Values_1.RecommendationJobContainerConfigSourceSpecifies mandatory fields for running an Inference Recommender job directly in the CreateInferenceRecommendationsJob API. The fields specified in ContainerConfig override the corresponding fields in the model package. Use ContainerConfig if you want to specify these fields for the recommendation job but don't want to edit them in your model package.
type nonrec t = {domain : Values_0.String_.t option;The machine learning domain of the model and its components. Valid Values: COMPUTER_VISION | NATURAL_LANGUAGE_PROCESSING | MACHINE_LEARNING
*)task : Values_0.String_.t option;The machine learning task that the model accomplishes. Valid Values: IMAGE_CLASSIFICATION | OBJECT_DETECTION | TEXT_GENERATION | IMAGE_SEGMENTATION | FILL_MASK | CLASSIFICATION | REGRESSION | OTHER
*)framework : Values_0.String_.t option;The machine learning framework of the container image. Valid Values: TENSORFLOW | PYTORCH | XGBOOST | SAGEMAKER-SCIKIT-LEARN
*)frameworkVersion : RecommendationJobFrameworkVersion.t option;The framework version of the container image.
*)payloadConfig : RecommendationJobPayloadConfig.t option;Specifies the SamplePayloadUrl and all other sample payload-related fields.
*)nearestModelName : Values_0.String_.t option;The name of a pre-trained machine learning model benchmarked by Amazon SageMaker Inference Recommender that matches your model. Valid Values: efficientnetb7 | unet | xgboost | faster-rcnn-resnet101 | nasnetlarge | vgg16 | inception-v3 | mask-rcnn | sagemaker-scikit-learn | densenet201-gluon | resnet18v2-gluon | xception | densenet201 | yolov4 | resnet152 | bert-base-cased | xceptionV1-keras | resnet50 | retinanet
*)supportedInstanceTypes : RecommendationJobSupportedInstanceTypes.t option;A list of the instance types that are used to generate inferences in real-time.
*)supportedEndpointType : RecommendationJobSupportedEndpointType.t option;The endpoint type to receive recommendations for. By default this is null, and the results of the inference recommendation job return a combined list of both real-time and serverless benchmarks. By specifying a value for this field, you can receive a longer list of benchmarks for the desired endpoint type.
*)dataInputConfig : RecommendationJobDataInputConfig.t option;Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form. This field is used for optimizing your model using SageMaker Neo. For more information, see DataInputConfig.
*)supportedResponseMIMETypes : RecommendationJobSupportedResponseMIMETypes.t
option;The supported MIME types for the output data.
*)}val to_value :
t ->
[> `Structure of
(string
* [> `Enum of string
| `List of [> `String of Values_0.String_.t ] list
| `String of Values_0.String_.t
| `Structure of
(string
* [> `List of
[> `String of RecommendationJobSupportedContentType.t ]
list
| `String of Values_0.S3Uri.t ])
list ])
list ]