public class CreateTrainingJobRequest extends AbstractModel
header, skipSign| Constructor and Description | 
|---|
| CreateTrainingJobRequest() | 
| CreateTrainingJobRequest(CreateTrainingJobRequest source)NOTE: Any ambiguous key set via .set("AnyKey", "value") will be a shallow copy,
       and any explicit key, i.e Foo, set via .setFoo("value") will be a deep copy. | 
| Modifier and Type | Method and Description | 
|---|---|
| AlgorithmSpecification | getAlgorithmSpecification()Get 算法镜像配置 | 
| EnvConfig[] | getEnvConfig()Get 环境变量配置 | 
| String | getHyperParameters()Get 算法超级参数 | 
| InputDataConfig[] | getInputDataConfig()Get 输入数据配置 | 
| OutputDataConfig | getOutputDataConfig()Get 输出数据配置 | 
| ResourceConfig | getResourceConfig()Get 资源实例配置 | 
| String | getRetryWhenResourceInsufficient()Get 在资源不足(ResourceInsufficient)时后台不定时尝试重新创建训练任务。可取值Enabled/Disabled
默认值为Disabled即不重新尝试。设为Enabled时重新尝试有一定的时间期限,定义在 StoppingCondition 中 MaxWaitTimeInSecond中 ,默认值为1天,超过该期限创建失败。 | 
| String | getRoleName()Get 角色名称 | 
| StoppingCondition | getStoppingCondition()Get 中止条件 | 
| String | getTrainingJobName()Get 训练任务名称 | 
| VpcConfig | getVpcConfig()Get 私有网络配置 | 
| void | setAlgorithmSpecification(AlgorithmSpecification AlgorithmSpecification)Set 算法镜像配置 | 
| void | setEnvConfig(EnvConfig[] EnvConfig)Set 环境变量配置 | 
| void | setHyperParameters(String HyperParameters)Set 算法超级参数 | 
| void | setInputDataConfig(InputDataConfig[] InputDataConfig)Set 输入数据配置 | 
| void | setOutputDataConfig(OutputDataConfig OutputDataConfig)Set 输出数据配置 | 
| void | setResourceConfig(ResourceConfig ResourceConfig)Set 资源实例配置 | 
| void | setRetryWhenResourceInsufficient(String RetryWhenResourceInsufficient)Set 在资源不足(ResourceInsufficient)时后台不定时尝试重新创建训练任务。可取值Enabled/Disabled
默认值为Disabled即不重新尝试。设为Enabled时重新尝试有一定的时间期限,定义在 StoppingCondition 中 MaxWaitTimeInSecond中 ,默认值为1天,超过该期限创建失败。 | 
| void | setRoleName(String RoleName)Set 角色名称 | 
| void | setStoppingCondition(StoppingCondition StoppingCondition)Set 中止条件 | 
| void | setTrainingJobName(String TrainingJobName)Set 训练任务名称 | 
| void | setVpcConfig(VpcConfig VpcConfig)Set 私有网络配置 | 
| void | toMap(HashMap<String,String> map,
     String prefix)Internal implementation, normal users should not use it. | 
any, fromJsonString, getBinaryParams, GetHeader, getMultipartRequestParams, getSkipSign, isStream, set, SetHeader, setParamArrayObj, setParamArraySimple, setParamObj, setParamSimple, setSkipSign, toJsonStringpublic CreateTrainingJobRequest()
public CreateTrainingJobRequest(CreateTrainingJobRequest source)
public AlgorithmSpecification getAlgorithmSpecification()
public void setAlgorithmSpecification(AlgorithmSpecification AlgorithmSpecification)
AlgorithmSpecification - 算法镜像配置public OutputDataConfig getOutputDataConfig()
public void setOutputDataConfig(OutputDataConfig OutputDataConfig)
OutputDataConfig - 输出数据配置public ResourceConfig getResourceConfig()
public void setResourceConfig(ResourceConfig ResourceConfig)
ResourceConfig - 资源实例配置public String getTrainingJobName()
public void setTrainingJobName(String TrainingJobName)
TrainingJobName - 训练任务名称public InputDataConfig[] getInputDataConfig()
public void setInputDataConfig(InputDataConfig[] InputDataConfig)
InputDataConfig - 输入数据配置public StoppingCondition getStoppingCondition()
public void setStoppingCondition(StoppingCondition StoppingCondition)
StoppingCondition - 中止条件public VpcConfig getVpcConfig()
public void setVpcConfig(VpcConfig VpcConfig)
VpcConfig - 私有网络配置public String getHyperParameters()
public void setHyperParameters(String HyperParameters)
HyperParameters - 算法超级参数public EnvConfig[] getEnvConfig()
public void setEnvConfig(EnvConfig[] EnvConfig)
EnvConfig - 环境变量配置public String getRoleName()
public void setRoleName(String RoleName)
RoleName - 角色名称public String getRetryWhenResourceInsufficient()
public void setRetryWhenResourceInsufficient(String RetryWhenResourceInsufficient)
RetryWhenResourceInsufficient - 在资源不足(ResourceInsufficient)时后台不定时尝试重新创建训练任务。可取值Enabled/Disabled
默认值为Disabled即不重新尝试。设为Enabled时重新尝试有一定的时间期限,定义在 StoppingCondition 中 MaxWaitTimeInSecond中 ,默认值为1天,超过该期限创建失败。Copyright © 2025. All rights reserved.