extract sweeplimits if present

sdk/ml/azure-ai-ml/azure/ai/ml/_schema/automl/image_vertical/image_sweep_settings.py

@@ -27,6 +27,9 @@ def conversion(self, data, **kwargs):
     def make(self, data, **kwargs):
         from azure.ai.ml.automl import ImageSweepSettings
 
+        if "limits" not in data:
+            return ImageSweepSettings(**data)
+
         limits = data.pop("limits")
         max_concurrent_trials = limits.max_concurrent_trials
         max_trials = limits.max_trials

sdk/ml/azure-ai-ml/tests/test_configs/automl_job/automl_image_job_limits_outside_sweep_mock.yml

@@ -0,0 +1,65 @@
+type: automl
+
+name: simpleautomlimagejob
+experiment_name: "automl"
+compute: azureml:cpu-cluster
+
+log_verbosity: debug
+task: image_object_detection
+primary_metric: mean_average_precision
+
+limits:
+  timeout_minutes: 60
+  max_concurrent_trials: 1
+  max_trials: 1
+
+target_column_name: label
+training_data:
+  path: azureml:image-training-data:1
+  type: mltable
+validation_data:
+  path: azureml:image-validation-data:1
+  type: mltable
+
+sweep:
+  sampling_algorithm: grid
+  early_termination:
+    type: bandit
+    evaluation_interval: 10
+    slack_factor: 0.2
+
+training_parameters:
+  checkpoint_frequency: 1
+  early_stopping: true
+  early_stopping_delay: 2
+  early_stopping_patience: 2
+  evaluation_frequency: 1
+
+search_space:
+  - model_name:
+      type: choice
+      values: [yolov5]
+    learning_rate:
+      type: uniform
+      min_value: 0.0001
+      max_value: 0.01
+    model_size:
+      type: choice
+      values: ['small', 'medium']
+
+  - model_name:
+      type: choice
+      values: [fasterrcnn_resnet50_fpn]
+    learning_rate:
+      type: uniform
+      min_value: 0.005
+      max_value: 0.05
+    warmup_cosine_lr_warmup_epochs:
+      type: choice
+      values: [0, 3]
+    optimizer:
+      type: choice
+      values: ['sgd', 'adam', 'adamw']
+    min_size:
+      type: choice
+      values: [600, 800]