模型自訂的程式碼範例

首先，建立名為 FineTuningData.json 的文字檔案。將 JSON 程式碼從下方複製到文字檔案，將 ${training-bucket} 和 ${output-bucket} 取代為您的 S3 儲存貯體名稱。

{
    "trainingDataConfig": {
        "s3Uri": "s3://${training-bucket}/train.jsonl"
    },
    "outputDataConfig": {
        "s3Uri": "s3://${output-bucket}"
    }
}

若要提交模型自訂任務，請導覽至終端機中包含 FineTuningData.json 的資料夾，並在命令列中執行下列命令，以您設定的模型自訂角色取代 ${your-customization-role-arn}。

aws bedrock create-model-customization-job \
    --customization-type FINE_TUNING \
    --base-model-identifier arn:aws:bedrock:us-east-1::foundation-model/amazon.titan-text-express-v1 \
    --role-arn ${your-customization-role-arn} \
    --job-name MyFineTuningJob \
    --custom-model-name MyCustomModel \
    --hyper-parameters epochCount=1,batchSize=1,learningRate=.0001,learningRateWarmupSteps=0 \
    --cli-input-json file://FineTuningData.json
                

回應會傳回 jobArn。讓任務有時間完成。您可以使用下列命令來檢查其狀態。

aws bedrock get-model-customization-job \
    --job-identifier "jobArn"

當 status為 時COMPLETE，您可以在回應trainingMetrics中看到 。您可以執行下列命令，將 aet.et-bucket 取代為您的輸出儲存貯體名稱和 jobId 以自訂任務的 ID ( 中最後一個斜線後面的序列jobArn)，將成品下載到目前的資料夾。

aws s3 cp s3://${output-bucket}/model-customization-job-jobId . --recursive

使用下列命令為您的自訂模型購買無承諾佈建輸送量。

aws bedrock create-provisioned-model-throughput \
    --model-id MyCustomModel \
    --provisioned-model-name MyProvisionedCustomModel \
    --model-units 1

回應會傳回 provisionedModelArn。預留一些時間來建立佈建輸送量。若要檢查其狀態，請在下列命令provisioned-model-id中提供佈建模型的名稱或 ARN 做為 。

aws bedrock get-provisioned-model-throughput \
    --provisioned-model-id ${provisioned-model-arn}

當 status為 時InService，您可以使用下列命令，使用自訂模型執行推論。您必須提供佈建模型的 ARN 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

執行下列程式碼片段以提交微調任務。將 ${your-customization-role-arn} 取代為您設定的 MyCustomizationRole 的 ARN，並將 ${training-bucket} 和 ${output-bucket} 取代為您的 S3 儲存貯體名稱。

import boto3

bedrock = boto3.client(service_name='bedrock')
    
# Set parameters
customizationType = "FINE_TUNING"
baseModelIdentifier = "arn:aws:bedrock:us-east-1::foundation-model/amazon.titan-text-express-v1"
roleArn = "${your-customization-role-arn}"
jobName = "MyFineTuningJob"
customModelName = "MyCustomModel"
hyperParameters = {
        "epochCount": "1",
        "batchSize": "1",
        "learningRate": ".0001",
        "learningRateWarmupSteps": "0"
    }
trainingDataConfig = {"s3Uri": "s3://${training-bucket}/myInputData/train.jsonl"}
outputDataConfig = {"s3Uri": "s3://${output-bucket}/myOutputData"}

# Create job
response_ft = bedrock.create_model_customization_job(
    jobName=jobName, 
    customModelName=customModelName,
    roleArn=roleArn,
    baseModelIdentifier=baseModelIdentifier,
    hyperParameters=hyperParameters,
    trainingDataConfig=trainingDataConfig,
    outputDataConfig=outputDataConfig
)

jobArn = response_ft.get('jobArn')

回應會傳回 jobArn。讓任務有時間完成。您可以使用下列命令來檢查其狀態。

bedrock.get_model_customization_job(jobIdentifier=jobArn).get('status')

當 status為 時COMPLETE，您可以在 GetModelCustomizationJob 回應trainingMetrics中看到 。您也可以遵循下載物件的步驟來下載指標。

使用下列命令為您的自訂模型購買無承諾佈建輸送量。

response_pt = bedrock.create_provisioned_model_throughput(
    modelId="MyCustomModel",
    provisionedModelName="MyProvisionedCustomModel",
    modelUnits="1"
)

provisionedModelArn = response_pt.get('provisionedModelArn')

回應會傳回 provisionedModelArn。預留一些時間來建立佈建輸送量。若要檢查其狀態，請在下列命令provisionedModelId中提供佈建模型的名稱或 ARN，做為 。

bedrock.get_provisioned_model_throughput(provisionedModelId=provisionedModelArn)

當 status為 時InService，您可以使用下列命令，使用自訂模型執行推論。您必須提供佈建模型的 ARN 做為 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 = provisionedModelArn

        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()