Merge pull request #1265 from yhwang/update-component-yaml

Update component.yaml to kfp v2 sdk

Author: beat-buesser

utils/mlops/kubeflow/robustness_evaluation_fgsm_pytorch/component.yaml

@@ -24,23 +24,23 @@ description: |
 metadata:
   annotations: {platform: 'OpenSource'}
 inputs:
-  - {name: model_id,                       description: 'Required. Training model ID', default: 'training-dummy'}
-  - {name: epsilon,                        description: 'Required. Epsilon value for the FGSM attack', default: '0.2'}
-  - {name: model_class_file,               description: 'Required. pytorch model class file'}
-  - {name: model_class_name,               description: 'Required. pytorch model class name', default: 'model'}
-  - {name: feature_testset_path,           description: 'Required. Feature test dataset path in the data bucket'}
-  - {name: label_testset_path,             description: 'Required. Label test dataset path in the data bucket'}
-  - {name: loss_fn,                        description: 'Required. PyTorch model loss function'}
-  - {name: optimizer,                      description: 'Required. PyTorch model optimizer'}
-  - {name: clip_values,                    description: 'Required. PyTorch model clip_values allowed for features (min, max)'}
-  - {name: nb_classes,                     description: 'Required. The number of classes of the model'}
-  - {name: input_shape,                    description: 'Required. The shape of one input instance for the pytorch model'}
-  - {name: data_bucket_name,               description: 'Bucket that has the processed data',  default: 'training-data'}
-  - {name: result_bucket_name,             description: 'Bucket that has the training results', default: 'training-result'}
-  - {name: adversarial_accuracy_threshold, description: 'Model accuracy threshold on adversarial samples', default: '0.2'}
+  - {name: model_id, type: String,                       description: 'Required. Training model ID', default: 'training-dummy'}
+  - {name: epsilon, type: String,                        description: 'Required. Epsilon value for the FGSM attack', default: '0.2'}
+  - {name: model_class_file, type: String,               description: 'Required. pytorch model class file'}
+  - {name: model_class_name, type: String,               description: 'Required. pytorch model class name', default: 'model'}
+  - {name: feature_testset_path, type: String,           description: 'Required. Feature test dataset path in the data bucket'}
+  - {name: label_testset_path, type: String,             description: 'Required. Label test dataset path in the data bucket'}
+  - {name: loss_fn, type: String,                        description: 'Required. PyTorch model loss function'}
+  - {name: optimizer, type: String,                      description: 'Required. PyTorch model optimizer'}
+  - {name: clip_values, type: String,                    description: 'Required. PyTorch model clip_values allowed for features (min, max)'}
+  - {name: nb_classes, type: String,                     description: 'Required. The number of classes of the model'}
+  - {name: input_shape, type: String,                    description: 'Required. The shape of one input instance for the pytorch model'}
+  - {name: data_bucket_name, type: String,               description: 'Bucket that has the processed data',  default: 'training-data'}
+  - {name: result_bucket_name, type: String,             description: 'Bucket that has the training results', default: 'training-result'}
+  - {name: adversarial_accuracy_threshold, type: String, description: 'Model accuracy threshold on adversarial samples', default: '0.2'}
 outputs:
-  - {name: metric_path,                    description: 'Path for robustness check output'}
-  - {name: robust_status,                  description: 'Path for robustness status output'}
+  - {name: metric_path, type: String,                    description: 'Path for robustness check output'}
+  - {name: robust_status, type: String,                  description: 'Path for robustness status output'}
 implementation:
   container:
     image: aipipeline/robustness-evaluation:pytorch