要使用AWS SageMaker SKLearn，您需要将Scikit-learn代码包装成SageMaker的容器。以下是一个解决方法的示例：

1. 创建一个Python文件，例如sklearn_container.py，用于定义SageMaker的容器。

import os
import subprocess
import sys
from sagemaker_containers import _env

if __name__ == '__main__':
    subprocess.check_call([sys.executable, "-m", "pip", "install", "scikit-learn==0.24.2"])

    from sagemaker_sklearn_container import entry_point

    entry_point()

2. 创建一个Python文件，例如entry_point.py，用于定义Scikit-learn的入口点。

import os
import pandas as pd
from sklearn import svm
from sklearn.externals import joblib

def model_fn(model_dir):
    clf = joblib.load(os.path.join(model_dir, "model.joblib"))
    return clf

def input_fn(input_data, content_type):
    if content_type == 'text/csv':
        df = pd.read_csv(StringIO(input_data), header=None)
        return df
    else:
        raise ValueError(f"Invalid content type: {content_type}")

def predict_fn(input_data, model):
    return model.predict(input_data)

def output_fn(prediction, accept):
    if accept == 'application/json':
        return pd.Series(prediction).to_json(orient='values')
    else:
        raise ValueError(f"Invalid accept type: {accept}")

def train():
    # Load training data
    train_data = pd.read_csv(os.path.join("/opt/ml/input/data", "train", "train.csv"), header=None)
    train_labels = pd.read_csv(os.path.join("/opt/ml/input/data", "train", "train_labels.csv"), header=None)

    # Train model
    clf = svm.SVC(gamma='scale')
    clf.fit(train_data, train_labels)

    # Save model
    joblib.dump(clf, os.path.join("/opt/ml/model", "model.joblib"))

3. 在SageMaker中创建一个训练作业时，将训练脚本指定为sklearn_container.py。

import sagemaker
from sagemaker.sklearn.estimator import SKLearn

sagemaker_session = sagemaker.Session()

sklearn = SKLearn(entry_point='sklearn_container.py',
                  source_dir='.',
                  role=sagemaker.get_execution_role(),
                  train_instance_count=1,
                  train_instance_type='ml.c4.xlarge',
                  framework_version='0.24.2',
                  py_version='py3',
                  hyperparameters={'epochs': 10})

sklearn.fit({'train': 's3://path/to/train/data'})

通过这种方式，您可以在AWS SageMaker上使用Scikit-learn的0.24.2版本。请确保您将正确的框架版本指定为framework_version参数，并在容器中安装相应的Scikit-learn版本。