Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

feat: update create_training_pipeline samples #142

Merged
merged 2 commits into from Dec 22, 2020
Merged

feat: update create_training_pipeline samples #142

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
0910776
feat: update create_training_pipeline samples
dizcology Dec 22, 2020
be365d1
fix typo
dizcology Dec 22, 2020
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Jump to
Jump to file
Failed to load files.
Diff view
Diff view
53 changes: 19 additions & 34 deletions .sample_configs/process_configs.yaml
View file
Open in desktop
Expand Up @@ -69,48 +69,33 @@ create_hyperparameter_tuning_job_sample: {}
create_specialist_pool_sample: {}
create_training_pipeline_custom_job_sample: {}
create_training_pipeline_custom_training_managed_dataset_sample: {}
create_training_pipeline_entity_extraction_sample: {}
create_training_pipeline_image_classification_sample: {}
create_training_pipeline_image_object_detection_sample: {}
create_training_pipeline_image_classification_sample:
schema_types:
training_task_inputs_dict: trainingjob.definition.AutoMlImageClassificationInputs
create_training_pipeline_image_object_detection_sample:
schema_types:
training_task_inputs_dict: trainingjob.definition.AutoMlImageObjectDetectionInputs
create_training_pipeline_sample: {}
create_training_pipeline_tabular_classification_sample: {}
create_training_pipeline_tabular_regression_sample: {}
create_training_pipeline_text_classification_sample: {}
create_training_pipeline_text_classification_sample:
schema_types:
training_task_inputs_dict: trainingjob.definition.AutoMlTextClassificationInputs
create_training_pipeline_text_entity_extraction_sample:
skip:
- predict_schemata
- supported_export_formats
- container_spec
- deployed_models
- explanation_spec
schema_types:
training_task_inputs_dict: trainingjob.definition.AutoMlTextExtractionInputs
create_training_pipeline_text_sentiment_analysis_sample:
skip:
- predict_schemata
- supported_export_formats
- container_spec
- deployed_models
- explanation_spec
schema_types:
training_task_inputs_dict: trainingjob.definition.AutoMlTextSentimentInputs
create_training_pipeline_video_action_recognition_sample:
skip:
- predict_schemata
- supported_export_formats
- container_spec
- deployed_models
- explanation_spec
schema_types:
training_task_inputs_dict: trainingjob.definition.AutoMlVideoActionRecognitionInputs
create_training_pipeline_video_classification_sample:
skip:
- predict_schemata
- supported_export_formats
- container_spec
- deployed_models
- explanation_spec
schema_types:
training_task_inputs_dict: trainingjob.definition.AutoMlVideoClassificationInputs
create_training_pipeline_video_object_tracking_sample:
skip:
- predict_schemata
- supported_export_formats
- container_spec
- deployed_models
- explanation_spec
schema_types:
training_task_inputs_dict: trainingjob.definition.AutoMlVideoObjectTrackingInputs
delete_batch_prediction_job_sample: {}
delete_custom_job_sample: {}
delete_data_labeling_job_sample: {}
Expand Down
9 changes: 4 additions & 5 deletions samples/snippets/create_training_pipeline_image_classification_sample.py
View file
Open in desktop
Expand Up @@ -30,14 +30,13 @@ def create_training_pipeline_image_classification_sample(
# Initialize client that will be used to create and send requests.
# This client only needs to be created once, and can be reused for multiple requests.
client = aiplatform.gapic.PipelineServiceClient(client_options=client_options)

icn_training_inputs = trainingjob.definition.AutoMlImageClassificationInputs(
training_task_inputs_object = trainingjob.definition.AutoMlImageClassificationInputs(
multi_label=True,
model_type=trainingjob.definition.AutoMlImageClassificationInputs.ModelType.CLOUD,
model_type="CLOUD",
Copy link
Contributor Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

This will be fixed in a follow up PR. Tracking issue: #143

budget_milli_node_hours=8000,
disable_early_stopping=False
disable_early_stopping=False,
)
training_task_inputs = icn_training_inputs.to_value()
training_task_inputs = training_task_inputs_object.to_value()

training_pipeline = {
"display_name": display_name,
Expand Down
15 changes: 7 additions & 8 deletions samples/snippets/create_training_pipeline_image_object_detection_sample.py
View file
Open in desktop
Expand Up @@ -14,8 +14,7 @@

# [START aiplatform_create_training_pipeline_image_object_detection_sample]
from google.cloud import aiplatform
from google.protobuf import json_format
from google.protobuf.struct_pb2 import Value
from google.cloud.aiplatform.schema import trainingjob


def create_training_pipeline_image_object_detection_sample(
Expand All @@ -31,12 +30,12 @@ def create_training_pipeline_image_object_detection_sample(
# Initialize client that will be used to create and send requests.
# This client only needs to be created once, and can be reused for multiple requests.
client = aiplatform.gapic.PipelineServiceClient(client_options=client_options)
training_task_inputs_dict = {
"modelType": "CLOUD_HIGH_ACCURACY_1",
"budgetMilliNodeHours": 20000,
"disableEarlyStopping": False,
}
training_task_inputs = json_format.ParseDict(training_task_inputs_dict, Value())
training_task_inputs_object = trainingjob.definition.AutoMlImageObjectDetectionInputs(
model_type="CLOUD_HIGH_ACCURACY_1",
budget_milli_node_hours=20000,
disable_early_stopping=False,
)
training_task_inputs = training_task_inputs_object.to_value()

training_pipeline = {
"display_name": display_name,
Expand Down
9 changes: 5 additions & 4 deletions samples/snippets/create_training_pipeline_text_classification_sample.py
View file
Open in desktop
Expand Up @@ -14,8 +14,7 @@

# [START aiplatform_create_training_pipeline_text_classification_sample]
from google.cloud import aiplatform
from google.protobuf import json_format
from google.protobuf.struct_pb2 import Value
from google.cloud.aiplatform.schema import trainingjob


def create_training_pipeline_text_classification_sample(
Expand All @@ -31,8 +30,10 @@ def create_training_pipeline_text_classification_sample(
# Initialize client that will be used to create and send requests.
# This client only needs to be created once, and can be reused for multiple requests.
client = aiplatform.gapic.PipelineServiceClient(client_options=client_options)
training_task_inputs_dict = {}
training_task_inputs = json_format.ParseDict(training_task_inputs_dict, Value())
training_task_inputs_object = (
trainingjob.definition.AutoMlTextClassificationInputs()
)
training_task_inputs = training_task_inputs_object.to_value()

training_pipeline = {
"display_name": display_name,
Expand Down
7 changes: 3 additions & 4 deletions samples/snippets/create_training_pipeline_text_entity_extraction_sample.py
View file
Open in desktop
Expand Up @@ -14,8 +14,7 @@

# [START aiplatform_create_training_pipeline_text_entity_extraction_sample]
from google.cloud import aiplatform
from google.protobuf import json_format
from google.protobuf.struct_pb2 import Value
from google.cloud.aiplatform.schema import trainingjob


def create_training_pipeline_text_entity_extraction_sample(
Expand All @@ -31,8 +30,8 @@ def create_training_pipeline_text_entity_extraction_sample(
# Initialize client that will be used to create and send requests.
# This client only needs to be created once, and can be reused for multiple requests.
client = aiplatform.gapic.PipelineServiceClient(client_options=client_options)
training_task_inputs_dict = {}
training_task_inputs = json_format.ParseDict(training_task_inputs_dict, Value())
training_task_inputs_object = trainingjob.definition.AutoMlTextExtractionInputs()
training_task_inputs = training_task_inputs_object.to_value()

training_pipeline = {
"display_name": display_name,
Expand Down
9 changes: 5 additions & 4 deletions samples/snippets/create_training_pipeline_text_sentiment_analysis_sample.py
View file
Open in desktop
Expand Up @@ -14,8 +14,7 @@

# [START aiplatform_create_training_pipeline_text_sentiment_analysis_sample]
from google.cloud import aiplatform
from google.protobuf import json_format
from google.protobuf.struct_pb2 import Value
from google.cloud.aiplatform.schema import trainingjob


def create_training_pipeline_text_sentiment_analysis_sample(
Expand All @@ -32,8 +31,10 @@ def create_training_pipeline_text_sentiment_analysis_sample(
# This client only needs to be created once, and can be reused for multiple requests.
client = aiplatform.gapic.PipelineServiceClient(client_options=client_options)
# Use sentiment_max of 4
training_task_inputs_dict = {"sentiment_max": 4}
training_task_inputs = json_format.ParseDict(training_task_inputs_dict, Value())
training_task_inputs_object = trainingjob.definition.AutoMlTextSentimentInputs(
sentiment_max=4
)
training_task_inputs = training_task_inputs_object.to_value()

training_pipeline = {
"display_name": display_name,
Expand Down
13 changes: 6 additions & 7 deletions samples/snippets/create_training_pipeline_video_action_recognition_sample.py
View file
Open in desktop
Expand Up @@ -14,8 +14,7 @@

# [START aiplatform_create_training_pipeline_video_action_recognition_sample]
from google.cloud import aiplatform
from google.protobuf import json_format
from google.protobuf.struct_pb2 import Value
from google.cloud.aiplatform.schema import trainingjob


def create_training_pipeline_video_action_recognition_sample(
Expand All @@ -32,11 +31,11 @@ def create_training_pipeline_video_action_recognition_sample(
# Initialize client that will be used to create and send requests.
# This client only needs to be created once, and can be reused for multiple requests.
client = aiplatform.gapic.PipelineServiceClient(client_options=client_options)
training_task_inputs_dict = {
# modelType can be either 'CLOUD' or 'MOBILE_VERSATILE_1'
"modelType": model_type
}
training_task_inputs = json_format.ParseDict(training_task_inputs_dict, Value())
# modelType can be either 'CLOUD' or 'MOBILE_VERSATILE_1'
training_task_inputs_object = trainingjob.definition.AutoMlVideoActionRecognitionInputs(
model_type=model_type
)
training_task_inputs = training_task_inputs_object.to_value()

training_pipeline = {
"display_name": display_name,
Expand Down
9 changes: 5 additions & 4 deletions samples/snippets/create_training_pipeline_video_classification_sample.py
View file
Open in desktop
Expand Up @@ -14,8 +14,7 @@

# [START aiplatform_create_training_pipeline_video_classification_sample]
from google.cloud import aiplatform
from google.protobuf import json_format
from google.protobuf.struct_pb2 import Value
from google.cloud.aiplatform.schema import trainingjob


def create_training_pipeline_video_classification_sample(
Expand All @@ -31,8 +30,10 @@ def create_training_pipeline_video_classification_sample(
# Initialize client that will be used to create and send requests.
# This client only needs to be created once, and can be reused for multiple requests.
client = aiplatform.gapic.PipelineServiceClient(client_options=client_options)
training_task_inputs_dict = {}
training_task_inputs = json_format.ParseDict(training_task_inputs_dict, Value())
training_task_inputs_object = (
trainingjob.definition.AutoMlVideoClassificationInputs()
)
training_task_inputs = training_task_inputs_object.to_value()

training_pipeline = {
"display_name": display_name,
Expand Down
9 changes: 5 additions & 4 deletions samples/snippets/create_training_pipeline_video_object_tracking_sample.py
View file
Open in desktop
Expand Up @@ -14,8 +14,7 @@

# [START aiplatform_create_training_pipeline_video_object_tracking_sample]
from google.cloud import aiplatform
from google.protobuf import json_format
from google.protobuf.struct_pb2 import Value
from google.cloud.aiplatform.schema import trainingjob


def create_training_pipeline_video_object_tracking_sample(
Expand All @@ -31,8 +30,10 @@ def create_training_pipeline_video_object_tracking_sample(
# Initialize client that will be used to create and send requests.
# This client only needs to be created once, and can be reused for multiple requests.
client = aiplatform.gapic.PipelineServiceClient(client_options=client_options)
training_task_inputs_dict = {"modelType": "CLOUD"}
training_task_inputs = json_format.ParseDict(training_task_inputs_dict, Value())
training_task_inputs_object = trainingjob.definition.AutoMlVideoObjectTrackingInputs(
model_type="CLOUD"
)
training_task_inputs = training_task_inputs_object.to_value()

training_pipeline = {
"display_name": display_name,
Expand Down