以下是使用AWS SageMaker云形成模板的解决方法示例:
import boto3
# 创建SageMaker客户端
sagemaker = boto3.client('sagemaker')
# 定义实例配置
instance_type = 'ml.m5.xlarge' # 实例类型
instance_count = 1 # 实例数量
volume_size = 30 # 磁盘空间大小(GB)
# 创建SageMaker实例
response = sagemaker.create_notebook_instance(
NotebookInstanceName='my-notebook-instance',
InstanceType=instance_type,
VolumeSizeInGB=volume_size,
InstanceCount=instance_count
)
import boto3
# 创建SageMaker客户端
sagemaker = boto3.client('sagemaker')
# 创建SageMaker笔记本实例配置
instance_type = 'ml.t2.medium' # 笔记本实例类型
volume_size = 5 # 磁盘空间大小(GB)
# 创建SageMaker笔记本实例
response = sagemaker.create_notebook_instance(
NotebookInstanceName='my-notebook-instance',
InstanceType=instance_type,
VolumeSizeInGB=volume_size
)
import boto3
# 创建SageMaker客户端
sagemaker = boto3.client('sagemaker')
# 定义训练作业配置
training_image = 'image_uri' # 训练镜像URI
instance_type = 'ml.p2.xlarge' # 实例类型
instance_count = 1 # 实例数量
role_arn = 'role_arn' # IAM角色ARN
bucket = 's3_bucket' # S3存储桶
training_data = 's3://s3_bucket/training_data.csv' # 训练数据路径
output_path = 's3://s3_bucket/output' # 模型输出路径
# 创建SageMaker训练作业
response = sagemaker.create_training_job(
TrainingJobName='my-training-job',
AlgorithmSpecification={
'TrainingImage': training_image,
'TrainingInputMode': 'File'
},
RoleArn=role_arn,
InputDataConfig=[
{
'ChannelName': 'training',
'DataSource': {
'S3DataSource': {
'S3DataType': 'S3Prefix',
'S3Uri': training_data,
'S3DataDistributionType': 'FullyReplicated'
}
},
'ContentType': 'text/csv'
}
],
OutputDataConfig={
'S3OutputPath': output_path
},
ResourceConfig={
'InstanceType': instance_type,
'InstanceCount': instance_count,
'VolumeSizeInGB': volume_size
},
StoppingCondition={
'MaxRuntimeInSeconds': 86400
}
)
请注意,以上示例中的代码块仅为演示目的,并不能直接运行。您需要根据实际场景和需求进行相应的配置和修改。