Amazon 基岩中佈建輸送量的程式碼範例

下列程式碼範例示範如何使用和 Python SDK 建立、使用 AWS CLI 和管理佈建輸送量。

AWS CLI

透過在終端機中執行下列命令，MyPT以從 Anthropic Claude v2.1 模型自訂名為MyCustomModel的自訂模型，建立不承諾的佈建輸送量。

aws bedrock create-provisioned-model-throughput \
   --model-units 1 \
   --provisioned-model-name MyPT \
   --model-id arn:aws:bedrock:us-east-1::custom-model/anthropic.claude-v2:1:200k/MyCustomModel

回應會傳回一個provisioned-model-arn。需要一些時間才能完成創建。若要檢查其狀態，請按照下列命令提供已佈建模型provisioned-model-id的名稱或 ARN。

aws bedrock get-provisioned-model-throughput \
    --provisioned-model-id MyPT

變更佈建輸送量的名稱，並將其與從 Anthropic Claude v2.1 自訂的其他模型產生關聯。

aws bedrock update-provisioned-model-throughput \
    --provisioned-model-id MyPT \ 
    --desired-provisioned-model-name MyPT2 \
    --desired-model-id arn:aws:bedrock:us-east-1::custom-model/anthropic.claude-v2:1:200k/MyCustomModel2

使用下列命令，使用更新的佈建模型執行推論。您必須提供已佈建模型的 ARN (在回UpdateProvisionedModelThroughput應中傳回)，做為. model-id 輸出會寫入目前資料夾中名為 output.txt 的檔案。

aws bedrock-runtime invoke-model \
    --model-id ${provisioned-model-arn} \
    --body '{"inputText": "What is AWS?", "textGenerationConfig": {"temperature": 0.5}}' \
    --cli-binary-format raw-in-base64-out \
    output.txt

使用下列命令刪除佈建輸送量。您不再需要支付佈建輸送量的費用。

aws bedrock delete-provisioned-model-throughput 
  --provisioned-model-id MyPT2

Python (Boto)

透過執行下列程式碼片段，建立以從 Anthropic Claude v2.1 模型自訂名為MyCustomModel的自訂模型呼叫的無承諾佈建輸送量。MyPT

import boto3 
                    
bedrock = boto3.client(service_name='bedrock')
bedrock.create_provisioned_model_throughput(
    modelUnits=1,
    provisionedModelName='MyPT', 
    modelId='arn:aws:bedrock:us-east-1::custom-model/anthropic.claude-v2:1:200k/MyCustomModel' 
)

回應會傳回一個provisionedModelArn。需要一些時間才能完成創建。您可以使用以下代碼片段檢查其狀態。您可以將佈建輸送量的名稱或從回CreateProvisionedModelThroughput應傳回的 ARN 提供為。provisionedModelId

bedrock.get_provisioned_model_throughput(provisionedModelId='MyPT')

變更佈建輸送量的名稱，並將其與從 Anthropic Claude v2.1 自訂的其他模型產生關聯。然後傳送GetProvisionedModelThroughput要求，並將佈建模型的 ARN 儲存至變數以用於推論。

bedrock.update_provisioned_model_throughput(
    provisionedModelId='MyPT',
    desiredProvisionedModelName='MyPT2',
    desiredModelId='arn:aws:bedrock:us-east-1::custom-model/anthropic.claude-v2:1:200k/MyCustomModel2'
)
                
arn_MyPT2 = bedrock.get_provisioned_model_throughput(provisionedModelId='MyPT2').get('provisionedModelArn')

使用下列命令，使用更新的佈建模型執行推論。您必須將已佈建模型的 ARN 提供為。modelId

import json
import logging
import boto3

from botocore.exceptions import ClientError


class ImageError(Exception):
    "Custom exception for errors returned by the model"

    def __init__(self, message):
        self.message = message


logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)


def generate_text(model_id, body):
    """
    Generate text using your provisioned custom model.
    Args:
        model_id (str): The model ID to use.
        body (str) : The request body to use.
    Returns:
        response (json): The response from the model.
    """

    logger.info(
        "Generating text with your provisioned custom model %s", model_id)

    brt = boto3.client(service_name='bedrock-runtime')

    accept = "application/json"
    content_type = "application/json"

    response = brt.invoke_model(
        body=body, modelId=model_id, accept=accept, contentType=content_type
    )
    response_body = json.loads(response.get("body").read())

    finish_reason = response_body.get("error")

    if finish_reason is not None:
        raise ImageError(f"Text generation error. Error is {finish_reason}")

    logger.info(
        "Successfully generated text with provisioned custom model %s", model_id)

    return response_body


def main():
    """
    Entrypoint for example.
    """
    try:
        logging.basicConfig(level=logging.INFO,
                            format="%(levelname)s: %(message)s")

        model_id = arn_myPT2

        body = json.dumps({
            "inputText": "what is AWS?"
        })

        response_body = generate_text(model_id, body)
        print(f"Input token count: {response_body['inputTextTokenCount']}")

        for result in response_body['results']:
            print(f"Token count: {result['tokenCount']}")
            print(f"Output text: {result['outputText']}")
            print(f"Completion reason: {result['completionReason']}")

    except ClientError as err:
        message = err.response["Error"]["Message"]
        logger.error("A client error occurred: %s", message)
        print("A client error occured: " +
              format(message))
    except ImageError as err:
        logger.error(err.message)
        print(err.message)

    else:
        print(
            f"Finished generating text with your provisioned custom model {model_id}.")


if __name__ == "__main__":
    main()

使用下列程式碼片段刪除佈建輸送量。您不再需要支付佈建輸送量的費用。

bedrock.delete_provisioned_model_throughput(provisionedModelId='MyPT2')