"DATAFLOW_TF_PREDICT_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tf-predict:f98ec68488cfbac022893a87c5bca083f03f2065'\n",
51+
"KUBEFLOW_TF_TRAINER_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:f98ec68488cfbac022893a87c5bca083f03f2065'\n",
52+
"KUBEFLOW_TF_TRAINER_GPU_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer-gpu:f98ec68488cfbac022893a87c5bca083f03f2065'\n",
53+
"KUBEFLOW_DEPLOYER_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-deployer:f98ec68488cfbac022893a87c5bca083f03f2065'\n",
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"DEPLOYER_MODEL = 'notebook_tfx_taxi'\n",
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"DEPLOYER_VERSION_DEV = 'dev'\n",
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"DEPLOYER_VERSION_PROD = 'prod'\n",

‎samples/resnet-cmle/resnet-train-pipeline.py

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@@ -22,7 +22,7 @@ def resnet_preprocess_op(project_id: 'GcpProject', output: 'GcsUri', train_csv:
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validation_csv: 'GcsUri[text/csv]', labels, step_name='preprocess'):
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return dsl.ContainerOp(
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name = step_name,
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image = 'gcr.io/ml-pipeline/resnet-preprocess:7775692adf28d6f79098e76e839986c9ee55dd61',
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image = 'gcr.io/ml-pipeline/resnet-preprocess:f98ec68488cfbac022893a87c5bca083f03f2065',
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arguments = [
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'--project_id', project_id,
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'--output', output,
@@ -38,7 +38,7 @@ def resnet_train_op(data_dir, output: 'GcsUri', region: 'GcpRegion', depth: int,
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num_eval_images: int, num_label_classes: int, tf_version, step_name='train'):
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return dsl.ContainerOp(
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name = step_name,
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image = 'gcr.io/ml-pipeline/resnet-train:7775692adf28d6f79098e76e839986c9ee55dd61',
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image = 'gcr.io/ml-pipeline/resnet-train:f98ec68488cfbac022893a87c5bca083f03f2065',
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arguments = [
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'--data_dir', data_dir,
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'--output', output,
@@ -60,7 +60,7 @@ def resnet_deploy_op(model_dir, model, version, project_id: 'GcpProject', region
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tf_version, step_name='deploy'):
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return dsl.ContainerOp(
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name = step_name,
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image = 'gcr.io/ml-pipeline/resnet-deploy:7775692adf28d6f79098e76e839986c9ee55dd61',
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image = 'gcr.io/ml-pipeline/resnet-deploy:f98ec68488cfbac022893a87c5bca083f03f2065',
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arguments = [
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'--model', model,
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'--version', version,

‎samples/tfx/taxi-cab-classification-pipeline.py

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@@ -21,7 +21,7 @@
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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'):
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return dsl.ContainerOp(
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name = step_name,
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image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tfdv:7775692adf28d6f79098e76e839986c9ee55dd61',
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image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tfdv:f98ec68488cfbac022893a87c5bca083f03f2065',
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arguments = [
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'--csv-data-for-inference', inference_data,
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'--csv-data-to-validate', validation_data,
@@ -40,7 +40,7 @@ def dataflow_tf_data_validation_op(inference_data: 'GcsUri', validation_data: 'G
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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'):
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return dsl.ContainerOp(
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name = step_name,
43-
image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tft:7775692adf28d6f79098e76e839986c9ee55dd61',
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image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tft:f98ec68488cfbac022893a87c5bca083f03f2065',
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arguments = [
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'--train', train_data,
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'--eval', evaluation_data,
@@ -57,7 +57,7 @@ def dataflow_tf_transform_op(train_data: 'GcsUri', evaluation_data: 'GcsUri', sc
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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'):
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return dsl.ContainerOp(
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name = step_name,
60-
image = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:7775692adf28d6f79098e76e839986c9ee55dd61',
60+
image = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:f98ec68488cfbac022893a87c5bca083f03f2065',
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arguments = [
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'--transformed-data-dir', transformed_data_dir,
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'--schema', schema,
@@ -74,7 +74,7 @@ def tf_train_op(transformed_data_dir, schema: 'GcsUri[text/json]', learning_rate
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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'):
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return dsl.ContainerOp(
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name = step_name,
77-
image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tfma:7775692adf28d6f79098e76e839986c9ee55dd61',
77+
image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tfma:f98ec68488cfbac022893a87c5bca083f03f2065',
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arguments = [
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'--model', model,
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'--eval', evaluation_data,
@@ -91,7 +91,7 @@ def dataflow_tf_model_analyze_op(model: 'TensorFlow model', evaluation_data: 'Gc
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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'):
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return dsl.ContainerOp(
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name = step_name,
94-
image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tf-predict:7775692adf28d6f79098e76e839986c9ee55dd61',
94+
image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tf-predict:f98ec68488cfbac022893a87c5bca083f03f2065',
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arguments = [
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'--data', evaluation_data,
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'--schema', schema,
@@ -108,7 +108,7 @@ def dataflow_tf_predict_op(evaluation_data: 'GcsUri', schema: 'GcsUri[text/json]
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def confusion_matrix_op(predictions: 'GcsUri', output: 'GcsUri', step_name='confusion_matrix'):
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return dsl.ContainerOp(
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name=step_name,
111-
image='gcr.io/ml-pipeline/ml-pipeline-local-confusion-matrix:7775692adf28d6f79098e76e839986c9ee55dd61',
111+
image='gcr.io/ml-pipeline/ml-pipeline-local-confusion-matrix:f98ec68488cfbac022893a87c5bca083f03f2065',
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arguments=[
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'--output', '%s/{{workflow.name}}/confusionmatrix' % output,
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'--predictions', predictions,
@@ -119,7 +119,7 @@ def confusion_matrix_op(predictions: 'GcsUri', output: 'GcsUri', step_name='conf
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def roc_op(predictions: 'GcsUri', output: 'GcsUri', step_name='roc'):
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return dsl.ContainerOp(
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name=step_name,
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image='gcr.io/ml-pipeline/ml-pipeline-local-roc:7775692adf28d6f79098e76e839986c9ee55dd61',
122+
image='gcr.io/ml-pipeline/ml-pipeline-local-roc:f98ec68488cfbac022893a87c5bca083f03f2065',
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arguments=[
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'--output', '%s/{{workflow.name}}/roc' % output,
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'--predictions', predictions,
@@ -130,7 +130,7 @@ def roc_op(predictions: 'GcsUri', output: 'GcsUri', step_name='roc'):
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def kubeflow_deploy_op(model: 'TensorFlow model', tf_server_name, step_name='deploy'):
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return dsl.ContainerOp(
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name = step_name,
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image = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-deployer:7775692adf28d6f79098e76e839986c9ee55dd61',
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image = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-deployer:f98ec68488cfbac022893a87c5bca083f03f2065',
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arguments = [
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'--model-export-path', '%s/export/export' % model,
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'--server-name', tf_server_name

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