From 4930beebb101af9056dee133fe3eb5fb96e54344 Mon Sep 17 00:00:00 2001 From: Googler Date: Fri, 12 Jul 2024 14:42:35 -0700 Subject: [PATCH] chore(components): Bump image version for Structured Data pipelines Signed-off-by: Googler PiperOrigin-RevId: 651891065 --- components/google-cloud/RELEASE.md | 1 + .../forecasting/forecasting_ensemble.py | 2 +- .../forecasting/forecasting_stage_1_tuner.py | 4 +- .../forecasting/forecasting_stage_2_tuner.py | 4 +- .../learn_to_learn_forecasting_pipeline.yaml | 72 ++++++++-------- ...ence_to_sequence_forecasting_pipeline.yaml | 72 ++++++++-------- ...sion_transformer_forecasting_pipeline.yaml | 72 ++++++++-------- ...es_dense_encoder_forecasting_pipeline.yaml | 72 ++++++++-------- .../preview/automl/forecasting/utils.py | 56 +++++++++++-- .../tabular/auto_feature_engineering.py | 2 +- ...ml_tabular_feature_selection_pipeline.yaml | 78 +++++++++--------- .../tabular/automl_tabular_v2_pipeline.yaml | 82 +++++++++---------- ...illation_stage_feature_transform_engine.py | 4 +- .../automl/tabular/feature_selection.py | 4 +- .../tabular/feature_selection_pipeline.yaml | 8 +- .../tabular/feature_transform_engine.py | 6 +- .../tabnet_hyperparameter_tuning_job.py | 4 +- ...et_hyperparameter_tuning_job_pipeline.yaml | 30 +++---- .../preview/automl/tabular/tabnet_trainer.py | 4 +- .../tabular/tabnet_trainer_pipeline.yaml | 26 +++--- ...wide_and_deep_hyperparameter_tuning_job.py | 4 +- ...ep_hyperparameter_tuning_job_pipeline.yaml | 28 +++---- .../automl/tabular/wide_and_deep_trainer.py | 4 +- .../wide_and_deep_trainer_pipeline.yaml | 26 +++--- ...st_hyperparameter_tuning_job_pipeline.yaml | 28 +++---- .../tabular/xgboost_trainer_pipeline.yaml | 26 +++--- .../bqml_arima_predict_pipeline.yaml | 20 ++--- .../bqml_arima_train_pipeline.yaml | 62 +++++++------- .../forecasting/prophet_predict_pipeline.yaml | 26 +++--- .../v1/automl/forecasting/prophet_trainer.py | 6 +- .../forecasting/prophet_trainer_pipeline.yaml | 28 +++---- .../tabular/automl_tabular_pipeline.yaml | 74 ++++++++--------- .../v1/automl/tabular/cv_trainer.py | 4 +- .../v1/automl/tabular/ensemble.py | 4 +- .../v1/automl/tabular/finalizer.py | 2 +- .../v1/automl/tabular/infra_validator.py | 2 +- .../automl/tabular/split_materialized_data.py | 2 +- .../v1/automl/tabular/stage_1_tuner.py | 4 +- .../automl/tabular/stats_and_example_gen.py | 4 +- .../training_configurator_and_validator.py | 2 +- .../v1/automl/tabular/transform.py | 4 +- 41 files changed, 511 insertions(+), 452 deletions(-) diff --git a/components/google-cloud/RELEASE.md b/components/google-cloud/RELEASE.md index b438187c289..0a8b52659ee 100644 --- a/components/google-cloud/RELEASE.md +++ b/components/google-cloud/RELEASE.md @@ -1,6 +1,7 @@ ## Upcoming release * Updated the Starry Net pipeline's template gallery description, and added dataprep_nan_threshold and dataprep_zero_threshold args to the Starry Net pipeline. * Add support for running tasks on a `PersistentResource` (see [CustomJobSpec](https://cloud.google.com/vertex-ai/docs/reference/rest/v1beta1/CustomJobSpec)) via `persistent_resource_id` parameter on `v1.custom_job.CustomTrainingJobOp` and `v1.custom_job.create_custom_training_job_from_component` +* Bump image for Structured Data pipelines. ## Release 2.15.0 * Add Gemini batch prediction support to `v1.model_evaluation.autosxs_pipeline`. diff --git a/components/google-cloud/google_cloud_pipeline_components/preview/automl/forecasting/forecasting_ensemble.py b/components/google-cloud/google_cloud_pipeline_components/preview/automl/forecasting/forecasting_ensemble.py index da8dbf42239..62d40682618 100644 --- a/components/google-cloud/google_cloud_pipeline_components/preview/automl/forecasting/forecasting_ensemble.py +++ b/components/google-cloud/google_cloud_pipeline_components/preview/automl/forecasting/forecasting_ensemble.py @@ -72,7 +72,7 @@ def automl_forecasting_ensemble( # fmt: on job_id = dsl.PIPELINE_JOB_ID_PLACEHOLDER task_id = dsl.PIPELINE_TASK_ID_PLACEHOLDER - image_uri = 'us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240419_0625' + image_uri = 'us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240710_0625' display_name = f'automl-forecasting-ensemble-{job_id}-{task_id}' error_file_path = f'{root_dir}/{job_id}/{task_id}/error.pb' diff --git a/components/google-cloud/google_cloud_pipeline_components/preview/automl/forecasting/forecasting_stage_1_tuner.py b/components/google-cloud/google_cloud_pipeline_components/preview/automl/forecasting/forecasting_stage_1_tuner.py index 31709d6ff08..e055ba53881 100644 --- a/components/google-cloud/google_cloud_pipeline_components/preview/automl/forecasting/forecasting_stage_1_tuner.py +++ b/components/google-cloud/google_cloud_pipeline_components/preview/automl/forecasting/forecasting_stage_1_tuner.py @@ -99,14 +99,14 @@ def automl_forecasting_stage_1_tuner( ' 1, "machine_spec": {"machine_type": "n1-standard-8"},' ' "container_spec": {"image_uri":"' ), - 'us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240419_0625', + 'us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240710_0625', '", "args": ["forecasting_mp_l2l_stage_1_tuner', '", "--region=', location, '", "--transform_output_path=', transform_output.uri, '", "--training_docker_uri=', - 'us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240419_0625', + 'us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240710_0625', '", "--reduce_search_space_mode=', reduce_search_space_mode, f'", "--component_id={dsl.PIPELINE_TASK_ID_PLACEHOLDER}', diff --git a/components/google-cloud/google_cloud_pipeline_components/preview/automl/forecasting/forecasting_stage_2_tuner.py b/components/google-cloud/google_cloud_pipeline_components/preview/automl/forecasting/forecasting_stage_2_tuner.py index 3a39353a746..b2defcf1a1e 100644 --- a/components/google-cloud/google_cloud_pipeline_components/preview/automl/forecasting/forecasting_stage_2_tuner.py +++ b/components/google-cloud/google_cloud_pipeline_components/preview/automl/forecasting/forecasting_stage_2_tuner.py @@ -97,14 +97,14 @@ def automl_forecasting_stage_2_tuner( ' 1, "machine_spec": {"machine_type": "n1-standard-8"},' ' "container_spec": {"image_uri":"' ), - 'us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240419_0625', + 'us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240710_0625', '", "args": ["forecasting_mp_l2l_stage_2_tuner', '", "--region=', location, '", "--transform_output_path=', transform_output.uri, '", "--training_docker_uri=', - 'us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240419_0625', + 'us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240710_0625', f'", "--component_id={dsl.PIPELINE_TASK_ID_PLACEHOLDER}', '", "--training_base_dir=', root_dir, diff --git a/components/google-cloud/google_cloud_pipeline_components/preview/automl/forecasting/learn_to_learn_forecasting_pipeline.yaml b/components/google-cloud/google_cloud_pipeline_components/preview/automl/forecasting/learn_to_learn_forecasting_pipeline.yaml index 5c9a6e9b521..489b7a41395 100644 --- a/components/google-cloud/google_cloud_pipeline_components/preview/automl/forecasting/learn_to_learn_forecasting_pipeline.yaml +++ b/components/google-cloud/google_cloud_pipeline_components/preview/automl/forecasting/learn_to_learn_forecasting_pipeline.yaml @@ -1074,6 +1074,8 @@ components: componentInputParameter: pipelinechannel--dataflow_subnetwork dataflow_use_public_ips: componentInputParameter: pipelinechannel--dataflow_use_public_ips + dataflow_workers_num: + componentInputParameter: pipelinechannel--evaluation_dataflow_starting_num_workers encryption_spec_key_name: componentInputParameter: pipelinechannel--encryption_spec_key_name forecasting_quantiles: @@ -1795,6 +1797,8 @@ components: componentInputParameter: pipelinechannel--dataflow_subnetwork dataflow_use_public_ips: componentInputParameter: pipelinechannel--dataflow_use_public_ips + dataflow_workers_num: + componentInputParameter: pipelinechannel--evaluation_dataflow_starting_num_workers encryption_spec_key_name: componentInputParameter: pipelinechannel--encryption_spec_key_name forecasting_quantiles: @@ -5573,7 +5577,7 @@ deploymentSpec: - '{"display_name": "automl-forecasting-ensemble-{{$.pipeline_job_uuid}}-{{$.pipeline_task_uuid}}", "encryption_spec": {"kms_key_name": "{{$.inputs.parameters[''encryption_spec_key_name'']}}"}, "job_spec": {"worker_pool_specs": [{"replica_count": 1, "machine_spec": - {"machine_type": "n1-highmem-8"}, "container_spec": {"image_uri": "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240419_0625", + {"machine_type": "n1-highmem-8"}, "container_spec": {"image_uri": "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240710_0625", "args": ["forecasting_mp_ensemble", "--transform_output_path={{$.inputs.artifacts[''transform_output''].uri}}", "--error_file_path={{$.inputs.parameters[''root_dir'']}}/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/error.pb", "--metadata_path={{$.inputs.artifacts[''metadata''].uri}}", "--tuning_result_input_path={{$.inputs.artifacts[''tuning_result_input''].uri}}", @@ -5607,7 +5611,7 @@ deploymentSpec: - '{"display_name": "automl-forecasting-ensemble-{{$.pipeline_job_uuid}}-{{$.pipeline_task_uuid}}", "encryption_spec": {"kms_key_name": "{{$.inputs.parameters[''encryption_spec_key_name'']}}"}, "job_spec": {"worker_pool_specs": [{"replica_count": 1, "machine_spec": - {"machine_type": "n1-highmem-8"}, "container_spec": {"image_uri": "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240419_0625", + {"machine_type": "n1-highmem-8"}, "container_spec": {"image_uri": "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240710_0625", "args": ["forecasting_mp_ensemble", "--transform_output_path={{$.inputs.artifacts[''transform_output''].uri}}", "--error_file_path={{$.inputs.parameters[''root_dir'']}}/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/error.pb", "--metadata_path={{$.inputs.artifacts[''metadata''].uri}}", "--tuning_result_input_path={{$.inputs.artifacts[''tuning_result_input''].uri}}", @@ -5642,11 +5646,11 @@ deploymentSpec: \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}", "\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\": {\"machine_type\": \"n1-standard-8\"}, \"container_spec\": {\"image_uri\":\"", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240419_0625", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240710_0625", "\", \"args\": [\"forecasting_mp_l2l_stage_1_tuner", "\", \"--region=", "{{$.inputs.parameters[''location'']}}", "\", \"--transform_output_path=", "{{$.inputs.artifacts[''transform_output''].uri}}", "\", \"--training_docker_uri=", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240419_0625", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240710_0625", "\", \"--reduce_search_space_mode=", "{{$.inputs.parameters[''reduce_search_space_mode'']}}", "\", \"--component_id={{$.pipeline_task_uuid}}", "\", \"--training_base_dir=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/train", @@ -5685,11 +5689,11 @@ deploymentSpec: \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}", "\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\": {\"machine_type\": \"n1-standard-8\"}, \"container_spec\": {\"image_uri\":\"", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240419_0625", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240710_0625", "\", \"args\": [\"forecasting_mp_l2l_stage_2_tuner", "\", \"--region=", "{{$.inputs.parameters[''location'']}}", "\", \"--transform_output_path=", "{{$.inputs.artifacts[''transform_output''].uri}}", "\", \"--training_docker_uri=", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240419_0625", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240710_0625", "\", \"--component_id={{$.pipeline_task_uuid}}", "\", \"--training_base_dir=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/train", "\", \"--num_parallel_trial=", "{{$.inputs.parameters[''num_parallel_trials'']}}", @@ -5728,7 +5732,7 @@ deploymentSpec: \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}", "\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\": {\"machine_type\": \"n1-standard-8\"}, \"container_spec\": {\"image_uri\":\"", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", "\", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"args\": [\"cancel_l2l_tuner\", \"--error_file_path=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/error.pb\", \"--cleanup_lro_job_infos=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/lro\"]}}]}}"]}' @@ -5793,7 +5797,7 @@ deploymentSpec: \ 'stage_2_single_run_max_secs',\n ],\n )(\n stage_1_deadline_hours,\n\ \ stage_1_single_run_max_secs,\n stage_2_deadline_hours,\n \ \ stage_2_single_run_max_secs,\n )\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-calculate-training-parameters-2: container: args: @@ -5849,7 +5853,7 @@ deploymentSpec: \ 'stage_2_single_run_max_secs',\n ],\n )(\n stage_1_deadline_hours,\n\ \ stage_1_single_run_max_secs,\n stage_2_deadline_hours,\n \ \ stage_2_single_run_max_secs,\n )\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-feature-attribution: container: args: @@ -6040,8 +6044,8 @@ deploymentSpec: "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/dataflow_tmp"]}' - '{"Concat": ["--dataflow_max_num_workers=", "{{$.inputs.parameters[''dataflow_max_num_workers'']}}"]}' - '{"Concat": ["--dataflow_machine_type=", "{{$.inputs.parameters[''dataflow_machine_type'']}}"]}' - - --dataflow_worker_container_image=us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240419_0625 - - --feature_transform_engine_docker_uri=us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240419_0625 + - --dataflow_worker_container_image=us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240710_0625 + - --feature_transform_engine_docker_uri=us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240710_0625 - '{"Concat": ["--dataflow_disk_size_gb=", "{{$.inputs.parameters[''dataflow_disk_size_gb'']}}"]}' - '{"Concat": ["--dataflow_subnetwork_fully_qualified=", "{{$.inputs.parameters[''dataflow_subnetwork'']}}"]}' - '{"Concat": ["--dataflow_use_public_ips=", "{{$.inputs.parameters[''dataflow_use_public_ips'']}}"]}' @@ -6058,7 +6062,7 @@ deploymentSpec: - '{"IfPresent": {"InputName": "group_temporal_total_weight", "Then": {"Concat": ["--group_temporal_total_weight=", "{{$.inputs.parameters[''group_temporal_total_weight'']}}"]}}}' - '{"Concat": ["--encryption_spec_key_name=", "{{$.inputs.parameters[''encryption_spec_key_name'']}}"]}' - image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240710_0625 resources: cpuLimit: 8.0 memoryLimit: 30.0 @@ -6089,7 +6093,7 @@ deploymentSpec: \ = 'point'\n else:\n forecasting_type = 'quantile'\n\n return collections.namedtuple(\n\ \ 'Outputs',\n (\n 'forecasting_type',\n 'quantiles',\n\ \ ),\n )(forecasting_type, quantiles)\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-finalize-eval-quantile-parameters-2: container: args: @@ -6117,7 +6121,7 @@ deploymentSpec: \ = 'point'\n else:\n forecasting_type = 'quantile'\n\n return collections.namedtuple(\n\ \ 'Outputs',\n (\n 'forecasting_type',\n 'quantiles',\n\ \ ),\n )(forecasting_type, quantiles)\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-get-or-create-model-description: container: args: @@ -6146,7 +6150,7 @@ deploymentSpec: \ return f'{original_description} From: {pipeline_url}'\n\n # The pipeline\ \ url contains KFP placeholders injected at runtime.\n return f'Vertex\ \ forecasting model trained in the pipeline: {pipeline_url}'\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-get-or-create-model-description-2: container: args: @@ -6175,7 +6179,7 @@ deploymentSpec: \ return f'{original_description} From: {pipeline_url}'\n\n # The pipeline\ \ url contains KFP placeholders injected at runtime.\n return f'Vertex\ \ forecasting model trained in the pipeline: {pipeline_url}'\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-get-prediction-image-uri: container: args: @@ -6198,14 +6202,14 @@ deploymentSpec: Returns the prediction image corresponding to the given model type.\"\"\"\ \n # Keys come from AutoMlTimeSeriesForecastingTrainSpec.\n # The URIs\ \ must be hardcoded without any breaks in the code so string\n # replacement\ - \ will work correctly.\n images = {\n 'l2l': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-l2l:20240419_0625',\n\ - \ 'seq2seq': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-seq2seq:20240419_0625',\n\ - \ 'tft': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-tft:20240419_0625',\n\ - \ 'tide': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-tide:20240419_0625',\n\ + \ will work correctly.\n images = {\n 'l2l': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-l2l:20240710_0625',\n\ + \ 'seq2seq': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-seq2seq:20240710_0625',\n\ + \ 'tft': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-tft:20240710_0625',\n\ + \ 'tide': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-tide:20240710_0625',\n\ \ }\n if model_type not in images:\n raise ValueError(\n f'Invalid\ \ forecasting model type: {model_type}. Valid options are: '\n f'{images.keys()}.'\n\ \ )\n return images[model_type]\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-get-prediction-image-uri-2: container: args: @@ -6228,14 +6232,14 @@ deploymentSpec: Returns the prediction image corresponding to the given model type.\"\"\"\ \n # Keys come from AutoMlTimeSeriesForecastingTrainSpec.\n # The URIs\ \ must be hardcoded without any breaks in the code so string\n # replacement\ - \ will work correctly.\n images = {\n 'l2l': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-l2l:20240419_0625',\n\ - \ 'seq2seq': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-seq2seq:20240419_0625',\n\ - \ 'tft': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-tft:20240419_0625',\n\ - \ 'tide': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-tide:20240419_0625',\n\ + \ will work correctly.\n images = {\n 'l2l': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-l2l:20240710_0625',\n\ + \ 'seq2seq': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-seq2seq:20240710_0625',\n\ + \ 'tft': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-tft:20240710_0625',\n\ + \ 'tide': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-tide:20240710_0625',\n\ \ }\n if model_type not in images:\n raise ValueError(\n f'Invalid\ \ forecasting model type: {model_type}. Valid options are: '\n f'{images.keys()}.'\n\ \ )\n return images[model_type]\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-get-predictions-column: container: args: @@ -6258,7 +6262,7 @@ deploymentSpec: \ str) -> str:\n \"\"\"Generates the BP output's target column name.\"\"\ \"\n if forecasting_type == 'quantile':\n return f'predicted_{target_column}.quantile_predictions'\n\ \ return f'predicted_{target_column}.value'\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-get-predictions-column-2: container: args: @@ -6281,7 +6285,7 @@ deploymentSpec: \ str) -> str:\n \"\"\"Generates the BP output's target column name.\"\"\ \"\n if forecasting_type == 'quantile':\n return f'predicted_{target_column}.quantile_predictions'\n\ \ return f'predicted_{target_column}.value'\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-importer: importer: artifactUri: @@ -6813,7 +6817,7 @@ deploymentSpec: \ 'model_display_name',\n 'transformations',\n ],\n\ \ )(\n data_source_csv_filenames,\n data_source_bigquery_table_path,\n\ \ model_display_name,\n transformations,\n )\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-split-materialized-data: container: args: @@ -6859,7 +6863,7 @@ deploymentSpec: \ 'w') as f:\n f.write(file_patterns[0])\n\n with tf.io.gfile.GFile(materialized_eval_split,\ \ 'w') as f:\n f.write(file_patterns[1])\n\n with tf.io.gfile.GFile(materialized_test_split,\ \ 'w') as f:\n f.write(file_patterns[2])\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240710_0625 exec-string-not-empty: container: args: @@ -6883,7 +6887,7 @@ deploymentSpec: \n Returns:\n Boolean value. -> 'true' if empty, 'false' if not empty.\ \ We need to use str\n instead of bool due to a limitation in KFP compiler.\n\ \ \"\"\"\n return 'true' if value else 'false'\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-table-to-uri: container: args: @@ -6913,7 +6917,7 @@ deploymentSpec: \ if use_bq_prefix:\n bq_uri = 'bq://' + bq_uri\n outputs.append(bq_uri)\n\ \ return collections.namedtuple(\n 'Outputs',\n ['project_id',\ \ 'dataset_id', 'table_id', 'uri'],\n )(*outputs)\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-table-to-uri-2: container: args: @@ -6943,7 +6947,7 @@ deploymentSpec: \ if use_bq_prefix:\n bq_uri = 'bq://' + bq_uri\n outputs.append(bq_uri)\n\ \ return collections.namedtuple(\n 'Outputs',\n ['project_id',\ \ 'dataset_id', 'table_id', 'uri'],\n )(*outputs)\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-training-configurator-and-validator: container: args: @@ -6988,7 +6992,7 @@ deploymentSpec: ["--temporal_total_weight=", "{{$.inputs.parameters[''temporal_total_weight'']}}"]}}}' - '{"IfPresent": {"InputName": "group_temporal_total_weight", "Then": {"Concat": ["--group_temporal_total_weight=", "{{$.inputs.parameters[''group_temporal_total_weight'']}}"]}}}' - image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240710_0625 pipelineInfo: description: The AutoML Forecasting pipeline. name: learn-to-learn-forecasting diff --git a/components/google-cloud/google_cloud_pipeline_components/preview/automl/forecasting/sequence_to_sequence_forecasting_pipeline.yaml b/components/google-cloud/google_cloud_pipeline_components/preview/automl/forecasting/sequence_to_sequence_forecasting_pipeline.yaml index 2ea88a50d49..27224d4aa99 100644 --- a/components/google-cloud/google_cloud_pipeline_components/preview/automl/forecasting/sequence_to_sequence_forecasting_pipeline.yaml +++ b/components/google-cloud/google_cloud_pipeline_components/preview/automl/forecasting/sequence_to_sequence_forecasting_pipeline.yaml @@ -1069,6 +1069,8 @@ components: componentInputParameter: pipelinechannel--dataflow_subnetwork dataflow_use_public_ips: componentInputParameter: pipelinechannel--dataflow_use_public_ips + dataflow_workers_num: + componentInputParameter: pipelinechannel--evaluation_dataflow_starting_num_workers encryption_spec_key_name: componentInputParameter: pipelinechannel--encryption_spec_key_name forecasting_quantiles: @@ -1785,6 +1787,8 @@ components: componentInputParameter: pipelinechannel--dataflow_subnetwork dataflow_use_public_ips: componentInputParameter: pipelinechannel--dataflow_use_public_ips + dataflow_workers_num: + componentInputParameter: pipelinechannel--evaluation_dataflow_starting_num_workers encryption_spec_key_name: componentInputParameter: pipelinechannel--encryption_spec_key_name forecasting_quantiles: @@ -5555,7 +5559,7 @@ deploymentSpec: - '{"display_name": "automl-forecasting-ensemble-{{$.pipeline_job_uuid}}-{{$.pipeline_task_uuid}}", "encryption_spec": {"kms_key_name": "{{$.inputs.parameters[''encryption_spec_key_name'']}}"}, "job_spec": {"worker_pool_specs": [{"replica_count": 1, "machine_spec": - {"machine_type": "n1-highmem-8"}, "container_spec": {"image_uri": "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240419_0625", + {"machine_type": "n1-highmem-8"}, "container_spec": {"image_uri": "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240710_0625", "args": ["forecasting_mp_ensemble", "--transform_output_path={{$.inputs.artifacts[''transform_output''].uri}}", "--error_file_path={{$.inputs.parameters[''root_dir'']}}/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/error.pb", "--metadata_path={{$.inputs.artifacts[''metadata''].uri}}", "--tuning_result_input_path={{$.inputs.artifacts[''tuning_result_input''].uri}}", @@ -5589,7 +5593,7 @@ deploymentSpec: - '{"display_name": "automl-forecasting-ensemble-{{$.pipeline_job_uuid}}-{{$.pipeline_task_uuid}}", "encryption_spec": {"kms_key_name": "{{$.inputs.parameters[''encryption_spec_key_name'']}}"}, "job_spec": {"worker_pool_specs": [{"replica_count": 1, "machine_spec": - {"machine_type": "n1-highmem-8"}, "container_spec": {"image_uri": "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240419_0625", + {"machine_type": "n1-highmem-8"}, "container_spec": {"image_uri": "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240710_0625", "args": ["forecasting_mp_ensemble", "--transform_output_path={{$.inputs.artifacts[''transform_output''].uri}}", "--error_file_path={{$.inputs.parameters[''root_dir'']}}/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/error.pb", "--metadata_path={{$.inputs.artifacts[''metadata''].uri}}", "--tuning_result_input_path={{$.inputs.artifacts[''tuning_result_input''].uri}}", @@ -5624,11 +5628,11 @@ deploymentSpec: \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}", "\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\": {\"machine_type\": \"n1-standard-8\"}, \"container_spec\": {\"image_uri\":\"", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240419_0625", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240710_0625", "\", \"args\": [\"forecasting_mp_l2l_stage_1_tuner", "\", \"--region=", "{{$.inputs.parameters[''location'']}}", "\", \"--transform_output_path=", "{{$.inputs.artifacts[''transform_output''].uri}}", "\", \"--training_docker_uri=", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240419_0625", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240710_0625", "\", \"--reduce_search_space_mode=", "{{$.inputs.parameters[''reduce_search_space_mode'']}}", "\", \"--component_id={{$.pipeline_task_uuid}}", "\", \"--training_base_dir=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/train", @@ -5667,11 +5671,11 @@ deploymentSpec: \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}", "\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\": {\"machine_type\": \"n1-standard-8\"}, \"container_spec\": {\"image_uri\":\"", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240419_0625", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240710_0625", "\", \"args\": [\"forecasting_mp_l2l_stage_2_tuner", "\", \"--region=", "{{$.inputs.parameters[''location'']}}", "\", \"--transform_output_path=", "{{$.inputs.artifacts[''transform_output''].uri}}", "\", \"--training_docker_uri=", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240419_0625", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240710_0625", "\", \"--component_id={{$.pipeline_task_uuid}}", "\", \"--training_base_dir=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/train", "\", \"--num_parallel_trial=", "{{$.inputs.parameters[''num_parallel_trials'']}}", @@ -5710,7 +5714,7 @@ deploymentSpec: \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}", "\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\": {\"machine_type\": \"n1-standard-8\"}, \"container_spec\": {\"image_uri\":\"", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", "\", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"args\": [\"cancel_l2l_tuner\", \"--error_file_path=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/error.pb\", \"--cleanup_lro_job_infos=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/lro\"]}}]}}"]}' @@ -5775,7 +5779,7 @@ deploymentSpec: \ 'stage_2_single_run_max_secs',\n ],\n )(\n stage_1_deadline_hours,\n\ \ stage_1_single_run_max_secs,\n stage_2_deadline_hours,\n \ \ stage_2_single_run_max_secs,\n )\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-calculate-training-parameters-2: container: args: @@ -5831,7 +5835,7 @@ deploymentSpec: \ 'stage_2_single_run_max_secs',\n ],\n )(\n stage_1_deadline_hours,\n\ \ stage_1_single_run_max_secs,\n stage_2_deadline_hours,\n \ \ stage_2_single_run_max_secs,\n )\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-feature-attribution: container: args: @@ -6022,8 +6026,8 @@ deploymentSpec: "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/dataflow_tmp"]}' - '{"Concat": ["--dataflow_max_num_workers=", "{{$.inputs.parameters[''dataflow_max_num_workers'']}}"]}' - '{"Concat": ["--dataflow_machine_type=", "{{$.inputs.parameters[''dataflow_machine_type'']}}"]}' - - --dataflow_worker_container_image=us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240419_0625 - - --feature_transform_engine_docker_uri=us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240419_0625 + - --dataflow_worker_container_image=us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240710_0625 + - --feature_transform_engine_docker_uri=us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240710_0625 - '{"Concat": ["--dataflow_disk_size_gb=", "{{$.inputs.parameters[''dataflow_disk_size_gb'']}}"]}' - '{"Concat": ["--dataflow_subnetwork_fully_qualified=", "{{$.inputs.parameters[''dataflow_subnetwork'']}}"]}' - '{"Concat": ["--dataflow_use_public_ips=", "{{$.inputs.parameters[''dataflow_use_public_ips'']}}"]}' @@ -6040,7 +6044,7 @@ deploymentSpec: - '{"IfPresent": {"InputName": "group_temporal_total_weight", "Then": {"Concat": ["--group_temporal_total_weight=", "{{$.inputs.parameters[''group_temporal_total_weight'']}}"]}}}' - '{"Concat": ["--encryption_spec_key_name=", "{{$.inputs.parameters[''encryption_spec_key_name'']}}"]}' - image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240710_0625 resources: cpuLimit: 8.0 memoryLimit: 30.0 @@ -6071,7 +6075,7 @@ deploymentSpec: \ = 'point'\n else:\n forecasting_type = 'quantile'\n\n return collections.namedtuple(\n\ \ 'Outputs',\n (\n 'forecasting_type',\n 'quantiles',\n\ \ ),\n )(forecasting_type, quantiles)\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-finalize-eval-quantile-parameters-2: container: args: @@ -6099,7 +6103,7 @@ deploymentSpec: \ = 'point'\n else:\n forecasting_type = 'quantile'\n\n return collections.namedtuple(\n\ \ 'Outputs',\n (\n 'forecasting_type',\n 'quantiles',\n\ \ ),\n )(forecasting_type, quantiles)\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-get-or-create-model-description: container: args: @@ -6128,7 +6132,7 @@ deploymentSpec: \ return f'{original_description} From: {pipeline_url}'\n\n # The pipeline\ \ url contains KFP placeholders injected at runtime.\n return f'Vertex\ \ forecasting model trained in the pipeline: {pipeline_url}'\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-get-or-create-model-description-2: container: args: @@ -6157,7 +6161,7 @@ deploymentSpec: \ return f'{original_description} From: {pipeline_url}'\n\n # The pipeline\ \ url contains KFP placeholders injected at runtime.\n return f'Vertex\ \ forecasting model trained in the pipeline: {pipeline_url}'\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-get-prediction-image-uri: container: args: @@ -6180,14 +6184,14 @@ deploymentSpec: Returns the prediction image corresponding to the given model type.\"\"\"\ \n # Keys come from AutoMlTimeSeriesForecastingTrainSpec.\n # The URIs\ \ must be hardcoded without any breaks in the code so string\n # replacement\ - \ will work correctly.\n images = {\n 'l2l': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-l2l:20240419_0625',\n\ - \ 'seq2seq': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-seq2seq:20240419_0625',\n\ - \ 'tft': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-tft:20240419_0625',\n\ - \ 'tide': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-tide:20240419_0625',\n\ + \ will work correctly.\n images = {\n 'l2l': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-l2l:20240710_0625',\n\ + \ 'seq2seq': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-seq2seq:20240710_0625',\n\ + \ 'tft': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-tft:20240710_0625',\n\ + \ 'tide': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-tide:20240710_0625',\n\ \ }\n if model_type not in images:\n raise ValueError(\n f'Invalid\ \ forecasting model type: {model_type}. Valid options are: '\n f'{images.keys()}.'\n\ \ )\n return images[model_type]\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-get-prediction-image-uri-2: container: args: @@ -6210,14 +6214,14 @@ deploymentSpec: Returns the prediction image corresponding to the given model type.\"\"\"\ \n # Keys come from AutoMlTimeSeriesForecastingTrainSpec.\n # The URIs\ \ must be hardcoded without any breaks in the code so string\n # replacement\ - \ will work correctly.\n images = {\n 'l2l': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-l2l:20240419_0625',\n\ - \ 'seq2seq': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-seq2seq:20240419_0625',\n\ - \ 'tft': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-tft:20240419_0625',\n\ - \ 'tide': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-tide:20240419_0625',\n\ + \ will work correctly.\n images = {\n 'l2l': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-l2l:20240710_0625',\n\ + \ 'seq2seq': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-seq2seq:20240710_0625',\n\ + \ 'tft': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-tft:20240710_0625',\n\ + \ 'tide': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-tide:20240710_0625',\n\ \ }\n if model_type not in images:\n raise ValueError(\n f'Invalid\ \ forecasting model type: {model_type}. Valid options are: '\n f'{images.keys()}.'\n\ \ )\n return images[model_type]\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-get-predictions-column: container: args: @@ -6240,7 +6244,7 @@ deploymentSpec: \ str) -> str:\n \"\"\"Generates the BP output's target column name.\"\"\ \"\n if forecasting_type == 'quantile':\n return f'predicted_{target_column}.quantile_predictions'\n\ \ return f'predicted_{target_column}.value'\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-get-predictions-column-2: container: args: @@ -6263,7 +6267,7 @@ deploymentSpec: \ str) -> str:\n \"\"\"Generates the BP output's target column name.\"\"\ \"\n if forecasting_type == 'quantile':\n return f'predicted_{target_column}.quantile_predictions'\n\ \ return f'predicted_{target_column}.value'\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-importer: importer: artifactUri: @@ -6795,7 +6799,7 @@ deploymentSpec: \ 'model_display_name',\n 'transformations',\n ],\n\ \ )(\n data_source_csv_filenames,\n data_source_bigquery_table_path,\n\ \ model_display_name,\n transformations,\n )\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-split-materialized-data: container: args: @@ -6841,7 +6845,7 @@ deploymentSpec: \ 'w') as f:\n f.write(file_patterns[0])\n\n with tf.io.gfile.GFile(materialized_eval_split,\ \ 'w') as f:\n f.write(file_patterns[1])\n\n with tf.io.gfile.GFile(materialized_test_split,\ \ 'w') as f:\n f.write(file_patterns[2])\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240710_0625 exec-string-not-empty: container: args: @@ -6865,7 +6869,7 @@ deploymentSpec: \n Returns:\n Boolean value. -> 'true' if empty, 'false' if not empty.\ \ We need to use str\n instead of bool due to a limitation in KFP compiler.\n\ \ \"\"\"\n return 'true' if value else 'false'\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-table-to-uri: container: args: @@ -6895,7 +6899,7 @@ deploymentSpec: \ if use_bq_prefix:\n bq_uri = 'bq://' + bq_uri\n outputs.append(bq_uri)\n\ \ return collections.namedtuple(\n 'Outputs',\n ['project_id',\ \ 'dataset_id', 'table_id', 'uri'],\n )(*outputs)\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-table-to-uri-2: container: args: @@ -6925,7 +6929,7 @@ deploymentSpec: \ if use_bq_prefix:\n bq_uri = 'bq://' + bq_uri\n outputs.append(bq_uri)\n\ \ return collections.namedtuple(\n 'Outputs',\n ['project_id',\ \ 'dataset_id', 'table_id', 'uri'],\n )(*outputs)\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-training-configurator-and-validator: container: args: @@ -6970,7 +6974,7 @@ deploymentSpec: ["--temporal_total_weight=", "{{$.inputs.parameters[''temporal_total_weight'']}}"]}}}' - '{"IfPresent": {"InputName": "group_temporal_total_weight", "Then": {"Concat": ["--group_temporal_total_weight=", "{{$.inputs.parameters[''group_temporal_total_weight'']}}"]}}}' - image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240710_0625 pipelineInfo: description: The Sequence to Sequence (Seq2Seq) Forecasting pipeline. name: sequence-to-sequence-forecasting diff --git a/components/google-cloud/google_cloud_pipeline_components/preview/automl/forecasting/temporal_fusion_transformer_forecasting_pipeline.yaml b/components/google-cloud/google_cloud_pipeline_components/preview/automl/forecasting/temporal_fusion_transformer_forecasting_pipeline.yaml index 34eff08cb35..146efa9db2a 100644 --- a/components/google-cloud/google_cloud_pipeline_components/preview/automl/forecasting/temporal_fusion_transformer_forecasting_pipeline.yaml +++ b/components/google-cloud/google_cloud_pipeline_components/preview/automl/forecasting/temporal_fusion_transformer_forecasting_pipeline.yaml @@ -1068,6 +1068,8 @@ components: componentInputParameter: pipelinechannel--dataflow_subnetwork dataflow_use_public_ips: componentInputParameter: pipelinechannel--dataflow_use_public_ips + dataflow_workers_num: + componentInputParameter: pipelinechannel--evaluation_dataflow_starting_num_workers encryption_spec_key_name: componentInputParameter: pipelinechannel--encryption_spec_key_name forecasting_quantiles: @@ -1784,6 +1786,8 @@ components: componentInputParameter: pipelinechannel--dataflow_subnetwork dataflow_use_public_ips: componentInputParameter: pipelinechannel--dataflow_use_public_ips + dataflow_workers_num: + componentInputParameter: pipelinechannel--evaluation_dataflow_starting_num_workers encryption_spec_key_name: componentInputParameter: pipelinechannel--encryption_spec_key_name forecasting_quantiles: @@ -5548,7 +5552,7 @@ deploymentSpec: - '{"display_name": "automl-forecasting-ensemble-{{$.pipeline_job_uuid}}-{{$.pipeline_task_uuid}}", "encryption_spec": {"kms_key_name": "{{$.inputs.parameters[''encryption_spec_key_name'']}}"}, "job_spec": {"worker_pool_specs": [{"replica_count": 1, "machine_spec": - {"machine_type": "n1-highmem-8"}, "container_spec": {"image_uri": "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240419_0625", + {"machine_type": "n1-highmem-8"}, "container_spec": {"image_uri": "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240710_0625", "args": ["forecasting_mp_ensemble", "--transform_output_path={{$.inputs.artifacts[''transform_output''].uri}}", "--error_file_path={{$.inputs.parameters[''root_dir'']}}/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/error.pb", "--metadata_path={{$.inputs.artifacts[''metadata''].uri}}", "--tuning_result_input_path={{$.inputs.artifacts[''tuning_result_input''].uri}}", @@ -5582,7 +5586,7 @@ deploymentSpec: - '{"display_name": "automl-forecasting-ensemble-{{$.pipeline_job_uuid}}-{{$.pipeline_task_uuid}}", "encryption_spec": {"kms_key_name": "{{$.inputs.parameters[''encryption_spec_key_name'']}}"}, "job_spec": {"worker_pool_specs": [{"replica_count": 1, "machine_spec": - {"machine_type": "n1-highmem-8"}, "container_spec": {"image_uri": "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240419_0625", + {"machine_type": "n1-highmem-8"}, "container_spec": {"image_uri": "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240710_0625", "args": ["forecasting_mp_ensemble", "--transform_output_path={{$.inputs.artifacts[''transform_output''].uri}}", "--error_file_path={{$.inputs.parameters[''root_dir'']}}/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/error.pb", "--metadata_path={{$.inputs.artifacts[''metadata''].uri}}", "--tuning_result_input_path={{$.inputs.artifacts[''tuning_result_input''].uri}}", @@ -5617,11 +5621,11 @@ deploymentSpec: \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}", "\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\": {\"machine_type\": \"n1-standard-8\"}, \"container_spec\": {\"image_uri\":\"", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240419_0625", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240710_0625", "\", \"args\": [\"forecasting_mp_l2l_stage_1_tuner", "\", \"--region=", "{{$.inputs.parameters[''location'']}}", "\", \"--transform_output_path=", "{{$.inputs.artifacts[''transform_output''].uri}}", "\", \"--training_docker_uri=", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240419_0625", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240710_0625", "\", \"--reduce_search_space_mode=", "{{$.inputs.parameters[''reduce_search_space_mode'']}}", "\", \"--component_id={{$.pipeline_task_uuid}}", "\", \"--training_base_dir=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/train", @@ -5660,11 +5664,11 @@ deploymentSpec: \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}", "\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\": {\"machine_type\": \"n1-standard-8\"}, \"container_spec\": {\"image_uri\":\"", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240419_0625", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240710_0625", "\", \"args\": [\"forecasting_mp_l2l_stage_2_tuner", "\", \"--region=", "{{$.inputs.parameters[''location'']}}", "\", \"--transform_output_path=", "{{$.inputs.artifacts[''transform_output''].uri}}", "\", \"--training_docker_uri=", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240419_0625", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240710_0625", "\", \"--component_id={{$.pipeline_task_uuid}}", "\", \"--training_base_dir=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/train", "\", \"--num_parallel_trial=", "{{$.inputs.parameters[''num_parallel_trials'']}}", @@ -5703,7 +5707,7 @@ deploymentSpec: \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}", "\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\": {\"machine_type\": \"n1-standard-8\"}, \"container_spec\": {\"image_uri\":\"", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", "\", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"args\": [\"cancel_l2l_tuner\", \"--error_file_path=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/error.pb\", \"--cleanup_lro_job_infos=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/lro\"]}}]}}"]}' @@ -5768,7 +5772,7 @@ deploymentSpec: \ 'stage_2_single_run_max_secs',\n ],\n )(\n stage_1_deadline_hours,\n\ \ stage_1_single_run_max_secs,\n stage_2_deadline_hours,\n \ \ stage_2_single_run_max_secs,\n )\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-calculate-training-parameters-2: container: args: @@ -5824,7 +5828,7 @@ deploymentSpec: \ 'stage_2_single_run_max_secs',\n ],\n )(\n stage_1_deadline_hours,\n\ \ stage_1_single_run_max_secs,\n stage_2_deadline_hours,\n \ \ stage_2_single_run_max_secs,\n )\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-feature-attribution: container: args: @@ -6015,8 +6019,8 @@ deploymentSpec: "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/dataflow_tmp"]}' - '{"Concat": ["--dataflow_max_num_workers=", "{{$.inputs.parameters[''dataflow_max_num_workers'']}}"]}' - '{"Concat": ["--dataflow_machine_type=", "{{$.inputs.parameters[''dataflow_machine_type'']}}"]}' - - --dataflow_worker_container_image=us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240419_0625 - - --feature_transform_engine_docker_uri=us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240419_0625 + - --dataflow_worker_container_image=us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240710_0625 + - --feature_transform_engine_docker_uri=us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240710_0625 - '{"Concat": ["--dataflow_disk_size_gb=", "{{$.inputs.parameters[''dataflow_disk_size_gb'']}}"]}' - '{"Concat": ["--dataflow_subnetwork_fully_qualified=", "{{$.inputs.parameters[''dataflow_subnetwork'']}}"]}' - '{"Concat": ["--dataflow_use_public_ips=", "{{$.inputs.parameters[''dataflow_use_public_ips'']}}"]}' @@ -6033,7 +6037,7 @@ deploymentSpec: - '{"IfPresent": {"InputName": "group_temporal_total_weight", "Then": {"Concat": ["--group_temporal_total_weight=", "{{$.inputs.parameters[''group_temporal_total_weight'']}}"]}}}' - '{"Concat": ["--encryption_spec_key_name=", "{{$.inputs.parameters[''encryption_spec_key_name'']}}"]}' - image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240710_0625 resources: cpuLimit: 8.0 memoryLimit: 30.0 @@ -6064,7 +6068,7 @@ deploymentSpec: \ = 'point'\n else:\n forecasting_type = 'quantile'\n\n return collections.namedtuple(\n\ \ 'Outputs',\n (\n 'forecasting_type',\n 'quantiles',\n\ \ ),\n )(forecasting_type, quantiles)\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-finalize-eval-quantile-parameters-2: container: args: @@ -6092,7 +6096,7 @@ deploymentSpec: \ = 'point'\n else:\n forecasting_type = 'quantile'\n\n return collections.namedtuple(\n\ \ 'Outputs',\n (\n 'forecasting_type',\n 'quantiles',\n\ \ ),\n )(forecasting_type, quantiles)\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-get-or-create-model-description: container: args: @@ -6121,7 +6125,7 @@ deploymentSpec: \ return f'{original_description} From: {pipeline_url}'\n\n # The pipeline\ \ url contains KFP placeholders injected at runtime.\n return f'Vertex\ \ forecasting model trained in the pipeline: {pipeline_url}'\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-get-or-create-model-description-2: container: args: @@ -6150,7 +6154,7 @@ deploymentSpec: \ return f'{original_description} From: {pipeline_url}'\n\n # The pipeline\ \ url contains KFP placeholders injected at runtime.\n return f'Vertex\ \ forecasting model trained in the pipeline: {pipeline_url}'\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-get-prediction-image-uri: container: args: @@ -6173,14 +6177,14 @@ deploymentSpec: Returns the prediction image corresponding to the given model type.\"\"\"\ \n # Keys come from AutoMlTimeSeriesForecastingTrainSpec.\n # The URIs\ \ must be hardcoded without any breaks in the code so string\n # replacement\ - \ will work correctly.\n images = {\n 'l2l': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-l2l:20240419_0625',\n\ - \ 'seq2seq': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-seq2seq:20240419_0625',\n\ - \ 'tft': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-tft:20240419_0625',\n\ - \ 'tide': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-tide:20240419_0625',\n\ + \ will work correctly.\n images = {\n 'l2l': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-l2l:20240710_0625',\n\ + \ 'seq2seq': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-seq2seq:20240710_0625',\n\ + \ 'tft': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-tft:20240710_0625',\n\ + \ 'tide': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-tide:20240710_0625',\n\ \ }\n if model_type not in images:\n raise ValueError(\n f'Invalid\ \ forecasting model type: {model_type}. Valid options are: '\n f'{images.keys()}.'\n\ \ )\n return images[model_type]\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-get-prediction-image-uri-2: container: args: @@ -6203,14 +6207,14 @@ deploymentSpec: Returns the prediction image corresponding to the given model type.\"\"\"\ \n # Keys come from AutoMlTimeSeriesForecastingTrainSpec.\n # The URIs\ \ must be hardcoded without any breaks in the code so string\n # replacement\ - \ will work correctly.\n images = {\n 'l2l': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-l2l:20240419_0625',\n\ - \ 'seq2seq': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-seq2seq:20240419_0625',\n\ - \ 'tft': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-tft:20240419_0625',\n\ - \ 'tide': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-tide:20240419_0625',\n\ + \ will work correctly.\n images = {\n 'l2l': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-l2l:20240710_0625',\n\ + \ 'seq2seq': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-seq2seq:20240710_0625',\n\ + \ 'tft': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-tft:20240710_0625',\n\ + \ 'tide': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-tide:20240710_0625',\n\ \ }\n if model_type not in images:\n raise ValueError(\n f'Invalid\ \ forecasting model type: {model_type}. Valid options are: '\n f'{images.keys()}.'\n\ \ )\n return images[model_type]\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-get-predictions-column: container: args: @@ -6233,7 +6237,7 @@ deploymentSpec: \ str) -> str:\n \"\"\"Generates the BP output's target column name.\"\"\ \"\n if forecasting_type == 'quantile':\n return f'predicted_{target_column}.quantile_predictions'\n\ \ return f'predicted_{target_column}.value'\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-get-predictions-column-2: container: args: @@ -6256,7 +6260,7 @@ deploymentSpec: \ str) -> str:\n \"\"\"Generates the BP output's target column name.\"\"\ \"\n if forecasting_type == 'quantile':\n return f'predicted_{target_column}.quantile_predictions'\n\ \ return f'predicted_{target_column}.value'\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-importer: importer: artifactUri: @@ -6788,7 +6792,7 @@ deploymentSpec: \ 'model_display_name',\n 'transformations',\n ],\n\ \ )(\n data_source_csv_filenames,\n data_source_bigquery_table_path,\n\ \ model_display_name,\n transformations,\n )\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-split-materialized-data: container: args: @@ -6834,7 +6838,7 @@ deploymentSpec: \ 'w') as f:\n f.write(file_patterns[0])\n\n with tf.io.gfile.GFile(materialized_eval_split,\ \ 'w') as f:\n f.write(file_patterns[1])\n\n with tf.io.gfile.GFile(materialized_test_split,\ \ 'w') as f:\n f.write(file_patterns[2])\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240710_0625 exec-string-not-empty: container: args: @@ -6858,7 +6862,7 @@ deploymentSpec: \n Returns:\n Boolean value. -> 'true' if empty, 'false' if not empty.\ \ We need to use str\n instead of bool due to a limitation in KFP compiler.\n\ \ \"\"\"\n return 'true' if value else 'false'\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-table-to-uri: container: args: @@ -6888,7 +6892,7 @@ deploymentSpec: \ if use_bq_prefix:\n bq_uri = 'bq://' + bq_uri\n outputs.append(bq_uri)\n\ \ return collections.namedtuple(\n 'Outputs',\n ['project_id',\ \ 'dataset_id', 'table_id', 'uri'],\n )(*outputs)\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-table-to-uri-2: container: args: @@ -6918,7 +6922,7 @@ deploymentSpec: \ if use_bq_prefix:\n bq_uri = 'bq://' + bq_uri\n outputs.append(bq_uri)\n\ \ return collections.namedtuple(\n 'Outputs',\n ['project_id',\ \ 'dataset_id', 'table_id', 'uri'],\n )(*outputs)\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-training-configurator-and-validator: container: args: @@ -6963,7 +6967,7 @@ deploymentSpec: ["--temporal_total_weight=", "{{$.inputs.parameters[''temporal_total_weight'']}}"]}}}' - '{"IfPresent": {"InputName": "group_temporal_total_weight", "Then": {"Concat": ["--group_temporal_total_weight=", "{{$.inputs.parameters[''group_temporal_total_weight'']}}"]}}}' - image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240710_0625 pipelineInfo: description: The Temporal Fusion Transformer (TFT) Forecasting pipeline. name: temporal-fusion-transformer-forecasting diff --git a/components/google-cloud/google_cloud_pipeline_components/preview/automl/forecasting/time_series_dense_encoder_forecasting_pipeline.yaml b/components/google-cloud/google_cloud_pipeline_components/preview/automl/forecasting/time_series_dense_encoder_forecasting_pipeline.yaml index 49af02086c7..9b373275eb5 100644 --- a/components/google-cloud/google_cloud_pipeline_components/preview/automl/forecasting/time_series_dense_encoder_forecasting_pipeline.yaml +++ b/components/google-cloud/google_cloud_pipeline_components/preview/automl/forecasting/time_series_dense_encoder_forecasting_pipeline.yaml @@ -1074,6 +1074,8 @@ components: componentInputParameter: pipelinechannel--dataflow_subnetwork dataflow_use_public_ips: componentInputParameter: pipelinechannel--dataflow_use_public_ips + dataflow_workers_num: + componentInputParameter: pipelinechannel--evaluation_dataflow_starting_num_workers encryption_spec_key_name: componentInputParameter: pipelinechannel--encryption_spec_key_name forecasting_quantiles: @@ -1795,6 +1797,8 @@ components: componentInputParameter: pipelinechannel--dataflow_subnetwork dataflow_use_public_ips: componentInputParameter: pipelinechannel--dataflow_use_public_ips + dataflow_workers_num: + componentInputParameter: pipelinechannel--evaluation_dataflow_starting_num_workers encryption_spec_key_name: componentInputParameter: pipelinechannel--encryption_spec_key_name forecasting_quantiles: @@ -5573,7 +5577,7 @@ deploymentSpec: - '{"display_name": "automl-forecasting-ensemble-{{$.pipeline_job_uuid}}-{{$.pipeline_task_uuid}}", "encryption_spec": {"kms_key_name": "{{$.inputs.parameters[''encryption_spec_key_name'']}}"}, "job_spec": {"worker_pool_specs": [{"replica_count": 1, "machine_spec": - {"machine_type": "n1-highmem-8"}, "container_spec": {"image_uri": "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240419_0625", + {"machine_type": "n1-highmem-8"}, "container_spec": {"image_uri": "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240710_0625", "args": ["forecasting_mp_ensemble", "--transform_output_path={{$.inputs.artifacts[''transform_output''].uri}}", "--error_file_path={{$.inputs.parameters[''root_dir'']}}/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/error.pb", "--metadata_path={{$.inputs.artifacts[''metadata''].uri}}", "--tuning_result_input_path={{$.inputs.artifacts[''tuning_result_input''].uri}}", @@ -5607,7 +5611,7 @@ deploymentSpec: - '{"display_name": "automl-forecasting-ensemble-{{$.pipeline_job_uuid}}-{{$.pipeline_task_uuid}}", "encryption_spec": {"kms_key_name": "{{$.inputs.parameters[''encryption_spec_key_name'']}}"}, "job_spec": {"worker_pool_specs": [{"replica_count": 1, "machine_spec": - {"machine_type": "n1-highmem-8"}, "container_spec": {"image_uri": "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240419_0625", + {"machine_type": "n1-highmem-8"}, "container_spec": {"image_uri": "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240710_0625", "args": ["forecasting_mp_ensemble", "--transform_output_path={{$.inputs.artifacts[''transform_output''].uri}}", "--error_file_path={{$.inputs.parameters[''root_dir'']}}/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/error.pb", "--metadata_path={{$.inputs.artifacts[''metadata''].uri}}", "--tuning_result_input_path={{$.inputs.artifacts[''tuning_result_input''].uri}}", @@ -5642,11 +5646,11 @@ deploymentSpec: \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}", "\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\": {\"machine_type\": \"n1-standard-8\"}, \"container_spec\": {\"image_uri\":\"", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240419_0625", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240710_0625", "\", \"args\": [\"forecasting_mp_l2l_stage_1_tuner", "\", \"--region=", "{{$.inputs.parameters[''location'']}}", "\", \"--transform_output_path=", "{{$.inputs.artifacts[''transform_output''].uri}}", "\", \"--training_docker_uri=", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240419_0625", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240710_0625", "\", \"--reduce_search_space_mode=", "{{$.inputs.parameters[''reduce_search_space_mode'']}}", "\", \"--component_id={{$.pipeline_task_uuid}}", "\", \"--training_base_dir=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/train", @@ -5685,11 +5689,11 @@ deploymentSpec: \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}", "\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\": {\"machine_type\": \"n1-standard-8\"}, \"container_spec\": {\"image_uri\":\"", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240419_0625", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240710_0625", "\", \"args\": [\"forecasting_mp_l2l_stage_2_tuner", "\", \"--region=", "{{$.inputs.parameters[''location'']}}", "\", \"--transform_output_path=", "{{$.inputs.artifacts[''transform_output''].uri}}", "\", \"--training_docker_uri=", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240419_0625", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240710_0625", "\", \"--component_id={{$.pipeline_task_uuid}}", "\", \"--training_base_dir=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/train", "\", \"--num_parallel_trial=", "{{$.inputs.parameters[''num_parallel_trials'']}}", @@ -5728,7 +5732,7 @@ deploymentSpec: \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}", "\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\": {\"machine_type\": \"n1-standard-8\"}, \"container_spec\": {\"image_uri\":\"", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", "\", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"args\": [\"cancel_l2l_tuner\", \"--error_file_path=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/error.pb\", \"--cleanup_lro_job_infos=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/lro\"]}}]}}"]}' @@ -5793,7 +5797,7 @@ deploymentSpec: \ 'stage_2_single_run_max_secs',\n ],\n )(\n stage_1_deadline_hours,\n\ \ stage_1_single_run_max_secs,\n stage_2_deadline_hours,\n \ \ stage_2_single_run_max_secs,\n )\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-calculate-training-parameters-2: container: args: @@ -5849,7 +5853,7 @@ deploymentSpec: \ 'stage_2_single_run_max_secs',\n ],\n )(\n stage_1_deadline_hours,\n\ \ stage_1_single_run_max_secs,\n stage_2_deadline_hours,\n \ \ stage_2_single_run_max_secs,\n )\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-feature-attribution: container: args: @@ -6040,8 +6044,8 @@ deploymentSpec: "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/dataflow_tmp"]}' - '{"Concat": ["--dataflow_max_num_workers=", "{{$.inputs.parameters[''dataflow_max_num_workers'']}}"]}' - '{"Concat": ["--dataflow_machine_type=", "{{$.inputs.parameters[''dataflow_machine_type'']}}"]}' - - --dataflow_worker_container_image=us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240419_0625 - - --feature_transform_engine_docker_uri=us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240419_0625 + - --dataflow_worker_container_image=us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240710_0625 + - --feature_transform_engine_docker_uri=us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240710_0625 - '{"Concat": ["--dataflow_disk_size_gb=", "{{$.inputs.parameters[''dataflow_disk_size_gb'']}}"]}' - '{"Concat": ["--dataflow_subnetwork_fully_qualified=", "{{$.inputs.parameters[''dataflow_subnetwork'']}}"]}' - '{"Concat": ["--dataflow_use_public_ips=", "{{$.inputs.parameters[''dataflow_use_public_ips'']}}"]}' @@ -6058,7 +6062,7 @@ deploymentSpec: - '{"IfPresent": {"InputName": "group_temporal_total_weight", "Then": {"Concat": ["--group_temporal_total_weight=", "{{$.inputs.parameters[''group_temporal_total_weight'']}}"]}}}' - '{"Concat": ["--encryption_spec_key_name=", "{{$.inputs.parameters[''encryption_spec_key_name'']}}"]}' - image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240710_0625 resources: cpuLimit: 8.0 memoryLimit: 30.0 @@ -6089,7 +6093,7 @@ deploymentSpec: \ = 'point'\n else:\n forecasting_type = 'quantile'\n\n return collections.namedtuple(\n\ \ 'Outputs',\n (\n 'forecasting_type',\n 'quantiles',\n\ \ ),\n )(forecasting_type, quantiles)\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-finalize-eval-quantile-parameters-2: container: args: @@ -6117,7 +6121,7 @@ deploymentSpec: \ = 'point'\n else:\n forecasting_type = 'quantile'\n\n return collections.namedtuple(\n\ \ 'Outputs',\n (\n 'forecasting_type',\n 'quantiles',\n\ \ ),\n )(forecasting_type, quantiles)\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-get-or-create-model-description: container: args: @@ -6146,7 +6150,7 @@ deploymentSpec: \ return f'{original_description} From: {pipeline_url}'\n\n # The pipeline\ \ url contains KFP placeholders injected at runtime.\n return f'Vertex\ \ forecasting model trained in the pipeline: {pipeline_url}'\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-get-or-create-model-description-2: container: args: @@ -6175,7 +6179,7 @@ deploymentSpec: \ return f'{original_description} From: {pipeline_url}'\n\n # The pipeline\ \ url contains KFP placeholders injected at runtime.\n return f'Vertex\ \ forecasting model trained in the pipeline: {pipeline_url}'\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-get-prediction-image-uri: container: args: @@ -6198,14 +6202,14 @@ deploymentSpec: Returns the prediction image corresponding to the given model type.\"\"\"\ \n # Keys come from AutoMlTimeSeriesForecastingTrainSpec.\n # The URIs\ \ must be hardcoded without any breaks in the code so string\n # replacement\ - \ will work correctly.\n images = {\n 'l2l': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-l2l:20240419_0625',\n\ - \ 'seq2seq': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-seq2seq:20240419_0625',\n\ - \ 'tft': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-tft:20240419_0625',\n\ - \ 'tide': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-tide:20240419_0625',\n\ + \ will work correctly.\n images = {\n 'l2l': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-l2l:20240710_0625',\n\ + \ 'seq2seq': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-seq2seq:20240710_0625',\n\ + \ 'tft': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-tft:20240710_0625',\n\ + \ 'tide': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-tide:20240710_0625',\n\ \ }\n if model_type not in images:\n raise ValueError(\n f'Invalid\ \ forecasting model type: {model_type}. Valid options are: '\n f'{images.keys()}.'\n\ \ )\n return images[model_type]\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-get-prediction-image-uri-2: container: args: @@ -6228,14 +6232,14 @@ deploymentSpec: Returns the prediction image corresponding to the given model type.\"\"\"\ \n # Keys come from AutoMlTimeSeriesForecastingTrainSpec.\n # The URIs\ \ must be hardcoded without any breaks in the code so string\n # replacement\ - \ will work correctly.\n images = {\n 'l2l': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-l2l:20240419_0625',\n\ - \ 'seq2seq': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-seq2seq:20240419_0625',\n\ - \ 'tft': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-tft:20240419_0625',\n\ - \ 'tide': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-tide:20240419_0625',\n\ + \ will work correctly.\n images = {\n 'l2l': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-l2l:20240710_0625',\n\ + \ 'seq2seq': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-seq2seq:20240710_0625',\n\ + \ 'tft': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-tft:20240710_0625',\n\ + \ 'tide': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-tide:20240710_0625',\n\ \ }\n if model_type not in images:\n raise ValueError(\n f'Invalid\ \ forecasting model type: {model_type}. Valid options are: '\n f'{images.keys()}.'\n\ \ )\n return images[model_type]\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-get-predictions-column: container: args: @@ -6258,7 +6262,7 @@ deploymentSpec: \ str) -> str:\n \"\"\"Generates the BP output's target column name.\"\"\ \"\n if forecasting_type == 'quantile':\n return f'predicted_{target_column}.quantile_predictions'\n\ \ return f'predicted_{target_column}.value'\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-get-predictions-column-2: container: args: @@ -6281,7 +6285,7 @@ deploymentSpec: \ str) -> str:\n \"\"\"Generates the BP output's target column name.\"\"\ \"\n if forecasting_type == 'quantile':\n return f'predicted_{target_column}.quantile_predictions'\n\ \ return f'predicted_{target_column}.value'\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-importer: importer: artifactUri: @@ -6813,7 +6817,7 @@ deploymentSpec: \ 'model_display_name',\n 'transformations',\n ],\n\ \ )(\n data_source_csv_filenames,\n data_source_bigquery_table_path,\n\ \ model_display_name,\n transformations,\n )\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-split-materialized-data: container: args: @@ -6859,7 +6863,7 @@ deploymentSpec: \ 'w') as f:\n f.write(file_patterns[0])\n\n with tf.io.gfile.GFile(materialized_eval_split,\ \ 'w') as f:\n f.write(file_patterns[1])\n\n with tf.io.gfile.GFile(materialized_test_split,\ \ 'w') as f:\n f.write(file_patterns[2])\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240710_0625 exec-string-not-empty: container: args: @@ -6883,7 +6887,7 @@ deploymentSpec: \n Returns:\n Boolean value. -> 'true' if empty, 'false' if not empty.\ \ We need to use str\n instead of bool due to a limitation in KFP compiler.\n\ \ \"\"\"\n return 'true' if value else 'false'\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-table-to-uri: container: args: @@ -6913,7 +6917,7 @@ deploymentSpec: \ if use_bq_prefix:\n bq_uri = 'bq://' + bq_uri\n outputs.append(bq_uri)\n\ \ return collections.namedtuple(\n 'Outputs',\n ['project_id',\ \ 'dataset_id', 'table_id', 'uri'],\n )(*outputs)\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-table-to-uri-2: container: args: @@ -6943,7 +6947,7 @@ deploymentSpec: \ if use_bq_prefix:\n bq_uri = 'bq://' + bq_uri\n outputs.append(bq_uri)\n\ \ return collections.namedtuple(\n 'Outputs',\n ['project_id',\ \ 'dataset_id', 'table_id', 'uri'],\n )(*outputs)\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-training-configurator-and-validator: container: args: @@ -6988,7 +6992,7 @@ deploymentSpec: ["--temporal_total_weight=", "{{$.inputs.parameters[''temporal_total_weight'']}}"]}}}' - '{"IfPresent": {"InputName": "group_temporal_total_weight", "Then": {"Concat": ["--group_temporal_total_weight=", "{{$.inputs.parameters[''group_temporal_total_weight'']}}"]}}}' - image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240710_0625 pipelineInfo: description: The Timeseries Dense Encoder (TiDE) Forecasting pipeline. name: time-series-dense-encoder-forecasting diff --git a/components/google-cloud/google_cloud_pipeline_components/preview/automl/forecasting/utils.py b/components/google-cloud/google_cloud_pipeline_components/preview/automl/forecasting/utils.py index 0d894282cef..3a42f4c91fc 100644 --- a/components/google-cloud/google_cloud_pipeline_components/preview/automl/forecasting/utils.py +++ b/components/google-cloud/google_cloud_pipeline_components/preview/automl/forecasting/utils.py @@ -13,6 +13,20 @@ ]) +def _validate_start_max_parameters( + starting_worker_count: int, + max_worker_count: int, + starting_count_name: str, + max_count_name: str, +): + if starting_worker_count > max_worker_count: + raise ValueError( + 'Starting count must be less than or equal to max count.' + f' {starting_count_name}: {starting_worker_count}, {max_count_name}:' + f' {max_worker_count}' + ) + + def _get_base_forecasting_parameters( *, project: str, @@ -59,6 +73,7 @@ def _get_base_forecasting_parameters( evaluation_batch_predict_max_replica_count: int = 25, evaluation_dataflow_machine_type: str = 'n1-standard-16', evaluation_dataflow_max_num_workers: int = 25, + evaluation_dataflow_starting_num_workers: int = 22, evaluation_dataflow_disk_size_gb: int = 50, study_spec_parameters_override: Optional[List[Dict[str, Any]]] = None, stage_1_tuner_worker_pool_specs_override: Optional[Dict[str, Any]] = None, @@ -91,6 +106,20 @@ def _get_base_forecasting_parameters( ) time_series_identifier_columns = [time_series_identifier_column] + _validate_start_max_parameters( + starting_worker_count=evaluation_batch_predict_starting_replica_count, + max_worker_count=evaluation_batch_predict_max_replica_count, + starting_count_name='evaluation_batch_predict_starting_replica_count', + max_count_name='evaluation_batch_predict_max_replica_count', + ) + + _validate_start_max_parameters( + starting_worker_count=evaluation_dataflow_starting_num_workers, + max_worker_count=evaluation_dataflow_max_num_workers, + starting_count_name='evaluation_dataflow_starting_num_workers', + max_count_name='evaluation_dataflow_max_num_workers', + ) + parameter_values = {} parameters = { 'project': project, @@ -152,6 +181,9 @@ def _get_base_forecasting_parameters( 'evaluation_dataflow_max_num_workers': ( evaluation_dataflow_max_num_workers ), + 'evaluation_dataflow_starting_num_workers': ( + evaluation_dataflow_starting_num_workers + ), 'evaluation_dataflow_disk_size_gb': evaluation_dataflow_disk_size_gb, 'study_spec_parameters_override': study_spec_parameters_override, 'stage_1_tuner_worker_pool_specs_override': ( @@ -174,13 +206,11 @@ def _get_base_forecasting_parameters( # Filter out empty values and those excluded from the particular pipeline. # (example: TFT and Seq2Seq don't support `quantiles`.) - parameter_values.update( - { - param: value - for param, value in parameters.items() - if value is not None and param not in fields_to_exclude - } - ) + parameter_values.update({ + param: value + for param, value in parameters.items() + if value is not None and param not in fields_to_exclude + }) return parameter_values @@ -229,6 +259,7 @@ def get_learn_to_learn_forecasting_pipeline_and_parameters( evaluation_batch_predict_max_replica_count: int = 25, evaluation_dataflow_machine_type: str = 'n1-standard-16', evaluation_dataflow_max_num_workers: int = 25, + evaluation_dataflow_starting_num_workers: int = 22, evaluation_dataflow_disk_size_gb: int = 50, study_spec_parameters_override: Optional[List[Dict[str, Any]]] = None, stage_1_tuner_worker_pool_specs_override: Optional[Dict[str, Any]] = None, @@ -291,6 +322,7 @@ def get_learn_to_learn_forecasting_pipeline_and_parameters( evaluation_batch_predict_max_replica_count: The maximum count of replicas the batch prediction job can scale to. evaluation_dataflow_machine_type: Machine type for the dataflow job in evaluation, such as 'n1-standard-16'. evaluation_dataflow_max_num_workers: Maximum number of dataflow workers. + evaluation_dataflow_starting_num_workers: Starting number of dataflow workers. evaluation_dataflow_disk_size_gb: The disk space in GB for dataflow. study_spec_parameters_override: The list for overriding study spec. stage_1_tuner_worker_pool_specs_override: The dictionary for overriding stage 1 tuner worker pool spec. @@ -354,6 +386,7 @@ def get_learn_to_learn_forecasting_pipeline_and_parameters( evaluation_batch_predict_max_replica_count=evaluation_batch_predict_max_replica_count, evaluation_dataflow_machine_type=evaluation_dataflow_machine_type, evaluation_dataflow_max_num_workers=evaluation_dataflow_max_num_workers, + evaluation_dataflow_starting_num_workers=evaluation_dataflow_starting_num_workers, evaluation_dataflow_disk_size_gb=evaluation_dataflow_disk_size_gb, study_spec_parameters_override=study_spec_parameters_override, stage_1_tuner_worker_pool_specs_override=stage_1_tuner_worker_pool_specs_override, @@ -423,6 +456,7 @@ def get_time_series_dense_encoder_forecasting_pipeline_and_parameters( evaluation_batch_predict_max_replica_count: int = 25, evaluation_dataflow_machine_type: str = 'n1-standard-16', evaluation_dataflow_max_num_workers: int = 25, + evaluation_dataflow_starting_num_workers: int = 22, evaluation_dataflow_disk_size_gb: int = 50, study_spec_parameters_override: Optional[List[Dict[str, Any]]] = None, stage_1_tuner_worker_pool_specs_override: Optional[Dict[str, Any]] = None, @@ -485,6 +519,7 @@ def get_time_series_dense_encoder_forecasting_pipeline_and_parameters( evaluation_batch_predict_max_replica_count: The maximum count of replicas the batch prediction job can scale to. evaluation_dataflow_machine_type: Machine type for the dataflow job in evaluation, such as 'n1-standard-16'. evaluation_dataflow_max_num_workers: Maximum number of dataflow workers. + evaluation_dataflow_starting_num_workers: Starting number of dataflow workers. evaluation_dataflow_disk_size_gb: The disk space in GB for dataflow. study_spec_parameters_override: The list for overriding study spec. stage_1_tuner_worker_pool_specs_override: The dictionary for overriding stage 1 tuner worker pool spec. @@ -548,6 +583,7 @@ def get_time_series_dense_encoder_forecasting_pipeline_and_parameters( evaluation_batch_predict_max_replica_count=evaluation_batch_predict_max_replica_count, evaluation_dataflow_machine_type=evaluation_dataflow_machine_type, evaluation_dataflow_max_num_workers=evaluation_dataflow_max_num_workers, + evaluation_dataflow_starting_num_workers=evaluation_dataflow_starting_num_workers, evaluation_dataflow_disk_size_gb=evaluation_dataflow_disk_size_gb, study_spec_parameters_override=study_spec_parameters_override, stage_1_tuner_worker_pool_specs_override=stage_1_tuner_worker_pool_specs_override, @@ -616,6 +652,7 @@ def get_temporal_fusion_transformer_forecasting_pipeline_and_parameters( evaluation_batch_predict_max_replica_count: int = 25, evaluation_dataflow_machine_type: str = 'n1-standard-16', evaluation_dataflow_max_num_workers: int = 25, + evaluation_dataflow_starting_num_workers: int = 22, evaluation_dataflow_disk_size_gb: int = 50, study_spec_parameters_override: Optional[List[Dict[str, Any]]] = None, stage_1_tuner_worker_pool_specs_override: Optional[Dict[str, Any]] = None, @@ -671,6 +708,7 @@ def get_temporal_fusion_transformer_forecasting_pipeline_and_parameters( evaluation_batch_predict_max_replica_count: The maximum count of replicas the batch prediction job can scale to. evaluation_dataflow_machine_type: Machine type for the dataflow job in evaluation, such as 'n1-standard-16'. evaluation_dataflow_max_num_workers: Maximum number of dataflow workers. + evaluation_dataflow_starting_num_workers: Starting number of dataflow workers. evaluation_dataflow_disk_size_gb: The disk space in GB for dataflow. study_spec_parameters_override: The list for overriding study spec. stage_1_tuner_worker_pool_specs_override: The dictionary for overriding stage 1 tuner worker pool spec. @@ -731,6 +769,7 @@ def get_temporal_fusion_transformer_forecasting_pipeline_and_parameters( evaluation_batch_predict_max_replica_count=evaluation_batch_predict_max_replica_count, evaluation_dataflow_machine_type=evaluation_dataflow_machine_type, evaluation_dataflow_max_num_workers=evaluation_dataflow_max_num_workers, + evaluation_dataflow_starting_num_workers=evaluation_dataflow_starting_num_workers, evaluation_dataflow_disk_size_gb=evaluation_dataflow_disk_size_gb, study_spec_parameters_override=study_spec_parameters_override, stage_1_tuner_worker_pool_specs_override=stage_1_tuner_worker_pool_specs_override, @@ -795,6 +834,7 @@ def get_sequence_to_sequence_forecasting_pipeline_and_parameters( evaluation_batch_predict_max_replica_count: int = 25, evaluation_dataflow_machine_type: str = 'n1-standard-16', evaluation_dataflow_max_num_workers: int = 25, + evaluation_dataflow_starting_num_workers: int = 22, evaluation_dataflow_disk_size_gb: int = 50, study_spec_parameters_override: Optional[List[Dict[str, Any]]] = None, stage_1_tuner_worker_pool_specs_override: Optional[Dict[str, Any]] = None, @@ -851,6 +891,7 @@ def get_sequence_to_sequence_forecasting_pipeline_and_parameters( evaluation_batch_predict_max_replica_count: The maximum count of replicas the batch prediction job can scale to. evaluation_dataflow_machine_type: Machine type for the dataflow job in evaluation, such as 'n1-standard-16'. evaluation_dataflow_max_num_workers: Maximum number of dataflow workers. + evaluation_dataflow_starting_num_workers: Starting number of dataflow workers. evaluation_dataflow_disk_size_gb: The disk space in GB for dataflow. study_spec_parameters_override: The list for overriding study spec. stage_1_tuner_worker_pool_specs_override: The dictionary for overriding stage 1 tuner worker pool spec. @@ -908,6 +949,7 @@ def get_sequence_to_sequence_forecasting_pipeline_and_parameters( evaluation_batch_predict_max_replica_count=evaluation_batch_predict_max_replica_count, evaluation_dataflow_machine_type=evaluation_dataflow_machine_type, evaluation_dataflow_max_num_workers=evaluation_dataflow_max_num_workers, + evaluation_dataflow_starting_num_workers=evaluation_dataflow_starting_num_workers, evaluation_dataflow_disk_size_gb=evaluation_dataflow_disk_size_gb, study_spec_parameters_override=study_spec_parameters_override, stage_1_tuner_worker_pool_specs_override=stage_1_tuner_worker_pool_specs_override, diff --git a/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/auto_feature_engineering.py b/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/auto_feature_engineering.py index 3d9a569f5ae..3294c4e5f98 100644 --- a/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/auto_feature_engineering.py +++ b/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/auto_feature_engineering.py @@ -65,7 +65,7 @@ def automated_feature_engineering( ' 1, "machine_spec": {"machine_type": "n1-standard-16"},' ' "container_spec": {"image_uri":"' ), - 'us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625', + 'us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625', '", "args": ["feature_engineering", "--project=', project, '", "--location=', location, '", "--data_source_bigquery_table_path=', data_source_bigquery_table_path, diff --git a/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/automl_tabular_feature_selection_pipeline.yaml b/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/automl_tabular_feature_selection_pipeline.yaml index 46011a8a5dd..30314c514a7 100644 --- a/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/automl_tabular_feature_selection_pipeline.yaml +++ b/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/automl_tabular_feature_selection_pipeline.yaml @@ -8622,9 +8622,9 @@ deploymentSpec: \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}", "\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\": {\"machine_type\": \"n1-standard-8\"}, \"container_spec\": {\"image_uri\":\"", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", "\", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"args\": [\"l2l_cv_tuner\", \"--transform_output_path=", "{{$.inputs.artifacts[''transform_output''].uri}}", - "\", \"--training_docker_uri=", "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", + "\", \"--training_docker_uri=", "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"--component_id={{$.pipeline_task_uuid}}\", \"--training_base_dir=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/train\", \"--num_parallel_trial=", "{{$.inputs.parameters[''num_parallel_trials'']}}", @@ -8665,9 +8665,9 @@ deploymentSpec: \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}", "\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\": {\"machine_type\": \"n1-standard-8\"}, \"container_spec\": {\"image_uri\":\"", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", "\", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"args\": [\"l2l_cv_tuner\", \"--transform_output_path=", "{{$.inputs.artifacts[''transform_output''].uri}}", - "\", \"--training_docker_uri=", "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", + "\", \"--training_docker_uri=", "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"--component_id={{$.pipeline_task_uuid}}\", \"--training_base_dir=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/train\", \"--num_parallel_trial=", "{{$.inputs.parameters[''num_parallel_trials'']}}", @@ -8708,7 +8708,7 @@ deploymentSpec: \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}", "\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\": {\"machine_type\": \"n1-highmem-8\"}, \"container_spec\": {\"image_uri\":\"", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", "\", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"args\": [\"ensemble\", \"--transform_output_path=", "{{$.inputs.artifacts[''transform_output''].uri}}", "\", \"--model_output_path=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/model\", \"--custom_model_output_path=", "{{$.inputs.parameters[''root_dir'']}}", @@ -8720,7 +8720,7 @@ deploymentSpec: "\", \"--tuning_result_input_path=", "{{$.inputs.artifacts[''tuning_result_input''].uri}}", "\", \"--instance_baseline_path=", "{{$.inputs.artifacts[''instance_baseline''].uri}}", "\", \"--warmup_data=", "{{$.inputs.artifacts[''warmup_data''].uri}}", "\", - \"--prediction_docker_uri=", "us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240419_0625", + \"--prediction_docker_uri=", "us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240710_0625", "\", \"--model_path=", "{{$.outputs.artifacts[''model''].uri}}", "\", \"--custom_model_path=", "{{$.outputs.artifacts[''model_without_custom_ops''].uri}}", "\", \"--explanation_metadata_path=", "{{$.outputs.parameters[''explanation_metadata''].output_file}}", ",", "{{$.outputs.artifacts[''explanation_metadata_artifact''].uri}}", @@ -8749,7 +8749,7 @@ deploymentSpec: \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}", "\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\": {\"machine_type\": \"n1-highmem-8\"}, \"container_spec\": {\"image_uri\":\"", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", "\", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"args\": [\"ensemble\", \"--transform_output_path=", "{{$.inputs.artifacts[''transform_output''].uri}}", "\", \"--model_output_path=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/model\", \"--custom_model_output_path=", "{{$.inputs.parameters[''root_dir'']}}", @@ -8761,7 +8761,7 @@ deploymentSpec: "\", \"--tuning_result_input_path=", "{{$.inputs.artifacts[''tuning_result_input''].uri}}", "\", \"--instance_baseline_path=", "{{$.inputs.artifacts[''instance_baseline''].uri}}", "\", \"--warmup_data=", "{{$.inputs.artifacts[''warmup_data''].uri}}", "\", - \"--prediction_docker_uri=", "us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240419_0625", + \"--prediction_docker_uri=", "us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240710_0625", "\", \"--model_path=", "{{$.outputs.artifacts[''model''].uri}}", "\", \"--custom_model_path=", "{{$.outputs.artifacts[''model_without_custom_ops''].uri}}", "\", \"--explanation_metadata_path=", "{{$.outputs.parameters[''explanation_metadata''].output_file}}", ",", "{{$.outputs.artifacts[''explanation_metadata_artifact''].uri}}", @@ -8790,7 +8790,7 @@ deploymentSpec: \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}", "\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\": {\"machine_type\": \"n1-highmem-8\"}, \"container_spec\": {\"image_uri\":\"", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", "\", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"args\": [\"ensemble\", \"--transform_output_path=", "{{$.inputs.artifacts[''transform_output''].uri}}", "\", \"--model_output_path=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/model\", \"--custom_model_output_path=", "{{$.inputs.parameters[''root_dir'']}}", @@ -8802,7 +8802,7 @@ deploymentSpec: "\", \"--tuning_result_input_path=", "{{$.inputs.artifacts[''tuning_result_input''].uri}}", "\", \"--instance_baseline_path=", "{{$.inputs.artifacts[''instance_baseline''].uri}}", "\", \"--warmup_data=", "{{$.inputs.artifacts[''warmup_data''].uri}}", "\", - \"--prediction_docker_uri=", "us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240419_0625", + \"--prediction_docker_uri=", "us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240710_0625", "\", \"--model_path=", "{{$.outputs.artifacts[''model''].uri}}", "\", \"--custom_model_path=", "{{$.outputs.artifacts[''model_without_custom_ops''].uri}}", "\", \"--explanation_metadata_path=", "{{$.outputs.parameters[''explanation_metadata''].output_file}}", ",", "{{$.outputs.artifacts[''explanation_metadata_artifact''].uri}}", @@ -8831,7 +8831,7 @@ deploymentSpec: \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}", "\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\": {\"machine_type\": \"n1-standard-8\"}, \"container_spec\": {\"image_uri\":\"", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", "\", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"args\": [\"cancel_l2l_tuner\", \"--error_file_path=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/error.pb\", \"--cleanup_lro_job_infos=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/lro\"]}}]}}"]}' @@ -8846,7 +8846,7 @@ deploymentSpec: args: - --executor_input - '{{$}}' - image: us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240710_0625 resources: cpuLimit: 8.0 memoryLimit: 52.0 @@ -8855,7 +8855,7 @@ deploymentSpec: args: - --executor_input - '{{$}}' - image: us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240710_0625 resources: cpuLimit: 8.0 memoryLimit: 52.0 @@ -8864,7 +8864,7 @@ deploymentSpec: args: - --executor_input - '{{$}}' - image: us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240710_0625 resources: cpuLimit: 8.0 memoryLimit: 52.0 @@ -8884,9 +8884,9 @@ deploymentSpec: \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}", "\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\": {\"machine_type\": \"n1-standard-8\"}, \"container_spec\": {\"image_uri\":\"", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", "\", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"args\": [\"l2l_stage_1_tuner\", \"--transform_output_path=", "{{$.inputs.artifacts[''transform_output''].uri}}", - "\", \"--training_docker_uri=", "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", + "\", \"--training_docker_uri=", "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"--feature_selection_result_path=", "{{$.inputs.artifacts[''feature_ranking''].uri}}", "\", \"--disable_early_stopping=", "{{$.inputs.parameters[''disable_early_stopping'']}}", "\", \"--tune_feature_selection_rate=", "{{$.inputs.parameters[''tune_feature_selection_rate'']}}", @@ -8931,9 +8931,9 @@ deploymentSpec: \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}", "\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\": {\"machine_type\": \"n1-standard-8\"}, \"container_spec\": {\"image_uri\":\"", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", "\", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"args\": [\"l2l_stage_1_tuner\", \"--transform_output_path=", "{{$.inputs.artifacts[''transform_output''].uri}}", - "\", \"--training_docker_uri=", "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", + "\", \"--training_docker_uri=", "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"--feature_selection_result_path=", "{{$.inputs.artifacts[''feature_ranking''].uri}}", "\", \"--disable_early_stopping=", "{{$.inputs.parameters[''disable_early_stopping'']}}", "\", \"--tune_feature_selection_rate=", "{{$.inputs.parameters[''tune_feature_selection_rate'']}}", @@ -8978,7 +8978,7 @@ deploymentSpec: \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}", "\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\": {\"machine_type\": \"n1-standard-8\"}, \"container_spec\": {\"image_uri\":\"", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", "\", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"args\": [\"transform\", \"--is_mp=true\", \"--transform_output_artifact_path=", "{{$.outputs.artifacts[''transform_output''].uri}}", "\", \"--transform_output_path=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/transform\", @@ -8999,7 +8999,7 @@ deploymentSpec: \"--dataflow_tmp_dir=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/dataflow_tmp\", \"--dataflow_max_num_workers=", "{{$.inputs.parameters[''dataflow_max_num_workers'']}}", "\", \"--dataflow_machine_type=", "{{$.inputs.parameters[''dataflow_machine_type'']}}", - "\", \"--dataflow_worker_container_image=", "us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240419_0625", + "\", \"--dataflow_worker_container_image=", "us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240710_0625", "\", \"--dataflow_disk_size_gb=", "{{$.inputs.parameters[''dataflow_disk_size_gb'']}}", "\", \"--dataflow_subnetwork_fully_qualified=", "{{$.inputs.parameters[''dataflow_subnetwork'']}}", "\", \"--dataflow_use_public_ips=", "{{$.inputs.parameters[''dataflow_use_public_ips'']}}", @@ -9030,7 +9030,7 @@ deploymentSpec: \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}", "\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\": {\"machine_type\": \"n1-standard-8\"}, \"container_spec\": {\"image_uri\":\"", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", "\", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"args\": [\"transform\", \"--is_mp=true\", \"--transform_output_artifact_path=", "{{$.outputs.artifacts[''transform_output''].uri}}", "\", \"--transform_output_path=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/transform\", @@ -9051,7 +9051,7 @@ deploymentSpec: \"--dataflow_tmp_dir=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/dataflow_tmp\", \"--dataflow_max_num_workers=", "{{$.inputs.parameters[''dataflow_max_num_workers'']}}", "\", \"--dataflow_machine_type=", "{{$.inputs.parameters[''dataflow_machine_type'']}}", - "\", \"--dataflow_worker_container_image=", "us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240419_0625", + "\", \"--dataflow_worker_container_image=", "us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240710_0625", "\", \"--dataflow_disk_size_gb=", "{{$.inputs.parameters[''dataflow_disk_size_gb'']}}", "\", \"--dataflow_subnetwork_fully_qualified=", "{{$.inputs.parameters[''dataflow_subnetwork'']}}", "\", \"--dataflow_use_public_ips=", "{{$.inputs.parameters[''dataflow_use_public_ips'']}}", @@ -9087,7 +9087,7 @@ deploymentSpec: \ *\n\ndef _bool_identity(value: bool) -> str:\n \"\"\"Returns boolean\ \ value.\n\n Args:\n value: Boolean value to return\n\n Returns:\n\ \ Boolean value.\n \"\"\"\n return 'true' if value else 'false'\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-bool-identity-2: container: args: @@ -9109,7 +9109,7 @@ deploymentSpec: \ *\n\ndef _bool_identity(value: bool) -> str:\n \"\"\"Returns boolean\ \ value.\n\n Args:\n value: Boolean value to return\n\n Returns:\n\ \ Boolean value.\n \"\"\"\n return 'true' if value else 'false'\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-bool-identity-3: container: args: @@ -9131,7 +9131,7 @@ deploymentSpec: \ *\n\ndef _bool_identity(value: bool) -> str:\n \"\"\"Returns boolean\ \ value.\n\n Args:\n value: Boolean value to return\n\n Returns:\n\ \ Boolean value.\n \"\"\"\n return 'true' if value else 'false'\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-calculate-training-parameters: container: args: @@ -9223,7 +9223,7 @@ deploymentSpec: \ stage_1_single_run_max_secs,\n stage_2_deadline_hours,\n \ \ stage_2_single_run_max_secs,\n distill_stage_1_deadline_hours,\n\ \ reduce_search_space_mode,\n )\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-calculate-training-parameters-2: container: args: @@ -9315,7 +9315,7 @@ deploymentSpec: \ stage_1_single_run_max_secs,\n stage_2_deadline_hours,\n \ \ stage_2_single_run_max_secs,\n distill_stage_1_deadline_hours,\n\ \ reduce_search_space_mode,\n )\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-check-if-binary-classification: container: args: @@ -9343,7 +9343,7 @@ deploymentSpec: \ with open(example_gen_metadata, 'r') as f:\n metadata_path = f.read()\n\ \ metadata = json.loads(metadata_path)\n return str(metadata['objective']\ \ == 'binary_classification').lower()\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-feature-attribution: container: args: @@ -9536,7 +9536,7 @@ deploymentSpec: \ 'r') as f:\n split_0_content = f.read()\n with open(split_1, 'r')\ \ as f:\n split_1_content = f.read()\n with open(splits, 'w') as f:\n\ \ f.write(','.join([split_0_content, split_1_content]))\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-model-batch-explanation: container: args: @@ -10383,7 +10383,7 @@ deploymentSpec: \n train_spec['transformations'] = purged_transformation_list\n metadata['train_spec']\ \ = train_spec\n\n with open(output_metadata, 'w') as f:\n f.write(json.dumps(metadata))\n\ \n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-read-input-uri: container: args: @@ -10411,7 +10411,7 @@ deploymentSpec: \ import json\n # pylint: enable=g-import-not-at-top,import-outside-toplevel,redefined-outer-name,reimported\n\ \ with open(split_uri, 'r') as f:\n data_source = json.loads(f.read())\n\ \ return data_source['tf_record_data_source']['file_patterns']\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-read-input-uri-2: container: args: @@ -10439,7 +10439,7 @@ deploymentSpec: \ import json\n # pylint: enable=g-import-not-at-top,import-outside-toplevel,redefined-outer-name,reimported\n\ \ with open(split_uri, 'r') as f:\n data_source = json.loads(f.read())\n\ \ return data_source['tf_record_data_source']['file_patterns']\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-string-not-empty: container: args: @@ -10463,7 +10463,7 @@ deploymentSpec: \n Returns:\n Boolean value. -> 'true' if empty, 'false' if not empty.\ \ We need to use str\n instead of bool due to a limitation in KFP compiler.\n\ \ \"\"\"\n return 'true' if value else 'false'\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-tabular-feature-ranking-and-selection: container: args: @@ -10480,7 +10480,7 @@ deploymentSpec: \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}", "\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\": {\"machine_type\": \"n1-standard-8\"}, \"container_spec\": {\"image_uri\":\"", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", "\", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"args\": [\"feature_selection\", \"--data_source=", "{{$.inputs.artifacts[''data_source''].uri}}", "\", \"--target_column=", "{{$.inputs.parameters[''target_column_name'']}}", "\", \"--prediction_type=", "{{$.inputs.parameters[''prediction_type'']}}", @@ -10493,7 +10493,7 @@ deploymentSpec: \"--dataflow_staging_dir=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/dataflow_staging\", \"--dataflow_tmp_dir=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/dataflow_tmp\", \"--dataflow_max_num_workers=", "{{$.inputs.parameters[''dataflow_max_num_workers'']}}", - "\", \"--dataflow_worker_container_image=", "us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240419_0625", + "\", \"--dataflow_worker_container_image=", "us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240710_0625", "\", \"--dataflow_machine_type=", "{{$.inputs.parameters[''dataflow_machine_type'']}}", "\", \"--dataflow_disk_size_gb=", "{{$.inputs.parameters[''dataflow_disk_size_gb'']}}", "\", \"--dataflow_subnetwork_fully_qualified=", "{{$.inputs.parameters[''dataflow_subnetwork'']}}", @@ -10526,7 +10526,7 @@ deploymentSpec: \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}", "\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\": {\"machine_type\": \"n1-standard-8\"}, \"container_spec\": {\"image_uri\":\"", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", "\", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"args\": [\"stats_generator\",", "\"--train_spec={\\\"prediction_type\\\": \\\"", "{{$.inputs.parameters[''prediction_type'']}}", "\\\", \\\"target_column\\\": \\\"", "{{$.inputs.parameters[''target_column_name'']}}", "\\\", \\\"optimization_objective\\\": @@ -10559,7 +10559,7 @@ deploymentSpec: \"--dataflow_staging_dir=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/dataflow_staging\", \"--dataflow_tmp_dir=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/dataflow_tmp\", \"--dataflow_max_num_workers=", "{{$.inputs.parameters[''dataflow_max_num_workers'']}}", - "\", \"--dataflow_worker_container_image=", "us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240419_0625", + "\", \"--dataflow_worker_container_image=", "us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240710_0625", "\", \"--dataflow_machine_type=", "{{$.inputs.parameters[''dataflow_machine_type'']}}", "\", \"--dataflow_disk_size_gb=", "{{$.inputs.parameters[''dataflow_disk_size_gb'']}}", "\", \"--dataflow_kms_key=", "{{$.inputs.parameters[''encryption_spec_key_name'']}}", @@ -10614,7 +10614,7 @@ deploymentSpec: \ f'{directory}/prediction.results-*',\n ],\n 'coder':\ \ 'PROTO_VALUE',\n },\n }\n with open(result, 'w') as f:\n f.write(json.dumps(data_source))\n\ \n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-write-bp-result-path-2: container: args: @@ -10644,7 +10644,7 @@ deploymentSpec: \ f'{directory}/prediction.results-*',\n ],\n 'coder':\ \ 'PROTO_VALUE',\n },\n }\n with open(result, 'w') as f:\n f.write(json.dumps(data_source))\n\ \n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 pipelineInfo: description: The AutoML Tabular pipeline. name: automl-tabular-feature-selection-pipeline diff --git a/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/automl_tabular_v2_pipeline.yaml b/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/automl_tabular_v2_pipeline.yaml index 3798ab00402..3a1239e5001 100644 --- a/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/automl_tabular_v2_pipeline.yaml +++ b/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/automl_tabular_v2_pipeline.yaml @@ -9452,9 +9452,9 @@ deploymentSpec: \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}", "\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\": {\"machine_type\": \"n1-standard-8\"}, \"container_spec\": {\"image_uri\":\"", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", "\", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"args\": [\"l2l_cv_tuner\", \"--transform_output_path=", "{{$.inputs.artifacts[''transform_output''].uri}}", - "\", \"--training_docker_uri=", "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", + "\", \"--training_docker_uri=", "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"--component_id={{$.pipeline_task_uuid}}\", \"--training_base_dir=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/train\", \"--num_parallel_trial=", "{{$.inputs.parameters[''num_parallel_trials'']}}", @@ -9495,9 +9495,9 @@ deploymentSpec: \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}", "\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\": {\"machine_type\": \"n1-standard-8\"}, \"container_spec\": {\"image_uri\":\"", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", "\", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"args\": [\"l2l_cv_tuner\", \"--transform_output_path=", "{{$.inputs.artifacts[''transform_output''].uri}}", - "\", \"--training_docker_uri=", "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", + "\", \"--training_docker_uri=", "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"--component_id={{$.pipeline_task_uuid}}\", \"--training_base_dir=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/train\", \"--num_parallel_trial=", "{{$.inputs.parameters[''num_parallel_trials'']}}", @@ -9538,7 +9538,7 @@ deploymentSpec: \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}", "\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\": {\"machine_type\": \"n1-highmem-8\"}, \"container_spec\": {\"image_uri\":\"", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", "\", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"args\": [\"ensemble\", \"--transform_output_path=", "{{$.inputs.artifacts[''transform_output''].uri}}", "\", \"--model_output_path=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/model\", \"--custom_model_output_path=", "{{$.inputs.parameters[''root_dir'']}}", @@ -9550,7 +9550,7 @@ deploymentSpec: "\", \"--tuning_result_input_path=", "{{$.inputs.artifacts[''tuning_result_input''].uri}}", "\", \"--instance_baseline_path=", "{{$.inputs.artifacts[''instance_baseline''].uri}}", "\", \"--warmup_data=", "{{$.inputs.artifacts[''warmup_data''].uri}}", "\", - \"--prediction_docker_uri=", "us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240419_0625", + \"--prediction_docker_uri=", "us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240710_0625", "\", \"--model_path=", "{{$.outputs.artifacts[''model''].uri}}", "\", \"--custom_model_path=", "{{$.outputs.artifacts[''model_without_custom_ops''].uri}}", "\", \"--explanation_metadata_path=", "{{$.outputs.parameters[''explanation_metadata''].output_file}}", ",", "{{$.outputs.artifacts[''explanation_metadata_artifact''].uri}}", @@ -9579,7 +9579,7 @@ deploymentSpec: \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}", "\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\": {\"machine_type\": \"n1-highmem-8\"}, \"container_spec\": {\"image_uri\":\"", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", "\", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"args\": [\"ensemble\", \"--transform_output_path=", "{{$.inputs.artifacts[''transform_output''].uri}}", "\", \"--model_output_path=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/model\", \"--custom_model_output_path=", "{{$.inputs.parameters[''root_dir'']}}", @@ -9591,7 +9591,7 @@ deploymentSpec: "\", \"--tuning_result_input_path=", "{{$.inputs.artifacts[''tuning_result_input''].uri}}", "\", \"--instance_baseline_path=", "{{$.inputs.artifacts[''instance_baseline''].uri}}", "\", \"--warmup_data=", "{{$.inputs.artifacts[''warmup_data''].uri}}", "\", - \"--prediction_docker_uri=", "us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240419_0625", + \"--prediction_docker_uri=", "us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240710_0625", "\", \"--model_path=", "{{$.outputs.artifacts[''model''].uri}}", "\", \"--custom_model_path=", "{{$.outputs.artifacts[''model_without_custom_ops''].uri}}", "\", \"--explanation_metadata_path=", "{{$.outputs.parameters[''explanation_metadata''].output_file}}", ",", "{{$.outputs.artifacts[''explanation_metadata_artifact''].uri}}", @@ -9620,7 +9620,7 @@ deploymentSpec: \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}", "\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\": {\"machine_type\": \"n1-highmem-8\"}, \"container_spec\": {\"image_uri\":\"", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", "\", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"args\": [\"ensemble\", \"--transform_output_path=", "{{$.inputs.artifacts[''transform_output''].uri}}", "\", \"--model_output_path=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/model\", \"--custom_model_output_path=", "{{$.inputs.parameters[''root_dir'']}}", @@ -9632,7 +9632,7 @@ deploymentSpec: "\", \"--tuning_result_input_path=", "{{$.inputs.artifacts[''tuning_result_input''].uri}}", "\", \"--instance_baseline_path=", "{{$.inputs.artifacts[''instance_baseline''].uri}}", "\", \"--warmup_data=", "{{$.inputs.artifacts[''warmup_data''].uri}}", "\", - \"--prediction_docker_uri=", "us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240419_0625", + \"--prediction_docker_uri=", "us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240710_0625", "\", \"--model_path=", "{{$.outputs.artifacts[''model''].uri}}", "\", \"--custom_model_path=", "{{$.outputs.artifacts[''model_without_custom_ops''].uri}}", "\", \"--explanation_metadata_path=", "{{$.outputs.parameters[''explanation_metadata''].output_file}}", ",", "{{$.outputs.artifacts[''explanation_metadata_artifact''].uri}}", @@ -9661,7 +9661,7 @@ deploymentSpec: \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}", "\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\": {\"machine_type\": \"n1-standard-8\"}, \"container_spec\": {\"image_uri\":\"", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", "\", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"args\": [\"cancel_l2l_tuner\", \"--error_file_path=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/error.pb\", \"--cleanup_lro_job_infos=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/lro\"]}}]}}"]}' @@ -9676,7 +9676,7 @@ deploymentSpec: args: - --executor_input - '{{$}}' - image: us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240710_0625 resources: cpuLimit: 8.0 memoryLimit: 52.0 @@ -9685,7 +9685,7 @@ deploymentSpec: args: - --executor_input - '{{$}}' - image: us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240710_0625 resources: cpuLimit: 8.0 memoryLimit: 52.0 @@ -9694,7 +9694,7 @@ deploymentSpec: args: - --executor_input - '{{$}}' - image: us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240710_0625 resources: cpuLimit: 8.0 memoryLimit: 52.0 @@ -9714,9 +9714,9 @@ deploymentSpec: \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}", "\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\": {\"machine_type\": \"n1-standard-8\"}, \"container_spec\": {\"image_uri\":\"", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", "\", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"args\": [\"l2l_stage_1_tuner\", \"--transform_output_path=", "{{$.inputs.artifacts[''transform_output''].uri}}", - "\", \"--training_docker_uri=", "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", + "\", \"--training_docker_uri=", "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"--feature_selection_result_path=", "{{$.inputs.artifacts[''feature_ranking''].uri}}", "\", \"--disable_early_stopping=", "{{$.inputs.parameters[''disable_early_stopping'']}}", "\", \"--tune_feature_selection_rate=", "{{$.inputs.parameters[''tune_feature_selection_rate'']}}", @@ -9761,9 +9761,9 @@ deploymentSpec: \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}", "\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\": {\"machine_type\": \"n1-standard-8\"}, \"container_spec\": {\"image_uri\":\"", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", "\", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"args\": [\"l2l_stage_1_tuner\", \"--transform_output_path=", "{{$.inputs.artifacts[''transform_output''].uri}}", - "\", \"--training_docker_uri=", "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", + "\", \"--training_docker_uri=", "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"--feature_selection_result_path=", "{{$.inputs.artifacts[''feature_ranking''].uri}}", "\", \"--disable_early_stopping=", "{{$.inputs.parameters[''disable_early_stopping'']}}", "\", \"--tune_feature_selection_rate=", "{{$.inputs.parameters[''tune_feature_selection_rate'']}}", @@ -9813,7 +9813,7 @@ deploymentSpec: \ *\n\ndef _bool_identity(value: bool) -> str:\n \"\"\"Returns boolean\ \ value.\n\n Args:\n value: Boolean value to return\n\n Returns:\n\ \ Boolean value.\n \"\"\"\n return 'true' if value else 'false'\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-bool-identity-2: container: args: @@ -9835,7 +9835,7 @@ deploymentSpec: \ *\n\ndef _bool_identity(value: bool) -> str:\n \"\"\"Returns boolean\ \ value.\n\n Args:\n value: Boolean value to return\n\n Returns:\n\ \ Boolean value.\n \"\"\"\n return 'true' if value else 'false'\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-bool-identity-3: container: args: @@ -9857,7 +9857,7 @@ deploymentSpec: \ *\n\ndef _bool_identity(value: bool) -> str:\n \"\"\"Returns boolean\ \ value.\n\n Args:\n value: Boolean value to return\n\n Returns:\n\ \ Boolean value.\n \"\"\"\n return 'true' if value else 'false'\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-calculate-training-parameters: container: args: @@ -9949,7 +9949,7 @@ deploymentSpec: \ stage_1_single_run_max_secs,\n stage_2_deadline_hours,\n \ \ stage_2_single_run_max_secs,\n distill_stage_1_deadline_hours,\n\ \ reduce_search_space_mode,\n )\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-calculate-training-parameters-2: container: args: @@ -10041,7 +10041,7 @@ deploymentSpec: \ stage_1_single_run_max_secs,\n stage_2_deadline_hours,\n \ \ stage_2_single_run_max_secs,\n distill_stage_1_deadline_hours,\n\ \ reduce_search_space_mode,\n )\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-distillation-stage-feature-transform-engine: container: args: @@ -10075,14 +10075,14 @@ deploymentSpec: "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/dataflow_tmp"]}' - '{"Concat": ["--dataflow_max_num_workers=", "{{$.inputs.parameters[''dataflow_max_num_workers'']}}"]}' - '{"Concat": ["--dataflow_machine_type=", "{{$.inputs.parameters[''dataflow_machine_type'']}}"]}' - - --dataflow_worker_container_image=us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240419_0625 + - --dataflow_worker_container_image=us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240710_0625 - '{"Concat": ["--dataflow_disk_size_gb=", "{{$.inputs.parameters[''dataflow_disk_size_gb'']}}"]}' - '{"Concat": ["--dataflow_subnetwork_fully_qualified=", "{{$.inputs.parameters[''dataflow_subnetwork'']}}"]}' - '{"Concat": ["--dataflow_use_public_ips=", "{{$.inputs.parameters[''dataflow_use_public_ips'']}}"]}' - '{"Concat": ["--dataflow_service_account=", "{{$.inputs.parameters[''dataflow_service_account'']}}"]}' - '{"Concat": ["--dataflow_kms_key=", "{{$.inputs.parameters[''encryption_spec_key_name'']}}"]}' - '{"Concat": ["--gcp_resources_path=", "{{$.outputs.parameters[''gcp_resources''].output_file}}"]}' - image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240710_0625 resources: cpuLimit: 8.0 memoryLimit: 30.0 @@ -10329,8 +10329,8 @@ deploymentSpec: "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/dataflow_tmp"]}' - '{"Concat": ["--dataflow_max_num_workers=", "{{$.inputs.parameters[''dataflow_max_num_workers'']}}"]}' - '{"Concat": ["--dataflow_machine_type=", "{{$.inputs.parameters[''dataflow_machine_type'']}}"]}' - - --dataflow_worker_container_image=us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240419_0625 - - --feature_transform_engine_docker_uri=us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240419_0625 + - --dataflow_worker_container_image=us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240710_0625 + - --feature_transform_engine_docker_uri=us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240710_0625 - '{"Concat": ["--dataflow_disk_size_gb=", "{{$.inputs.parameters[''dataflow_disk_size_gb'']}}"]}' - '{"Concat": ["--dataflow_subnetwork_fully_qualified=", "{{$.inputs.parameters[''dataflow_subnetwork'']}}"]}' - '{"Concat": ["--dataflow_use_public_ips=", "{{$.inputs.parameters[''dataflow_use_public_ips'']}}"]}' @@ -10347,7 +10347,7 @@ deploymentSpec: - '{"IfPresent": {"InputName": "group_temporal_total_weight", "Then": {"Concat": ["--group_temporal_total_weight=", "{{$.inputs.parameters[''group_temporal_total_weight'']}}"]}}}' - '{"Concat": ["--encryption_spec_key_name=", "{{$.inputs.parameters[''encryption_spec_key_name'']}}"]}' - image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240710_0625 resources: cpuLimit: 8.0 memoryLimit: 30.0 @@ -10382,7 +10382,7 @@ deploymentSpec: \ collections.namedtuple(\n 'Outputs',\n [\n 'bigquery_destination_output_uri',\n\ \ ],\n )(\n f'{bigquery_staging_dataset_uri}.{table_prefix}{model_display_name}{curr_time}',\n\ \ )\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-get-bigquery-destination-output-uri-2: container: args: @@ -10414,7 +10414,7 @@ deploymentSpec: \ collections.namedtuple(\n 'Outputs',\n [\n 'bigquery_destination_output_uri',\n\ \ ],\n )(\n f'{bigquery_staging_dataset_uri}.{table_prefix}{model_display_name}{curr_time}',\n\ \ )\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-get-bp-bq-output-table: container: args: @@ -10442,7 +10442,7 @@ deploymentSpec: \n return collections.namedtuple(\n 'Outputs',\n [\n \ \ 'bq_output_table_uri',\n ],\n )(\n f\"{bp_job.metadata['bigqueryOutputDataset']}.{bp_job.metadata['bigqueryOutputTable']}\"\ ,\n )\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-get-bp-bq-output-table-2: container: args: @@ -10470,7 +10470,7 @@ deploymentSpec: \n return collections.namedtuple(\n 'Outputs',\n [\n \ \ 'bq_output_table_uri',\n ],\n )(\n f\"{bp_job.metadata['bigqueryOutputDataset']}.{bp_job.metadata['bigqueryOutputTable']}\"\ ,\n )\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-get-model-display-name: container: args: @@ -10497,7 +10497,7 @@ deploymentSpec: \n return collections.namedtuple(\n 'Outputs',\n [\n \ \ 'model_display_name',\n ],\n )(\n model_display_name,\n )\n\ \n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-get-transform-config-path: container: args: @@ -10530,7 +10530,7 @@ deploymentSpec: \ )\n\n return collections.namedtuple(\n 'Outputs',\n [\n \ \ 'transform_config_path',\n ],\n )(\n transform_config_path,\n\ \ )\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-importer: importer: artifactUri: @@ -10564,7 +10564,7 @@ deploymentSpec: \ 'r') as f:\n split_0_content = f.read()\n with open(split_1, 'r')\ \ as f:\n split_1_content = f.read()\n with open(splits, 'w') as f:\n\ \ f.write(','.join([split_0_content, split_1_content]))\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-model-batch-explanation: container: args: @@ -11409,7 +11409,7 @@ deploymentSpec: \ 'data_source_csv_filenames',\n 'data_source_bigquery_table_path',\n\ \ ],\n )(\n data_source_csv_filenames,\n data_source_bigquery_table_path,\n\ \ )\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-split-materialized-data: container: args: @@ -11455,7 +11455,7 @@ deploymentSpec: \ 'w') as f:\n f.write(file_patterns[0])\n\n with tf.io.gfile.GFile(materialized_eval_split,\ \ 'w') as f:\n f.write(file_patterns[1])\n\n with tf.io.gfile.GFile(materialized_test_split,\ \ 'w') as f:\n f.write(file_patterns[2])\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240710_0625 exec-split-materialized-data-2: container: args: @@ -11501,7 +11501,7 @@ deploymentSpec: \ 'w') as f:\n f.write(file_patterns[0])\n\n with tf.io.gfile.GFile(materialized_eval_split,\ \ 'w') as f:\n f.write(file_patterns[1])\n\n with tf.io.gfile.GFile(materialized_test_split,\ \ 'w') as f:\n f.write(file_patterns[2])\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240710_0625 exec-string-not-empty: container: args: @@ -11525,7 +11525,7 @@ deploymentSpec: \n Returns:\n Boolean value. -> 'true' if empty, 'false' if not empty.\ \ We need to use str\n instead of bool due to a limitation in KFP compiler.\n\ \ \"\"\"\n return 'true' if value else 'false'\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-training-configurator-and-validator: container: args: @@ -11570,7 +11570,7 @@ deploymentSpec: ["--temporal_total_weight=", "{{$.inputs.parameters[''temporal_total_weight'']}}"]}}}' - '{"IfPresent": {"InputName": "group_temporal_total_weight", "Then": {"Concat": ["--group_temporal_total_weight=", "{{$.inputs.parameters[''group_temporal_total_weight'']}}"]}}}' - image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240710_0625 exec-training-configurator-and-validator-2: container: args: @@ -11615,7 +11615,7 @@ deploymentSpec: ["--temporal_total_weight=", "{{$.inputs.parameters[''temporal_total_weight'']}}"]}}}' - '{"IfPresent": {"InputName": "group_temporal_total_weight", "Then": {"Concat": ["--group_temporal_total_weight=", "{{$.inputs.parameters[''group_temporal_total_weight'']}}"]}}}' - image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240710_0625 pipelineInfo: description: The AutoML Tabular pipeline v2. name: automl-tabular-v2 diff --git a/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/distillation_stage_feature_transform_engine.py b/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/distillation_stage_feature_transform_engine.py index adee4e2d36b..d9e14b0d391 100644 --- a/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/distillation_stage_feature_transform_engine.py +++ b/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/distillation_stage_feature_transform_engine.py @@ -77,7 +77,7 @@ def distillation_stage_feature_transform_engine( # fmt: on return dsl.ContainerSpec( - image='us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240419_0625', + image='us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240710_0625', command=[], args=[ 'distillation_stage_feature_transform_engine', @@ -185,7 +185,7 @@ def distillation_stage_feature_transform_engine( dataflow_machine_type, ] ), - '--dataflow_worker_container_image=us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240419_0625', + '--dataflow_worker_container_image=us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240710_0625', dsl.ConcatPlaceholder( items=[ '--dataflow_disk_size_gb=', diff --git a/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/feature_selection.py b/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/feature_selection.py index 083da657717..92bb09870c3 100644 --- a/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/feature_selection.py +++ b/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/feature_selection.py @@ -100,7 +100,7 @@ def tabular_feature_ranking_and_selection( ' 1, "machine_spec": {"machine_type": "n1-standard-8"},' ' "container_spec": {"image_uri":"' ), - 'us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625', + 'us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625', '", "args": ["feature_selection", "--data_source=', data_source.uri, '", "--target_column=', @@ -137,7 +137,7 @@ def tabular_feature_ranking_and_selection( ), dataflow_max_num_workers, '", "--dataflow_worker_container_image=', - 'us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240419_0625', + 'us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240710_0625', '", "--dataflow_machine_type=', dataflow_machine_type, '", "--dataflow_disk_size_gb=', diff --git a/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/feature_selection_pipeline.yaml b/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/feature_selection_pipeline.yaml index 91ca188ca3c..6b6233e2835 100644 --- a/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/feature_selection_pipeline.yaml +++ b/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/feature_selection_pipeline.yaml @@ -983,8 +983,8 @@ deploymentSpec: "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/dataflow_tmp"]}' - '{"Concat": ["--dataflow_max_num_workers=", "{{$.inputs.parameters[''dataflow_max_num_workers'']}}"]}' - '{"Concat": ["--dataflow_machine_type=", "{{$.inputs.parameters[''dataflow_machine_type'']}}"]}' - - --dataflow_worker_container_image=us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240419_0625 - - --feature_transform_engine_docker_uri=us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240419_0625 + - --dataflow_worker_container_image=us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240710_0625 + - --feature_transform_engine_docker_uri=us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240710_0625 - '{"Concat": ["--dataflow_disk_size_gb=", "{{$.inputs.parameters[''dataflow_disk_size_gb'']}}"]}' - '{"Concat": ["--dataflow_subnetwork_fully_qualified=", "{{$.inputs.parameters[''dataflow_subnetwork'']}}"]}' - '{"Concat": ["--dataflow_use_public_ips=", "{{$.inputs.parameters[''dataflow_use_public_ips'']}}"]}' @@ -1001,7 +1001,7 @@ deploymentSpec: - '{"IfPresent": {"InputName": "group_temporal_total_weight", "Then": {"Concat": ["--group_temporal_total_weight=", "{{$.inputs.parameters[''group_temporal_total_weight'']}}"]}}}' - '{"Concat": ["--encryption_spec_key_name=", "{{$.inputs.parameters[''encryption_spec_key_name'']}}"]}' - image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240710_0625 resources: cpuLimit: 8.0 memoryLimit: 30.0 @@ -1049,7 +1049,7 @@ deploymentSpec: ["--temporal_total_weight=", "{{$.inputs.parameters[''temporal_total_weight'']}}"]}}}' - '{"IfPresent": {"InputName": "group_temporal_total_weight", "Then": {"Concat": ["--group_temporal_total_weight=", "{{$.inputs.parameters[''group_temporal_total_weight'']}}"]}}}' - image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240710_0625 pipelineInfo: description: Defines pipeline for feature transform engine component. name: feature-selection diff --git a/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/feature_transform_engine.py b/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/feature_transform_engine.py index a0f669043b1..db85e257cfe 100644 --- a/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/feature_transform_engine.py +++ b/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/feature_transform_engine.py @@ -308,7 +308,7 @@ def feature_transform_engine( # fmt: on return dsl.ContainerSpec( - image='us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240419_0625', + image='us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240710_0625', command=[], args=[ 'feature_transform_engine', @@ -637,8 +637,8 @@ def feature_transform_engine( dsl.ConcatPlaceholder( items=['--dataflow_machine_type=', dataflow_machine_type] ), - '--dataflow_worker_container_image=us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240419_0625', - '--feature_transform_engine_docker_uri=us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240419_0625', + '--dataflow_worker_container_image=us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240710_0625', + '--feature_transform_engine_docker_uri=us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240710_0625', dsl.ConcatPlaceholder( items=['--dataflow_disk_size_gb=', dataflow_disk_size_gb] ), diff --git a/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/tabnet_hyperparameter_tuning_job.py b/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/tabnet_hyperparameter_tuning_job.py index 3f36cc4709b..4f5aff349cd 100644 --- a/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/tabnet_hyperparameter_tuning_job.py +++ b/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/tabnet_hyperparameter_tuning_job.py @@ -158,7 +158,7 @@ def tabnet_hyperparameter_tuning_job( ', "disk_spec": ', training_disk_spec, ', "container_spec": {"image_uri":"', - 'us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/tabnet-training:20240419_0625', + 'us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/tabnet-training:20240710_0625', '", "args": ["--target_column=', target_column, '", "--weight_column=', @@ -166,7 +166,7 @@ def tabnet_hyperparameter_tuning_job( '", "--model_type=', prediction_type, '", "--prediction_docker_uri=', - 'us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240419_0625', + 'us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240710_0625', '", "--prediction_docker_uri_artifact_path=', prediction_docker_uri_output, '", "--baseline_path=', diff --git a/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/tabnet_hyperparameter_tuning_job_pipeline.yaml b/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/tabnet_hyperparameter_tuning_job_pipeline.yaml index 60d182fd05f..ee813bebfd8 100644 --- a/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/tabnet_hyperparameter_tuning_job_pipeline.yaml +++ b/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/tabnet_hyperparameter_tuning_job_pipeline.yaml @@ -2826,7 +2826,7 @@ deploymentSpec: \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}", "\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\": {\"machine_type\": \"n1-standard-8\"}, \"container_spec\": {\"image_uri\":\"", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", "\", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"args\": [\"cancel_l2l_tuner\", \"--error_file_path=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/error.pb\", \"--cleanup_lro_job_infos=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/lro\"]}}]}}"]}' @@ -2841,7 +2841,7 @@ deploymentSpec: args: - --executor_input - '{{$}}' - image: us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240710_0625 resources: cpuLimit: 8.0 memoryLimit: 52.0 @@ -2866,7 +2866,7 @@ deploymentSpec: \ *\n\ndef _bool_identity(value: bool) -> str:\n \"\"\"Returns boolean\ \ value.\n\n Args:\n value: Boolean value to return\n\n Returns:\n\ \ Boolean value.\n \"\"\"\n return 'true' if value else 'false'\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-feature-transform-engine: container: args: @@ -2951,8 +2951,8 @@ deploymentSpec: "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/dataflow_tmp"]}' - '{"Concat": ["--dataflow_max_num_workers=", "{{$.inputs.parameters[''dataflow_max_num_workers'']}}"]}' - '{"Concat": ["--dataflow_machine_type=", "{{$.inputs.parameters[''dataflow_machine_type'']}}"]}' - - --dataflow_worker_container_image=us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240419_0625 - - --feature_transform_engine_docker_uri=us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240419_0625 + - --dataflow_worker_container_image=us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240710_0625 + - --feature_transform_engine_docker_uri=us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240710_0625 - '{"Concat": ["--dataflow_disk_size_gb=", "{{$.inputs.parameters[''dataflow_disk_size_gb'']}}"]}' - '{"Concat": ["--dataflow_subnetwork_fully_qualified=", "{{$.inputs.parameters[''dataflow_subnetwork'']}}"]}' - '{"Concat": ["--dataflow_use_public_ips=", "{{$.inputs.parameters[''dataflow_use_public_ips'']}}"]}' @@ -2969,7 +2969,7 @@ deploymentSpec: - '{"IfPresent": {"InputName": "group_temporal_total_weight", "Then": {"Concat": ["--group_temporal_total_weight=", "{{$.inputs.parameters[''group_temporal_total_weight'']}}"]}}}' - '{"Concat": ["--encryption_spec_key_name=", "{{$.inputs.parameters[''encryption_spec_key_name'']}}"]}' - image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240710_0625 resources: cpuLimit: 8.0 memoryLimit: 30.0 @@ -3037,7 +3037,7 @@ deploymentSpec: \ = {\n 'instanceSchemaUri': instance_schema_uri,\n 'predictionSchemaUri':\ \ prediction_schema_uri,\n }\n unmanaged_container_model.uri = os.path.join(\n\ \ trials_dir, 'trial_{}'.format(best_trial['id']), 'model'\n )\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-get-model-display-name: container: args: @@ -3064,7 +3064,7 @@ deploymentSpec: \n return collections.namedtuple(\n 'Outputs',\n [\n \ \ 'model_display_name',\n ],\n )(\n model_display_name,\n )\n\ \n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-get-tabnet-study-spec-parameters: container: args: @@ -3580,7 +3580,7 @@ deploymentSpec: \ = ', '.join(extra_overrides)\n warnings.warn(\n f'The overrides\ \ {extra_override_str} were not found in the params and '\n 'will\ \ be ignored.'\n )\n\n return study_spec_parameters\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-model-batch-predict: container: args: @@ -3821,7 +3821,7 @@ deploymentSpec: \ 'training_disk_spec',\n 'eval_machine_spec',\n 'eval_replica_count',\n\ \ ],\n )(\n training_machine_spec,\n training_disk_spec,\n\ \ eval_machine_spec,\n eval_replica_count,\n )\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-set-optional-inputs: container: args: @@ -3869,7 +3869,7 @@ deploymentSpec: \ 'data_source_csv_filenames',\n 'data_source_bigquery_table_path',\n\ \ ],\n )(\n data_source_csv_filenames,\n data_source_bigquery_table_path,\n\ \ )\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-split-materialized-data: container: args: @@ -3915,7 +3915,7 @@ deploymentSpec: \ 'w') as f:\n f.write(file_patterns[0])\n\n with tf.io.gfile.GFile(materialized_eval_split,\ \ 'w') as f:\n f.write(file_patterns[1])\n\n with tf.io.gfile.GFile(materialized_test_split,\ \ 'w') as f:\n f.write(file_patterns[2])\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240710_0625 exec-tabnet-hyperparameter-tuning-job: container: args: @@ -3943,11 +3943,11 @@ deploymentSpec: ", \"trial_job_spec\": {\"worker_pool_specs\": [{\"replica_count\":\"", "1", "\", \"machine_spec\": ", "{{$.inputs.parameters[''training_machine_spec'']}}", ", \"disk_spec\": ", "{{$.inputs.parameters[''training_disk_spec'']}}", - ", \"container_spec\": {\"image_uri\":\"", "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/tabnet-training:20240419_0625", + ", \"container_spec\": {\"image_uri\":\"", "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/tabnet-training:20240710_0625", "\", \"args\": [\"--target_column=", "{{$.inputs.parameters[''target_column'']}}", "\", \"--weight_column=", "{{$.inputs.parameters[''weight_column'']}}", "\", \"--model_type=", "{{$.inputs.parameters[''prediction_type'']}}", "\", - \"--prediction_docker_uri=", "us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240419_0625", + \"--prediction_docker_uri=", "us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240710_0625", "\", \"--prediction_docker_uri_artifact_path=", "{{$.outputs.parameters[''prediction_docker_uri_output''].output_file}}", "\", \"--baseline_path=", "{{$.inputs.artifacts[''instance_baseline''].uri}}", "\", \"--metadata_path=", "{{$.inputs.artifacts[''metadata''].uri}}", "\", @@ -4016,7 +4016,7 @@ deploymentSpec: ["--temporal_total_weight=", "{{$.inputs.parameters[''temporal_total_weight'']}}"]}}}' - '{"IfPresent": {"InputName": "group_temporal_total_weight", "Then": {"Concat": ["--group_temporal_total_weight=", "{{$.inputs.parameters[''group_temporal_total_weight'']}}"]}}}' - image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240710_0625 pipelineInfo: description: The TabNet built-in algorithm HyperparameterTuningJob pipeline. name: automl-tabular-tabnet-hyperparameter-tuning-job diff --git a/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/tabnet_trainer.py b/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/tabnet_trainer.py index ae3b551a152..d95cb79d58e 100644 --- a/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/tabnet_trainer.py +++ b/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/tabnet_trainer.py @@ -165,7 +165,7 @@ def tabnet_trainer( ', "disk_spec": ', training_disk_spec, ', "container_spec": {"image_uri":"', - 'us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/tabnet-training:20240419_0625', + 'us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/tabnet-training:20240710_0625', '", "args": ["--target_column=', target_column, '", "--weight_column=', @@ -173,7 +173,7 @@ def tabnet_trainer( '", "--model_type=', prediction_type, '", "--prediction_docker_uri=', - 'us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240419_0625', + 'us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240710_0625', '", "--baseline_path=', instance_baseline.uri, '", "--metadata_path=', diff --git a/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/tabnet_trainer_pipeline.yaml b/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/tabnet_trainer_pipeline.yaml index cc28b94ec63..8255c1e4a6d 100644 --- a/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/tabnet_trainer_pipeline.yaml +++ b/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/tabnet_trainer_pipeline.yaml @@ -2875,7 +2875,7 @@ deploymentSpec: \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}", "\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\": {\"machine_type\": \"n1-standard-8\"}, \"container_spec\": {\"image_uri\":\"", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", "\", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"args\": [\"cancel_l2l_tuner\", \"--error_file_path=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/error.pb\", \"--cleanup_lro_job_infos=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/lro\"]}}]}}"]}' @@ -2890,7 +2890,7 @@ deploymentSpec: args: - --executor_input - '{{$}}' - image: us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240710_0625 resources: cpuLimit: 8.0 memoryLimit: 52.0 @@ -2915,7 +2915,7 @@ deploymentSpec: \ *\n\ndef _bool_identity(value: bool) -> str:\n \"\"\"Returns boolean\ \ value.\n\n Args:\n value: Boolean value to return\n\n Returns:\n\ \ Boolean value.\n \"\"\"\n return 'true' if value else 'false'\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-feature-transform-engine: container: args: @@ -3000,8 +3000,8 @@ deploymentSpec: "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/dataflow_tmp"]}' - '{"Concat": ["--dataflow_max_num_workers=", "{{$.inputs.parameters[''dataflow_max_num_workers'']}}"]}' - '{"Concat": ["--dataflow_machine_type=", "{{$.inputs.parameters[''dataflow_machine_type'']}}"]}' - - --dataflow_worker_container_image=us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240419_0625 - - --feature_transform_engine_docker_uri=us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240419_0625 + - --dataflow_worker_container_image=us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240710_0625 + - --feature_transform_engine_docker_uri=us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240710_0625 - '{"Concat": ["--dataflow_disk_size_gb=", "{{$.inputs.parameters[''dataflow_disk_size_gb'']}}"]}' - '{"Concat": ["--dataflow_subnetwork_fully_qualified=", "{{$.inputs.parameters[''dataflow_subnetwork'']}}"]}' - '{"Concat": ["--dataflow_use_public_ips=", "{{$.inputs.parameters[''dataflow_use_public_ips'']}}"]}' @@ -3018,7 +3018,7 @@ deploymentSpec: - '{"IfPresent": {"InputName": "group_temporal_total_weight", "Then": {"Concat": ["--group_temporal_total_weight=", "{{$.inputs.parameters[''group_temporal_total_weight'']}}"]}}}' - '{"Concat": ["--encryption_spec_key_name=", "{{$.inputs.parameters[''encryption_spec_key_name'']}}"]}' - image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240710_0625 resources: cpuLimit: 8.0 memoryLimit: 30.0 @@ -3048,7 +3048,7 @@ deploymentSpec: \n return collections.namedtuple(\n 'Outputs',\n [\n \ \ 'model_display_name',\n ],\n )(\n model_display_name,\n )\n\ \n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-model-batch-predict: container: args: @@ -3289,7 +3289,7 @@ deploymentSpec: \ 'training_disk_spec',\n 'eval_machine_spec',\n 'eval_replica_count',\n\ \ ],\n )(\n training_machine_spec,\n training_disk_spec,\n\ \ eval_machine_spec,\n eval_replica_count,\n )\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-set-optional-inputs: container: args: @@ -3337,7 +3337,7 @@ deploymentSpec: \ 'data_source_csv_filenames',\n 'data_source_bigquery_table_path',\n\ \ ],\n )(\n data_source_csv_filenames,\n data_source_bigquery_table_path,\n\ \ )\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-split-materialized-data: container: args: @@ -3383,7 +3383,7 @@ deploymentSpec: \ 'w') as f:\n f.write(file_patterns[0])\n\n with tf.io.gfile.GFile(materialized_eval_split,\ \ 'w') as f:\n f.write(file_patterns[1])\n\n with tf.io.gfile.GFile(materialized_test_split,\ \ 'w') as f:\n f.write(file_patterns[2])\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240710_0625 exec-tabnet-trainer: container: args: @@ -3401,11 +3401,11 @@ deploymentSpec: "\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\":\"", "1", "\", \"machine_spec\": ", "{{$.inputs.parameters[''training_machine_spec'']}}", ", \"disk_spec\": ", "{{$.inputs.parameters[''training_disk_spec'']}}", - ", \"container_spec\": {\"image_uri\":\"", "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/tabnet-training:20240419_0625", + ", \"container_spec\": {\"image_uri\":\"", "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/tabnet-training:20240710_0625", "\", \"args\": [\"--target_column=", "{{$.inputs.parameters[''target_column'']}}", "\", \"--weight_column=", "{{$.inputs.parameters[''weight_column'']}}", "\", \"--model_type=", "{{$.inputs.parameters[''prediction_type'']}}", "\", - \"--prediction_docker_uri=", "us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240419_0625", + \"--prediction_docker_uri=", "us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240710_0625", "\", \"--baseline_path=", "{{$.inputs.artifacts[''instance_baseline''].uri}}", "\", \"--metadata_path=", "{{$.inputs.artifacts[''metadata''].uri}}", "\", \"--transform_output_path=", "{{$.inputs.artifacts[''transform_output''].uri}}", @@ -3492,7 +3492,7 @@ deploymentSpec: ["--temporal_total_weight=", "{{$.inputs.parameters[''temporal_total_weight'']}}"]}}}' - '{"IfPresent": {"InputName": "group_temporal_total_weight", "Then": {"Concat": ["--group_temporal_total_weight=", "{{$.inputs.parameters[''group_temporal_total_weight'']}}"]}}}' - image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240710_0625 pipelineInfo: description: 'Train a model using the Tabular Workflow for TabNet pipelines. diff --git a/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/wide_and_deep_hyperparameter_tuning_job.py b/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/wide_and_deep_hyperparameter_tuning_job.py index d0c901ec25a..8577c54ed31 100644 --- a/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/wide_and_deep_hyperparameter_tuning_job.py +++ b/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/wide_and_deep_hyperparameter_tuning_job.py @@ -158,7 +158,7 @@ def wide_and_deep_hyperparameter_tuning_job( ', "disk_spec": ', training_disk_spec, ', "container_spec": {"image_uri":"', - 'us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/wide-and-deep-training:20240419_0625', + 'us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/wide-and-deep-training:20240710_0625', '", "args": ["--target_column=', target_column, '", "--weight_column=', @@ -166,7 +166,7 @@ def wide_and_deep_hyperparameter_tuning_job( '", "--model_type=', prediction_type, '", "--prediction_docker_uri=', - 'us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240419_0625', + 'us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240710_0625', '", "--prediction_docker_uri_artifact_path=', prediction_docker_uri_output, '", "--baseline_path=', diff --git a/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/wide_and_deep_hyperparameter_tuning_job_pipeline.yaml b/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/wide_and_deep_hyperparameter_tuning_job_pipeline.yaml index 056bca0d92d..7003138669c 100644 --- a/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/wide_and_deep_hyperparameter_tuning_job_pipeline.yaml +++ b/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/wide_and_deep_hyperparameter_tuning_job_pipeline.yaml @@ -2632,7 +2632,7 @@ deploymentSpec: \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}", "\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\": {\"machine_type\": \"n1-standard-8\"}, \"container_spec\": {\"image_uri\":\"", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", "\", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"args\": [\"cancel_l2l_tuner\", \"--error_file_path=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/error.pb\", \"--cleanup_lro_job_infos=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/lro\"]}}]}}"]}' @@ -2647,7 +2647,7 @@ deploymentSpec: args: - --executor_input - '{{$}}' - image: us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240710_0625 resources: cpuLimit: 8.0 memoryLimit: 52.0 @@ -2672,7 +2672,7 @@ deploymentSpec: \ *\n\ndef _bool_identity(value: bool) -> str:\n \"\"\"Returns boolean\ \ value.\n\n Args:\n value: Boolean value to return\n\n Returns:\n\ \ Boolean value.\n \"\"\"\n return 'true' if value else 'false'\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-feature-transform-engine: container: args: @@ -2757,8 +2757,8 @@ deploymentSpec: "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/dataflow_tmp"]}' - '{"Concat": ["--dataflow_max_num_workers=", "{{$.inputs.parameters[''dataflow_max_num_workers'']}}"]}' - '{"Concat": ["--dataflow_machine_type=", "{{$.inputs.parameters[''dataflow_machine_type'']}}"]}' - - --dataflow_worker_container_image=us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240419_0625 - - --feature_transform_engine_docker_uri=us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240419_0625 + - --dataflow_worker_container_image=us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240710_0625 + - --feature_transform_engine_docker_uri=us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240710_0625 - '{"Concat": ["--dataflow_disk_size_gb=", "{{$.inputs.parameters[''dataflow_disk_size_gb'']}}"]}' - '{"Concat": ["--dataflow_subnetwork_fully_qualified=", "{{$.inputs.parameters[''dataflow_subnetwork'']}}"]}' - '{"Concat": ["--dataflow_use_public_ips=", "{{$.inputs.parameters[''dataflow_use_public_ips'']}}"]}' @@ -2775,7 +2775,7 @@ deploymentSpec: - '{"IfPresent": {"InputName": "group_temporal_total_weight", "Then": {"Concat": ["--group_temporal_total_weight=", "{{$.inputs.parameters[''group_temporal_total_weight'']}}"]}}}' - '{"Concat": ["--encryption_spec_key_name=", "{{$.inputs.parameters[''encryption_spec_key_name'']}}"]}' - image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240710_0625 resources: cpuLimit: 8.0 memoryLimit: 30.0 @@ -2843,7 +2843,7 @@ deploymentSpec: \ = {\n 'instanceSchemaUri': instance_schema_uri,\n 'predictionSchemaUri':\ \ prediction_schema_uri,\n }\n unmanaged_container_model.uri = os.path.join(\n\ \ trials_dir, 'trial_{}'.format(best_trial['id']), 'model'\n )\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-get-model-display-name: container: args: @@ -2870,7 +2870,7 @@ deploymentSpec: \n return collections.namedtuple(\n 'Outputs',\n [\n \ \ 'model_display_name',\n ],\n )(\n model_display_name,\n )\n\ \n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-get-wide-and-deep-study-spec-parameters: container: args: @@ -3147,7 +3147,7 @@ deploymentSpec: \ 'training_disk_spec',\n 'eval_machine_spec',\n 'eval_replica_count',\n\ \ ],\n )(\n training_machine_spec,\n training_disk_spec,\n\ \ eval_machine_spec,\n eval_replica_count,\n )\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-set-optional-inputs: container: args: @@ -3195,7 +3195,7 @@ deploymentSpec: \ 'data_source_csv_filenames',\n 'data_source_bigquery_table_path',\n\ \ ],\n )(\n data_source_csv_filenames,\n data_source_bigquery_table_path,\n\ \ )\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-split-materialized-data: container: args: @@ -3241,7 +3241,7 @@ deploymentSpec: \ 'w') as f:\n f.write(file_patterns[0])\n\n with tf.io.gfile.GFile(materialized_eval_split,\ \ 'w') as f:\n f.write(file_patterns[1])\n\n with tf.io.gfile.GFile(materialized_test_split,\ \ 'w') as f:\n f.write(file_patterns[2])\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240710_0625 exec-training-configurator-and-validator: container: args: @@ -3286,7 +3286,7 @@ deploymentSpec: ["--temporal_total_weight=", "{{$.inputs.parameters[''temporal_total_weight'']}}"]}}}' - '{"IfPresent": {"InputName": "group_temporal_total_weight", "Then": {"Concat": ["--group_temporal_total_weight=", "{{$.inputs.parameters[''group_temporal_total_weight'']}}"]}}}' - image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240710_0625 exec-wide-and-deep-hyperparameter-tuning-job: container: args: @@ -3314,11 +3314,11 @@ deploymentSpec: ", \"trial_job_spec\": {\"worker_pool_specs\": [{\"replica_count\":\"", "1", "\", \"machine_spec\": ", "{{$.inputs.parameters[''training_machine_spec'']}}", ", \"disk_spec\": ", "{{$.inputs.parameters[''training_disk_spec'']}}", - ", \"container_spec\": {\"image_uri\":\"", "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/wide-and-deep-training:20240419_0625", + ", \"container_spec\": {\"image_uri\":\"", "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/wide-and-deep-training:20240710_0625", "\", \"args\": [\"--target_column=", "{{$.inputs.parameters[''target_column'']}}", "\", \"--weight_column=", "{{$.inputs.parameters[''weight_column'']}}", "\", \"--model_type=", "{{$.inputs.parameters[''prediction_type'']}}", "\", - \"--prediction_docker_uri=", "us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240419_0625", + \"--prediction_docker_uri=", "us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240710_0625", "\", \"--prediction_docker_uri_artifact_path=", "{{$.outputs.parameters[''prediction_docker_uri_output''].output_file}}", "\", \"--baseline_path=", "{{$.inputs.artifacts[''instance_baseline''].uri}}", "\", \"--metadata_path=", "{{$.inputs.artifacts[''metadata''].uri}}", "\", diff --git a/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/wide_and_deep_trainer.py b/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/wide_and_deep_trainer.py index 9c93acc867b..e4b7ced41ff 100644 --- a/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/wide_and_deep_trainer.py +++ b/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/wide_and_deep_trainer.py @@ -161,7 +161,7 @@ def wide_and_deep_trainer( ', "disk_spec": ', training_disk_spec, ', "container_spec": {"image_uri":"', - 'us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/wide-and-deep-training:20240419_0625', + 'us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/wide-and-deep-training:20240710_0625', '", "args": ["--target_column=', target_column, '", "--weight_column=', @@ -169,7 +169,7 @@ def wide_and_deep_trainer( '", "--model_type=', prediction_type, '", "--prediction_docker_uri=', - 'us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240419_0625', + 'us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240710_0625', '", "--baseline_path=', instance_baseline.uri, '", "--metadata_path=', diff --git a/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/wide_and_deep_trainer_pipeline.yaml b/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/wide_and_deep_trainer_pipeline.yaml index ac50e50ee2b..fb1345499ae 100644 --- a/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/wide_and_deep_trainer_pipeline.yaml +++ b/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/wide_and_deep_trainer_pipeline.yaml @@ -2674,7 +2674,7 @@ deploymentSpec: \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}", "\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\": {\"machine_type\": \"n1-standard-8\"}, \"container_spec\": {\"image_uri\":\"", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", "\", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"args\": [\"cancel_l2l_tuner\", \"--error_file_path=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/error.pb\", \"--cleanup_lro_job_infos=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/lro\"]}}]}}"]}' @@ -2689,7 +2689,7 @@ deploymentSpec: args: - --executor_input - '{{$}}' - image: us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240710_0625 resources: cpuLimit: 8.0 memoryLimit: 52.0 @@ -2714,7 +2714,7 @@ deploymentSpec: \ *\n\ndef _bool_identity(value: bool) -> str:\n \"\"\"Returns boolean\ \ value.\n\n Args:\n value: Boolean value to return\n\n Returns:\n\ \ Boolean value.\n \"\"\"\n return 'true' if value else 'false'\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-feature-transform-engine: container: args: @@ -2799,8 +2799,8 @@ deploymentSpec: "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/dataflow_tmp"]}' - '{"Concat": ["--dataflow_max_num_workers=", "{{$.inputs.parameters[''dataflow_max_num_workers'']}}"]}' - '{"Concat": ["--dataflow_machine_type=", "{{$.inputs.parameters[''dataflow_machine_type'']}}"]}' - - --dataflow_worker_container_image=us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240419_0625 - - --feature_transform_engine_docker_uri=us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240419_0625 + - --dataflow_worker_container_image=us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240710_0625 + - --feature_transform_engine_docker_uri=us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240710_0625 - '{"Concat": ["--dataflow_disk_size_gb=", "{{$.inputs.parameters[''dataflow_disk_size_gb'']}}"]}' - '{"Concat": ["--dataflow_subnetwork_fully_qualified=", "{{$.inputs.parameters[''dataflow_subnetwork'']}}"]}' - '{"Concat": ["--dataflow_use_public_ips=", "{{$.inputs.parameters[''dataflow_use_public_ips'']}}"]}' @@ -2817,7 +2817,7 @@ deploymentSpec: - '{"IfPresent": {"InputName": "group_temporal_total_weight", "Then": {"Concat": ["--group_temporal_total_weight=", "{{$.inputs.parameters[''group_temporal_total_weight'']}}"]}}}' - '{"Concat": ["--encryption_spec_key_name=", "{{$.inputs.parameters[''encryption_spec_key_name'']}}"]}' - image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240710_0625 resources: cpuLimit: 8.0 memoryLimit: 30.0 @@ -2847,7 +2847,7 @@ deploymentSpec: \n return collections.namedtuple(\n 'Outputs',\n [\n \ \ 'model_display_name',\n ],\n )(\n model_display_name,\n )\n\ \n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-model-batch-predict: container: args: @@ -3040,7 +3040,7 @@ deploymentSpec: \ 'training_disk_spec',\n 'eval_machine_spec',\n 'eval_replica_count',\n\ \ ],\n )(\n training_machine_spec,\n training_disk_spec,\n\ \ eval_machine_spec,\n eval_replica_count,\n )\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-set-optional-inputs: container: args: @@ -3088,7 +3088,7 @@ deploymentSpec: \ 'data_source_csv_filenames',\n 'data_source_bigquery_table_path',\n\ \ ],\n )(\n data_source_csv_filenames,\n data_source_bigquery_table_path,\n\ \ )\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-split-materialized-data: container: args: @@ -3134,7 +3134,7 @@ deploymentSpec: \ 'w') as f:\n f.write(file_patterns[0])\n\n with tf.io.gfile.GFile(materialized_eval_split,\ \ 'w') as f:\n f.write(file_patterns[1])\n\n with tf.io.gfile.GFile(materialized_test_split,\ \ 'w') as f:\n f.write(file_patterns[2])\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240710_0625 exec-training-configurator-and-validator: container: args: @@ -3179,7 +3179,7 @@ deploymentSpec: ["--temporal_total_weight=", "{{$.inputs.parameters[''temporal_total_weight'']}}"]}}}' - '{"IfPresent": {"InputName": "group_temporal_total_weight", "Then": {"Concat": ["--group_temporal_total_weight=", "{{$.inputs.parameters[''group_temporal_total_weight'']}}"]}}}' - image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240710_0625 exec-wide-and-deep-trainer: container: args: @@ -3197,11 +3197,11 @@ deploymentSpec: "\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\":\"", "1", "\", \"machine_spec\": ", "{{$.inputs.parameters[''training_machine_spec'']}}", ", \"disk_spec\": ", "{{$.inputs.parameters[''training_disk_spec'']}}", - ", \"container_spec\": {\"image_uri\":\"", "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/wide-and-deep-training:20240419_0625", + ", \"container_spec\": {\"image_uri\":\"", "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/wide-and-deep-training:20240710_0625", "\", \"args\": [\"--target_column=", "{{$.inputs.parameters[''target_column'']}}", "\", \"--weight_column=", "{{$.inputs.parameters[''weight_column'']}}", "\", \"--model_type=", "{{$.inputs.parameters[''prediction_type'']}}", "\", - \"--prediction_docker_uri=", "us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240419_0625", + \"--prediction_docker_uri=", "us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240710_0625", "\", \"--baseline_path=", "{{$.inputs.artifacts[''instance_baseline''].uri}}", "\", \"--metadata_path=", "{{$.inputs.artifacts[''metadata''].uri}}", "\", \"--transform_output_path=", "{{$.inputs.artifacts[''transform_output''].uri}}", diff --git a/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/xgboost_hyperparameter_tuning_job_pipeline.yaml b/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/xgboost_hyperparameter_tuning_job_pipeline.yaml index 7b6890aa39d..bdb51e66927 100644 --- a/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/xgboost_hyperparameter_tuning_job_pipeline.yaml +++ b/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/xgboost_hyperparameter_tuning_job_pipeline.yaml @@ -2620,7 +2620,7 @@ deploymentSpec: \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}", "\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\": {\"machine_type\": \"n1-standard-8\"}, \"container_spec\": {\"image_uri\":\"", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", "\", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"args\": [\"cancel_l2l_tuner\", \"--error_file_path=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/error.pb\", \"--cleanup_lro_job_infos=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/lro\"]}}]}}"]}' @@ -2651,7 +2651,7 @@ deploymentSpec: \ *\n\ndef _bool_identity(value: bool) -> str:\n \"\"\"Returns boolean\ \ value.\n\n Args:\n value: Boolean value to return\n\n Returns:\n\ \ Boolean value.\n \"\"\"\n return 'true' if value else 'false'\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-feature-transform-engine: container: args: @@ -2736,8 +2736,8 @@ deploymentSpec: "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/dataflow_tmp"]}' - '{"Concat": ["--dataflow_max_num_workers=", "{{$.inputs.parameters[''dataflow_max_num_workers'']}}"]}' - '{"Concat": ["--dataflow_machine_type=", "{{$.inputs.parameters[''dataflow_machine_type'']}}"]}' - - --dataflow_worker_container_image=us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240419_0625 - - --feature_transform_engine_docker_uri=us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240419_0625 + - --dataflow_worker_container_image=us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240710_0625 + - --feature_transform_engine_docker_uri=us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240710_0625 - '{"Concat": ["--dataflow_disk_size_gb=", "{{$.inputs.parameters[''dataflow_disk_size_gb'']}}"]}' - '{"Concat": ["--dataflow_subnetwork_fully_qualified=", "{{$.inputs.parameters[''dataflow_subnetwork'']}}"]}' - '{"Concat": ["--dataflow_use_public_ips=", "{{$.inputs.parameters[''dataflow_use_public_ips'']}}"]}' @@ -2754,7 +2754,7 @@ deploymentSpec: - '{"IfPresent": {"InputName": "group_temporal_total_weight", "Then": {"Concat": ["--group_temporal_total_weight=", "{{$.inputs.parameters[''group_temporal_total_weight'']}}"]}}}' - '{"Concat": ["--encryption_spec_key_name=", "{{$.inputs.parameters[''encryption_spec_key_name'']}}"]}' - image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240710_0625 resources: cpuLimit: 8.0 memoryLimit: 30.0 @@ -2818,7 +2818,7 @@ deploymentSpec: \ return re.sub(r'^/gcs/', r'gs://', path)\n\n master_worker_pool_spec\ \ = {\n 'replica_count': 1,\n 'machine_spec': {\n 'machine_type':\ \ machine_type,\n },\n 'container_spec': {\n 'image_uri':\ - \ 'us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/xgboost-training:20240419_0625',\n\ + \ 'us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/xgboost-training:20240710_0625',\n\ \ 'args': [\n f'--job_dir={get_gcs_path(job_dir)}',\n\ \ f'--instance_schema_path={get_gcs_path(instance_schema_uri)}',\n\ \ f'--prediction_schema_path={get_gcs_path(prediction_schema_uri)}',\n\ @@ -2831,7 +2831,7 @@ deploymentSpec: \ f'--baseline_path={get_gcs_path(instance_baseline)}',\n \ \ f'--eval_metric={eval_metric}',\n f'--disable_default_eval_metric={disable_default_eval_metric}',\n\ \ f'--seed={seed}',\n f'--seed_per_iteration={seed_per_iteration}',\n\ - \ '--prediction_docker_uri=us-docker.pkg.dev/vertex-ai/automl-tabular/xgboost-prediction-server:20240419_0625',\n\ + \ '--prediction_docker_uri=us-docker.pkg.dev/vertex-ai/automl-tabular/xgboost-prediction-server:20240710_0625',\n\ \ ],\n },\n }\n\n # Add optional arguments if set\n if\ \ weight_column:\n master_worker_pool_spec['container_spec']['args'].append(\n\ \ f'--weight_column={weight_column}'\n )\n\n # Add accelerator_type\ @@ -2850,7 +2850,7 @@ deploymentSpec: \ ],\n )(\n worker_pool_specs_lst,\n get_gcs_path(instance_schema_uri),\n\ \ get_gcs_path(prediction_schema_uri),\n get_gcs_path(trials),\n\ \ get_gcs_path(prediction_docker_uri_output),\n )\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-get-best-hyperparameter-tuning-job-trial: container: args: @@ -2915,7 +2915,7 @@ deploymentSpec: \ = {\n 'instanceSchemaUri': instance_schema_uri,\n 'predictionSchemaUri':\ \ prediction_schema_uri,\n }\n unmanaged_container_model.uri = os.path.join(\n\ \ trials_dir, 'trial_{}'.format(best_trial['id']), 'model'\n )\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-get-model-display-name: container: args: @@ -2942,7 +2942,7 @@ deploymentSpec: \n return collections.namedtuple(\n 'Outputs',\n [\n \ \ 'model_display_name',\n ],\n )(\n model_display_name,\n )\n\ \n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-get-prediction-type-for-xgboost: container: args: @@ -2971,7 +2971,7 @@ deploymentSpec: \ Must be one of'\n ' [reg:squarederror, reg:squaredlogerror, reg:logistic,\ \ reg:gamma,'\n ' reg:tweedie, reg:pseudohubererror, binary:logistic,'\n\ \ ' multi:softprob].'\n )\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-get-xgboost-study-spec-parameters: container: args: @@ -3546,7 +3546,7 @@ deploymentSpec: \ 'data_source_csv_filenames',\n 'data_source_bigquery_table_path',\n\ \ ],\n )(\n data_source_csv_filenames,\n data_source_bigquery_table_path,\n\ \ )\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-split-materialized-data: container: args: @@ -3592,7 +3592,7 @@ deploymentSpec: \ 'w') as f:\n f.write(file_patterns[0])\n\n with tf.io.gfile.GFile(materialized_eval_split,\ \ 'w') as f:\n f.write(file_patterns[1])\n\n with tf.io.gfile.GFile(materialized_test_split,\ \ 'w') as f:\n f.write(file_patterns[2])\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240710_0625 exec-training-configurator-and-validator: container: args: @@ -3637,7 +3637,7 @@ deploymentSpec: ["--temporal_total_weight=", "{{$.inputs.parameters[''temporal_total_weight'']}}"]}}}' - '{"IfPresent": {"InputName": "group_temporal_total_weight", "Then": {"Concat": ["--group_temporal_total_weight=", "{{$.inputs.parameters[''group_temporal_total_weight'']}}"]}}}' - image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240710_0625 exec-xgboost-hyperparameter-tuning-job: container: args: diff --git a/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/xgboost_trainer_pipeline.yaml b/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/xgboost_trainer_pipeline.yaml index 06da514bb74..280c7e27842 100644 --- a/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/xgboost_trainer_pipeline.yaml +++ b/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/xgboost_trainer_pipeline.yaml @@ -2844,7 +2844,7 @@ deploymentSpec: \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}", "\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\": {\"machine_type\": \"n1-standard-8\"}, \"container_spec\": {\"image_uri\":\"", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", "\", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"args\": [\"cancel_l2l_tuner\", \"--error_file_path=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/error.pb\", \"--cleanup_lro_job_infos=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/lro\"]}}]}}"]}' @@ -2875,7 +2875,7 @@ deploymentSpec: \ *\n\ndef _bool_identity(value: bool) -> str:\n \"\"\"Returns boolean\ \ value.\n\n Args:\n value: Boolean value to return\n\n Returns:\n\ \ Boolean value.\n \"\"\"\n return 'true' if value else 'false'\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-feature-transform-engine: container: args: @@ -2960,8 +2960,8 @@ deploymentSpec: "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/dataflow_tmp"]}' - '{"Concat": ["--dataflow_max_num_workers=", "{{$.inputs.parameters[''dataflow_max_num_workers'']}}"]}' - '{"Concat": ["--dataflow_machine_type=", "{{$.inputs.parameters[''dataflow_machine_type'']}}"]}' - - --dataflow_worker_container_image=us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240419_0625 - - --feature_transform_engine_docker_uri=us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240419_0625 + - --dataflow_worker_container_image=us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240710_0625 + - --feature_transform_engine_docker_uri=us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240710_0625 - '{"Concat": ["--dataflow_disk_size_gb=", "{{$.inputs.parameters[''dataflow_disk_size_gb'']}}"]}' - '{"Concat": ["--dataflow_subnetwork_fully_qualified=", "{{$.inputs.parameters[''dataflow_subnetwork'']}}"]}' - '{"Concat": ["--dataflow_use_public_ips=", "{{$.inputs.parameters[''dataflow_use_public_ips'']}}"]}' @@ -2978,7 +2978,7 @@ deploymentSpec: - '{"IfPresent": {"InputName": "group_temporal_total_weight", "Then": {"Concat": ["--group_temporal_total_weight=", "{{$.inputs.parameters[''group_temporal_total_weight'']}}"]}}}' - '{"Concat": ["--encryption_spec_key_name=", "{{$.inputs.parameters[''encryption_spec_key_name'']}}"]}' - image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240710_0625 resources: cpuLimit: 8.0 memoryLimit: 30.0 @@ -3098,10 +3098,10 @@ deploymentSpec: \ worker pool specs.\n \"\"\"\n import copy\n import collections\n import\ \ os\n import re\n\n def get_gcs_path(path):\n return re.sub(r'/gcs/',\ \ 'gs://', path)\n\n formatted_job_dir = get_gcs_path(job_dir)\n prediction_docker_uri\ - \ = (\n 'us-docker.pkg.dev/vertex-ai/automl-tabular/xgboost-prediction-server:20240419_0625'\n\ + \ = (\n 'us-docker.pkg.dev/vertex-ai/automl-tabular/xgboost-prediction-server:20240710_0625'\n\ \ )\n master_worker_pool_spec = {\n 'replica_count': 1,\n 'machine_spec':\ \ {\n 'machine_type': machine_type,\n },\n 'container_spec':\ - \ {\n 'image_uri': 'us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/xgboost-training:20240419_0625',\n\ + \ {\n 'image_uri': 'us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/xgboost-training:20240710_0625',\n\ \ 'args': [\n f'--job_dir={formatted_job_dir}',\n\ \ f'--target_column={target_column}',\n f'--objective={objective}',\n\ \ f'--training_data_path={get_gcs_path(materialized_train_split)}',\n\ @@ -3159,7 +3159,7 @@ deploymentSpec: \ 'predictionSchemaUri': os.path.join(model_dir, 'prediction_schema.yaml'),\n\ \ }\n unmanaged_container_model.uri = model_dir\n\n return collections.namedtuple('Outputs',\ \ ['worker_pool_specs'])(\n worker_pool_specs_lst\n )\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-get-model-display-name: container: args: @@ -3186,7 +3186,7 @@ deploymentSpec: \n return collections.namedtuple(\n 'Outputs',\n [\n \ \ 'model_display_name',\n ],\n )(\n model_display_name,\n )\n\ \n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-get-prediction-type-for-xgboost: container: args: @@ -3215,7 +3215,7 @@ deploymentSpec: \ Must be one of'\n ' [reg:squarederror, reg:squaredlogerror, reg:logistic,\ \ reg:gamma,'\n ' reg:tweedie, reg:pseudohubererror, binary:logistic,'\n\ \ ' multi:softprob].'\n )\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-model-batch-predict: container: args: @@ -3407,7 +3407,7 @@ deploymentSpec: \ 'data_source_csv_filenames',\n 'data_source_bigquery_table_path',\n\ \ ],\n )(\n data_source_csv_filenames,\n data_source_bigquery_table_path,\n\ \ )\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-split-materialized-data: container: args: @@ -3453,7 +3453,7 @@ deploymentSpec: \ 'w') as f:\n f.write(file_patterns[0])\n\n with tf.io.gfile.GFile(materialized_eval_split,\ \ 'w') as f:\n f.write(file_patterns[1])\n\n with tf.io.gfile.GFile(materialized_test_split,\ \ 'w') as f:\n f.write(file_patterns[2])\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240710_0625 exec-training-configurator-and-validator: container: args: @@ -3498,7 +3498,7 @@ deploymentSpec: ["--temporal_total_weight=", "{{$.inputs.parameters[''temporal_total_weight'']}}"]}}}' - '{"IfPresent": {"InputName": "group_temporal_total_weight", "Then": {"Concat": ["--group_temporal_total_weight=", "{{$.inputs.parameters[''group_temporal_total_weight'']}}"]}}}' - image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240710_0625 exec-xgboost-trainer: container: args: diff --git a/components/google-cloud/google_cloud_pipeline_components/v1/automl/forecasting/bqml_arima_predict_pipeline.yaml b/components/google-cloud/google_cloud_pipeline_components/v1/automl/forecasting/bqml_arima_predict_pipeline.yaml index 46b0f89f162..58abf5fd9f8 100644 --- a/components/google-cloud/google_cloud_pipeline_components/v1/automl/forecasting/bqml_arima_predict_pipeline.yaml +++ b/components/google-cloud/google_cloud_pipeline_components/v1/automl/forecasting/bqml_arima_predict_pipeline.yaml @@ -658,7 +658,7 @@ deploymentSpec: \ = client.create_dataset(dataset=dataset, exists_ok=exists_ok)\n return\ \ collections.namedtuple('Outputs', ['project_id', 'dataset_id'])(\n \ \ ref.project, ref.dataset_id)\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-bigquery-create-dataset-2: container: args: @@ -693,7 +693,7 @@ deploymentSpec: \ = client.create_dataset(dataset=dataset, exists_ok=exists_ok)\n return\ \ collections.namedtuple('Outputs', ['project_id', 'dataset_id'])(\n \ \ ref.project, ref.dataset_id)\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-bigquery-delete-dataset-with-prefix: container: args: @@ -727,7 +727,7 @@ deploymentSpec: \ if dataset.dataset_id.startswith(dataset_prefix):\n client.delete_dataset(\n\ \ dataset=dataset.dataset_id,\n delete_contents=delete_contents)\n\ \n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-bigquery-query-job: container: args: @@ -788,7 +788,7 @@ deploymentSpec: \ 'datasetId': dataset_id,\n 'tableId': table_id,\n }\n\ \ if write_disposition:\n config['write_disposition'] = write_disposition\n\ \ return config\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-get-first-valid: container: args: @@ -812,7 +812,7 @@ deploymentSpec: \ import json\n # pylint: enable=g-import-not-at-top,import-outside-toplevel,redefined-outer-name,reimported\n\ \n for value in json.loads(values):\n if value:\n return value\n\ \ raise ValueError('No valid values.')\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-get-model-metadata: container: args: @@ -851,7 +851,7 @@ deploymentSpec: \ 'forecast_horizon',\n ],\n )(\n options.time_series_timestamp_column,\n\ \ options.time_series_id_column,\n options.time_series_data_column,\n\ \ options.horizon,\n )\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-get-table-location: container: args: @@ -887,7 +887,7 @@ deploymentSpec: \ if table.startswith('bq://'):\n table = table[len('bq://'):]\n elif\ \ table.startswith('bigquery://'):\n table = table[len('bigquery://'):]\n\ \ return client.get_table(table).location\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-load-table-from-uri: container: args: @@ -928,7 +928,7 @@ deploymentSpec: \ source_format=source_format)\n client.load_table_from_uri(\n source_uris=csv_list,\n\ \ destination=destination,\n project=project,\n location=location,\n\ \ job_config=job_config).result()\n return destination\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-maybe-replace-with-default: container: args: @@ -950,7 +950,7 @@ deploymentSpec: \ *\n\ndef maybe_replace_with_default(value: str, default: str = '') ->\ \ str:\n \"\"\"Replaces string with another value if it is a dash.\"\"\"\ \n return default if not value else value\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-validate-inputs: container: args: @@ -1046,7 +1046,7 @@ deploymentSpec: \ raise ValueError(\n 'Granularity unit should be one of the\ \ following: '\n f'{valid_data_granularity_units}, got: {data_granularity_unit}.')\n\ \n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 pipelineInfo: description: Forecasts using a BQML ARIMA_PLUS model. name: automl-tabular-bqml-arima-prediction diff --git a/components/google-cloud/google_cloud_pipeline_components/v1/automl/forecasting/bqml_arima_train_pipeline.yaml b/components/google-cloud/google_cloud_pipeline_components/v1/automl/forecasting/bqml_arima_train_pipeline.yaml index 7e2ada1f233..5453f600e59 100644 --- a/components/google-cloud/google_cloud_pipeline_components/v1/automl/forecasting/bqml_arima_train_pipeline.yaml +++ b/components/google-cloud/google_cloud_pipeline_components/v1/automl/forecasting/bqml_arima_train_pipeline.yaml @@ -3399,7 +3399,7 @@ deploymentSpec: \ = client.create_dataset(dataset=dataset, exists_ok=exists_ok)\n return\ \ collections.namedtuple('Outputs', ['project_id', 'dataset_id'])(\n \ \ ref.project, ref.dataset_id)\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-bigquery-create-dataset-2: container: args: @@ -3434,7 +3434,7 @@ deploymentSpec: \ = client.create_dataset(dataset=dataset, exists_ok=exists_ok)\n return\ \ collections.namedtuple('Outputs', ['project_id', 'dataset_id'])(\n \ \ ref.project, ref.dataset_id)\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-bigquery-create-model-job: container: args: @@ -3494,7 +3494,7 @@ deploymentSpec: \ if dataset.dataset_id.startswith(dataset_prefix):\n client.delete_dataset(\n\ \ dataset=dataset.dataset_id,\n delete_contents=delete_contents)\n\ \n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-bigquery-list-rows: container: args: @@ -3532,7 +3532,7 @@ deploymentSpec: \ metadata['datasetId'], metadata['tableId']]))\n result = []\n for row\ \ in rows:\n result.append({col: str(value) for col, value in dict(row).items()})\n\ \ return result\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-bigquery-list-rows-2: container: args: @@ -3570,7 +3570,7 @@ deploymentSpec: \ metadata['datasetId'], metadata['tableId']]))\n result = []\n for row\ \ in rows:\n result.append({col: str(value) for col, value in dict(row).items()})\n\ \ return result\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-bigquery-query-job: container: args: @@ -3739,7 +3739,7 @@ deploymentSpec: \ 'datasetId': dataset_id,\n 'tableId': table_id,\n }\n\ \ if write_disposition:\n config['write_disposition'] = write_disposition\n\ \ return config\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-build-job-configuration-query-2: container: args: @@ -3773,7 +3773,7 @@ deploymentSpec: \ 'datasetId': dataset_id,\n 'tableId': table_id,\n }\n\ \ if write_disposition:\n config['write_disposition'] = write_disposition\n\ \ return config\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-build-job-configuration-query-3: container: args: @@ -3807,7 +3807,7 @@ deploymentSpec: \ 'datasetId': dataset_id,\n 'tableId': table_id,\n }\n\ \ if write_disposition:\n config['write_disposition'] = write_disposition\n\ \ return config\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-build-job-configuration-query-4: container: args: @@ -3841,7 +3841,7 @@ deploymentSpec: \ 'datasetId': dataset_id,\n 'tableId': table_id,\n }\n\ \ if write_disposition:\n config['write_disposition'] = write_disposition\n\ \ return config\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-build-job-configuration-query-5: container: args: @@ -3875,7 +3875,7 @@ deploymentSpec: \ 'datasetId': dataset_id,\n 'tableId': table_id,\n }\n\ \ if write_disposition:\n config['write_disposition'] = write_disposition\n\ \ return config\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-build-job-configuration-query-6: container: args: @@ -3909,7 +3909,7 @@ deploymentSpec: \ 'datasetId': dataset_id,\n 'tableId': table_id,\n }\n\ \ if write_disposition:\n config['write_disposition'] = write_disposition\n\ \ return config\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-build-serialized-query-parameters: container: args: @@ -3980,7 +3980,7 @@ deploymentSpec: \ 'name': 'start_time',\n 'parameterType': {\n 'type':\ \ 'TIMESTAMP'\n },\n 'parameterValue': {\n 'value': start_time\n\ \ },\n })\n return query_parameters\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-build-serialized-query-parameters-2: container: args: @@ -4051,7 +4051,7 @@ deploymentSpec: \ 'name': 'start_time',\n 'parameterType': {\n 'type':\ \ 'TIMESTAMP'\n },\n 'parameterValue': {\n 'value': start_time\n\ \ },\n })\n return query_parameters\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-build-serialized-query-parameters-3: container: args: @@ -4122,7 +4122,7 @@ deploymentSpec: \ 'name': 'start_time',\n 'parameterType': {\n 'type':\ \ 'TIMESTAMP'\n },\n 'parameterValue': {\n 'value': start_time\n\ \ },\n })\n return query_parameters\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-cond: container: args: @@ -4144,7 +4144,7 @@ deploymentSpec: \ *\n\ndef cond(predicate: bool, true_str: str, false_str: str) -> str:\n\ \ \"\"\"Returns true_str if predicate is true, else false_str.\"\"\"\n\ \ return true_str if predicate else false_str\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-create-metrics-artifact: container: args: @@ -4170,7 +4170,7 @@ deploymentSpec: \ 'MAPE': 'meanAbsolutePercentageError',\n }\n metrics = {metric_name_map[k]:\ \ v for k, v in dict(metrics_rows[0]).items()}\n evaluation_metrics.metadata\ \ = metrics\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-feature-transform-engine: container: args: @@ -4255,8 +4255,8 @@ deploymentSpec: "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/dataflow_tmp"]}' - '{"Concat": ["--dataflow_max_num_workers=", "{{$.inputs.parameters[''dataflow_max_num_workers'']}}"]}' - '{"Concat": ["--dataflow_machine_type=", "{{$.inputs.parameters[''dataflow_machine_type'']}}"]}' - - --dataflow_worker_container_image=us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240419_0625 - - --feature_transform_engine_docker_uri=us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240419_0625 + - --dataflow_worker_container_image=us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240710_0625 + - --feature_transform_engine_docker_uri=us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240710_0625 - '{"Concat": ["--dataflow_disk_size_gb=", "{{$.inputs.parameters[''dataflow_disk_size_gb'']}}"]}' - '{"Concat": ["--dataflow_subnetwork_fully_qualified=", "{{$.inputs.parameters[''dataflow_subnetwork'']}}"]}' - '{"Concat": ["--dataflow_use_public_ips=", "{{$.inputs.parameters[''dataflow_use_public_ips'']}}"]}' @@ -4273,7 +4273,7 @@ deploymentSpec: - '{"IfPresent": {"InputName": "group_temporal_total_weight", "Then": {"Concat": ["--group_temporal_total_weight=", "{{$.inputs.parameters[''group_temporal_total_weight'']}}"]}}}' - '{"Concat": ["--encryption_spec_key_name=", "{{$.inputs.parameters[''encryption_spec_key_name'']}}"]}' - image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240710_0625 exec-get-fte-suffix: container: args: @@ -4301,7 +4301,7 @@ deploymentSpec: \ table.table_id.startswith(fte_table):\n return table.table_id[len(fte_table)\ \ + 1:]\n raise ValueError(\n f'No FTE output tables found in {bigquery_staging_full_dataset_id}.')\n\ \n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-get-table-location: container: args: @@ -4337,7 +4337,7 @@ deploymentSpec: \ if table.startswith('bq://'):\n table = table[len('bq://'):]\n elif\ \ table.startswith('bigquery://'):\n table = table[len('bigquery://'):]\n\ \ return client.get_table(table).location\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-get-value: container: args: @@ -4358,7 +4358,7 @@ deploymentSpec: - "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\ \ *\n\ndef get_value(d: Dict[str, str], key: str) -> str:\n return d[key]\n\ \n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-get-window-query-priority: container: args: @@ -4382,7 +4382,7 @@ deploymentSpec: \ depending on the window number.\"\"\"\n if int(window['window_number'])\ \ <= max_interactive:\n return 'INTERACTIVE'\n else:\n return 'BATCH'\n\ \n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-maybe-replace-with-default: container: args: @@ -4404,7 +4404,7 @@ deploymentSpec: \ *\n\ndef maybe_replace_with_default(value: str, default: str = '') ->\ \ str:\n \"\"\"Replaces string with another value if it is a dash.\"\"\"\ \n return default if not value else value\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-query-with-retry: container: args: @@ -4458,7 +4458,7 @@ deploymentSpec: \ 'Query failed with %s. Retrying after %d seconds.', e, wait_time)\n\ \ time.sleep(wait_time)\n retry_count += 1\n return destination_uri\n\ \n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-query-with-retry-2: container: args: @@ -4512,7 +4512,7 @@ deploymentSpec: \ 'Query failed with %s. Retrying after %d seconds.', e, wait_time)\n\ \ time.sleep(wait_time)\n retry_count += 1\n return destination_uri\n\ \n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-query-with-retry-3: container: args: @@ -4566,7 +4566,7 @@ deploymentSpec: \ 'Query failed with %s. Retrying after %d seconds.', e, wait_time)\n\ \ time.sleep(wait_time)\n retry_count += 1\n return destination_uri\n\ \n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-table-to-uri: container: args: @@ -4596,7 +4596,7 @@ deploymentSpec: \ if use_bq_prefix:\n bq_uri = 'bq://' + bq_uri\n outputs.append(bq_uri)\n\ \ return collections.namedtuple(\n 'Outputs',\n ['project_id',\ \ 'dataset_id', 'table_id', 'uri'],\n )(*outputs)\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-table-to-uri-2: container: args: @@ -4626,7 +4626,7 @@ deploymentSpec: \ if use_bq_prefix:\n bq_uri = 'bq://' + bq_uri\n outputs.append(bq_uri)\n\ \ return collections.namedtuple(\n 'Outputs',\n ['project_id',\ \ 'dataset_id', 'table_id', 'uri'],\n )(*outputs)\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-validate-inputs: container: args: @@ -4722,7 +4722,7 @@ deploymentSpec: \ raise ValueError(\n 'Granularity unit should be one of the\ \ following: '\n f'{valid_data_granularity_units}, got: {data_granularity_unit}.')\n\ \n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-wrapped-in-list: container: args: @@ -4743,7 +4743,7 @@ deploymentSpec: - "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\ \ *\n\ndef wrapped_in_list(value: str) -> List[str]:\n \"\"\"Wraps a string\ \ in a list.\"\"\"\n return [value]\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 pipelineInfo: description: Trains a BQML ARIMA_PLUS model. name: automl-tabular-bqml-arima-train diff --git a/components/google-cloud/google_cloud_pipeline_components/v1/automl/forecasting/prophet_predict_pipeline.yaml b/components/google-cloud/google_cloud_pipeline_components/v1/automl/forecasting/prophet_predict_pipeline.yaml index abc17963e6f..8cfde9c7862 100644 --- a/components/google-cloud/google_cloud_pipeline_components/v1/automl/forecasting/prophet_predict_pipeline.yaml +++ b/components/google-cloud/google_cloud_pipeline_components/v1/automl/forecasting/prophet_predict_pipeline.yaml @@ -1461,7 +1461,7 @@ deploymentSpec: \ = client.create_dataset(dataset=dataset, exists_ok=exists_ok)\n return\ \ collections.namedtuple('Outputs', ['project_id', 'dataset_id'])(\n \ \ ref.project, ref.dataset_id)\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-bigquery-delete-dataset-with-prefix: container: args: @@ -1495,7 +1495,7 @@ deploymentSpec: \ if dataset.dataset_id.startswith(dataset_prefix):\n client.delete_dataset(\n\ \ dataset=dataset.dataset_id,\n delete_contents=delete_contents)\n\ \n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-bigquery-query-job: container: args: @@ -1583,7 +1583,7 @@ deploymentSpec: \ 'datasetId': dataset_id,\n 'tableId': table_id,\n }\n\ \ if write_disposition:\n config['write_disposition'] = write_disposition\n\ \ return config\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-build-job-configuration-query-2: container: args: @@ -1617,7 +1617,7 @@ deploymentSpec: \ 'datasetId': dataset_id,\n 'tableId': table_id,\n }\n\ \ if write_disposition:\n config['write_disposition'] = write_disposition\n\ \ return config\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-get-first-valid: container: args: @@ -1641,7 +1641,7 @@ deploymentSpec: \ import json\n # pylint: enable=g-import-not-at-top,import-outside-toplevel,redefined-outer-name,reimported\n\ \n for value in json.loads(values):\n if value:\n return value\n\ \ raise ValueError('No valid values.')\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-get-table-location: container: args: @@ -1677,7 +1677,7 @@ deploymentSpec: \ if table.startswith('bq://'):\n table = table[len('bq://'):]\n elif\ \ table.startswith('bigquery://'):\n table = table[len('bigquery://'):]\n\ \ return client.get_table(table).location\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-get-table-location-2: container: args: @@ -1713,7 +1713,7 @@ deploymentSpec: \ if table.startswith('bq://'):\n table = table[len('bq://'):]\n elif\ \ table.startswith('bigquery://'):\n table = table[len('bigquery://'):]\n\ \ return client.get_table(table).location\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-load-table-from-uri: container: args: @@ -1754,7 +1754,7 @@ deploymentSpec: \ source_format=source_format)\n client.load_table_from_uri(\n source_uris=csv_list,\n\ \ destination=destination,\n project=project,\n location=location,\n\ \ job_config=job_config).result()\n return destination\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-make-vertex-model-artifact: container: args: @@ -1778,7 +1778,7 @@ deploymentSpec: Creates a google.VertexModel artifact.\"\"\"\n vertex_model.metadata =\ \ {'resourceName': model_resource_name}\n vertex_model.uri = (f'https://{location}-aiplatform.googleapis.com'\n\ \ f'/v1/{model_resource_name}')\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-maybe-replace-with-default: container: args: @@ -1800,7 +1800,7 @@ deploymentSpec: \ *\n\ndef maybe_replace_with_default(value: str, default: str = '') ->\ \ str:\n \"\"\"Replaces string with another value if it is a dash.\"\"\"\ \n return default if not value else value\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-model-batch-predict: container: args: @@ -1879,7 +1879,7 @@ deploymentSpec: \ if use_bq_prefix:\n bq_uri = 'bq://' + bq_uri\n outputs.append(bq_uri)\n\ \ return collections.namedtuple(\n 'Outputs',\n ['project_id',\ \ 'dataset_id', 'table_id', 'uri'],\n )(*outputs)\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-table-to-uri-2: container: args: @@ -1909,7 +1909,7 @@ deploymentSpec: \ if use_bq_prefix:\n bq_uri = 'bq://' + bq_uri\n outputs.append(bq_uri)\n\ \ return collections.namedtuple(\n 'Outputs',\n ['project_id',\ \ 'dataset_id', 'table_id', 'uri'],\n )(*outputs)\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-validate-inputs: container: args: @@ -2005,7 +2005,7 @@ deploymentSpec: \ raise ValueError(\n 'Granularity unit should be one of the\ \ following: '\n f'{valid_data_granularity_units}, got: {data_granularity_unit}.')\n\ \n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 pipelineInfo: description: Creates a batch prediction using a Prophet model. name: prophet-predict diff --git a/components/google-cloud/google_cloud_pipeline_components/v1/automl/forecasting/prophet_trainer.py b/components/google-cloud/google_cloud_pipeline_components/v1/automl/forecasting/prophet_trainer.py index 36eb1c6aadf..3deb39701f3 100644 --- a/components/google-cloud/google_cloud_pipeline_components/v1/automl/forecasting/prophet_trainer.py +++ b/components/google-cloud/google_cloud_pipeline_components/v1/automl/forecasting/prophet_trainer.py @@ -108,17 +108,17 @@ def prophet_trainer( '"machine_spec": {"machine_type": "n1-standard-4"}, ', ( '"container_spec":' - ' {"image_uri":"us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", ' + ' {"image_uri":"us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", ' ), '"args": ["prophet_trainer", "', ( f'--job_name=dataflow-{dsl.PIPELINE_JOB_NAME_PLACEHOLDER}", "' ), ( - '--dataflow_worker_container_image=us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240419_0625", "' + '--dataflow_worker_container_image=us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240710_0625", "' ), ( - '--prediction_container_image=us-docker.pkg.dev/vertex-ai/automl-tabular/fte-prediction-server:20240419_0625", "' + '--prediction_container_image=us-docker.pkg.dev/vertex-ai/automl-tabular/fte-prediction-server:20240710_0625", "' ), '--artifacts_dir=', root_dir, diff --git a/components/google-cloud/google_cloud_pipeline_components/v1/automl/forecasting/prophet_trainer_pipeline.yaml b/components/google-cloud/google_cloud_pipeline_components/v1/automl/forecasting/prophet_trainer_pipeline.yaml index bdffa54a80a..ef78345ec6a 100644 --- a/components/google-cloud/google_cloud_pipeline_components/v1/automl/forecasting/prophet_trainer_pipeline.yaml +++ b/components/google-cloud/google_cloud_pipeline_components/v1/automl/forecasting/prophet_trainer_pipeline.yaml @@ -2021,7 +2021,7 @@ deploymentSpec: \ = client.create_dataset(dataset=dataset, exists_ok=exists_ok)\n return\ \ collections.namedtuple('Outputs', ['project_id', 'dataset_id'])(\n \ \ ref.project, ref.dataset_id)\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-bigquery-delete-dataset-with-prefix: container: args: @@ -2055,7 +2055,7 @@ deploymentSpec: \ if dataset.dataset_id.startswith(dataset_prefix):\n client.delete_dataset(\n\ \ dataset=dataset.dataset_id,\n delete_contents=delete_contents)\n\ \n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-bigquery-query-job: container: args: @@ -2116,7 +2116,7 @@ deploymentSpec: \ 'datasetId': dataset_id,\n 'tableId': table_id,\n }\n\ \ if write_disposition:\n config['write_disposition'] = write_disposition\n\ \ return config\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-feature-transform-engine: container: args: @@ -2201,8 +2201,8 @@ deploymentSpec: "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/dataflow_tmp"]}' - '{"Concat": ["--dataflow_max_num_workers=", "{{$.inputs.parameters[''dataflow_max_num_workers'']}}"]}' - '{"Concat": ["--dataflow_machine_type=", "{{$.inputs.parameters[''dataflow_machine_type'']}}"]}' - - --dataflow_worker_container_image=us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240419_0625 - - --feature_transform_engine_docker_uri=us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240419_0625 + - --dataflow_worker_container_image=us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240710_0625 + - --feature_transform_engine_docker_uri=us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240710_0625 - '{"Concat": ["--dataflow_disk_size_gb=", "{{$.inputs.parameters[''dataflow_disk_size_gb'']}}"]}' - '{"Concat": ["--dataflow_subnetwork_fully_qualified=", "{{$.inputs.parameters[''dataflow_subnetwork'']}}"]}' - '{"Concat": ["--dataflow_use_public_ips=", "{{$.inputs.parameters[''dataflow_use_public_ips'']}}"]}' @@ -2219,7 +2219,7 @@ deploymentSpec: - '{"IfPresent": {"InputName": "group_temporal_total_weight", "Then": {"Concat": ["--group_temporal_total_weight=", "{{$.inputs.parameters[''group_temporal_total_weight'']}}"]}}}' - '{"Concat": ["--encryption_spec_key_name=", "{{$.inputs.parameters[''encryption_spec_key_name'']}}"]}' - image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240710_0625 exec-get-fte-suffix: container: args: @@ -2247,7 +2247,7 @@ deploymentSpec: \ table.table_id.startswith(fte_table):\n return table.table_id[len(fte_table)\ \ + 1:]\n raise ValueError(\n f'No FTE output tables found in {bigquery_staging_full_dataset_id}.')\n\ \n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-get-table-location: container: args: @@ -2283,7 +2283,7 @@ deploymentSpec: \ if table.startswith('bq://'):\n table = table[len('bq://'):]\n elif\ \ table.startswith('bigquery://'):\n table = table[len('bigquery://'):]\n\ \ return client.get_table(table).location\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-model-evaluation-regression: container: args: @@ -2394,10 +2394,10 @@ deploymentSpec: ", "\"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}", "\"}, ", "\"job_spec\": {\"worker_pool_specs\": [{\"replica_count\":\"1\", ", "\"machine_spec\": {\"machine_type\": \"n1-standard-4\"}, ", "\"container_spec\": - {\"image_uri\":\"us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625\", + {\"image_uri\":\"us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625\", ", "\"args\": [\"prophet_trainer\", \"", "--job_name=dataflow-{{$.pipeline_job_name}}\", - \"", "--dataflow_worker_container_image=us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240419_0625\", - \"", "--prediction_container_image=us-docker.pkg.dev/vertex-ai/automl-tabular/fte-prediction-server:20240419_0625\", + \"", "--dataflow_worker_container_image=us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240710_0625\", + \"", "--prediction_container_image=us-docker.pkg.dev/vertex-ai/automl-tabular/fte-prediction-server:20240710_0625\", \"", "--artifacts_dir=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/model/\", \"", "--evaluated_examples_dir=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/eval/\", \"", "--region=", "{{$.inputs.parameters[''location'']}}", @@ -2458,7 +2458,7 @@ deploymentSpec: \ if use_bq_prefix:\n bq_uri = 'bq://' + bq_uri\n outputs.append(bq_uri)\n\ \ return collections.namedtuple(\n 'Outputs',\n ['project_id',\ \ 'dataset_id', 'table_id', 'uri'],\n )(*outputs)\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-validate-inputs: container: args: @@ -2554,7 +2554,7 @@ deploymentSpec: \ raise ValueError(\n 'Granularity unit should be one of the\ \ following: '\n f'{valid_data_granularity_units}, got: {data_granularity_unit}.')\n\ \n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-wrapped-in-list: container: args: @@ -2575,7 +2575,7 @@ deploymentSpec: - "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\ \ *\n\ndef wrapped_in_list(value: str) -> List[str]:\n \"\"\"Wraps a string\ \ in a list.\"\"\"\n return [value]\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 pipelineInfo: description: Trains one Prophet model per time series. name: prophet-train diff --git a/components/google-cloud/google_cloud_pipeline_components/v1/automl/tabular/automl_tabular_pipeline.yaml b/components/google-cloud/google_cloud_pipeline_components/v1/automl/tabular/automl_tabular_pipeline.yaml index 60e4669658a..1a705ce5125 100644 --- a/components/google-cloud/google_cloud_pipeline_components/v1/automl/tabular/automl_tabular_pipeline.yaml +++ b/components/google-cloud/google_cloud_pipeline_components/v1/automl/tabular/automl_tabular_pipeline.yaml @@ -8420,9 +8420,9 @@ deploymentSpec: \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}", "\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\": {\"machine_type\": \"n1-standard-8\"}, \"container_spec\": {\"image_uri\":\"", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", "\", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"args\": [\"l2l_cv_tuner\", \"--transform_output_path=", "{{$.inputs.artifacts[''transform_output''].uri}}", - "\", \"--training_docker_uri=", "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", + "\", \"--training_docker_uri=", "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"--component_id={{$.pipeline_task_uuid}}\", \"--training_base_dir=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/train\", \"--num_parallel_trial=", "{{$.inputs.parameters[''num_parallel_trials'']}}", @@ -8463,9 +8463,9 @@ deploymentSpec: \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}", "\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\": {\"machine_type\": \"n1-standard-8\"}, \"container_spec\": {\"image_uri\":\"", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", "\", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"args\": [\"l2l_cv_tuner\", \"--transform_output_path=", "{{$.inputs.artifacts[''transform_output''].uri}}", - "\", \"--training_docker_uri=", "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", + "\", \"--training_docker_uri=", "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"--component_id={{$.pipeline_task_uuid}}\", \"--training_base_dir=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/train\", \"--num_parallel_trial=", "{{$.inputs.parameters[''num_parallel_trials'']}}", @@ -8506,7 +8506,7 @@ deploymentSpec: \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}", "\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\": {\"machine_type\": \"n1-highmem-8\"}, \"container_spec\": {\"image_uri\":\"", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", "\", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"args\": [\"ensemble\", \"--transform_output_path=", "{{$.inputs.artifacts[''transform_output''].uri}}", "\", \"--model_output_path=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/model\", \"--custom_model_output_path=", "{{$.inputs.parameters[''root_dir'']}}", @@ -8518,7 +8518,7 @@ deploymentSpec: "\", \"--tuning_result_input_path=", "{{$.inputs.artifacts[''tuning_result_input''].uri}}", "\", \"--instance_baseline_path=", "{{$.inputs.artifacts[''instance_baseline''].uri}}", "\", \"--warmup_data=", "{{$.inputs.artifacts[''warmup_data''].uri}}", "\", - \"--prediction_docker_uri=", "us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240419_0625", + \"--prediction_docker_uri=", "us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240710_0625", "\", \"--model_path=", "{{$.outputs.artifacts[''model''].uri}}", "\", \"--custom_model_path=", "{{$.outputs.artifacts[''model_without_custom_ops''].uri}}", "\", \"--explanation_metadata_path=", "{{$.outputs.parameters[''explanation_metadata''].output_file}}", ",", "{{$.outputs.artifacts[''explanation_metadata_artifact''].uri}}", @@ -8547,7 +8547,7 @@ deploymentSpec: \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}", "\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\": {\"machine_type\": \"n1-highmem-8\"}, \"container_spec\": {\"image_uri\":\"", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", "\", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"args\": [\"ensemble\", \"--transform_output_path=", "{{$.inputs.artifacts[''transform_output''].uri}}", "\", \"--model_output_path=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/model\", \"--custom_model_output_path=", "{{$.inputs.parameters[''root_dir'']}}", @@ -8559,7 +8559,7 @@ deploymentSpec: "\", \"--tuning_result_input_path=", "{{$.inputs.artifacts[''tuning_result_input''].uri}}", "\", \"--instance_baseline_path=", "{{$.inputs.artifacts[''instance_baseline''].uri}}", "\", \"--warmup_data=", "{{$.inputs.artifacts[''warmup_data''].uri}}", "\", - \"--prediction_docker_uri=", "us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240419_0625", + \"--prediction_docker_uri=", "us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240710_0625", "\", \"--model_path=", "{{$.outputs.artifacts[''model''].uri}}", "\", \"--custom_model_path=", "{{$.outputs.artifacts[''model_without_custom_ops''].uri}}", "\", \"--explanation_metadata_path=", "{{$.outputs.parameters[''explanation_metadata''].output_file}}", ",", "{{$.outputs.artifacts[''explanation_metadata_artifact''].uri}}", @@ -8588,7 +8588,7 @@ deploymentSpec: \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}", "\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\": {\"machine_type\": \"n1-highmem-8\"}, \"container_spec\": {\"image_uri\":\"", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", "\", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"args\": [\"ensemble\", \"--transform_output_path=", "{{$.inputs.artifacts[''transform_output''].uri}}", "\", \"--model_output_path=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/model\", \"--custom_model_output_path=", "{{$.inputs.parameters[''root_dir'']}}", @@ -8600,7 +8600,7 @@ deploymentSpec: "\", \"--tuning_result_input_path=", "{{$.inputs.artifacts[''tuning_result_input''].uri}}", "\", \"--instance_baseline_path=", "{{$.inputs.artifacts[''instance_baseline''].uri}}", "\", \"--warmup_data=", "{{$.inputs.artifacts[''warmup_data''].uri}}", "\", - \"--prediction_docker_uri=", "us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240419_0625", + \"--prediction_docker_uri=", "us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240710_0625", "\", \"--model_path=", "{{$.outputs.artifacts[''model''].uri}}", "\", \"--custom_model_path=", "{{$.outputs.artifacts[''model_without_custom_ops''].uri}}", "\", \"--explanation_metadata_path=", "{{$.outputs.parameters[''explanation_metadata''].output_file}}", ",", "{{$.outputs.artifacts[''explanation_metadata_artifact''].uri}}", @@ -8629,7 +8629,7 @@ deploymentSpec: \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}", "\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\": {\"machine_type\": \"n1-standard-8\"}, \"container_spec\": {\"image_uri\":\"", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", "\", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"args\": [\"cancel_l2l_tuner\", \"--error_file_path=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/error.pb\", \"--cleanup_lro_job_infos=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/lro\"]}}]}}"]}' @@ -8644,7 +8644,7 @@ deploymentSpec: args: - --executor_input - '{{$}}' - image: us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240710_0625 resources: cpuLimit: 8.0 memoryLimit: 52.0 @@ -8653,7 +8653,7 @@ deploymentSpec: args: - --executor_input - '{{$}}' - image: us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240710_0625 resources: cpuLimit: 8.0 memoryLimit: 52.0 @@ -8662,7 +8662,7 @@ deploymentSpec: args: - --executor_input - '{{$}}' - image: us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240710_0625 resources: cpuLimit: 8.0 memoryLimit: 52.0 @@ -8682,9 +8682,9 @@ deploymentSpec: \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}", "\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\": {\"machine_type\": \"n1-standard-8\"}, \"container_spec\": {\"image_uri\":\"", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", "\", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"args\": [\"l2l_stage_1_tuner\", \"--transform_output_path=", "{{$.inputs.artifacts[''transform_output''].uri}}", - "\", \"--training_docker_uri=", "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", + "\", \"--training_docker_uri=", "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"--feature_selection_result_path=", "{{$.inputs.artifacts[''feature_ranking''].uri}}", "\", \"--disable_early_stopping=", "{{$.inputs.parameters[''disable_early_stopping'']}}", "\", \"--tune_feature_selection_rate=", "{{$.inputs.parameters[''tune_feature_selection_rate'']}}", @@ -8729,9 +8729,9 @@ deploymentSpec: \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}", "\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\": {\"machine_type\": \"n1-standard-8\"}, \"container_spec\": {\"image_uri\":\"", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", "\", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"args\": [\"l2l_stage_1_tuner\", \"--transform_output_path=", "{{$.inputs.artifacts[''transform_output''].uri}}", - "\", \"--training_docker_uri=", "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", + "\", \"--training_docker_uri=", "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"--feature_selection_result_path=", "{{$.inputs.artifacts[''feature_ranking''].uri}}", "\", \"--disable_early_stopping=", "{{$.inputs.parameters[''disable_early_stopping'']}}", "\", \"--tune_feature_selection_rate=", "{{$.inputs.parameters[''tune_feature_selection_rate'']}}", @@ -8776,7 +8776,7 @@ deploymentSpec: \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}", "\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\": {\"machine_type\": \"n1-standard-8\"}, \"container_spec\": {\"image_uri\":\"", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", "\", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"args\": [\"transform\", \"--is_mp=true\", \"--transform_output_artifact_path=", "{{$.outputs.artifacts[''transform_output''].uri}}", "\", \"--transform_output_path=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/transform\", @@ -8797,7 +8797,7 @@ deploymentSpec: \"--dataflow_tmp_dir=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/dataflow_tmp\", \"--dataflow_max_num_workers=", "{{$.inputs.parameters[''dataflow_max_num_workers'']}}", "\", \"--dataflow_machine_type=", "{{$.inputs.parameters[''dataflow_machine_type'']}}", - "\", \"--dataflow_worker_container_image=", "us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240419_0625", + "\", \"--dataflow_worker_container_image=", "us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240710_0625", "\", \"--dataflow_disk_size_gb=", "{{$.inputs.parameters[''dataflow_disk_size_gb'']}}", "\", \"--dataflow_subnetwork_fully_qualified=", "{{$.inputs.parameters[''dataflow_subnetwork'']}}", "\", \"--dataflow_use_public_ips=", "{{$.inputs.parameters[''dataflow_use_public_ips'']}}", @@ -8828,7 +8828,7 @@ deploymentSpec: \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}", "\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\": {\"machine_type\": \"n1-standard-8\"}, \"container_spec\": {\"image_uri\":\"", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", "\", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"args\": [\"transform\", \"--is_mp=true\", \"--transform_output_artifact_path=", "{{$.outputs.artifacts[''transform_output''].uri}}", "\", \"--transform_output_path=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/transform\", @@ -8849,7 +8849,7 @@ deploymentSpec: \"--dataflow_tmp_dir=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/dataflow_tmp\", \"--dataflow_max_num_workers=", "{{$.inputs.parameters[''dataflow_max_num_workers'']}}", "\", \"--dataflow_machine_type=", "{{$.inputs.parameters[''dataflow_machine_type'']}}", - "\", \"--dataflow_worker_container_image=", "us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240419_0625", + "\", \"--dataflow_worker_container_image=", "us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240710_0625", "\", \"--dataflow_disk_size_gb=", "{{$.inputs.parameters[''dataflow_disk_size_gb'']}}", "\", \"--dataflow_subnetwork_fully_qualified=", "{{$.inputs.parameters[''dataflow_subnetwork'']}}", "\", \"--dataflow_use_public_ips=", "{{$.inputs.parameters[''dataflow_use_public_ips'']}}", @@ -8885,7 +8885,7 @@ deploymentSpec: \ *\n\ndef _bool_identity(value: bool) -> str:\n \"\"\"Returns boolean\ \ value.\n\n Args:\n value: Boolean value to return\n\n Returns:\n\ \ Boolean value.\n \"\"\"\n return 'true' if value else 'false'\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-bool-identity-2: container: args: @@ -8907,7 +8907,7 @@ deploymentSpec: \ *\n\ndef _bool_identity(value: bool) -> str:\n \"\"\"Returns boolean\ \ value.\n\n Args:\n value: Boolean value to return\n\n Returns:\n\ \ Boolean value.\n \"\"\"\n return 'true' if value else 'false'\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-bool-identity-3: container: args: @@ -8929,7 +8929,7 @@ deploymentSpec: \ *\n\ndef _bool_identity(value: bool) -> str:\n \"\"\"Returns boolean\ \ value.\n\n Args:\n value: Boolean value to return\n\n Returns:\n\ \ Boolean value.\n \"\"\"\n return 'true' if value else 'false'\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-calculate-training-parameters: container: args: @@ -9021,7 +9021,7 @@ deploymentSpec: \ stage_1_single_run_max_secs,\n stage_2_deadline_hours,\n \ \ stage_2_single_run_max_secs,\n distill_stage_1_deadline_hours,\n\ \ reduce_search_space_mode,\n )\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-calculate-training-parameters-2: container: args: @@ -9113,7 +9113,7 @@ deploymentSpec: \ stage_1_single_run_max_secs,\n stage_2_deadline_hours,\n \ \ stage_2_single_run_max_secs,\n distill_stage_1_deadline_hours,\n\ \ reduce_search_space_mode,\n )\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-feature-attribution: container: args: @@ -9299,7 +9299,7 @@ deploymentSpec: \n return collections.namedtuple(\n 'Outputs',\n [\n \ \ 'model_display_name',\n ],\n )(\n model_display_name,\n )\n\ \n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-importer: importer: artifactUri: @@ -9333,7 +9333,7 @@ deploymentSpec: \ 'r') as f:\n split_0_content = f.read()\n with open(split_1, 'r')\ \ as f:\n split_1_content = f.read()\n with open(splits, 'w') as f:\n\ \ f.write(','.join([split_0_content, split_1_content]))\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-model-batch-explanation: container: args: @@ -10158,7 +10158,7 @@ deploymentSpec: \ import json\n # pylint: enable=g-import-not-at-top,import-outside-toplevel,redefined-outer-name,reimported\n\ \ with open(split_uri, 'r') as f:\n data_source = json.loads(f.read())\n\ \ return data_source['tf_record_data_source']['file_patterns']\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-read-input-uri-2: container: args: @@ -10186,7 +10186,7 @@ deploymentSpec: \ import json\n # pylint: enable=g-import-not-at-top,import-outside-toplevel,redefined-outer-name,reimported\n\ \ with open(split_uri, 'r') as f:\n data_source = json.loads(f.read())\n\ \ return data_source['tf_record_data_source']['file_patterns']\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-set-optional-inputs: container: args: @@ -10234,7 +10234,7 @@ deploymentSpec: \ 'data_source_csv_filenames',\n 'data_source_bigquery_table_path',\n\ \ ],\n )(\n data_source_csv_filenames,\n data_source_bigquery_table_path,\n\ \ )\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-string-not-empty: container: args: @@ -10258,7 +10258,7 @@ deploymentSpec: \n Returns:\n Boolean value. -> 'true' if empty, 'false' if not empty.\ \ We need to use str\n instead of bool due to a limitation in KFP compiler.\n\ \ \"\"\"\n return 'true' if value else 'false'\n\n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-tabular-stats-and-example-gen: container: args: @@ -10275,7 +10275,7 @@ deploymentSpec: \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}", "\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\": {\"machine_type\": \"n1-standard-8\"}, \"container_spec\": {\"image_uri\":\"", - "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625", "\", + "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625", "\", \"args\": [\"stats_generator\",", "\"--train_spec={\\\"prediction_type\\\": \\\"", "{{$.inputs.parameters[''prediction_type'']}}", "\\\", \\\"target_column\\\": \\\"", "{{$.inputs.parameters[''target_column_name'']}}", "\\\", \\\"optimization_objective\\\": @@ -10308,7 +10308,7 @@ deploymentSpec: \"--dataflow_staging_dir=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/dataflow_staging\", \"--dataflow_tmp_dir=", "{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/dataflow_tmp\", \"--dataflow_max_num_workers=", "{{$.inputs.parameters[''dataflow_max_num_workers'']}}", - "\", \"--dataflow_worker_container_image=", "us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240419_0625", + "\", \"--dataflow_worker_container_image=", "us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240710_0625", "\", \"--dataflow_machine_type=", "{{$.inputs.parameters[''dataflow_machine_type'']}}", "\", \"--dataflow_disk_size_gb=", "{{$.inputs.parameters[''dataflow_disk_size_gb'']}}", "\", \"--dataflow_kms_key=", "{{$.inputs.parameters[''encryption_spec_key_name'']}}", @@ -10363,7 +10363,7 @@ deploymentSpec: \ f'{directory}/prediction.results-*',\n ],\n 'coder':\ \ 'PROTO_VALUE',\n },\n }\n with open(result, 'w') as f:\n f.write(json.dumps(data_source))\n\ \n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 exec-write-bp-result-path-2: container: args: @@ -10393,7 +10393,7 @@ deploymentSpec: \ f'{directory}/prediction.results-*',\n ],\n 'coder':\ \ 'PROTO_VALUE',\n },\n }\n with open(result, 'w') as f:\n f.write(json.dumps(data_source))\n\ \n" - image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240419_0625 + image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240710_0625 pipelineInfo: description: 'Complete AutoML Tables pipeline. diff --git a/components/google-cloud/google_cloud_pipeline_components/v1/automl/tabular/cv_trainer.py b/components/google-cloud/google_cloud_pipeline_components/v1/automl/tabular/cv_trainer.py index 52611565721..36469fbfb31 100644 --- a/components/google-cloud/google_cloud_pipeline_components/v1/automl/tabular/cv_trainer.py +++ b/components/google-cloud/google_cloud_pipeline_components/v1/automl/tabular/cv_trainer.py @@ -99,11 +99,11 @@ def automl_tabular_cv_trainer( ' 1, "machine_spec": {"machine_type": "n1-standard-8"},' ' "container_spec": {"image_uri":"' ), - 'us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625', + 'us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625', '", "args": ["l2l_cv_tuner", "--transform_output_path=', transform_output.uri, '", "--training_docker_uri=', - 'us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625', + 'us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625', ( f'", "--component_id={dsl.PIPELINE_TASK_ID_PLACEHOLDER}",' ' "--training_base_dir=' diff --git a/components/google-cloud/google_cloud_pipeline_components/v1/automl/tabular/ensemble.py b/components/google-cloud/google_cloud_pipeline_components/v1/automl/tabular/ensemble.py index 286c214f4d5..0c01b0be714 100644 --- a/components/google-cloud/google_cloud_pipeline_components/v1/automl/tabular/ensemble.py +++ b/components/google-cloud/google_cloud_pipeline_components/v1/automl/tabular/ensemble.py @@ -106,7 +106,7 @@ def automl_tabular_ensemble( ' 1, "machine_spec": {"machine_type": "n1-highmem-8"},' ' "container_spec": {"image_uri":"' ), - 'us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625', + 'us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625', '", "args": ["ensemble", "--transform_output_path=', transform_output.uri, '", "--model_output_path=', @@ -137,7 +137,7 @@ def automl_tabular_ensemble( '", "--warmup_data=', warmup_data.uri, '", "--prediction_docker_uri=', - 'us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240419_0625', + 'us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240710_0625', '", "--model_path=', model.uri, '", "--custom_model_path=', diff --git a/components/google-cloud/google_cloud_pipeline_components/v1/automl/tabular/finalizer.py b/components/google-cloud/google_cloud_pipeline_components/v1/automl/tabular/finalizer.py index 19133fca49a..3e863412aac 100644 --- a/components/google-cloud/google_cloud_pipeline_components/v1/automl/tabular/finalizer.py +++ b/components/google-cloud/google_cloud_pipeline_components/v1/automl/tabular/finalizer.py @@ -72,7 +72,7 @@ def automl_tabular_finalizer( ' 1, "machine_spec": {"machine_type": "n1-standard-8"},' ' "container_spec": {"image_uri":"' ), - 'us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625', + 'us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625', '", "args": ["cancel_l2l_tuner", "--error_file_path=', root_dir, ( diff --git a/components/google-cloud/google_cloud_pipeline_components/v1/automl/tabular/infra_validator.py b/components/google-cloud/google_cloud_pipeline_components/v1/automl/tabular/infra_validator.py index d979338b7bd..b88bc0f9149 100644 --- a/components/google-cloud/google_cloud_pipeline_components/v1/automl/tabular/infra_validator.py +++ b/components/google-cloud/google_cloud_pipeline_components/v1/automl/tabular/infra_validator.py @@ -32,7 +32,7 @@ def automl_tabular_infra_validator( # fmt: on return dsl.ContainerSpec( - image='us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240419_0625', + image='us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:20240710_0625', command=[], args=['--executor_input', '{{$}}'], ) diff --git a/components/google-cloud/google_cloud_pipeline_components/v1/automl/tabular/split_materialized_data.py b/components/google-cloud/google_cloud_pipeline_components/v1/automl/tabular/split_materialized_data.py index 1f17b627215..c492e1bdc75 100644 --- a/components/google-cloud/google_cloud_pipeline_components/v1/automl/tabular/split_materialized_data.py +++ b/components/google-cloud/google_cloud_pipeline_components/v1/automl/tabular/split_materialized_data.py @@ -52,7 +52,7 @@ def split_materialized_data( # fmt: on return dsl.ContainerSpec( - image='us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240419_0625', + image='us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240710_0625', command=[ 'sh', '-ec', diff --git a/components/google-cloud/google_cloud_pipeline_components/v1/automl/tabular/stage_1_tuner.py b/components/google-cloud/google_cloud_pipeline_components/v1/automl/tabular/stage_1_tuner.py index aebe535be46..cc8bdef19dd 100644 --- a/components/google-cloud/google_cloud_pipeline_components/v1/automl/tabular/stage_1_tuner.py +++ b/components/google-cloud/google_cloud_pipeline_components/v1/automl/tabular/stage_1_tuner.py @@ -109,11 +109,11 @@ def automl_tabular_stage_1_tuner( ' 1, "machine_spec": {"machine_type": "n1-standard-8"},' ' "container_spec": {"image_uri":"' ), - 'us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625', + 'us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625', '", "args": ["l2l_stage_1_tuner", "--transform_output_path=', transform_output.uri, '", "--training_docker_uri=', - 'us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625', + 'us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625', '", "--feature_selection_result_path=', feature_ranking.uri, '", "--disable_early_stopping=', diff --git a/components/google-cloud/google_cloud_pipeline_components/v1/automl/tabular/stats_and_example_gen.py b/components/google-cloud/google_cloud_pipeline_components/v1/automl/tabular/stats_and_example_gen.py index 61e699f5a6d..0c5af3deb66 100644 --- a/components/google-cloud/google_cloud_pipeline_components/v1/automl/tabular/stats_and_example_gen.py +++ b/components/google-cloud/google_cloud_pipeline_components/v1/automl/tabular/stats_and_example_gen.py @@ -136,7 +136,7 @@ def tabular_stats_and_example_gen( ' 1, "machine_spec": {"machine_type": "n1-standard-8"},' ' "container_spec": {"image_uri":"' ), - 'us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625', + 'us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625', '", "args": ["stats_generator",', '"--train_spec={\\"prediction_type\\": \\"', prediction_type, @@ -215,7 +215,7 @@ def tabular_stats_and_example_gen( ), dataflow_max_num_workers, '", "--dataflow_worker_container_image=', - 'us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240419_0625', + 'us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240710_0625', '", "--dataflow_machine_type=', dataflow_machine_type, '", "--dataflow_disk_size_gb=', diff --git a/components/google-cloud/google_cloud_pipeline_components/v1/automl/tabular/training_configurator_and_validator.py b/components/google-cloud/google_cloud_pipeline_components/v1/automl/tabular/training_configurator_and_validator.py index 43f28dcc48f..c1461a4fcc8 100644 --- a/components/google-cloud/google_cloud_pipeline_components/v1/automl/tabular/training_configurator_and_validator.py +++ b/components/google-cloud/google_cloud_pipeline_components/v1/automl/tabular/training_configurator_and_validator.py @@ -95,7 +95,7 @@ def training_configurator_and_validator( # fmt: on return dsl.ContainerSpec( - image='us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240419_0625', + image='us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240710_0625', command=[], args=[ 'training_configurator_and_validator', diff --git a/components/google-cloud/google_cloud_pipeline_components/v1/automl/tabular/transform.py b/components/google-cloud/google_cloud_pipeline_components/v1/automl/tabular/transform.py index 4896370cad8..a2b23fe7dd5 100644 --- a/components/google-cloud/google_cloud_pipeline_components/v1/automl/tabular/transform.py +++ b/components/google-cloud/google_cloud_pipeline_components/v1/automl/tabular/transform.py @@ -108,7 +108,7 @@ def automl_tabular_transform( ' 1, "machine_spec": {"machine_type": "n1-standard-8"},' ' "container_spec": {"image_uri":"' ), - 'us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240419_0625', + 'us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240710_0625', ( '", "args": ["transform", "--is_mp=true",' ' "--transform_output_artifact_path=' @@ -167,7 +167,7 @@ def automl_tabular_transform( '", "--dataflow_machine_type=', dataflow_machine_type, '", "--dataflow_worker_container_image=', - 'us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240419_0625', + 'us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240710_0625', '", "--dataflow_disk_size_gb=', dataflow_disk_size_gb, '", "--dataflow_subnetwork_fully_qualified=',