@Generated(value="com.amazonaws:aws-java-sdk-code-generator") public class CreateMlflowTrackingServerRequest extends AmazonWebServiceRequest implements Serializable, Cloneable
NOOP
Constructor and Description |
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CreateMlflowTrackingServerRequest() |
Modifier and Type | Method and Description |
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CreateMlflowTrackingServerRequest |
clone()
Creates a shallow clone of this object for all fields except the handler context.
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boolean |
equals(Object obj) |
String |
getArtifactStoreUri()
The S3 URI for a general purpose bucket to use as the MLflow Tracking Server artifact store.
|
Boolean |
getAutomaticModelRegistration()
Whether to enable or disable automatic registration of new MLflow models to the SageMaker Model Registry.
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String |
getMlflowVersion()
The version of MLflow that the tracking server uses.
|
String |
getRoleArn()
The Amazon Resource Name (ARN) for an IAM role in your account that the MLflow Tracking Server uses to access the
artifact store in Amazon S3.
|
List<Tag> |
getTags()
Tags consisting of key-value pairs used to manage metadata for the tracking server.
|
String |
getTrackingServerName()
A unique string identifying the tracking server name.
|
String |
getTrackingServerSize()
The size of the tracking server you want to create.
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String |
getWeeklyMaintenanceWindowStart()
The day and time of the week in Coordinated Universal Time (UTC) 24-hour standard time that weekly maintenance
updates are scheduled.
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int |
hashCode() |
Boolean |
isAutomaticModelRegistration()
Whether to enable or disable automatic registration of new MLflow models to the SageMaker Model Registry.
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void |
setArtifactStoreUri(String artifactStoreUri)
The S3 URI for a general purpose bucket to use as the MLflow Tracking Server artifact store.
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void |
setAutomaticModelRegistration(Boolean automaticModelRegistration)
Whether to enable or disable automatic registration of new MLflow models to the SageMaker Model Registry.
|
void |
setMlflowVersion(String mlflowVersion)
The version of MLflow that the tracking server uses.
|
void |
setRoleArn(String roleArn)
The Amazon Resource Name (ARN) for an IAM role in your account that the MLflow Tracking Server uses to access the
artifact store in Amazon S3.
|
void |
setTags(Collection<Tag> tags)
Tags consisting of key-value pairs used to manage metadata for the tracking server.
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void |
setTrackingServerName(String trackingServerName)
A unique string identifying the tracking server name.
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void |
setTrackingServerSize(String trackingServerSize)
The size of the tracking server you want to create.
|
void |
setWeeklyMaintenanceWindowStart(String weeklyMaintenanceWindowStart)
The day and time of the week in Coordinated Universal Time (UTC) 24-hour standard time that weekly maintenance
updates are scheduled.
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String |
toString()
Returns a string representation of this object.
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CreateMlflowTrackingServerRequest |
withArtifactStoreUri(String artifactStoreUri)
The S3 URI for a general purpose bucket to use as the MLflow Tracking Server artifact store.
|
CreateMlflowTrackingServerRequest |
withAutomaticModelRegistration(Boolean automaticModelRegistration)
Whether to enable or disable automatic registration of new MLflow models to the SageMaker Model Registry.
|
CreateMlflowTrackingServerRequest |
withMlflowVersion(String mlflowVersion)
The version of MLflow that the tracking server uses.
|
CreateMlflowTrackingServerRequest |
withRoleArn(String roleArn)
The Amazon Resource Name (ARN) for an IAM role in your account that the MLflow Tracking Server uses to access the
artifact store in Amazon S3.
|
CreateMlflowTrackingServerRequest |
withTags(Collection<Tag> tags)
Tags consisting of key-value pairs used to manage metadata for the tracking server.
|
CreateMlflowTrackingServerRequest |
withTags(Tag... tags)
Tags consisting of key-value pairs used to manage metadata for the tracking server.
|
CreateMlflowTrackingServerRequest |
withTrackingServerName(String trackingServerName)
A unique string identifying the tracking server name.
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CreateMlflowTrackingServerRequest |
withTrackingServerSize(String trackingServerSize)
The size of the tracking server you want to create.
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CreateMlflowTrackingServerRequest |
withTrackingServerSize(TrackingServerSize trackingServerSize)
The size of the tracking server you want to create.
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CreateMlflowTrackingServerRequest |
withWeeklyMaintenanceWindowStart(String weeklyMaintenanceWindowStart)
The day and time of the week in Coordinated Universal Time (UTC) 24-hour standard time that weekly maintenance
updates are scheduled.
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addHandlerContext, getCloneRoot, getCloneSource, getCustomQueryParameters, getCustomRequestHeaders, getGeneralProgressListener, getHandlerContext, getReadLimit, getRequestClientOptions, getRequestCredentials, getRequestCredentialsProvider, getRequestMetricCollector, getSdkClientExecutionTimeout, getSdkRequestTimeout, putCustomQueryParameter, putCustomRequestHeader, setGeneralProgressListener, setRequestCredentials, setRequestCredentialsProvider, setRequestMetricCollector, setSdkClientExecutionTimeout, setSdkRequestTimeout, withGeneralProgressListener, withRequestCredentialsProvider, withRequestMetricCollector, withSdkClientExecutionTimeout, withSdkRequestTimeout
public void setTrackingServerName(String trackingServerName)
A unique string identifying the tracking server name. This string is part of the tracking server ARN.
trackingServerName
- A unique string identifying the tracking server name. This string is part of the tracking server ARN.public String getTrackingServerName()
A unique string identifying the tracking server name. This string is part of the tracking server ARN.
public CreateMlflowTrackingServerRequest withTrackingServerName(String trackingServerName)
A unique string identifying the tracking server name. This string is part of the tracking server ARN.
trackingServerName
- A unique string identifying the tracking server name. This string is part of the tracking server ARN.public void setArtifactStoreUri(String artifactStoreUri)
The S3 URI for a general purpose bucket to use as the MLflow Tracking Server artifact store.
artifactStoreUri
- The S3 URI for a general purpose bucket to use as the MLflow Tracking Server artifact store.public String getArtifactStoreUri()
The S3 URI for a general purpose bucket to use as the MLflow Tracking Server artifact store.
public CreateMlflowTrackingServerRequest withArtifactStoreUri(String artifactStoreUri)
The S3 URI for a general purpose bucket to use as the MLflow Tracking Server artifact store.
artifactStoreUri
- The S3 URI for a general purpose bucket to use as the MLflow Tracking Server artifact store.public void setTrackingServerSize(String trackingServerSize)
The size of the tracking server you want to create. You can choose between "Small"
,
"Medium"
, and "Large"
. The default MLflow Tracking Server configuration size is
"Small"
. You can choose a size depending on the projected use of the tracking server such as the
volume of data logged, number of users, and frequency of use.
We recommend using a small tracking server for teams of up to 25 users, a medium tracking server for teams of up to 50 users, and a large tracking server for teams of up to 100 users.
trackingServerSize
- The size of the tracking server you want to create. You can choose between "Small"
,
"Medium"
, and "Large"
. The default MLflow Tracking Server configuration size is
"Small"
. You can choose a size depending on the projected use of the tracking server such as
the volume of data logged, number of users, and frequency of use.
We recommend using a small tracking server for teams of up to 25 users, a medium tracking server for teams of up to 50 users, and a large tracking server for teams of up to 100 users.
TrackingServerSize
public String getTrackingServerSize()
The size of the tracking server you want to create. You can choose between "Small"
,
"Medium"
, and "Large"
. The default MLflow Tracking Server configuration size is
"Small"
. You can choose a size depending on the projected use of the tracking server such as the
volume of data logged, number of users, and frequency of use.
We recommend using a small tracking server for teams of up to 25 users, a medium tracking server for teams of up to 50 users, and a large tracking server for teams of up to 100 users.
"Small"
,
"Medium"
, and "Large"
. The default MLflow Tracking Server configuration size is
"Small"
. You can choose a size depending on the projected use of the tracking server such as
the volume of data logged, number of users, and frequency of use.
We recommend using a small tracking server for teams of up to 25 users, a medium tracking server for teams of up to 50 users, and a large tracking server for teams of up to 100 users.
TrackingServerSize
public CreateMlflowTrackingServerRequest withTrackingServerSize(String trackingServerSize)
The size of the tracking server you want to create. You can choose between "Small"
,
"Medium"
, and "Large"
. The default MLflow Tracking Server configuration size is
"Small"
. You can choose a size depending on the projected use of the tracking server such as the
volume of data logged, number of users, and frequency of use.
We recommend using a small tracking server for teams of up to 25 users, a medium tracking server for teams of up to 50 users, and a large tracking server for teams of up to 100 users.
trackingServerSize
- The size of the tracking server you want to create. You can choose between "Small"
,
"Medium"
, and "Large"
. The default MLflow Tracking Server configuration size is
"Small"
. You can choose a size depending on the projected use of the tracking server such as
the volume of data logged, number of users, and frequency of use.
We recommend using a small tracking server for teams of up to 25 users, a medium tracking server for teams of up to 50 users, and a large tracking server for teams of up to 100 users.
TrackingServerSize
public CreateMlflowTrackingServerRequest withTrackingServerSize(TrackingServerSize trackingServerSize)
The size of the tracking server you want to create. You can choose between "Small"
,
"Medium"
, and "Large"
. The default MLflow Tracking Server configuration size is
"Small"
. You can choose a size depending on the projected use of the tracking server such as the
volume of data logged, number of users, and frequency of use.
We recommend using a small tracking server for teams of up to 25 users, a medium tracking server for teams of up to 50 users, and a large tracking server for teams of up to 100 users.
trackingServerSize
- The size of the tracking server you want to create. You can choose between "Small"
,
"Medium"
, and "Large"
. The default MLflow Tracking Server configuration size is
"Small"
. You can choose a size depending on the projected use of the tracking server such as
the volume of data logged, number of users, and frequency of use.
We recommend using a small tracking server for teams of up to 25 users, a medium tracking server for teams of up to 50 users, and a large tracking server for teams of up to 100 users.
TrackingServerSize
public void setMlflowVersion(String mlflowVersion)
The version of MLflow that the tracking server uses. To see which MLflow versions are available to use, see How it works.
mlflowVersion
- The version of MLflow that the tracking server uses. To see which MLflow versions are available to use,
see How it works.public String getMlflowVersion()
The version of MLflow that the tracking server uses. To see which MLflow versions are available to use, see How it works.
public CreateMlflowTrackingServerRequest withMlflowVersion(String mlflowVersion)
The version of MLflow that the tracking server uses. To see which MLflow versions are available to use, see How it works.
mlflowVersion
- The version of MLflow that the tracking server uses. To see which MLflow versions are available to use,
see How it works.public void setRoleArn(String roleArn)
The Amazon Resource Name (ARN) for an IAM role in your account that the MLflow Tracking Server uses to access the
artifact store in Amazon S3. The role should have AmazonS3FullAccess
permissions. For more
information on IAM permissions for tracking server creation, see Set up IAM
permissions for MLflow.
roleArn
- The Amazon Resource Name (ARN) for an IAM role in your account that the MLflow Tracking Server uses to
access the artifact store in Amazon S3. The role should have AmazonS3FullAccess
permissions.
For more information on IAM permissions for tracking server creation, see Set up IAM
permissions for MLflow.public String getRoleArn()
The Amazon Resource Name (ARN) for an IAM role in your account that the MLflow Tracking Server uses to access the
artifact store in Amazon S3. The role should have AmazonS3FullAccess
permissions. For more
information on IAM permissions for tracking server creation, see Set up IAM
permissions for MLflow.
AmazonS3FullAccess
permissions.
For more information on IAM permissions for tracking server creation, see Set up IAM
permissions for MLflow.public CreateMlflowTrackingServerRequest withRoleArn(String roleArn)
The Amazon Resource Name (ARN) for an IAM role in your account that the MLflow Tracking Server uses to access the
artifact store in Amazon S3. The role should have AmazonS3FullAccess
permissions. For more
information on IAM permissions for tracking server creation, see Set up IAM
permissions for MLflow.
roleArn
- The Amazon Resource Name (ARN) for an IAM role in your account that the MLflow Tracking Server uses to
access the artifact store in Amazon S3. The role should have AmazonS3FullAccess
permissions.
For more information on IAM permissions for tracking server creation, see Set up IAM
permissions for MLflow.public void setAutomaticModelRegistration(Boolean automaticModelRegistration)
Whether to enable or disable automatic registration of new MLflow models to the SageMaker Model Registry. To
enable automatic model registration, set this value to True
. To disable automatic model
registration, set this value to False
. If not specified, AutomaticModelRegistration
defaults to False
.
automaticModelRegistration
- Whether to enable or disable automatic registration of new MLflow models to the SageMaker Model Registry.
To enable automatic model registration, set this value to True
. To disable automatic model
registration, set this value to False
. If not specified,
AutomaticModelRegistration
defaults to False
.public Boolean getAutomaticModelRegistration()
Whether to enable or disable automatic registration of new MLflow models to the SageMaker Model Registry. To
enable automatic model registration, set this value to True
. To disable automatic model
registration, set this value to False
. If not specified, AutomaticModelRegistration
defaults to False
.
True
. To disable automatic model
registration, set this value to False
. If not specified,
AutomaticModelRegistration
defaults to False
.public CreateMlflowTrackingServerRequest withAutomaticModelRegistration(Boolean automaticModelRegistration)
Whether to enable or disable automatic registration of new MLflow models to the SageMaker Model Registry. To
enable automatic model registration, set this value to True
. To disable automatic model
registration, set this value to False
. If not specified, AutomaticModelRegistration
defaults to False
.
automaticModelRegistration
- Whether to enable or disable automatic registration of new MLflow models to the SageMaker Model Registry.
To enable automatic model registration, set this value to True
. To disable automatic model
registration, set this value to False
. If not specified,
AutomaticModelRegistration
defaults to False
.public Boolean isAutomaticModelRegistration()
Whether to enable or disable automatic registration of new MLflow models to the SageMaker Model Registry. To
enable automatic model registration, set this value to True
. To disable automatic model
registration, set this value to False
. If not specified, AutomaticModelRegistration
defaults to False
.
True
. To disable automatic model
registration, set this value to False
. If not specified,
AutomaticModelRegistration
defaults to False
.public void setWeeklyMaintenanceWindowStart(String weeklyMaintenanceWindowStart)
The day and time of the week in Coordinated Universal Time (UTC) 24-hour standard time that weekly maintenance updates are scheduled. For example: TUE:03:30.
weeklyMaintenanceWindowStart
- The day and time of the week in Coordinated Universal Time (UTC) 24-hour standard time that weekly
maintenance updates are scheduled. For example: TUE:03:30.public String getWeeklyMaintenanceWindowStart()
The day and time of the week in Coordinated Universal Time (UTC) 24-hour standard time that weekly maintenance updates are scheduled. For example: TUE:03:30.
public CreateMlflowTrackingServerRequest withWeeklyMaintenanceWindowStart(String weeklyMaintenanceWindowStart)
The day and time of the week in Coordinated Universal Time (UTC) 24-hour standard time that weekly maintenance updates are scheduled. For example: TUE:03:30.
weeklyMaintenanceWindowStart
- The day and time of the week in Coordinated Universal Time (UTC) 24-hour standard time that weekly
maintenance updates are scheduled. For example: TUE:03:30.public List<Tag> getTags()
Tags consisting of key-value pairs used to manage metadata for the tracking server.
public void setTags(Collection<Tag> tags)
Tags consisting of key-value pairs used to manage metadata for the tracking server.
tags
- Tags consisting of key-value pairs used to manage metadata for the tracking server.public CreateMlflowTrackingServerRequest withTags(Tag... tags)
Tags consisting of key-value pairs used to manage metadata for the tracking server.
NOTE: This method appends the values to the existing list (if any). Use
setTags(java.util.Collection)
or withTags(java.util.Collection)
if you want to override the
existing values.
tags
- Tags consisting of key-value pairs used to manage metadata for the tracking server.public CreateMlflowTrackingServerRequest withTags(Collection<Tag> tags)
Tags consisting of key-value pairs used to manage metadata for the tracking server.
tags
- Tags consisting of key-value pairs used to manage metadata for the tracking server.public String toString()
toString
in class Object
Object.toString()
public CreateMlflowTrackingServerRequest clone()
AmazonWebServiceRequest
clone
in class AmazonWebServiceRequest
Object.clone()