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Revert dataflow component version in samples to unblock release #817

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Feb 15, 2019
Merged

Revert dataflow component version in samples to unblock release #817

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8 changes: 4 additions & 4 deletions samples/notebooks/KubeFlow Pipeline Using TFX OSS Components.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -43,10 +43,10 @@
"EVAL_DATA = 'gs://ml-pipeline-playground/tfx/taxi-cab-classification/eval.csv'\n",
"HIDDEN_LAYER_SIZE = '1500'\n",
"STEPS = 3000\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:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f'\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:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f'\n",
"DATAFLOW_TFDV_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tfdv:6ad2601ec7d04e842c212c50d5c78e548e12ddea'\n",
"DATAFLOW_TFT_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tft:6ad2601ec7d04e842c212c50d5c78e548e12ddea'\n",
"DATAFLOW_TFMA_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tfma:6ad2601ec7d04e842c212c50d5c78e548e12ddea'\n",
"DATAFLOW_TF_PREDICT_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tf-predict: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:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f'\n",
"KUBEFLOW_DEPLOYER_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-deployer:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f'\n",
Expand Down
8 changes: 4 additions & 4 deletions samples/tfx/taxi-cab-classification-pipeline.py
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Original file line number Diff line number Diff line change
Expand Up @@ -21,7 +21,7 @@
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:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f',
image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tfdv:6ad2601ec7d04e842c212c50d5c78e548e12ddea',
arguments = [
'--csv-data-for-inference', inference_data,
'--csv-data-to-validate', validation_data,
Expand All @@ -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'):
return dsl.ContainerOp(
name = step_name,
image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tft:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f',
image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tft:6ad2601ec7d04e842c212c50d5c78e548e12ddea',
arguments = [
'--train', train_data,
'--eval', evaluation_data,
Expand Down Expand Up @@ -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'):
return dsl.ContainerOp(
name = step_name,
image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tfma:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f',
image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tfma:6ad2601ec7d04e842c212c50d5c78e548e12ddea',
arguments = [
'--model', model,
'--eval', evaluation_data,
Expand All @@ -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'):
return dsl.ContainerOp(
name = step_name,
image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tf-predict:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f',
image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tf-predict:6ad2601ec7d04e842c212c50d5c78e548e12ddea',
arguments = [
'--data', evaluation_data,
'--schema', schema,
Expand Down