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‎components/kubeflow/launcher/kubeflow_tfjob_launcher_op.py

+1-1
Original file line numberDiff line numberDiff line change
@@ -17,7 +17,7 @@
1717
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'):
1818
return dsl.ContainerOp(
1919
name = step_name,
20-
image = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf:65d0f6a1a3b1a4c2254a4398cc6b92550803fe62',
20+
image = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf:d3c4add0a95e930c70a330466d0923827784eb9a',
2121
arguments = [
2222
'--workers', number_of_workers,
2323
'--pss', number_of_parameter_servers,

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

+3-3
Original file line numberDiff line numberDiff line change
@@ -26,7 +26,7 @@ spec:
2626
spec:
2727
containers:
2828
- name: tensorflow
29-
image: gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:65d0f6a1a3b1a4c2254a4398cc6b92550803fe62
29+
image: gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:d3c4add0a95e930c70a330466d0923827784eb9a
3030
command:
3131
- python
3232
- -m
@@ -38,7 +38,7 @@ spec:
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spec:
3939
containers:
4040
- name: tensorflow
41-
image: gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:65d0f6a1a3b1a4c2254a4398cc6b92550803fe62
41+
image: gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:d3c4add0a95e930c70a330466d0923827784eb9a
4242
command:
4343
- python
4444
- -m
@@ -50,7 +50,7 @@ spec:
5050
spec:
5151
containers:
5252
- name: tensorflow
53-
image: gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:65d0f6a1a3b1a4c2254a4398cc6b92550803fe62
53+
image: gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:d3c4add0a95e930c70a330466d0923827784eb9a
5454
command:
5555
- python
5656
- -m

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

+5-5
Original file line numberDiff line numberDiff line change
@@ -21,7 +21,7 @@
2121
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'):
2222
return dsl.ContainerOp(
2323
name = step_name,
24-
image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tft:65d0f6a1a3b1a4c2254a4398cc6b92550803fe62',
24+
image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tft:d3c4add0a95e930c70a330466d0923827784eb9a',
2525
arguments = [
2626
'--train', train_data,
2727
'--eval', evaluation_data,
@@ -38,7 +38,7 @@ def dataflow_tf_transform_op(train_data: 'GcsUri', evaluation_data: 'GcsUri', sc
3838
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):
3939
kubeflow_tf_training_op = dsl.ContainerOp(
4040
name = step_name,
41-
image = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:65d0f6a1a3b1a4c2254a4398cc6b92550803fe62',
41+
image = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:d3c4add0a95e930c70a330466d0923827784eb9a',
4242
arguments = [
4343
'--transformed-data-dir', transformed_data_dir,
4444
'--schema', schema,
@@ -52,15 +52,15 @@ def kubeflow_tf_training_op(transformed_data_dir, schema: 'GcsUri[text/json]', l
5252
file_outputs = {'train': '/output.txt'}
5353
)
5454
if use_gpu:
55-
kubeflow_tf_training_op.image = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer-gpu:65d0f6a1a3b1a4c2254a4398cc6b92550803fe62'
55+
kubeflow_tf_training_op.image = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer-gpu:d3c4add0a95e930c70a330466d0923827784eb9a'
5656
kubeflow_tf_training_op.set_gpu_limit(1)
5757

5858
return kubeflow_tf_training_op
5959

6060
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'):
6161
return dsl.ContainerOp(
6262
name = step_name,
63-
image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tf-predict:65d0f6a1a3b1a4c2254a4398cc6b92550803fe62',
63+
image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tf-predict:d3c4add0a95e930c70a330466d0923827784eb9a',
6464
arguments = [
6565
'--data', evaluation_data,
6666
'--schema', schema,
@@ -76,7 +76,7 @@ def dataflow_tf_predict_op(evaluation_data: 'GcsUri', schema: 'GcsUri[text/json]
7676
def confusion_matrix_op(predictions, output, step_name='confusionmatrix'):
7777
return dsl.ContainerOp(
7878
name = step_name,
79-
image = 'gcr.io/ml-pipeline/ml-pipeline-local-confusion-matrix:65d0f6a1a3b1a4c2254a4398cc6b92550803fe62',
79+
image = 'gcr.io/ml-pipeline/ml-pipeline-local-confusion-matrix:d3c4add0a95e930c70a330466d0923827784eb9a',
8080
arguments = [
8181
'--predictions', predictions,
8282
'--output', output,

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

+7-7
Original file line numberDiff line numberDiff line change
@@ -43,13 +43,13 @@
4343
"EVAL_DATA = 'gs://ml-pipeline-playground/tfx/taxi-cab-classification/eval.csv'\n",
4444
"HIDDEN_LAYER_SIZE = '1500'\n",
4545
"STEPS = 3000\n",
46-
"DATAFLOW_TFDV_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tfdv:65d0f6a1a3b1a4c2254a4398cc6b92550803fe62'\n",
47-
"DATAFLOW_TFT_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tft:65d0f6a1a3b1a4c2254a4398cc6b92550803fe62'\n",
48-
"DATAFLOW_TFMA_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tfma:65d0f6a1a3b1a4c2254a4398cc6b92550803fe62'\n",
49-
"DATAFLOW_TF_PREDICT_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tf-predict:65d0f6a1a3b1a4c2254a4398cc6b92550803fe62'\n",
50-
"KUBEFLOW_TF_TRAINER_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:65d0f6a1a3b1a4c2254a4398cc6b92550803fe62'\n",
51-
"KUBEFLOW_TF_TRAINER_GPU_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer-gpu:65d0f6a1a3b1a4c2254a4398cc6b92550803fe62'\n",
52-
"KUBEFLOW_DEPLOYER_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-deployer:65d0f6a1a3b1a4c2254a4398cc6b92550803fe62'\n",
46+
"DATAFLOW_TFDV_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tfdv:d3c4add0a95e930c70a330466d0923827784eb9a'\n",
47+
"DATAFLOW_TFT_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tft:d3c4add0a95e930c70a330466d0923827784eb9a'\n",
48+
"DATAFLOW_TFMA_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tfma:d3c4add0a95e930c70a330466d0923827784eb9a'\n",
49+
"DATAFLOW_TF_PREDICT_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tf-predict:d3c4add0a95e930c70a330466d0923827784eb9a'\n",
50+
"KUBEFLOW_TF_TRAINER_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:d3c4add0a95e930c70a330466d0923827784eb9a'\n",
51+
"KUBEFLOW_TF_TRAINER_GPU_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer-gpu:d3c4add0a95e930c70a330466d0923827784eb9a'\n",
52+
"KUBEFLOW_DEPLOYER_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-deployer:d3c4add0a95e930c70a330466d0923827784eb9a'\n",
5353
"DEPLOYER_MODEL = 'notebook_tfx_taxi'\n",
5454
"DEPLOYER_VERSION_DEV = 'dev'\n",
5555
"DEPLOYER_VERSION_PROD = 'prod'"

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

+3-3
Original file line numberDiff line numberDiff line change
@@ -22,7 +22,7 @@ def resnet_preprocess_op(project_id: 'GcpProject', output: 'GcsUri', train_csv:
2222
validation_csv: 'GcsUri[text/csv]', labels, step_name='preprocess'):
2323
return dsl.ContainerOp(
2424
name = step_name,
25-
image = 'gcr.io/ml-pipeline/resnet-preprocess:65d0f6a1a3b1a4c2254a4398cc6b92550803fe62',
25+
image = 'gcr.io/ml-pipeline/resnet-preprocess:d3c4add0a95e930c70a330466d0923827784eb9a',
2626
arguments = [
2727
'--project_id', project_id,
2828
'--output', output,
@@ -38,7 +38,7 @@ def resnet_train_op(data_dir, output: 'GcsUri', region: 'GcpRegion', depth: int,
3838
num_eval_images: int, num_label_classes: int, tf_version, step_name='train'):
3939
return dsl.ContainerOp(
4040
name = step_name,
41-
image = 'gcr.io/ml-pipeline/resnet-train:65d0f6a1a3b1a4c2254a4398cc6b92550803fe62',
41+
image = 'gcr.io/ml-pipeline/resnet-train:d3c4add0a95e930c70a330466d0923827784eb9a',
4242
arguments = [
4343
'--data_dir', data_dir,
4444
'--output', output,
@@ -60,7 +60,7 @@ def resnet_deploy_op(model_dir, model, version, project_id: 'GcpProject', region
6060
tf_version, step_name='deploy'):
6161
return dsl.ContainerOp(
6262
name = step_name,
63-
image = 'gcr.io/ml-pipeline/resnet-deploy:65d0f6a1a3b1a4c2254a4398cc6b92550803fe62',
63+
image = 'gcr.io/ml-pipeline/resnet-deploy:d3c4add0a95e930c70a330466d0923827784eb9a',
6464
arguments = [
6565
'--model', model,
6666
'--version', version,

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

+8-8
Original file line numberDiff line numberDiff line change
@@ -21,7 +21,7 @@
2121
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'):
2222
return dsl.ContainerOp(
2323
name = step_name,
24-
image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tfdv:65d0f6a1a3b1a4c2254a4398cc6b92550803fe62',
24+
image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tfdv:d3c4add0a95e930c70a330466d0923827784eb9a',
2525
arguments = [
2626
'--csv-data-for-inference', inference_data,
2727
'--csv-data-to-validate', validation_data,
@@ -40,7 +40,7 @@ def dataflow_tf_data_validation_op(inference_data: 'GcsUri', validation_data: 'G
4040
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'):
4141
return dsl.ContainerOp(
4242
name = step_name,
43-
image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tft:65d0f6a1a3b1a4c2254a4398cc6b92550803fe62',
43+
image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tft:d3c4add0a95e930c70a330466d0923827784eb9a',
4444
arguments = [
4545
'--train', train_data,
4646
'--eval', evaluation_data,
@@ -57,7 +57,7 @@ def dataflow_tf_transform_op(train_data: 'GcsUri', evaluation_data: 'GcsUri', sc
5757
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'):
5858
return dsl.ContainerOp(
5959
name = step_name,
60-
image = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:65d0f6a1a3b1a4c2254a4398cc6b92550803fe62',
60+
image = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:d3c4add0a95e930c70a330466d0923827784eb9a',
6161
arguments = [
6262
'--transformed-data-dir', transformed_data_dir,
6363
'--schema', schema,
@@ -74,7 +74,7 @@ def tf_train_op(transformed_data_dir, schema: 'GcsUri[text/json]', learning_rate
7474
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'):
7575
return dsl.ContainerOp(
7676
name = step_name,
77-
image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tfma:65d0f6a1a3b1a4c2254a4398cc6b92550803fe62',
77+
image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tfma:d3c4add0a95e930c70a330466d0923827784eb9a',
7878
arguments = [
7979
'--model', model,
8080
'--eval', evaluation_data,
@@ -91,7 +91,7 @@ def dataflow_tf_model_analyze_op(model: 'TensorFlow model', evaluation_data: 'Gc
9191
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'):
9292
return dsl.ContainerOp(
9393
name = step_name,
94-
image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tf-predict:65d0f6a1a3b1a4c2254a4398cc6b92550803fe62',
94+
image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tf-predict:d3c4add0a95e930c70a330466d0923827784eb9a',
9595
arguments = [
9696
'--data', evaluation_data,
9797
'--schema', schema,
@@ -108,7 +108,7 @@ def dataflow_tf_predict_op(evaluation_data: 'GcsUri', schema: 'GcsUri[text/json]
108108
def confusion_matrix_op(predictions: 'GcsUri', output: 'GcsUri', step_name='confusion_matrix'):
109109
return dsl.ContainerOp(
110110
name=step_name,
111-
image='gcr.io/ml-pipeline/ml-pipeline-local-confusion-matrix:65d0f6a1a3b1a4c2254a4398cc6b92550803fe62',
111+
image='gcr.io/ml-pipeline/ml-pipeline-local-confusion-matrix:d3c4add0a95e930c70a330466d0923827784eb9a',
112112
arguments=[
113113
'--output', '%s/{{workflow.name}}/confusionmatrix' % output,
114114
'--predictions', predictions,
@@ -119,7 +119,7 @@ def confusion_matrix_op(predictions: 'GcsUri', output: 'GcsUri', step_name='conf
119119
def roc_op(predictions: 'GcsUri', output: 'GcsUri', step_name='roc'):
120120
return dsl.ContainerOp(
121121
name=step_name,
122-
image='gcr.io/ml-pipeline/ml-pipeline-local-roc:65d0f6a1a3b1a4c2254a4398cc6b92550803fe62',
122+
image='gcr.io/ml-pipeline/ml-pipeline-local-roc:d3c4add0a95e930c70a330466d0923827784eb9a',
123123
arguments=[
124124
'--output', '%s/{{workflow.name}}/roc' % output,
125125
'--predictions', predictions,
@@ -130,7 +130,7 @@ def roc_op(predictions: 'GcsUri', output: 'GcsUri', step_name='roc'):
130130
def kubeflow_deploy_op(model: 'TensorFlow model', tf_server_name, step_name='deploy'):
131131
return dsl.ContainerOp(
132132
name = step_name,
133-
image = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-deployer:65d0f6a1a3b1a4c2254a4398cc6b92550803fe62',
133+
image = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-deployer:d3c4add0a95e930c70a330466d0923827784eb9a',
134134
arguments = [
135135
'--model-path', model,
136136
'--server-name', tf_server_name

‎samples/xgboost-spark/xgboost-training-cm.py

+8-8
Original file line numberDiff line numberDiff line change
@@ -26,7 +26,7 @@ class CreateClusterOp(dsl.ContainerOp):
2626
def __init__(self, name, project, region, staging):
2727
super(CreateClusterOp, self).__init__(
2828
name=name,
29-
image='gcr.io/ml-pipeline/ml-pipeline-dataproc-create-cluster:65d0f6a1a3b1a4c2254a4398cc6b92550803fe62',
29+
image='gcr.io/ml-pipeline/ml-pipeline-dataproc-create-cluster:d3c4add0a95e930c70a330466d0923827784eb9a',
3030
arguments=[
3131
'--project', project,
3232
'--region', region,
@@ -41,7 +41,7 @@ class DeleteClusterOp(dsl.ContainerOp):
4141
def __init__(self, name, project, region):
4242
super(DeleteClusterOp, self).__init__(
4343
name=name,
44-
image='gcr.io/ml-pipeline/ml-pipeline-dataproc-delete-cluster:65d0f6a1a3b1a4c2254a4398cc6b92550803fe62',
44+
image='gcr.io/ml-pipeline/ml-pipeline-dataproc-delete-cluster:d3c4add0a95e930c70a330466d0923827784eb9a',
4545
arguments=[
4646
'--project', project,
4747
'--region', region,
@@ -55,7 +55,7 @@ class AnalyzeOp(dsl.ContainerOp):
5555
def __init__(self, name, project, region, cluster_name, schema, train_data, output):
5656
super(AnalyzeOp, self).__init__(
5757
name=name,
58-
image='gcr.io/ml-pipeline/ml-pipeline-dataproc-analyze:65d0f6a1a3b1a4c2254a4398cc6b92550803fe62',
58+
image='gcr.io/ml-pipeline/ml-pipeline-dataproc-analyze:d3c4add0a95e930c70a330466d0923827784eb9a',
5959
arguments=[
6060
'--project', project,
6161
'--region', region,
@@ -73,7 +73,7 @@ def __init__(self, name, project, region, cluster_name, train_data, eval_data,
7373
target, analysis, output):
7474
super(TransformOp, self).__init__(
7575
name=name,
76-
image='gcr.io/ml-pipeline/ml-pipeline-dataproc-transform:65d0f6a1a3b1a4c2254a4398cc6b92550803fe62',
76+
image='gcr.io/ml-pipeline/ml-pipeline-dataproc-transform:d3c4add0a95e930c70a330466d0923827784eb9a',
7777
arguments=[
7878
'--project', project,
7979
'--region', region,
@@ -98,7 +98,7 @@ def __init__(self, name, project, region, cluster_name, train_data, eval_data,
9898

9999
super(TrainerOp, self).__init__(
100100
name=name,
101-
image='gcr.io/ml-pipeline/ml-pipeline-dataproc-train:65d0f6a1a3b1a4c2254a4398cc6b92550803fe62',
101+
image='gcr.io/ml-pipeline/ml-pipeline-dataproc-train:d3c4add0a95e930c70a330466d0923827784eb9a',
102102
arguments=[
103103
'--project', project,
104104
'--region', region,
@@ -121,7 +121,7 @@ class PredictOp(dsl.ContainerOp):
121121
def __init__(self, name, project, region, cluster_name, data, model, target, analysis, output):
122122
super(PredictOp, self).__init__(
123123
name=name,
124-
image='gcr.io/ml-pipeline/ml-pipeline-dataproc-predict:65d0f6a1a3b1a4c2254a4398cc6b92550803fe62',
124+
image='gcr.io/ml-pipeline/ml-pipeline-dataproc-predict:d3c4add0a95e930c70a330466d0923827784eb9a',
125125
arguments=[
126126
'--project', project,
127127
'--region', region,
@@ -141,7 +141,7 @@ class ConfusionMatrixOp(dsl.ContainerOp):
141141
def __init__(self, name, predictions, output):
142142
super(ConfusionMatrixOp, self).__init__(
143143
name=name,
144-
image='gcr.io/ml-pipeline/ml-pipeline-local-confusion-matrix:65d0f6a1a3b1a4c2254a4398cc6b92550803fe62',
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image='gcr.io/ml-pipeline/ml-pipeline-local-confusion-matrix:d3c4add0a95e930c70a330466d0923827784eb9a',
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arguments=[
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'--output', output,
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'--predictions', predictions
@@ -153,7 +153,7 @@ class RocOp(dsl.ContainerOp):
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def __init__(self, name, predictions, trueclass, output):
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super(RocOp, self).__init__(
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name=name,
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image='gcr.io/ml-pipeline/ml-pipeline-local-roc:65d0f6a1a3b1a4c2254a4398cc6b92550803fe62',
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image='gcr.io/ml-pipeline/ml-pipeline-local-roc:d3c4add0a95e930c70a330466d0923827784eb9a',
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arguments=[
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'--output', output,
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'--predictions', predictions,

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