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TheMichaelHuGoogle Cloud Pipeline Components maintainers
TheMichaelHu
authored and
Google Cloud Pipeline Components maintainers
committed
chore(components): Sync AutoML components
PiperOrigin-RevId: 597743182
1 parent a79b36c commit 1cc31bb

39 files changed

+396
-401
lines changed

‎components/google-cloud/google_cloud_pipeline_components/preview/automl/forecasting/forecasting_ensemble.py

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -72,7 +72,7 @@ def automl_forecasting_ensemble(
7272
# fmt: on
7373
job_id = dsl.PIPELINE_JOB_ID_PLACEHOLDER
7474
task_id = dsl.PIPELINE_TASK_ID_PLACEHOLDER
75-
image_uri = 'us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20231029_0125'
75+
image_uri = 'us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240108_1325'
7676
display_name = f'automl-forecasting-ensemble-{job_id}-{task_id}'
7777

7878
error_file_path = f'{root_dir}/{job_id}/{task_id}/error.pb'

‎components/google-cloud/google_cloud_pipeline_components/preview/automl/forecasting/forecasting_stage_1_tuner.py

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -99,14 +99,14 @@ def automl_forecasting_stage_1_tuner(
9999
' 1, "machine_spec": {"machine_type": "n1-standard-8"},'
100100
' "container_spec": {"image_uri":"'
101101
),
102-
'us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20231029_0125',
102+
'us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240108_1325',
103103
'", "args": ["forecasting_mp_l2l_stage_1_tuner',
104104
'", "--region=',
105105
location,
106106
'", "--transform_output_path=',
107107
transform_output.uri,
108108
'", "--training_docker_uri=',
109-
'us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20231029_0125',
109+
'us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240108_1325',
110110
'", "--reduce_search_space_mode=',
111111
reduce_search_space_mode,
112112
f'", "--component_id={dsl.PIPELINE_TASK_ID_PLACEHOLDER}',

‎components/google-cloud/google_cloud_pipeline_components/preview/automl/forecasting/forecasting_stage_2_tuner.py

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -97,14 +97,14 @@ def automl_forecasting_stage_2_tuner(
9797
' 1, "machine_spec": {"machine_type": "n1-standard-8"},'
9898
' "container_spec": {"image_uri":"'
9999
),
100-
'us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20231029_0125',
100+
'us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240108_1325',
101101
'", "args": ["forecasting_mp_l2l_stage_2_tuner',
102102
'", "--region=',
103103
location,
104104
'", "--transform_output_path=',
105105
transform_output.uri,
106106
'", "--training_docker_uri=',
107-
'us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20231029_0125',
107+
'us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240108_1325',
108108
f'", "--component_id={dsl.PIPELINE_TASK_ID_PLACEHOLDER}',
109109
'", "--training_base_dir=',
110110
root_dir,

‎components/google-cloud/google_cloud_pipeline_components/preview/automl/forecasting/learn_to_learn_forecasting_pipeline.yaml

Lines changed: 27 additions & 27 deletions
Original file line numberDiff line numberDiff line change
@@ -5806,7 +5806,7 @@ deploymentSpec:
58065806
- '{"display_name": "automl-forecasting-ensemble-{{$.pipeline_job_uuid}}-{{$.pipeline_task_uuid}}",
58075807
"encryption_spec": {"kms_key_name": "{{$.inputs.parameters[''encryption_spec_key_name'']}}"},
58085808
"job_spec": {"worker_pool_specs": [{"replica_count": 1, "machine_spec":
5809-
{"machine_type": "n1-highmem-8"}, "container_spec": {"image_uri": "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20231029_0125",
5809+
{"machine_type": "n1-highmem-8"}, "container_spec": {"image_uri": "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240108_1325",
58105810
"args": ["forecasting_mp_ensemble", "--transform_output_path={{$.inputs.artifacts[''transform_output''].uri}}",
58115811
"--error_file_path={{$.inputs.parameters[''root_dir'']}}/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/error.pb",
58125812
"--metadata_path={{$.inputs.artifacts[''metadata''].uri}}", "--tuning_result_input_path={{$.inputs.artifacts[''tuning_result_input''].uri}}",
@@ -5840,7 +5840,7 @@ deploymentSpec:
58405840
- '{"display_name": "automl-forecasting-ensemble-{{$.pipeline_job_uuid}}-{{$.pipeline_task_uuid}}",
58415841
"encryption_spec": {"kms_key_name": "{{$.inputs.parameters[''encryption_spec_key_name'']}}"},
58425842
"job_spec": {"worker_pool_specs": [{"replica_count": 1, "machine_spec":
5843-
{"machine_type": "n1-highmem-8"}, "container_spec": {"image_uri": "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20231029_0125",
5843+
{"machine_type": "n1-highmem-8"}, "container_spec": {"image_uri": "us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240108_1325",
58445844
"args": ["forecasting_mp_ensemble", "--transform_output_path={{$.inputs.artifacts[''transform_output''].uri}}",
58455845
"--error_file_path={{$.inputs.parameters[''root_dir'']}}/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/error.pb",
58465846
"--metadata_path={{$.inputs.artifacts[''metadata''].uri}}", "--tuning_result_input_path={{$.inputs.artifacts[''tuning_result_input''].uri}}",
@@ -5875,11 +5875,11 @@ deploymentSpec:
58755875
\"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}",
58765876
"\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\":
58775877
{\"machine_type\": \"n1-standard-8\"}, \"container_spec\": {\"image_uri\":\"",
5878-
"us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20231029_0125",
5878+
"us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240108_1325",
58795879
"\", \"args\": [\"forecasting_mp_l2l_stage_1_tuner", "\", \"--region=",
58805880
"{{$.inputs.parameters[''location'']}}", "\", \"--transform_output_path=",
58815881
"{{$.inputs.artifacts[''transform_output''].uri}}", "\", \"--training_docker_uri=",
5882-
"us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20231029_0125",
5882+
"us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240108_1325",
58835883
"\", \"--reduce_search_space_mode=", "{{$.inputs.parameters[''reduce_search_space_mode'']}}",
58845884
"\", \"--component_id={{$.pipeline_task_uuid}}", "\", \"--training_base_dir=",
58855885
"{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/train",
@@ -5918,11 +5918,11 @@ deploymentSpec:
59185918
\"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}",
59195919
"\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\":
59205920
{\"machine_type\": \"n1-standard-8\"}, \"container_spec\": {\"image_uri\":\"",
5921-
"us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20231029_0125",
5921+
"us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240108_1325",
59225922
"\", \"args\": [\"forecasting_mp_l2l_stage_2_tuner", "\", \"--region=",
59235923
"{{$.inputs.parameters[''location'']}}", "\", \"--transform_output_path=",
59245924
"{{$.inputs.artifacts[''transform_output''].uri}}", "\", \"--training_docker_uri=",
5925-
"us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20231029_0125",
5925+
"us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/forecasting-training:20240108_1325",
59265926
"\", \"--component_id={{$.pipeline_task_uuid}}", "\", \"--training_base_dir=",
59275927
"{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/train",
59285928
"\", \"--num_parallel_trial=", "{{$.inputs.parameters[''num_parallel_trials'']}}",
@@ -5961,7 +5961,7 @@ deploymentSpec:
59615961
\"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}",
59625962
"\"}, \"job_spec\": {\"worker_pool_specs\": [{\"replica_count\": 1, \"machine_spec\":
59635963
{\"machine_type\": \"n1-standard-8\"}, \"container_spec\": {\"image_uri\":\"",
5964-
"us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20231029_0125", "\",
5964+
"us-docker.pkg.dev/vertex-ai-restricted/automl-tabular/training:20240108_1325", "\",
59655965
\"args\": [\"cancel_l2l_tuner\", \"--error_file_path=", "{{$.inputs.parameters[''root_dir'']}}",
59665966
"/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/error.pb\", \"--cleanup_lro_job_infos=",
59675967
"{{$.inputs.parameters[''root_dir'']}}", "/{{$.pipeline_job_uuid}}/lro\"]}}]}}"]}'
@@ -6285,8 +6285,8 @@ deploymentSpec:
62856285
"/{{$.pipeline_job_uuid}}/{{$.pipeline_task_uuid}}/dataflow_tmp"]}'
62866286
- '{"Concat": ["--dataflow_max_num_workers=", "{{$.inputs.parameters[''dataflow_max_num_workers'']}}"]}'
62876287
- '{"Concat": ["--dataflow_machine_type=", "{{$.inputs.parameters[''dataflow_machine_type'']}}"]}'
6288-
- --dataflow_worker_container_image=us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20231029_0125
6289-
- --feature_transform_engine_docker_uri=us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20231029_0125
6288+
- --dataflow_worker_container_image=us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240108_1325
6289+
- --feature_transform_engine_docker_uri=us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240108_1325
62906290
- '{"Concat": ["--dataflow_disk_size_gb=", "{{$.inputs.parameters[''dataflow_disk_size_gb'']}}"]}'
62916291
- '{"Concat": ["--dataflow_subnetwork_fully_qualified=", "{{$.inputs.parameters[''dataflow_subnetwork'']}}"]}'
62926292
- '{"Concat": ["--dataflow_use_public_ips=", "{{$.inputs.parameters[''dataflow_use_public_ips'']}}"]}'
@@ -6303,7 +6303,7 @@ deploymentSpec:
63036303
- '{"IfPresent": {"InputName": "group_temporal_total_weight", "Then": {"Concat":
63046304
["--group_temporal_total_weight=", "{{$.inputs.parameters[''group_temporal_total_weight'']}}"]}}}'
63056305
- '{"Concat": ["--encryption_spec_key_name=", "{{$.inputs.parameters[''encryption_spec_key_name'']}}"]}'
6306-
image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20231029_0125
6306+
image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240108_1325
63076307
resources:
63086308
cpuLimit: 8.0
63096309
memoryLimit: 30.0
@@ -6473,10 +6473,10 @@ deploymentSpec:
64736473
Returns the prediction image corresponding to the given model type.\"\"\"\
64746474
\n # Keys come from AutoMlTimeSeriesForecastingTrainSpec.\n # The URIs\
64756475
\ must be hardcoded without any breaks in the code so string\n # replacement\
6476-
\ will work correctly.\n images = {\n 'l2l': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-l2l:20231029_0125',\n\
6477-
\ 'seq2seq': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-seq2seq:20231029_0125',\n\
6478-
\ 'tft': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-tft:20231029_0125',\n\
6479-
\ 'tide': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-tide:20231029_0125',\n\
6476+
\ will work correctly.\n images = {\n 'l2l': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-l2l:20240108_1325',\n\
6477+
\ 'seq2seq': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-seq2seq:20240108_1325',\n\
6478+
\ 'tft': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-tft:20240108_1325',\n\
6479+
\ 'tide': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-tide:20240108_1325',\n\
64806480
\ }\n if model_type not in images:\n raise ValueError(\n f'Invalid\
64816481
\ forecasting model type: {model_type}. Valid options are: '\n f'{images.keys()}.'\n\
64826482
\ )\n return images[model_type]\n\n"
@@ -6509,10 +6509,10 @@ deploymentSpec:
65096509
Returns the prediction image corresponding to the given model type.\"\"\"\
65106510
\n # Keys come from AutoMlTimeSeriesForecastingTrainSpec.\n # The URIs\
65116511
\ must be hardcoded without any breaks in the code so string\n # replacement\
6512-
\ will work correctly.\n images = {\n 'l2l': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-l2l:20231029_0125',\n\
6513-
\ 'seq2seq': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-seq2seq:20231029_0125',\n\
6514-
\ 'tft': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-tft:20231029_0125',\n\
6515-
\ 'tide': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-tide:20231029_0125',\n\
6512+
\ will work correctly.\n images = {\n 'l2l': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-l2l:20240108_1325',\n\
6513+
\ 'seq2seq': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-seq2seq:20240108_1325',\n\
6514+
\ 'tft': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-tft:20240108_1325',\n\
6515+
\ 'tide': 'us-docker.pkg.dev/vertex-ai/automl-tabular/forecasting-prediction-server-tide:20240108_1325',\n\
65166516
\ }\n if model_type not in images:\n raise ValueError(\n f'Invalid\
65176517
\ forecasting model type: {model_type}. Valid options are: '\n f'{images.keys()}.'\n\
65186518
\ )\n return images[model_type]\n\n"
@@ -6545,7 +6545,7 @@ deploymentSpec:
65456545
\ str) -> str:\n \"\"\"Generates the BP output's target column name.\"\"\
65466546
\"\n if forecasting_type == 'quantile':\n return f'predicted_{target_column}.quantile_predictions'\n\
65476547
\ return f'predicted_{target_column}.value'\n\n"
6548-
image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20231029_0125
6548+
image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240108_1325
65496549
exec-get-predictions-column-2:
65506550
container:
65516551
args:
@@ -6574,7 +6574,7 @@ deploymentSpec:
65746574
\ str) -> str:\n \"\"\"Generates the BP output's target column name.\"\"\
65756575
\"\n if forecasting_type == 'quantile':\n return f'predicted_{target_column}.quantile_predictions'\n\
65766576
\ return f'predicted_{target_column}.value'\n\n"
6577-
image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20231029_0125
6577+
image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240108_1325
65786578
exec-importer:
65796579
importer:
65806580
artifactUri:
@@ -7020,7 +7020,7 @@ deploymentSpec:
70207020
- -u
70217021
- -m
70227022
- launcher
7023-
image: gcr.io/ml-pipeline/automl-tables-private:1.0.15
7023+
image: gcr.io/ml-pipeline/automl-tables-private:1.0.17
70247024
exec-model-upload-2:
70257025
container:
70267026
args:
@@ -7049,7 +7049,7 @@ deploymentSpec:
70497049
- -u
70507050
- -m
70517051
- launcher
7052-
image: gcr.io/ml-pipeline/automl-tables-private:1.0.15
7052+
image: gcr.io/ml-pipeline/automl-tables-private:1.0.17
70537053
exec-set-optional-inputs:
70547054
container:
70557055
args:
@@ -7112,7 +7112,7 @@ deploymentSpec:
71127112
\ 'model_display_name',\n 'transformations',\n ],\n\
71137113
\ )(\n data_source_csv_filenames,\n data_source_bigquery_table_path,\n\
71147114
\ model_display_name,\n transformations,\n )\n\n"
7115-
image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20231029_0125
7115+
image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240108_1325
71167116
exec-split-materialized-data:
71177117
container:
71187118
args:
@@ -7158,7 +7158,7 @@ deploymentSpec:
71587158
\ 'w') as f:\n f.write(file_patterns[0])\n\n with tf.io.gfile.GFile(materialized_eval_split,\
71597159
\ 'w') as f:\n f.write(file_patterns[1])\n\n with tf.io.gfile.GFile(materialized_test_split,\
71607160
\ 'w') as f:\n f.write(file_patterns[2])\n\n"
7161-
image: us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20231029_0125
7161+
image: us-docker.pkg.dev/vertex-ai/automl-tabular/dataflow-worker:20240108_1325
71627162
exec-string-not-empty:
71637163
container:
71647164
args:
@@ -7224,7 +7224,7 @@ deploymentSpec:
72247224
\ if use_bq_prefix:\n bq_uri = 'bq://' + bq_uri\n outputs.append(bq_uri)\n\
72257225
\ return collections.namedtuple(\n 'Outputs',\n ['project_id',\
72267226
\ 'dataset_id', 'table_id', 'uri'],\n )(*outputs)\n\n"
7227-
image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20231029_0125
7227+
image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240108_1325
72287228
exec-table-to-uri-2:
72297229
container:
72307230
args:
@@ -7260,7 +7260,7 @@ deploymentSpec:
72607260
\ if use_bq_prefix:\n bq_uri = 'bq://' + bq_uri\n outputs.append(bq_uri)\n\
72617261
\ return collections.namedtuple(\n 'Outputs',\n ['project_id',\
72627262
\ 'dataset_id', 'table_id', 'uri'],\n )(*outputs)\n\n"
7263-
image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20231029_0125
7263+
image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:20240108_1325
72647264
exec-training-configurator-and-validator:
72657265
container:
72667266
args:
@@ -7305,7 +7305,7 @@ deploymentSpec:
73057305
["--temporal_total_weight=", "{{$.inputs.parameters[''temporal_total_weight'']}}"]}}}'
73067306
- '{"IfPresent": {"InputName": "group_temporal_total_weight", "Then": {"Concat":
73077307
["--group_temporal_total_weight=", "{{$.inputs.parameters[''group_temporal_total_weight'']}}"]}}}'
7308-
image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20231029_0125
7308+
image: us-docker.pkg.dev/vertex-ai/automl-tabular/feature-transform-engine:20240108_1325
73097309
pipelineInfo:
73107310
description: The AutoML Forecasting pipeline.
73117311
name: learn-to-learn-forecasting

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