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Rolling back TFMA image #1095

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Apr 6, 2019
Merged

Rolling back TFMA image #1095

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2 changes: 1 addition & 1 deletion components/dataflow/tfma/component.yaml
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Expand Up @@ -17,7 +17,7 @@ outputs:
- {name: Analysis results dir, type: GCPPath, description: GCS or local directory where the analysis results should were written.} # type: {GCSPath: {path_type: Directory}}
implementation:
container:
image: gcr.io/ml-pipeline/ml-pipeline-dataflow-tfma:a277f87ea1d4707bf860d080d06639b7caf9a1cf
image: gcr.io/ml-pipeline/ml-pipeline-dataflow-tfma:2c2445df83fa879387a200747cc20f72a7ee9727
command: [python2, /ml/model_analysis.py]
args: [
--model, {inputValue: Model},
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2 changes: 1 addition & 1 deletion samples/notebooks/KubeFlow Pipeline Using TFX OSS Components.ipynb
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Expand Up @@ -46,7 +46,7 @@
"STEPS = 3000\n",
"DATAFLOW_TFDV_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tfdv:a277f87ea1d4707bf860d080d06639b7caf9a1cf'\n",
"DATAFLOW_TFT_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tft:a277f87ea1d4707bf860d080d06639b7caf9a1cf'\n",
"DATAFLOW_TFMA_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tfma:a277f87ea1d4707bf860d080d06639b7caf9a1cf'\n",
"DATAFLOW_TFMA_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tfma:2c2445df83fa879387a200747cc20f72a7ee9727'\n",
"DATAFLOW_TF_PREDICT_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tf-predict:a277f87ea1d4707bf860d080d06639b7caf9a1cf'\n",
"KUBEFLOW_TF_TRAINER_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:a277f87ea1d4707bf860d080d06639b7caf9a1cf'\n",
"KUBEFLOW_TF_TRAINER_GPU_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer-gpu:a277f87ea1d4707bf860d080d06639b7caf9a1cf'\n",
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2 changes: 1 addition & 1 deletion samples/tfx/taxi-cab-classification-pipeline.py
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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:a277f87ea1d4707bf860d080d06639b7caf9a1cf',
image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tfma:2c2445df83fa879387a200747cc20f72a7ee9727',
arguments = [
'--model', model,
'--eval', evaluation_data,
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