Bedrock / Client / get_custom_model

get_custom_model¶

Bedrock.Client.get_custom_model(**kwargs)¶

Get the properties associated with a Amazon Bedrock custom model that you have created. For more information, see Custom models in the Amazon Bedrock User Guide.

Request Syntax

response = client.get_custom_model(
    modelIdentifier='string'
)

Parameters:

modelIdentifier (string) –

[REQUIRED]

Name or Amazon Resource Name (ARN) of the custom model.

Return type:

dict

Returns:

Response Syntax

{
    'modelArn': 'string',
    'modelName': 'string',
    'jobName': 'string',
    'jobArn': 'string',
    'baseModelArn': 'string',
    'customizationType': 'FINE_TUNING'|'CONTINUED_PRE_TRAINING'|'DISTILLATION'|'REINFORCEMENT_FINE_TUNING'|'IMPORTED',
    'modelKmsKeyArn': 'string',
    'hyperParameters': {
        'string': 'string'
    },
    'trainingDataConfig': {
        's3Uri': 'string',
        'invocationLogsConfig': {
            'usePromptResponse': True|False,
            'invocationLogSource': {
                's3Uri': 'string'
            },
            'requestMetadataFilters': {
                'equals': {
                    'string': 'string'
                },
                'notEquals': {
                    'string': 'string'
                },
                'andAll': [
                    {
                        'equals': {
                            'string': 'string'
                        },
                        'notEquals': {
                            'string': 'string'
                        }
                    },
                ],
                'orAll': [
                    {
                        'equals': {
                            'string': 'string'
                        },
                        'notEquals': {
                            'string': 'string'
                        }
                    },
                ]
            }
        }
    },
    'validationDataConfig': {
        'validators': [
            {
                's3Uri': 'string'
            },
        ]
    },
    'outputDataConfig': {
        's3Uri': 'string'
    },
    'trainingMetrics': {
        'trainingLoss': ...
    },
    'validationMetrics': [
        {
            'validationLoss': ...
        },
    ],
    'creationTime': datetime(2015, 1, 1),
    'customizationConfig': {
        'distillationConfig': {
            'teacherModelConfig': {
                'teacherModelIdentifier': 'string',
                'maxResponseLengthForInference': 123
            }
        },
        'rftConfig': {
            'graderConfig': {
                'lambdaGrader': {
                    'lambdaArn': 'string'
                }
            },
            'hyperParameters': {
                'epochCount': 123,
                'batchSize': 123,
                'learningRate': ...,
                'maxPromptLength': 123,
                'trainingSamplePerPrompt': 123,
                'inferenceMaxTokens': 123,
                'reasoningEffort': 'low'|'medium'|'high',
                'evalInterval': 123
            }
        }
    },
    'modelStatus': 'Active'|'Creating'|'Failed',
    'failureMessage': 'string'
}

Response Structure

(dict) –
- modelArn (string) –
  
  Amazon Resource Name (ARN) associated with this model.
- modelName (string) –
  
  Model name associated with this model.
- jobName (string) –
  
  Job name associated with this model.
- jobArn (string) –
  
  Job Amazon Resource Name (ARN) associated with this model. For models that you create with the CreateCustomModel API operation, this is NULL.
- baseModelArn (string) –
  
  Amazon Resource Name (ARN) of the base model.
- customizationType (string) –
  
  The type of model customization.
- modelKmsKeyArn (string) –
  
  The custom model is encrypted at rest using this key.
- hyperParameters (dict) –
  
  Hyperparameter values associated with this model. For details on the format for different models, see Custom model hyperparameters.
  - (string) –
    - (string) –
- trainingDataConfig (dict) –
  
  Contains information about the training dataset.
  - s3Uri (string) –
    
    The S3 URI where the training data is stored.
  - invocationLogsConfig (dict) –
    
    Settings for using invocation logs to customize a model.
    - usePromptResponse (boolean) –
      
      Whether to use the model’s response for training, or just the prompt. The default value is False.
    - invocationLogSource (dict) –
      
      The source of the invocation logs.
      Note
      This is a Tagged Union structure. Only one of the following top level keys will be set: s3Uri. If a client receives an unknown member it will set SDK_UNKNOWN_MEMBER as the top level key, which maps to the name or tag of the unknown member. The structure of SDK_UNKNOWN_MEMBER is as follows:
      
      'SDK_UNKNOWN_MEMBER': {'name': 'UnknownMemberName'}
      - s3Uri (string) –
        
        The URI of an invocation log in a bucket.
    - requestMetadataFilters (dict) –
      
      Rules for filtering invocation logs based on request metadata.
      Note
      This is a Tagged Union structure. Only one of the following top level keys will be set: equals, notEquals, andAll, orAll. If a client receives an unknown member it will set SDK_UNKNOWN_MEMBER as the top level key, which maps to the name or tag of the unknown member. The structure of SDK_UNKNOWN_MEMBER is as follows:
      
      'SDK_UNKNOWN_MEMBER': {'name': 'UnknownMemberName'}
      - equals (dict) –
        
        Include results where the key equals the value.
        
        (string) –
        
        (string) –
      - notEquals (dict) –
        
        Include results where the key does not equal the value.
        
        (string) –
        
        (string) –
      - andAll (list) –
        
        Include results where all of the based filters match.
        
        (dict) –
        
        A mapping of a metadata key to a value that it should or should not equal.
        
        equals (dict) –
        
        Include results where the key equals the value.
        
        (string) –
        
        (string) –
        
        notEquals (dict) –
        
        Include results where the key does not equal the value.
        
        (string) –
        
        (string) –
      - orAll (list) –
        
        Include results where any of the base filters match.
        
        (dict) –
        
        A mapping of a metadata key to a value that it should or should not equal.
        
        equals (dict) –
        
        Include results where the key equals the value.
        
        (string) –
        
        (string) –
        
        notEquals (dict) –
        
        Include results where the key does not equal the value.
        
        (string) –
        
        (string) –
- validationDataConfig (dict) –
  
  Contains information about the validation dataset.
  - validators (list) –
    
    Information about the validators.
    - (dict) –
      
      Information about a validator.
      - s3Uri (string) –
        
        The S3 URI where the validation data is stored.
- outputDataConfig (dict) –
  
  Output data configuration associated with this custom model.
  - s3Uri (string) –
    
    The S3 URI where the output data is stored.
- trainingMetrics (dict) –
  
  Contains training metrics from the job creation.
  - trainingLoss (float) –
    
    Loss metric associated with the custom job.
- validationMetrics (list) –
  
  The validation metrics from the job creation.
  - (dict) –
    
    The metric for the validator.
    - validationLoss (float) –
      
      The validation loss associated with this validator.
- creationTime (datetime) –
  
  Creation time of the model.
- customizationConfig (dict) –
  
  The customization configuration for the custom model.
  Note
  This is a Tagged Union structure. Only one of the following top level keys will be set: distillationConfig, rftConfig. If a client receives an unknown member it will set SDK_UNKNOWN_MEMBER as the top level key, which maps to the name or tag of the unknown member. The structure of SDK_UNKNOWN_MEMBER is as follows:
  'SDK_UNKNOWN_MEMBER': {'name': 'UnknownMemberName'}
  - distillationConfig (dict) –
    
    The Distillation configuration for the custom model.
    - teacherModelConfig (dict) –
      
      The teacher model configuration.
      - teacherModelIdentifier (string) –
        
        The identifier of the teacher model.
      - maxResponseLengthForInference (integer) –
        
        The maximum number of tokens requested when the customization job invokes the teacher model.
  - rftConfig (dict) –
    
    Configuration settings for reinforcement fine-tuning (RFT) model customization, including grader configuration and hyperparameters.
    - graderConfig (dict) –
      
      Configuration for the grader that evaluates model responses and provides reward signals during RFT training.
      Note
      This is a Tagged Union structure. Only one of the following top level keys will be set: lambdaGrader. If a client receives an unknown member it will set SDK_UNKNOWN_MEMBER as the top level key, which maps to the name or tag of the unknown member. The structure of SDK_UNKNOWN_MEMBER is as follows:
      
      'SDK_UNKNOWN_MEMBER': {'name': 'UnknownMemberName'}
      - lambdaGrader (dict) –
        
        Configuration for using an AWS Lambda function as the grader for evaluating model responses and provide reward signals in reinforcement fine-tuning.
        
        lambdaArn (string) –
        
        ARN of the AWS Lambda function that will evaluate model responses and return reward scores for RFT training.
    - hyperParameters (dict) –
      
      Hyperparameters that control the reinforcement fine-tuning training process, including learning rate, batch size, and epoch count.
      - epochCount (integer) –
        
        Number of training epochs to run during reinforcement fine-tuning. Higher values may improve performance but increase training time.
      - batchSize (integer) –
        
        Number of training samples processed in each batch during reinforcement fine-tuning (RFT) training. Larger batches may improve training stability.
      - learningRate (float) –
        
        Learning rate for the reinforcement fine-tuning. Controls how quickly the model adapts to reward signals.
      - maxPromptLength (integer) –
        
        Maximum length of input prompts during RFT training, measured in tokens. Longer prompts allow more context but increase memory usage and training-time.
      - trainingSamplePerPrompt (integer) –
        
        Number of response samples generated per prompt during RFT training. More samples provide better reward signal estimation.
      - inferenceMaxTokens (integer) –
        
        Maximum number of tokens the model can generate in response to each prompt during RFT training.
      - reasoningEffort (string) –
        
        Level of reasoning effort applied during RFT training. Higher values may improve response quality but increase training time.
      - evalInterval (integer) –
        
        Interval between evaluation runs during RFT training, measured in training steps. More frequent evaluation provides better monitoring.
- modelStatus (string) –
  
  The current status of the custom model. Possible values include:
  - Creating - The model is being created and validated.
  - Active - The model has been successfully created and is ready for use.
  - Failed - The model creation process failed. Check the failureMessage field for details.
- failureMessage (string) –
  
  A failure message for any issues that occurred when creating the custom model. This is included for only a failed CreateCustomModel operation.

Exceptions

Bedrock.Client.exceptions.ResourceNotFoundException
Bedrock.Client.exceptions.AccessDeniedException
Bedrock.Client.exceptions.ValidationException
Bedrock.Client.exceptions.InternalServerException
Bedrock.Client.exceptions.ThrottlingException