Sample updates for release v0.1.10 (#816)
This commit is contained in:
hongye-sun
Kubernetes Prow Robot
parent
5df2cdc1ed
commit
7c633a97d7
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@ -17,7 +17,7 @@ from kfp import dsl
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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:6ad2601ec7d04e842c212c50d5c78e548e12ddea',
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image = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f',
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arguments = [
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'--workers', number_of_workers,
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'--pss', number_of_parameter_servers,
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@ -26,7 +26,7 @@ spec:
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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:6ad2601ec7d04e842c212c50d5c78e548e12ddea
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image: gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f
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command:
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- python
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- -m
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@ -49,7 +49,7 @@ spec:
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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:6ad2601ec7d04e842c212c50d5c78e548e12ddea
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image: gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f
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command:
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- python
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- -m
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@ -72,7 +72,7 @@ spec:
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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:6ad2601ec7d04e842c212c50d5c78e548e12ddea
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image: gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f
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command:
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- python
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- -m
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@ -21,7 +21,7 @@ import datetime
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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:6ad2601ec7d04e842c212c50d5c78e548e12ddea',
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image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tft:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f',
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arguments = [
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'--train', train_data,
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'--eval', evaluation_data,
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@ -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:6ad2601ec7d04e842c212c50d5c78e548e12ddea',
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image = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f',
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arguments = [
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'--transformed-data-dir', transformed_data_dir,
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'--schema', schema,
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@ -52,7 +52,7 @@ 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:6ad2601ec7d04e842c212c50d5c78e548e12ddea'
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kubeflow_tf_training_op.image = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer-gpu:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f'
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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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@ -60,7 +60,7 @@ def kubeflow_tf_training_op(transformed_data_dir, schema: 'GcsUri[text/json]', l
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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:6ad2601ec7d04e842c212c50d5c78e548e12ddea',
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image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tf-predict:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f',
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arguments = [
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'--data', evaluation_data,
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'--schema', schema,
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@ -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:6ad2601ec7d04e842c212c50d5c78e548e12ddea',
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image = 'gcr.io/ml-pipeline/ml-pipeline-local-confusion-matrix:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f',
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arguments = [
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'--predictions', predictions,
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'--output', output,
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@ -43,13 +43,13 @@
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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:6ad2601ec7d04e842c212c50d5c78e548e12ddea'\n",
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"DATAFLOW_TFT_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tft:6ad2601ec7d04e842c212c50d5c78e548e12ddea'\n",
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"DATAFLOW_TFMA_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tfma:6ad2601ec7d04e842c212c50d5c78e548e12ddea'\n",
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"DATAFLOW_TF_PREDICT_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tf-predict:6ad2601ec7d04e842c212c50d5c78e548e12ddea'\n",
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"KUBEFLOW_TF_TRAINER_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:6ad2601ec7d04e842c212c50d5c78e548e12ddea'\n",
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"KUBEFLOW_TF_TRAINER_GPU_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer-gpu:6ad2601ec7d04e842c212c50d5c78e548e12ddea'\n",
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"KUBEFLOW_DEPLOYER_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-deployer:6ad2601ec7d04e842c212c50d5c78e548e12ddea'\n",
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"DATAFLOW_TFDV_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tfdv:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f'\n",
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"DATAFLOW_TFT_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tft:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f'\n",
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"DATAFLOW_TFMA_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tfma:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f'\n",
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"DATAFLOW_TF_PREDICT_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tf-predict:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f'\n",
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"KUBEFLOW_TF_TRAINER_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f'\n",
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"KUBEFLOW_TF_TRAINER_GPU_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer-gpu:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f'\n",
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"KUBEFLOW_DEPLOYER_IMAGE = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-deployer:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f'\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'"
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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:6ad2601ec7d04e842c212c50d5c78e548e12ddea',
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image = 'gcr.io/ml-pipeline/resnet-preprocess:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f',
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arguments = [
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'--project_id', project_id,
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'--output', output,
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@ -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:6ad2601ec7d04e842c212c50d5c78e548e12ddea',
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image = 'gcr.io/ml-pipeline/resnet-train:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f',
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arguments = [
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'--data_dir', data_dir,
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'--output', output,
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@ -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:6ad2601ec7d04e842c212c50d5c78e548e12ddea',
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image = 'gcr.io/ml-pipeline/resnet-deploy:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f',
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arguments = [
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'--model', model,
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'--version', version,
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@ -21,7 +21,7 @@ import datetime
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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:6ad2601ec7d04e842c212c50d5c78e548e12ddea',
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image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tfdv:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f',
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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,
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@ -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,
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image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tft:6ad2601ec7d04e842c212c50d5c78e548e12ddea',
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image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tft:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f',
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arguments = [
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'--train', train_data,
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'--eval', evaluation_data,
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@ -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,
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image = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:6ad2601ec7d04e842c212c50d5c78e548e12ddea',
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image = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f',
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arguments = [
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'--transformed-data-dir', transformed_data_dir,
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'--schema', schema,
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@ -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,
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image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tfma:6ad2601ec7d04e842c212c50d5c78e548e12ddea',
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image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tfma:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f',
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arguments = [
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'--model', model,
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'--eval', evaluation_data,
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@ -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,
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image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tf-predict:6ad2601ec7d04e842c212c50d5c78e548e12ddea',
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image = 'gcr.io/ml-pipeline/ml-pipeline-dataflow-tf-predict:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f',
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arguments = [
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'--data', evaluation_data,
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'--schema', schema,
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@ -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,
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image='gcr.io/ml-pipeline/ml-pipeline-local-confusion-matrix:6ad2601ec7d04e842c212c50d5c78e548e12ddea',
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image='gcr.io/ml-pipeline/ml-pipeline-local-confusion-matrix:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f',
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arguments=[
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'--output', '%s/{{workflow.name}}/confusionmatrix' % output,
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'--predictions', predictions,
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@ -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:6ad2601ec7d04e842c212c50d5c78e548e12ddea',
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image='gcr.io/ml-pipeline/ml-pipeline-local-roc:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f',
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arguments=[
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'--output', '%s/{{workflow.name}}/roc' % output,
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'--predictions', predictions,
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@ -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:6ad2601ec7d04e842c212c50d5c78e548e12ddea',
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image = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-deployer:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f',
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arguments = [
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'--model-path', model,
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'--server-name', tf_server_name
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@ -26,7 +26,7 @@ class CreateClusterOp(dsl.ContainerOp):
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def __init__(self, name, project, region, staging):
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super(CreateClusterOp, self).__init__(
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name=name,
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image='gcr.io/ml-pipeline/ml-pipeline-dataproc-create-cluster:6ad2601ec7d04e842c212c50d5c78e548e12ddea',
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image='gcr.io/ml-pipeline/ml-pipeline-dataproc-create-cluster:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f',
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arguments=[
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'--project', project,
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'--region', region,
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@ -41,7 +41,7 @@ class DeleteClusterOp(dsl.ContainerOp):
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def __init__(self, name, project, region):
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super(DeleteClusterOp, self).__init__(
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name=name,
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image='gcr.io/ml-pipeline/ml-pipeline-dataproc-delete-cluster:6ad2601ec7d04e842c212c50d5c78e548e12ddea',
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image='gcr.io/ml-pipeline/ml-pipeline-dataproc-delete-cluster:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f',
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arguments=[
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'--project', project,
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'--region', region,
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@ -55,7 +55,7 @@ class AnalyzeOp(dsl.ContainerOp):
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def __init__(self, name, project, region, cluster_name, schema, train_data, output):
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super(AnalyzeOp, self).__init__(
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name=name,
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image='gcr.io/ml-pipeline/ml-pipeline-dataproc-analyze:6ad2601ec7d04e842c212c50d5c78e548e12ddea',
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image='gcr.io/ml-pipeline/ml-pipeline-dataproc-analyze:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f',
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arguments=[
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'--project', project,
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'--region', region,
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@ -73,7 +73,7 @@ class TransformOp(dsl.ContainerOp):
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target, analysis, output):
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super(TransformOp, self).__init__(
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name=name,
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image='gcr.io/ml-pipeline/ml-pipeline-dataproc-transform:6ad2601ec7d04e842c212c50d5c78e548e12ddea',
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image='gcr.io/ml-pipeline/ml-pipeline-dataproc-transform:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f',
|
||||
arguments=[
|
||||
'--project', project,
|
||||
'--region', region,
|
||||
|
|
@ -98,7 +98,7 @@ class TrainerOp(dsl.ContainerOp):
|
|||
|
||||
super(TrainerOp, self).__init__(
|
||||
name=name,
|
||||
image='gcr.io/ml-pipeline/ml-pipeline-dataproc-train:6ad2601ec7d04e842c212c50d5c78e548e12ddea',
|
||||
image='gcr.io/ml-pipeline/ml-pipeline-dataproc-train:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f',
|
||||
arguments=[
|
||||
'--project', project,
|
||||
'--region', region,
|
||||
|
|
@ -121,7 +121,7 @@ class PredictOp(dsl.ContainerOp):
|
|||
def __init__(self, name, project, region, cluster_name, data, model, target, analysis, output):
|
||||
super(PredictOp, self).__init__(
|
||||
name=name,
|
||||
image='gcr.io/ml-pipeline/ml-pipeline-dataproc-predict:6ad2601ec7d04e842c212c50d5c78e548e12ddea',
|
||||
image='gcr.io/ml-pipeline/ml-pipeline-dataproc-predict:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f',
|
||||
arguments=[
|
||||
'--project', project,
|
||||
'--region', region,
|
||||
|
|
@ -141,7 +141,7 @@ class ConfusionMatrixOp(dsl.ContainerOp):
|
|||
def __init__(self, name, predictions, output):
|
||||
super(ConfusionMatrixOp, self).__init__(
|
||||
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=[
|
||||
'--output', output,
|
||||
'--predictions', predictions
|
||||
|
|
@ -153,7 +153,7 @@ class RocOp(dsl.ContainerOp):
|
|||
def __init__(self, name, predictions, trueclass, output):
|
||||
super(RocOp, self).__init__(
|
||||
name=name,
|
||||
image='gcr.io/ml-pipeline/ml-pipeline-local-roc:6ad2601ec7d04e842c212c50d5c78e548e12ddea',
|
||||
image='gcr.io/ml-pipeline/ml-pipeline-local-roc:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f',
|
||||
arguments=[
|
||||
'--output', output,
|
||||
'--predictions', predictions,
|
||||
|
|
|
|||
Loading…
Reference in New Issue