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0D feat: Adds the temporal fusion transformer (TFT) forecasting job · googleapis/python-aiplatform@99313e0 · GitHub
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feat: Adds the temporal fusion transformer (TFT) forecasting job
COPYBARA_INTEGRATE_REVIEW=#1817 from mikelawrence-google:mikealawrence-add-tft-model-support dde8ac0 PiperOrigin-RevId: 494251134
1 parent 43468bd commit 99313e0

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5 files changed

+36
-86
lines changed

5 files changed

+36
-86
lines changed

‎google/cloud/aiplatform/__init__.py

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@@ -68,6 +68,7 @@
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AutoMLTabularTrainingJob,
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AutoMLForecastingTrainingJob,
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SequenceToSequencePlusForecastingTrainingJob,
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TemporalFusionTransformerForecastingTrainingJob,
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AutoMLImageTrainingJob,
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AutoMLTextTrainingJob,
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AutoMLVideoTrainingJob,
@@ -162,6 +163,7 @@
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"TensorboardRun",
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"TensorboardTimeSeries",
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"TextDataset",
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"TemporalFusionTransformerForecastingTrainingJob",
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"TimeSeriesDataset",
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"VideoDataset",
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)

‎google/cloud/aiplatform/schema.py

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@@ -24,6 +24,7 @@ class definition:
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automl_tabular = "gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_tabular_1.0.0.yaml"
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automl_forecasting = "gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_time_series_forecasting_1.0.0.yaml"
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seq2seq_plus_forecasting = "gs://google-cloud-aiplatform/schema/trainingjob/definition/seq2seq_plus_time_series_forecasting_1.0.0.yaml"
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tft_forecasting = "gs://google-cloud-aiplatform/schema/trainingjob/definition/temporal_fusion_transformer_time_series_forecasting_1.0.0.yaml"
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automl_image_classification = "gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_image_classification_1.0.0.yaml"
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automl_image_object_detection = "gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_image_object_detection_1.0.0.yaml"
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automl_text_classification = "gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_text_classification_1.0.0.yaml"

‎google/cloud/aiplatform/training_jobs.py

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@@ -5204,19 +5204,31 @@ class column_data_types:
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class AutoMLForecastingTrainingJob(_ForecastingTrainingJob):
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"""Class to train AutoML forecasting models."""
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_model_type = "AutoML"
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_training_task_definition = schema.training_job.definition.automl_forecasting
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_supported_training_schemas = (schema.training_job.definition.automl_forecasting,)
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class SequenceToSequencePlusForecastingTrainingJob(_ForecastingTrainingJob):
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"""Class to train Sequence to Sequence (Seq2Seq) forecasting models."""
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_model_type = "Seq2Seq"
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_training_task_definition = schema.training_job.definition.seq2seq_plus_forecasting
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_supported_training_schemas = (
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schema.training_job.definition.seq2seq_plus_forecasting,
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)
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5224+
class TemporalFusionTransformerForecastingTrainingJob(_ForecastingTrainingJob):
5225+
"""Class to train Temporal Fusion Transformer (TFT) forecasting models."""
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_model_type = "TFT"
5228+
_training_task_definition = schema.training_job.definition.tft_forecasting
5229+
_supported_training_schemas = (schema.training_job.definition.tft_forecasting,)
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class AutoMLImageTrainingJob(_TrainingJob):
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_supported_training_schemas = (
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schema.training_job.definition.automl_image_classification,

‎tests/system/aiplatform/test_e2e_forecasting.py

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Original file line numberDiff line numberDiff line change
@@ -40,9 +40,10 @@ class TestEndToEndForecasting(e2e_base.TestEndToEnd):
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"training_job",
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[
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training_jobs.AutoMLForecastingTrainingJob,
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training_jobs.SequenceToSequencePlusForecastingTrainingJob,
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pytest.param(
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training_jobs.SequenceToSequencePlusForecastingTrainingJob,
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marks=pytest.mark.skip(reason="Seq2Seq not yet released."),
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training_jobs.TemporalFusionTransformerForecastingTrainingJob,
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marks=pytest.mark.skip(reason="TFT not yet released."),
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),
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],
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)

‎tests/unit/aiplatform/test_automl_forecasting_training_jobs.py

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@@ -183,6 +183,12 @@
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_TEST_SPLIT_PREDEFINED_COLUMN_NAME = "split"
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_TEST_SPLIT_TIMESTAMP_COLUMN_NAME = "timestamp"
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186+
_FORECASTING_JOB_MODEL_TYPES = [
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training_jobs.AutoMLForecastingTrainingJob,
188+
training_jobs.SequenceToSequencePlusForecastingTrainingJob,
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training_jobs.TemporalFusionTransformerForecastingTrainingJob,
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]
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@pytest.fixture
188194
def mock_pipeline_service_create():
@@ -293,13 +299,7 @@ def teardown_method(self):
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@mock.patch.object(training_jobs, "_JOB_WAIT_TIME", 1)
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@mock.patch.object(training_jobs, "_LOG_WAIT_TIME", 1)
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@pytest.mark.parametrize("sync", [True, False])
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@pytest.mark.parametrize(
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"training_job",
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[
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training_jobs.AutoMLForecastingTrainingJob,
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training_jobs.SequenceToSequencePlusForecastingTrainingJob,
301-
],
302-
)
302+
@pytest.mark.parametrize("training_job", _FORECASTING_JOB_MODEL_TYPES)
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def test_run_call_pipeline_service_create(
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self,
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mock_pipeline_service_create,
@@ -401,13 +401,7 @@ def test_run_call_pipeline_service_create(
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@mock.patch.object(training_jobs, "_JOB_WAIT_TIME", 1)
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@mock.patch.object(training_jobs, "_LOG_WAIT_TIME", 1)
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@pytest.mark.parametrize("sync", [True, False])
404-
@pytest.mark.parametrize(
405-
"training_job",
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[
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training_jobs.AutoMLForecastingTrainingJob,
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training_jobs.SequenceToSequencePlusForecastingTrainingJob,
409-
],
410-
)
404+
@pytest.mark.parametrize("training_job", _FORECASTING_JOB_MODEL_TYPES)
411405
def test_run_call_pipeline_service_create_with_timeout(
412406
self,
413407
mock_pipeline_service_create,
@@ -496,13 +490,7 @@ def test_run_call_pipeline_service_create_with_timeout(
496490
@mock.patch.object(training_jobs, "_LOG_WAIT_TIME", 1)
497491
@pytest.mark.usefixtures("mock_pipeline_service_get")
498492
@pytest.mark.parametrize("sync", [True, False])
499-
@pytest.mark.parametrize(
500-
"training_job",
501-
[
502-
training_jobs.AutoMLForecastingTrainingJob,
503-
training_jobs.SequenceToSequencePlusForecastingTrainingJob,
504-
],
505-
)
493+
@pytest.mark.parametrize("training_job", _FORECASTING_JOB_MODEL_TYPES)
506494
def test_run_call_pipeline_if_no_model_display_name_nor_model_labels(
507495
self,
508496
mock_pipeline_service_create,
@@ -584,13 +572,7 @@ def test_run_call_pipeline_if_no_model_display_name_nor_model_labels(
584572
@mock.patch.object(training_jobs, "_LOG_WAIT_TIME", 1)
585573
@pytest.mark.usefixtures("mock_pipeline_service_get")
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@pytest.mark.parametrize("sync", [True, False])
587-
@pytest.mark.parametrize(
588-
"training_job",
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[
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training_jobs.AutoMLForecastingTrainingJob,
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training_jobs.SequenceToSequencePlusForecastingTrainingJob,
592-
],
593-
)
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@pytest.mark.parametrize("training_job", _FORECASTING_JOB_MODEL_TYPES)
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def test_run_call_pipeline_if_set_additional_experiments(
595577
self,
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mock_pipeline_service_create,
@@ -675,13 +657,7 @@ def test_run_call_pipeline_if_set_additional_experiments(
675657
"mock_model_service_get",
676658
)
677659
@pytest.mark.parametrize("sync", [True, False])
678-
@pytest.mark.parametrize(
679-
"training_job",
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[
681-
training_jobs.AutoMLForecastingTrainingJob,
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training_jobs.SequenceToSequencePlusForecastingTrainingJob,
683-
],
684-
)
660+
@pytest.mark.parametrize("training_job", _FORECASTING_JOB_MODEL_TYPES)
685661
def test_run_called_twice_raises(
686662
self,
687663
mock_dataset_time_series,
@@ -762,13 +738,7 @@ def test_run_called_twice_raises(
762738
@mock.patch.object(training_jobs, "_JOB_WAIT_TIME", 1)
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@mock.patch.object(training_jobs, "_LOG_WAIT_TIME", 1)
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@pytest.mark.parametrize("sync", [True, False])
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@pytest.mark.parametrize(
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"training_job",
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[
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training_jobs.AutoMLForecastingTrainingJob,
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training_jobs.SequenceToSequencePlusForecastingTrainingJob,
770-
],
771-
)
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@pytest.mark.parametrize("training_job", _FORECASTING_JOB_MODEL_TYPES)
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def test_run_raises_if_pipeline_fails(
773743
self,
774744
mock_pipeline_service_create_and_get_with_fail,
@@ -823,13 +793,7 @@ def test_run_raises_if_pipeline_fails(
823793
with pytest.raises(RuntimeError):
824794
job.get_model()
825795

826-
@pytest.mark.parametrize(
827-
"training_job",
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[
829-
training_jobs.AutoMLForecastingTrainingJob,
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training_jobs.SequenceToSequencePlusForecastingTrainingJob,
831-
],
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)
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@pytest.mark.parametrize("training_job", _FORECASTING_JOB_MODEL_TYPES)
833797
def test_raises_before_run_is_called(
834798
self,
835799
mock_pipeline_service_create,
@@ -855,13 +819,7 @@ def test_raises_before_run_is_called(
855819
@mock.patch.object(training_jobs, "_JOB_WAIT_TIME", 1)
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@mock.patch.object(training_jobs, "_LOG_WAIT_TIME", 1)
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@pytest.mark.parametrize("sync", [True, False])
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@pytest.mark.parametrize(
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"training_job",
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[
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training_jobs.AutoMLForecastingTrainingJob,
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training_jobs.SequenceToSequencePlusForecastingTrainingJob,
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],
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)
822+
@pytest.mark.parametrize("training_job", _FORECASTING_JOB_MODEL_TYPES)
865823
def test_splits_fraction(
866824
self,
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mock_pipeline_service_create,
@@ -960,13 +918,7 @@ def test_splits_fraction(
960918
@mock.patch.object(training_jobs, "_JOB_WAIT_TIME", 1)
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@mock.patch.object(training_jobs, "_LOG_WAIT_TIME", 1)
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@pytest.mark.parametrize("sync", [True, False])
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@pytest.mark.parametrize(
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"training_job",
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[
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training_jobs.AutoMLForecastingTrainingJob,
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training_jobs.SequenceToSequencePlusForecastingTrainingJob,
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],
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)
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@pytest.mark.parametrize("training_job", _FORECASTING_JOB_MODEL_TYPES)
970922
def test_splits_timestamp(
971923
self,
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mock_pipeline_service_create,
@@ -1067,13 +1019,7 @@ def test_splits_timestamp(
10671019
@mock.patch.object(training_jobs, "_JOB_WAIT_TIME", 1)
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@mock.patch.object(training_jobs, "_LOG_WAIT_TIME", 1)
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@pytest.mark.parametrize("sync", [True, False])
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@pytest.mark.parametrize(
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"training_job",
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[
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training_jobs.AutoMLForecastingTrainingJob,
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training_jobs.SequenceToSequencePlusForecastingTrainingJob,
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],
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)
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@pytest.mark.parametrize("training_job", _FORECASTING_JOB_MODEL_TYPES)
10771023
def test_splits_predefined(
10781024
self,
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mock_pipeline_service_create,
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11681114
@mock.patch.object(training_jobs, "_JOB_WAIT_TIME", 1)
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@mock.patch.object(training_jobs, "_LOG_WAIT_TIME", 1)
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@pytest.mark.parametrize("sync", [True, False])
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@pytest.mark.parametrize(
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"training_job",
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[
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training_jobs.AutoMLForecastingTrainingJob,
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training_jobs.SequenceToSequencePlusForecastingTrainingJob,
1176-
],
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)
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@pytest.mark.parametrize("training_job", _FORECASTING_JOB_MODEL_TYPES)
11781118
def test_splits_default(
11791119
self,
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mock_pipeline_service_create,
@@ -1264,13 +1204,7 @@ def test_splits_default(
12641204
@mock.patch.object(training_jobs, "_LOG_WAIT_TIME", 1)
12651205
@pytest.mark.usefixtures("mock_pipeline_service_get")
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@pytest.mark.parametrize("sync", [True, False])
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@pytest.mark.parametrize(
1268-
"training_job",
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[
1270-
training_jobs.AutoMLForecastingTrainingJob,
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training_jobs.SequenceToSequencePlusForecastingTrainingJob,
1272-
],
1273-
)
1207+
@pytest.mark.parametrize("training_job", _FORECASTING_JOB_MODEL_TYPES)
12741208
def test_run_call_pipeline_if_set_additional_experiments_probabilistic_inference(
12751209
self,
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mock_pipeline_service_create,

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