@Generated(value="software.amazon.awssdk:codegen") public interface SageMakerAsyncClient extends SdkClient
builder()
method.
Definition of the public APIs exposed by SageMakerModifier and Type | Field and Description |
---|---|
static String |
SERVICE_NAME |
Modifier and Type | Method and Description |
---|---|
default CompletableFuture<AddTagsResponse> |
addTags(AddTagsRequest addTagsRequest)
Adds or overwrites one or more tags for the specified Amazon SageMaker resource.
|
default CompletableFuture<AddTagsResponse> |
addTags(Consumer<AddTagsRequest.Builder> addTagsRequest)
Adds or overwrites one or more tags for the specified Amazon SageMaker resource.
|
static SageMakerAsyncClientBuilder |
builder()
Create a builder that can be used to configure and create a
SageMakerAsyncClient . |
static SageMakerAsyncClient |
create()
Create a
SageMakerAsyncClient with the region loaded from the
DefaultAwsRegionProviderChain and credentials loaded from the
DefaultCredentialsProvider . |
default CompletableFuture<CreateEndpointResponse> |
createEndpoint(Consumer<CreateEndpointRequest.Builder> createEndpointRequest)
Creates an endpoint using the endpoint configuration specified in the request.
|
default CompletableFuture<CreateEndpointResponse> |
createEndpoint(CreateEndpointRequest createEndpointRequest)
Creates an endpoint using the endpoint configuration specified in the request.
|
default CompletableFuture<CreateEndpointConfigResponse> |
createEndpointConfig(Consumer<CreateEndpointConfigRequest.Builder> createEndpointConfigRequest)
Creates an endpoint configuration that Amazon SageMaker hosting services uses to deploy models.
|
default CompletableFuture<CreateEndpointConfigResponse> |
createEndpointConfig(CreateEndpointConfigRequest createEndpointConfigRequest)
Creates an endpoint configuration that Amazon SageMaker hosting services uses to deploy models.
|
default CompletableFuture<CreateModelResponse> |
createModel(Consumer<CreateModelRequest.Builder> createModelRequest)
Creates a model in Amazon SageMaker.
|
default CompletableFuture<CreateModelResponse> |
createModel(CreateModelRequest createModelRequest)
Creates a model in Amazon SageMaker.
|
default CompletableFuture<CreateNotebookInstanceResponse> |
createNotebookInstance(Consumer<CreateNotebookInstanceRequest.Builder> createNotebookInstanceRequest)
Creates an Amazon SageMaker notebook instance.
|
default CompletableFuture<CreateNotebookInstanceResponse> |
createNotebookInstance(CreateNotebookInstanceRequest createNotebookInstanceRequest)
Creates an Amazon SageMaker notebook instance.
|
default CompletableFuture<CreatePresignedNotebookInstanceUrlResponse> |
createPresignedNotebookInstanceUrl(Consumer<CreatePresignedNotebookInstanceUrlRequest.Builder> createPresignedNotebookInstanceUrlRequest)
Returns a URL that you can use to connect to the Juypter server from a notebook instance.
|
default CompletableFuture<CreatePresignedNotebookInstanceUrlResponse> |
createPresignedNotebookInstanceUrl(CreatePresignedNotebookInstanceUrlRequest createPresignedNotebookInstanceUrlRequest)
Returns a URL that you can use to connect to the Juypter server from a notebook instance.
|
default CompletableFuture<CreateTrainingJobResponse> |
createTrainingJob(Consumer<CreateTrainingJobRequest.Builder> createTrainingJobRequest)
Starts a model training job.
|
default CompletableFuture<CreateTrainingJobResponse> |
createTrainingJob(CreateTrainingJobRequest createTrainingJobRequest)
Starts a model training job.
|
default CompletableFuture<DeleteEndpointResponse> |
deleteEndpoint(Consumer<DeleteEndpointRequest.Builder> deleteEndpointRequest)
Deletes an endpoint.
|
default CompletableFuture<DeleteEndpointResponse> |
deleteEndpoint(DeleteEndpointRequest deleteEndpointRequest)
Deletes an endpoint.
|
default CompletableFuture<DeleteEndpointConfigResponse> |
deleteEndpointConfig(Consumer<DeleteEndpointConfigRequest.Builder> deleteEndpointConfigRequest)
Deletes an endpoint configuration.
|
default CompletableFuture<DeleteEndpointConfigResponse> |
deleteEndpointConfig(DeleteEndpointConfigRequest deleteEndpointConfigRequest)
Deletes an endpoint configuration.
|
default CompletableFuture<DeleteModelResponse> |
deleteModel(Consumer<DeleteModelRequest.Builder> deleteModelRequest)
Deletes a model.
|
default CompletableFuture<DeleteModelResponse> |
deleteModel(DeleteModelRequest deleteModelRequest)
Deletes a model.
|
default CompletableFuture<DeleteNotebookInstanceResponse> |
deleteNotebookInstance(Consumer<DeleteNotebookInstanceRequest.Builder> deleteNotebookInstanceRequest)
Deletes an Amazon SageMaker notebook instance.
|
default CompletableFuture<DeleteNotebookInstanceResponse> |
deleteNotebookInstance(DeleteNotebookInstanceRequest deleteNotebookInstanceRequest)
Deletes an Amazon SageMaker notebook instance.
|
default CompletableFuture<DeleteTagsResponse> |
deleteTags(Consumer<DeleteTagsRequest.Builder> deleteTagsRequest)
Deletes the specified tags from an Amazon SageMaker resource.
|
default CompletableFuture<DeleteTagsResponse> |
deleteTags(DeleteTagsRequest deleteTagsRequest)
Deletes the specified tags from an Amazon SageMaker resource.
|
default CompletableFuture<DescribeEndpointResponse> |
describeEndpoint(Consumer<DescribeEndpointRequest.Builder> describeEndpointRequest)
Returns the description of an endpoint.
|
default CompletableFuture<DescribeEndpointResponse> |
describeEndpoint(DescribeEndpointRequest describeEndpointRequest)
Returns the description of an endpoint.
|
default CompletableFuture<DescribeEndpointConfigResponse> |
describeEndpointConfig(Consumer<DescribeEndpointConfigRequest.Builder> describeEndpointConfigRequest)
Returns the description of an endpoint configuration created using the
CreateEndpointConfig API. |
default CompletableFuture<DescribeEndpointConfigResponse> |
describeEndpointConfig(DescribeEndpointConfigRequest describeEndpointConfigRequest)
Returns the description of an endpoint configuration created using the
CreateEndpointConfig API. |
default CompletableFuture<DescribeModelResponse> |
describeModel(Consumer<DescribeModelRequest.Builder> describeModelRequest)
Describes a model that you created using the
CreateModel API. |
default CompletableFuture<DescribeModelResponse> |
describeModel(DescribeModelRequest describeModelRequest)
Describes a model that you created using the
CreateModel API. |
default CompletableFuture<DescribeNotebookInstanceResponse> |
describeNotebookInstance(Consumer<DescribeNotebookInstanceRequest.Builder> describeNotebookInstanceRequest)
Returns information about a notebook instance.
|
default CompletableFuture<DescribeNotebookInstanceResponse> |
describeNotebookInstance(DescribeNotebookInstanceRequest describeNotebookInstanceRequest)
Returns information about a notebook instance.
|
default CompletableFuture<DescribeTrainingJobResponse> |
describeTrainingJob(Consumer<DescribeTrainingJobRequest.Builder> describeTrainingJobRequest)
Returns information about a training job.
|
default CompletableFuture<DescribeTrainingJobResponse> |
describeTrainingJob(DescribeTrainingJobRequest describeTrainingJobRequest)
Returns information about a training job.
|
default CompletableFuture<ListEndpointConfigsResponse> |
listEndpointConfigs()
Lists endpoint configurations.
|
default CompletableFuture<ListEndpointConfigsResponse> |
listEndpointConfigs(Consumer<ListEndpointConfigsRequest.Builder> listEndpointConfigsRequest)
Lists endpoint configurations.
|
default CompletableFuture<ListEndpointConfigsResponse> |
listEndpointConfigs(ListEndpointConfigsRequest listEndpointConfigsRequest)
Lists endpoint configurations.
|
default ListEndpointConfigsPublisher |
listEndpointConfigsPaginator()
Lists endpoint configurations.
|
default ListEndpointConfigsPublisher |
listEndpointConfigsPaginator(Consumer<ListEndpointConfigsRequest.Builder> listEndpointConfigsRequest)
Lists endpoint configurations.
|
default ListEndpointConfigsPublisher |
listEndpointConfigsPaginator(ListEndpointConfigsRequest listEndpointConfigsRequest)
Lists endpoint configurations.
|
default CompletableFuture<ListEndpointsResponse> |
listEndpoints()
Lists endpoints.
|
default CompletableFuture<ListEndpointsResponse> |
listEndpoints(Consumer<ListEndpointsRequest.Builder> listEndpointsRequest)
Lists endpoints.
|
default CompletableFuture<ListEndpointsResponse> |
listEndpoints(ListEndpointsRequest listEndpointsRequest)
Lists endpoints.
|
default ListEndpointsPublisher |
listEndpointsPaginator()
Lists endpoints.
|
default ListEndpointsPublisher |
listEndpointsPaginator(Consumer<ListEndpointsRequest.Builder> listEndpointsRequest)
Lists endpoints.
|
default ListEndpointsPublisher |
listEndpointsPaginator(ListEndpointsRequest listEndpointsRequest)
Lists endpoints.
|
default CompletableFuture<ListModelsResponse> |
listModels()
Lists models created with the CreateModel API.
|
default CompletableFuture<ListModelsResponse> |
listModels(Consumer<ListModelsRequest.Builder> listModelsRequest)
Lists models created with the CreateModel API.
|
default CompletableFuture<ListModelsResponse> |
listModels(ListModelsRequest listModelsRequest)
Lists models created with the CreateModel API.
|
default ListModelsPublisher |
listModelsPaginator()
Lists models created with the CreateModel API.
|
default ListModelsPublisher |
listModelsPaginator(Consumer<ListModelsRequest.Builder> listModelsRequest)
Lists models created with the CreateModel API.
|
default ListModelsPublisher |
listModelsPaginator(ListModelsRequest listModelsRequest)
Lists models created with the CreateModel API.
|
default CompletableFuture<ListNotebookInstancesResponse> |
listNotebookInstances()
Returns a list of the Amazon SageMaker notebook instances in the requester's account in an AWS Region.
|
default CompletableFuture<ListNotebookInstancesResponse> |
listNotebookInstances(Consumer<ListNotebookInstancesRequest.Builder> listNotebookInstancesRequest)
Returns a list of the Amazon SageMaker notebook instances in the requester's account in an AWS Region.
|
default CompletableFuture<ListNotebookInstancesResponse> |
listNotebookInstances(ListNotebookInstancesRequest listNotebookInstancesRequest)
Returns a list of the Amazon SageMaker notebook instances in the requester's account in an AWS Region.
|
default ListNotebookInstancesPublisher |
listNotebookInstancesPaginator()
Returns a list of the Amazon SageMaker notebook instances in the requester's account in an AWS Region.
|
default ListNotebookInstancesPublisher |
listNotebookInstancesPaginator(Consumer<ListNotebookInstancesRequest.Builder> listNotebookInstancesRequest)
Returns a list of the Amazon SageMaker notebook instances in the requester's account in an AWS Region.
|
default ListNotebookInstancesPublisher |
listNotebookInstancesPaginator(ListNotebookInstancesRequest listNotebookInstancesRequest)
Returns a list of the Amazon SageMaker notebook instances in the requester's account in an AWS Region.
|
default CompletableFuture<ListTagsResponse> |
listTags(Consumer<ListTagsRequest.Builder> listTagsRequest)
Returns the tags for the specified Amazon SageMaker resource.
|
default CompletableFuture<ListTagsResponse> |
listTags(ListTagsRequest listTagsRequest)
Returns the tags for the specified Amazon SageMaker resource.
|
default ListTagsPublisher |
listTagsPaginator(Consumer<ListTagsRequest.Builder> listTagsRequest)
Returns the tags for the specified Amazon SageMaker resource.
|
default ListTagsPublisher |
listTagsPaginator(ListTagsRequest listTagsRequest)
Returns the tags for the specified Amazon SageMaker resource.
|
default CompletableFuture<ListTrainingJobsResponse> |
listTrainingJobs()
Lists training jobs.
|
default CompletableFuture<ListTrainingJobsResponse> |
listTrainingJobs(Consumer<ListTrainingJobsRequest.Builder> listTrainingJobsRequest)
Lists training jobs.
|
default CompletableFuture<ListTrainingJobsResponse> |
listTrainingJobs(ListTrainingJobsRequest listTrainingJobsRequest)
Lists training jobs.
|
default ListTrainingJobsPublisher |
listTrainingJobsPaginator()
Lists training jobs.
|
default ListTrainingJobsPublisher |
listTrainingJobsPaginator(Consumer<ListTrainingJobsRequest.Builder> listTrainingJobsRequest)
Lists training jobs.
|
default ListTrainingJobsPublisher |
listTrainingJobsPaginator(ListTrainingJobsRequest listTrainingJobsRequest)
Lists training jobs.
|
default CompletableFuture<StartNotebookInstanceResponse> |
startNotebookInstance(Consumer<StartNotebookInstanceRequest.Builder> startNotebookInstanceRequest)
Launches an ML compute instance with the latest version of the libraries and attaches your ML storage volume.
|
default CompletableFuture<StartNotebookInstanceResponse> |
startNotebookInstance(StartNotebookInstanceRequest startNotebookInstanceRequest)
Launches an ML compute instance with the latest version of the libraries and attaches your ML storage volume.
|
default CompletableFuture<StopNotebookInstanceResponse> |
stopNotebookInstance(Consumer<StopNotebookInstanceRequest.Builder> stopNotebookInstanceRequest)
Terminates the ML compute instance.
|
default CompletableFuture<StopNotebookInstanceResponse> |
stopNotebookInstance(StopNotebookInstanceRequest stopNotebookInstanceRequest)
Terminates the ML compute instance.
|
default CompletableFuture<StopTrainingJobResponse> |
stopTrainingJob(Consumer<StopTrainingJobRequest.Builder> stopTrainingJobRequest)
Stops a training job.
|
default CompletableFuture<StopTrainingJobResponse> |
stopTrainingJob(StopTrainingJobRequest stopTrainingJobRequest)
Stops a training job.
|
default CompletableFuture<UpdateEndpointResponse> |
updateEndpoint(Consumer<UpdateEndpointRequest.Builder> updateEndpointRequest)
Deploys the new
EndpointConfig specified in the request, switches to using newly created endpoint,
and then deletes resources provisioned for the endpoint using the previous EndpointConfig (there is
no availability loss). |
default CompletableFuture<UpdateEndpointResponse> |
updateEndpoint(UpdateEndpointRequest updateEndpointRequest)
Deploys the new
EndpointConfig specified in the request, switches to using newly created endpoint,
and then deletes resources provisioned for the endpoint using the previous EndpointConfig (there is
no availability loss). |
default CompletableFuture<UpdateEndpointWeightsAndCapacitiesResponse> |
updateEndpointWeightsAndCapacities(Consumer<UpdateEndpointWeightsAndCapacitiesRequest.Builder> updateEndpointWeightsAndCapacitiesRequest)
Updates variant weight, capacity, or both of one or more variants associated with an endpoint.
|
default CompletableFuture<UpdateEndpointWeightsAndCapacitiesResponse> |
updateEndpointWeightsAndCapacities(UpdateEndpointWeightsAndCapacitiesRequest updateEndpointWeightsAndCapacitiesRequest)
Updates variant weight, capacity, or both of one or more variants associated with an endpoint.
|
default CompletableFuture<UpdateNotebookInstanceResponse> |
updateNotebookInstance(Consumer<UpdateNotebookInstanceRequest.Builder> updateNotebookInstanceRequest)
Updates a notebook instance.
|
default CompletableFuture<UpdateNotebookInstanceResponse> |
updateNotebookInstance(UpdateNotebookInstanceRequest updateNotebookInstanceRequest)
Updates a notebook instance.
|
serviceName
close
static final String SERVICE_NAME
static SageMakerAsyncClient create()
SageMakerAsyncClient
with the region loaded from the
DefaultAwsRegionProviderChain
and credentials loaded from the
DefaultCredentialsProvider
.static SageMakerAsyncClientBuilder builder()
SageMakerAsyncClient
.default CompletableFuture<AddTagsResponse> addTags(AddTagsRequest addTagsRequest)
Adds or overwrites one or more tags for the specified Amazon SageMaker resource. You can add tags to notebook instances, training jobs, models, endpoint configurations, and endpoints.
Each tag consists of a key and an optional value. Tag keys must be unique per resource. For more information about tags, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.
addTagsRequest
- default CompletableFuture<AddTagsResponse> addTags(Consumer<AddTagsRequest.Builder> addTagsRequest)
Adds or overwrites one or more tags for the specified Amazon SageMaker resource. You can add tags to notebook instances, training jobs, models, endpoint configurations, and endpoints.
Each tag consists of a key and an optional value. Tag keys must be unique per resource. For more information about tags, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.
This is a convenience which creates an instance of the AddTagsRequest.Builder
avoiding the need to create
one manually via AddTagsRequest.builder()
addTagsRequest
- A Consumer
that will call methods on AddTagsInput.Builder
to create a request.default CompletableFuture<CreateEndpointResponse> createEndpoint(CreateEndpointRequest createEndpointRequest)
Creates an endpoint using the endpoint configuration specified in the request. Amazon SageMaker uses the endpoint to provision resources and deploy models. You create the endpoint configuration with the CreateEndpointConfig API.
Use this API only for hosting models using Amazon SageMaker hosting services.
The endpoint name must be unique within an AWS Region in your AWS account.
When it receives the request, Amazon SageMaker creates the endpoint, launches the resources (ML compute instances), and deploys the model(s) on them.
When Amazon SageMaker receives the request, it sets the endpoint status to Creating
. After it
creates the endpoint, it sets the status to InService
. Amazon SageMaker can then process incoming
requests for inferences. To check the status of an endpoint, use the DescribeEndpoint API.
For an example, see Exercise 1: Using the K-Means Algorithm Provided by Amazon SageMaker.
createEndpointRequest
- default CompletableFuture<CreateEndpointResponse> createEndpoint(Consumer<CreateEndpointRequest.Builder> createEndpointRequest)
Creates an endpoint using the endpoint configuration specified in the request. Amazon SageMaker uses the endpoint to provision resources and deploy models. You create the endpoint configuration with the CreateEndpointConfig API.
Use this API only for hosting models using Amazon SageMaker hosting services.
The endpoint name must be unique within an AWS Region in your AWS account.
When it receives the request, Amazon SageMaker creates the endpoint, launches the resources (ML compute instances), and deploys the model(s) on them.
When Amazon SageMaker receives the request, it sets the endpoint status to Creating
. After it
creates the endpoint, it sets the status to InService
. Amazon SageMaker can then process incoming
requests for inferences. To check the status of an endpoint, use the DescribeEndpoint API.
For an example, see Exercise 1: Using the K-Means Algorithm Provided by Amazon SageMaker.
This is a convenience which creates an instance of the CreateEndpointRequest.Builder
avoiding the need to
create one manually via CreateEndpointRequest.builder()
createEndpointRequest
- A Consumer
that will call methods on CreateEndpointInput.Builder
to create a request.default CompletableFuture<CreateEndpointConfigResponse> createEndpointConfig(CreateEndpointConfigRequest createEndpointConfigRequest)
Creates an endpoint configuration that Amazon SageMaker hosting services uses to deploy models. In the
configuration, you identify one or more models, created using the CreateModel
API, to deploy and the
resources that you want Amazon SageMaker to provision. Then you call the CreateEndpoint API.
Use this API only if you want to use Amazon SageMaker hosting services to deploy models into production.
In the request, you define one or more ProductionVariant
s, each of which identifies a model. Each
ProductionVariant
parameter also describes the resources that you want Amazon SageMaker to
provision. This includes the number and type of ML compute instances to deploy.
If you are hosting multiple models, you also assign a VariantWeight
to specify how much traffic you
want to allocate to each model. For example, suppose that you want to host two models, A and B, and you assign
traffic weight 2 for model A and 1 for model B. Amazon SageMaker distributes two-thirds of the traffic to Model
A, and one-third to model B.
createEndpointConfigRequest
- default CompletableFuture<CreateEndpointConfigResponse> createEndpointConfig(Consumer<CreateEndpointConfigRequest.Builder> createEndpointConfigRequest)
Creates an endpoint configuration that Amazon SageMaker hosting services uses to deploy models. In the
configuration, you identify one or more models, created using the CreateModel
API, to deploy and the
resources that you want Amazon SageMaker to provision. Then you call the CreateEndpoint API.
Use this API only if you want to use Amazon SageMaker hosting services to deploy models into production.
In the request, you define one or more ProductionVariant
s, each of which identifies a model. Each
ProductionVariant
parameter also describes the resources that you want Amazon SageMaker to
provision. This includes the number and type of ML compute instances to deploy.
If you are hosting multiple models, you also assign a VariantWeight
to specify how much traffic you
want to allocate to each model. For example, suppose that you want to host two models, A and B, and you assign
traffic weight 2 for model A and 1 for model B. Amazon SageMaker distributes two-thirds of the traffic to Model
A, and one-third to model B.
This is a convenience which creates an instance of the CreateEndpointConfigRequest.Builder
avoiding the
need to create one manually via CreateEndpointConfigRequest.builder()
createEndpointConfigRequest
- A Consumer
that will call methods on CreateEndpointConfigInput.Builder
to create a
request.default CompletableFuture<CreateModelResponse> createModel(CreateModelRequest createModelRequest)
Creates a model in Amazon SageMaker. In the request, you name the model and describe one or more containers. For each container, you specify the docker image containing inference code, artifacts (from prior training), and custom environment map that the inference code uses when you deploy the model into production.
Use this API to create a model only if you want to use Amazon SageMaker hosting services. To host your model, you
create an endpoint configuration with the CreateEndpointConfig
API, and then create an endpoint with
the CreateEndpoint
API.
Amazon SageMaker then deploys all of the containers that you defined for the model in the hosting environment.
In the CreateModel
request, you must define a container with the PrimaryContainer
parameter.
In the request, you also provide an IAM role that Amazon SageMaker can assume to access model artifacts and docker image for deployment on ML compute hosting instances. In addition, you also use the IAM role to manage permissions the inference code needs. For example, if the inference code access any other AWS resources, you grant necessary permissions via this role.
createModelRequest
- default CompletableFuture<CreateModelResponse> createModel(Consumer<CreateModelRequest.Builder> createModelRequest)
Creates a model in Amazon SageMaker. In the request, you name the model and describe one or more containers. For each container, you specify the docker image containing inference code, artifacts (from prior training), and custom environment map that the inference code uses when you deploy the model into production.
Use this API to create a model only if you want to use Amazon SageMaker hosting services. To host your model, you
create an endpoint configuration with the CreateEndpointConfig
API, and then create an endpoint with
the CreateEndpoint
API.
Amazon SageMaker then deploys all of the containers that you defined for the model in the hosting environment.
In the CreateModel
request, you must define a container with the PrimaryContainer
parameter.
In the request, you also provide an IAM role that Amazon SageMaker can assume to access model artifacts and docker image for deployment on ML compute hosting instances. In addition, you also use the IAM role to manage permissions the inference code needs. For example, if the inference code access any other AWS resources, you grant necessary permissions via this role.
This is a convenience which creates an instance of the CreateModelRequest.Builder
avoiding the need to
create one manually via CreateModelRequest.builder()
createModelRequest
- A Consumer
that will call methods on CreateModelInput.Builder
to create a request.default CompletableFuture<CreateNotebookInstanceResponse> createNotebookInstance(CreateNotebookInstanceRequest createNotebookInstanceRequest)
Creates an Amazon SageMaker notebook instance. A notebook instance is an ML compute instance running on a Jupyter notebook.
In a CreateNotebookInstance
request, you specify the type of ML compute instance that you want to
run. Amazon SageMaker launches the instance, installs common libraries that you can use to explore datasets for
model training, and attaches an ML storage volume to the notebook instance.
Amazon SageMaker also provides a set of example notebooks. Each notebook demonstrates how to use Amazon SageMaker with a specific an algorithm or with a machine learning framework.
After receiving the request, Amazon SageMaker does the following:
Creates a network interface in the Amazon SageMaker VPC.
(Option) If you specified SubnetId
, creates a network interface in your own VPC, which is inferred
from the subnet ID that you provide in the input. When creating this network interface, Amazon SageMaker attaches
the security group that you specified in the request to the network interface that it creates in your VPC.
Launches an EC2 instance of the type specified in the request in the Amazon SageMaker VPC. If you specified
SubnetId
of your VPC, Amazon SageMaker specifies both network interfaces when launching this
instance. This enables inbound traffic from your own VPC to the notebook instance, assuming that the security
groups allow it.
After creating the notebook instance, Amazon SageMaker returns its Amazon Resource Name (ARN).
After Amazon SageMaker creates the notebook instance, you can connect to the Jupyter server and work in Jupyter notebooks. For example, you can write code to explore a dataset that you can use for model training, train a model, host models by creating Amazon SageMaker endpoints, and validate hosted models.
For more information, see How It Works.
createNotebookInstanceRequest
- default CompletableFuture<CreateNotebookInstanceResponse> createNotebookInstance(Consumer<CreateNotebookInstanceRequest.Builder> createNotebookInstanceRequest)
Creates an Amazon SageMaker notebook instance. A notebook instance is an ML compute instance running on a Jupyter notebook.
In a CreateNotebookInstance
request, you specify the type of ML compute instance that you want to
run. Amazon SageMaker launches the instance, installs common libraries that you can use to explore datasets for
model training, and attaches an ML storage volume to the notebook instance.
Amazon SageMaker also provides a set of example notebooks. Each notebook demonstrates how to use Amazon SageMaker with a specific an algorithm or with a machine learning framework.
After receiving the request, Amazon SageMaker does the following:
Creates a network interface in the Amazon SageMaker VPC.
(Option) If you specified SubnetId
, creates a network interface in your own VPC, which is inferred
from the subnet ID that you provide in the input. When creating this network interface, Amazon SageMaker attaches
the security group that you specified in the request to the network interface that it creates in your VPC.
Launches an EC2 instance of the type specified in the request in the Amazon SageMaker VPC. If you specified
SubnetId
of your VPC, Amazon SageMaker specifies both network interfaces when launching this
instance. This enables inbound traffic from your own VPC to the notebook instance, assuming that the security
groups allow it.
After creating the notebook instance, Amazon SageMaker returns its Amazon Resource Name (ARN).
After Amazon SageMaker creates the notebook instance, you can connect to the Jupyter server and work in Jupyter notebooks. For example, you can write code to explore a dataset that you can use for model training, train a model, host models by creating Amazon SageMaker endpoints, and validate hosted models.
For more information, see How It Works.
This is a convenience which creates an instance of the CreateNotebookInstanceRequest.Builder
avoiding the
need to create one manually via CreateNotebookInstanceRequest.builder()
createNotebookInstanceRequest
- A Consumer
that will call methods on CreateNotebookInstanceInput.Builder
to create a
request.default CompletableFuture<CreatePresignedNotebookInstanceUrlResponse> createPresignedNotebookInstanceUrl(CreatePresignedNotebookInstanceUrlRequest createPresignedNotebookInstanceUrlRequest)
Returns a URL that you can use to connect to the Juypter server from a notebook instance. In the Amazon SageMaker
console, when you choose Open
next to a notebook instance, Amazon SageMaker opens a new tab showing
the Jupyter server home page from the notebook instance. The console uses this API to get the URL and show the
page.
createPresignedNotebookInstanceUrlRequest
- default CompletableFuture<CreatePresignedNotebookInstanceUrlResponse> createPresignedNotebookInstanceUrl(Consumer<CreatePresignedNotebookInstanceUrlRequest.Builder> createPresignedNotebookInstanceUrlRequest)
Returns a URL that you can use to connect to the Juypter server from a notebook instance. In the Amazon SageMaker
console, when you choose Open
next to a notebook instance, Amazon SageMaker opens a new tab showing
the Jupyter server home page from the notebook instance. The console uses this API to get the URL and show the
page.
This is a convenience which creates an instance of the CreatePresignedNotebookInstanceUrlRequest.Builder
avoiding the need to create one manually via CreatePresignedNotebookInstanceUrlRequest.builder()
createPresignedNotebookInstanceUrlRequest
- A Consumer
that will call methods on CreatePresignedNotebookInstanceUrlInput.Builder
to
create a request.default CompletableFuture<CreateTrainingJobResponse> createTrainingJob(CreateTrainingJobRequest createTrainingJobRequest)
Starts a model training job. After training completes, Amazon SageMaker saves the resulting model artifacts to an Amazon S3 location that you specify.
If you choose to host your model using Amazon SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts in a deep learning service other than Amazon SageMaker, provided that you know how to use them for inferences.
In the request body, you provide the following:
AlgorithmSpecification
- Identifies the training algorithm to use.
HyperParameters
- Specify these algorithm-specific parameters to influence the quality of the final
model. For a list of hyperparameters for each training algorithm provided by Amazon SageMaker, see Algorithms.
InputDataConfig
- Describes the training dataset and the Amazon S3 location where it is stored.
OutputDataConfig
- Identifies the Amazon S3 location where you want Amazon SageMaker to save the
results of model training.
ResourceConfig
- Identifies the resources, ML compute instances, and ML storage volumes to deploy
for model training. In distributed training, you specify more than one instance.
RoleARN
- The Amazon Resource Number (ARN) that Amazon SageMaker assumes to perform tasks on your
behalf during model training. You must grant this role the necessary permissions so that Amazon SageMaker can
successfully complete model training.
StoppingCondition
- Sets a duration for training. Use this parameter to cap model training costs.
For more information about Amazon SageMaker, see How It Works.
createTrainingJobRequest
- default CompletableFuture<CreateTrainingJobResponse> createTrainingJob(Consumer<CreateTrainingJobRequest.Builder> createTrainingJobRequest)
Starts a model training job. After training completes, Amazon SageMaker saves the resulting model artifacts to an Amazon S3 location that you specify.
If you choose to host your model using Amazon SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts in a deep learning service other than Amazon SageMaker, provided that you know how to use them for inferences.
In the request body, you provide the following:
AlgorithmSpecification
- Identifies the training algorithm to use.
HyperParameters
- Specify these algorithm-specific parameters to influence the quality of the final
model. For a list of hyperparameters for each training algorithm provided by Amazon SageMaker, see Algorithms.
InputDataConfig
- Describes the training dataset and the Amazon S3 location where it is stored.
OutputDataConfig
- Identifies the Amazon S3 location where you want Amazon SageMaker to save the
results of model training.
ResourceConfig
- Identifies the resources, ML compute instances, and ML storage volumes to deploy
for model training. In distributed training, you specify more than one instance.
RoleARN
- The Amazon Resource Number (ARN) that Amazon SageMaker assumes to perform tasks on your
behalf during model training. You must grant this role the necessary permissions so that Amazon SageMaker can
successfully complete model training.
StoppingCondition
- Sets a duration for training. Use this parameter to cap model training costs.
For more information about Amazon SageMaker, see How It Works.
This is a convenience which creates an instance of the CreateTrainingJobRequest.Builder
avoiding the need
to create one manually via CreateTrainingJobRequest.builder()
createTrainingJobRequest
- A Consumer
that will call methods on CreateTrainingJobRequest.Builder
to create a request.default CompletableFuture<DeleteEndpointResponse> deleteEndpoint(DeleteEndpointRequest deleteEndpointRequest)
Deletes an endpoint. Amazon SageMaker frees up all of the resources that were deployed when the endpoint was created.
deleteEndpointRequest
- default CompletableFuture<DeleteEndpointResponse> deleteEndpoint(Consumer<DeleteEndpointRequest.Builder> deleteEndpointRequest)
Deletes an endpoint. Amazon SageMaker frees up all of the resources that were deployed when the endpoint was created.
This is a convenience which creates an instance of the DeleteEndpointRequest.Builder
avoiding the need to
create one manually via DeleteEndpointRequest.builder()
deleteEndpointRequest
- A Consumer
that will call methods on DeleteEndpointInput.Builder
to create a request.default CompletableFuture<DeleteEndpointConfigResponse> deleteEndpointConfig(DeleteEndpointConfigRequest deleteEndpointConfigRequest)
Deletes an endpoint configuration. The DeleteEndpoingConfig
API deletes only the specified
configuration. It does not delete endpoints created using the configuration.
deleteEndpointConfigRequest
- default CompletableFuture<DeleteEndpointConfigResponse> deleteEndpointConfig(Consumer<DeleteEndpointConfigRequest.Builder> deleteEndpointConfigRequest)
Deletes an endpoint configuration. The DeleteEndpoingConfig
API deletes only the specified
configuration. It does not delete endpoints created using the configuration.
This is a convenience which creates an instance of the DeleteEndpointConfigRequest.Builder
avoiding the
need to create one manually via DeleteEndpointConfigRequest.builder()
deleteEndpointConfigRequest
- A Consumer
that will call methods on DeleteEndpointConfigInput.Builder
to create a
request.default CompletableFuture<DeleteModelResponse> deleteModel(DeleteModelRequest deleteModelRequest)
Deletes a model. The DeleteModel
API deletes only the model entry that was created in Amazon
SageMaker when you called the CreateModel API. It does not
delete model artifacts, inference code, or the IAM role that you specified when creating the model.
deleteModelRequest
- default CompletableFuture<DeleteModelResponse> deleteModel(Consumer<DeleteModelRequest.Builder> deleteModelRequest)
Deletes a model. The DeleteModel
API deletes only the model entry that was created in Amazon
SageMaker when you called the CreateModel API. It does not
delete model artifacts, inference code, or the IAM role that you specified when creating the model.
This is a convenience which creates an instance of the DeleteModelRequest.Builder
avoiding the need to
create one manually via DeleteModelRequest.builder()
deleteModelRequest
- A Consumer
that will call methods on DeleteModelInput.Builder
to create a request.default CompletableFuture<DeleteNotebookInstanceResponse> deleteNotebookInstance(DeleteNotebookInstanceRequest deleteNotebookInstanceRequest)
Deletes an Amazon SageMaker notebook instance. Before you can delete a notebook instance, you must call the
StopNotebookInstance
API.
When you delete a notebook instance, you lose all of your data. Amazon SageMaker removes the ML compute instance, and deletes the ML storage volume and the network interface associated with the notebook instance.
deleteNotebookInstanceRequest
- default CompletableFuture<DeleteNotebookInstanceResponse> deleteNotebookInstance(Consumer<DeleteNotebookInstanceRequest.Builder> deleteNotebookInstanceRequest)
Deletes an Amazon SageMaker notebook instance. Before you can delete a notebook instance, you must call the
StopNotebookInstance
API.
When you delete a notebook instance, you lose all of your data. Amazon SageMaker removes the ML compute instance, and deletes the ML storage volume and the network interface associated with the notebook instance.
This is a convenience which creates an instance of the DeleteNotebookInstanceRequest.Builder
avoiding the
need to create one manually via DeleteNotebookInstanceRequest.builder()
deleteNotebookInstanceRequest
- A Consumer
that will call methods on DeleteNotebookInstanceInput.Builder
to create a
request.default CompletableFuture<DeleteTagsResponse> deleteTags(DeleteTagsRequest deleteTagsRequest)
Deletes the specified tags from an Amazon SageMaker resource.
To list a resource's tags, use the ListTags
API.
deleteTagsRequest
- default CompletableFuture<DeleteTagsResponse> deleteTags(Consumer<DeleteTagsRequest.Builder> deleteTagsRequest)
Deletes the specified tags from an Amazon SageMaker resource.
To list a resource's tags, use the ListTags
API.
This is a convenience which creates an instance of the DeleteTagsRequest.Builder
avoiding the need to
create one manually via DeleteTagsRequest.builder()
deleteTagsRequest
- A Consumer
that will call methods on DeleteTagsInput.Builder
to create a request.default CompletableFuture<DescribeEndpointResponse> describeEndpoint(DescribeEndpointRequest describeEndpointRequest)
Returns the description of an endpoint.
describeEndpointRequest
- default CompletableFuture<DescribeEndpointResponse> describeEndpoint(Consumer<DescribeEndpointRequest.Builder> describeEndpointRequest)
Returns the description of an endpoint.
This is a convenience which creates an instance of the DescribeEndpointRequest.Builder
avoiding the need
to create one manually via DescribeEndpointRequest.builder()
describeEndpointRequest
- A Consumer
that will call methods on DescribeEndpointInput.Builder
to create a request.default CompletableFuture<DescribeEndpointConfigResponse> describeEndpointConfig(DescribeEndpointConfigRequest describeEndpointConfigRequest)
Returns the description of an endpoint configuration created using the CreateEndpointConfig
API.
describeEndpointConfigRequest
- default CompletableFuture<DescribeEndpointConfigResponse> describeEndpointConfig(Consumer<DescribeEndpointConfigRequest.Builder> describeEndpointConfigRequest)
Returns the description of an endpoint configuration created using the CreateEndpointConfig
API.
This is a convenience which creates an instance of the DescribeEndpointConfigRequest.Builder
avoiding the
need to create one manually via DescribeEndpointConfigRequest.builder()
describeEndpointConfigRequest
- A Consumer
that will call methods on DescribeEndpointConfigInput.Builder
to create a
request.default CompletableFuture<DescribeModelResponse> describeModel(DescribeModelRequest describeModelRequest)
Describes a model that you created using the CreateModel
API.
describeModelRequest
- default CompletableFuture<DescribeModelResponse> describeModel(Consumer<DescribeModelRequest.Builder> describeModelRequest)
Describes a model that you created using the CreateModel
API.
This is a convenience which creates an instance of the DescribeModelRequest.Builder
avoiding the need to
create one manually via DescribeModelRequest.builder()
describeModelRequest
- A Consumer
that will call methods on DescribeModelInput.Builder
to create a request.default CompletableFuture<DescribeNotebookInstanceResponse> describeNotebookInstance(DescribeNotebookInstanceRequest describeNotebookInstanceRequest)
Returns information about a notebook instance.
describeNotebookInstanceRequest
- default CompletableFuture<DescribeNotebookInstanceResponse> describeNotebookInstance(Consumer<DescribeNotebookInstanceRequest.Builder> describeNotebookInstanceRequest)
Returns information about a notebook instance.
This is a convenience which creates an instance of the DescribeNotebookInstanceRequest.Builder
avoiding
the need to create one manually via DescribeNotebookInstanceRequest.builder()
describeNotebookInstanceRequest
- A Consumer
that will call methods on DescribeNotebookInstanceInput.Builder
to create a
request.default CompletableFuture<DescribeTrainingJobResponse> describeTrainingJob(DescribeTrainingJobRequest describeTrainingJobRequest)
Returns information about a training job.
describeTrainingJobRequest
- default CompletableFuture<DescribeTrainingJobResponse> describeTrainingJob(Consumer<DescribeTrainingJobRequest.Builder> describeTrainingJobRequest)
Returns information about a training job.
This is a convenience which creates an instance of the DescribeTrainingJobRequest.Builder
avoiding the
need to create one manually via DescribeTrainingJobRequest.builder()
describeTrainingJobRequest
- A Consumer
that will call methods on DescribeTrainingJobRequest.Builder
to create a
request.default CompletableFuture<ListEndpointConfigsResponse> listEndpointConfigs(ListEndpointConfigsRequest listEndpointConfigsRequest)
Lists endpoint configurations.
listEndpointConfigsRequest
- default CompletableFuture<ListEndpointConfigsResponse> listEndpointConfigs(Consumer<ListEndpointConfigsRequest.Builder> listEndpointConfigsRequest)
Lists endpoint configurations.
This is a convenience which creates an instance of the ListEndpointConfigsRequest.Builder
avoiding the
need to create one manually via ListEndpointConfigsRequest.builder()
listEndpointConfigsRequest
- A Consumer
that will call methods on ListEndpointConfigsInput.Builder
to create a request.default CompletableFuture<ListEndpointConfigsResponse> listEndpointConfigs()
Lists endpoint configurations.
default ListEndpointConfigsPublisher listEndpointConfigsPaginator()
Lists endpoint configurations.
This is a variant of
listEndpointConfigs(software.amazon.awssdk.services.sagemaker.model.ListEndpointConfigsRequest)
operation. The return type is a custom publisher that can be subscribed to request a stream of response pages.
SDK will internally handle making service calls for you.
When the operation is called, an instance of this class is returned. At this point, no service calls are made yet
and so there is no guarantee that the request is valid. If there are errors in your request, you will see the
failures only after you start streaming the data. The subscribe method should be called as a request to start
streaming data. For more info, see
Publisher.subscribe(org.reactivestreams.Subscriber)
. Each call to the subscribe
method will result in a new Subscription
i.e., a new contract to stream data from the
starting request.
The following are few ways to use the response class:
1) Using the forEach helper method
software.amazon.awssdk.services.sagemaker.paginators.ListEndpointConfigsPublisher publisher = client.listEndpointConfigsPaginator(request);
CompletableFuture<Void> future = publisher.forEach(res -> { // Do something with the response });
future.get();
2) Using a custom subscriber
software.amazon.awssdk.services.sagemaker.paginators.ListEndpointConfigsPublisher publisher = client.listEndpointConfigsPaginator(request);
publisher.subscribe(new Subscriber<software.amazon.awssdk.services.sagemaker.model.ListEndpointConfigsResponse>() {
public void onSubscribe(org.reactivestreams.Subscriber subscription) { //... };
public void onNext(software.amazon.awssdk.services.sagemaker.model.ListEndpointConfigsResponse response) { //... };
});
As the response is a publisher, it can work well with third party reactive streams implementations like RxJava2.
Note: If you prefer to have control on service calls, use the
listEndpointConfigs(software.amazon.awssdk.services.sagemaker.model.ListEndpointConfigsRequest)
operation.
default ListEndpointConfigsPublisher listEndpointConfigsPaginator(ListEndpointConfigsRequest listEndpointConfigsRequest)
Lists endpoint configurations.
This is a variant of
listEndpointConfigs(software.amazon.awssdk.services.sagemaker.model.ListEndpointConfigsRequest)
operation. The return type is a custom publisher that can be subscribed to request a stream of response pages.
SDK will internally handle making service calls for you.
When the operation is called, an instance of this class is returned. At this point, no service calls are made yet
and so there is no guarantee that the request is valid. If there are errors in your request, you will see the
failures only after you start streaming the data. The subscribe method should be called as a request to start
streaming data. For more info, see
Publisher.subscribe(org.reactivestreams.Subscriber)
. Each call to the subscribe
method will result in a new Subscription
i.e., a new contract to stream data from the
starting request.
The following are few ways to use the response class:
1) Using the forEach helper method
software.amazon.awssdk.services.sagemaker.paginators.ListEndpointConfigsPublisher publisher = client.listEndpointConfigsPaginator(request);
CompletableFuture<Void> future = publisher.forEach(res -> { // Do something with the response });
future.get();
2) Using a custom subscriber
software.amazon.awssdk.services.sagemaker.paginators.ListEndpointConfigsPublisher publisher = client.listEndpointConfigsPaginator(request);
publisher.subscribe(new Subscriber<software.amazon.awssdk.services.sagemaker.model.ListEndpointConfigsResponse>() {
public void onSubscribe(org.reactivestreams.Subscriber subscription) { //... };
public void onNext(software.amazon.awssdk.services.sagemaker.model.ListEndpointConfigsResponse response) { //... };
});
As the response is a publisher, it can work well with third party reactive streams implementations like RxJava2.
Note: If you prefer to have control on service calls, use the
listEndpointConfigs(software.amazon.awssdk.services.sagemaker.model.ListEndpointConfigsRequest)
operation.
listEndpointConfigsRequest
- default ListEndpointConfigsPublisher listEndpointConfigsPaginator(Consumer<ListEndpointConfigsRequest.Builder> listEndpointConfigsRequest)
Lists endpoint configurations.
This is a variant of
listEndpointConfigs(software.amazon.awssdk.services.sagemaker.model.ListEndpointConfigsRequest)
operation. The return type is a custom publisher that can be subscribed to request a stream of response pages.
SDK will internally handle making service calls for you.
When the operation is called, an instance of this class is returned. At this point, no service calls are made yet
and so there is no guarantee that the request is valid. If there are errors in your request, you will see the
failures only after you start streaming the data. The subscribe method should be called as a request to start
streaming data. For more info, see
Publisher.subscribe(org.reactivestreams.Subscriber)
. Each call to the subscribe
method will result in a new Subscription
i.e., a new contract to stream data from the
starting request.
The following are few ways to use the response class:
1) Using the forEach helper method
software.amazon.awssdk.services.sagemaker.paginators.ListEndpointConfigsPublisher publisher = client.listEndpointConfigsPaginator(request);
CompletableFuture<Void> future = publisher.forEach(res -> { // Do something with the response });
future.get();
2) Using a custom subscriber
software.amazon.awssdk.services.sagemaker.paginators.ListEndpointConfigsPublisher publisher = client.listEndpointConfigsPaginator(request);
publisher.subscribe(new Subscriber<software.amazon.awssdk.services.sagemaker.model.ListEndpointConfigsResponse>() {
public void onSubscribe(org.reactivestreams.Subscriber subscription) { //... };
public void onNext(software.amazon.awssdk.services.sagemaker.model.ListEndpointConfigsResponse response) { //... };
});
As the response is a publisher, it can work well with third party reactive streams implementations like RxJava2.
Note: If you prefer to have control on service calls, use the
listEndpointConfigs(software.amazon.awssdk.services.sagemaker.model.ListEndpointConfigsRequest)
operation.
This is a convenience which creates an instance of the ListEndpointConfigsRequest.Builder
avoiding the
need to create one manually via ListEndpointConfigsRequest.builder()
listEndpointConfigsRequest
- A Consumer
that will call methods on ListEndpointConfigsInput.Builder
to create a request.default CompletableFuture<ListEndpointsResponse> listEndpoints(ListEndpointsRequest listEndpointsRequest)
Lists endpoints.
listEndpointsRequest
- default CompletableFuture<ListEndpointsResponse> listEndpoints(Consumer<ListEndpointsRequest.Builder> listEndpointsRequest)
Lists endpoints.
This is a convenience which creates an instance of the ListEndpointsRequest.Builder
avoiding the need to
create one manually via ListEndpointsRequest.builder()
listEndpointsRequest
- A Consumer
that will call methods on ListEndpointsInput.Builder
to create a request.default CompletableFuture<ListEndpointsResponse> listEndpoints()
Lists endpoints.
default ListEndpointsPublisher listEndpointsPaginator()
Lists endpoints.
This is a variant of listEndpoints(software.amazon.awssdk.services.sagemaker.model.ListEndpointsRequest)
operation. The return type is a custom publisher that can be subscribed to request a stream of response pages.
SDK will internally handle making service calls for you.
When the operation is called, an instance of this class is returned. At this point, no service calls are made yet
and so there is no guarantee that the request is valid. If there are errors in your request, you will see the
failures only after you start streaming the data. The subscribe method should be called as a request to start
streaming data. For more info, see
Publisher.subscribe(org.reactivestreams.Subscriber)
. Each call to the subscribe
method will result in a new Subscription
i.e., a new contract to stream data from the
starting request.
The following are few ways to use the response class:
1) Using the forEach helper method
software.amazon.awssdk.services.sagemaker.paginators.ListEndpointsPublisher publisher = client.listEndpointsPaginator(request);
CompletableFuture<Void> future = publisher.forEach(res -> { // Do something with the response });
future.get();
2) Using a custom subscriber
software.amazon.awssdk.services.sagemaker.paginators.ListEndpointsPublisher publisher = client.listEndpointsPaginator(request);
publisher.subscribe(new Subscriber<software.amazon.awssdk.services.sagemaker.model.ListEndpointsResponse>() {
public void onSubscribe(org.reactivestreams.Subscriber subscription) { //... };
public void onNext(software.amazon.awssdk.services.sagemaker.model.ListEndpointsResponse response) { //... };
});
As the response is a publisher, it can work well with third party reactive streams implementations like RxJava2.
Note: If you prefer to have control on service calls, use the
listEndpoints(software.amazon.awssdk.services.sagemaker.model.ListEndpointsRequest)
operation.
default ListEndpointsPublisher listEndpointsPaginator(ListEndpointsRequest listEndpointsRequest)
Lists endpoints.
This is a variant of listEndpoints(software.amazon.awssdk.services.sagemaker.model.ListEndpointsRequest)
operation. The return type is a custom publisher that can be subscribed to request a stream of response pages.
SDK will internally handle making service calls for you.
When the operation is called, an instance of this class is returned. At this point, no service calls are made yet
and so there is no guarantee that the request is valid. If there are errors in your request, you will see the
failures only after you start streaming the data. The subscribe method should be called as a request to start
streaming data. For more info, see
Publisher.subscribe(org.reactivestreams.Subscriber)
. Each call to the subscribe
method will result in a new Subscription
i.e., a new contract to stream data from the
starting request.
The following are few ways to use the response class:
1) Using the forEach helper method
software.amazon.awssdk.services.sagemaker.paginators.ListEndpointsPublisher publisher = client.listEndpointsPaginator(request);
CompletableFuture<Void> future = publisher.forEach(res -> { // Do something with the response });
future.get();
2) Using a custom subscriber
software.amazon.awssdk.services.sagemaker.paginators.ListEndpointsPublisher publisher = client.listEndpointsPaginator(request);
publisher.subscribe(new Subscriber<software.amazon.awssdk.services.sagemaker.model.ListEndpointsResponse>() {
public void onSubscribe(org.reactivestreams.Subscriber subscription) { //... };
public void onNext(software.amazon.awssdk.services.sagemaker.model.ListEndpointsResponse response) { //... };
});
As the response is a publisher, it can work well with third party reactive streams implementations like RxJava2.
Note: If you prefer to have control on service calls, use the
listEndpoints(software.amazon.awssdk.services.sagemaker.model.ListEndpointsRequest)
operation.
listEndpointsRequest
- default ListEndpointsPublisher listEndpointsPaginator(Consumer<ListEndpointsRequest.Builder> listEndpointsRequest)
Lists endpoints.
This is a variant of listEndpoints(software.amazon.awssdk.services.sagemaker.model.ListEndpointsRequest)
operation. The return type is a custom publisher that can be subscribed to request a stream of response pages.
SDK will internally handle making service calls for you.
When the operation is called, an instance of this class is returned. At this point, no service calls are made yet
and so there is no guarantee that the request is valid. If there are errors in your request, you will see the
failures only after you start streaming the data. The subscribe method should be called as a request to start
streaming data. For more info, see
Publisher.subscribe(org.reactivestreams.Subscriber)
. Each call to the subscribe
method will result in a new Subscription
i.e., a new contract to stream data from the
starting request.
The following are few ways to use the response class:
1) Using the forEach helper method
software.amazon.awssdk.services.sagemaker.paginators.ListEndpointsPublisher publisher = client.listEndpointsPaginator(request);
CompletableFuture<Void> future = publisher.forEach(res -> { // Do something with the response });
future.get();
2) Using a custom subscriber
software.amazon.awssdk.services.sagemaker.paginators.ListEndpointsPublisher publisher = client.listEndpointsPaginator(request);
publisher.subscribe(new Subscriber<software.amazon.awssdk.services.sagemaker.model.ListEndpointsResponse>() {
public void onSubscribe(org.reactivestreams.Subscriber subscription) { //... };
public void onNext(software.amazon.awssdk.services.sagemaker.model.ListEndpointsResponse response) { //... };
});
As the response is a publisher, it can work well with third party reactive streams implementations like RxJava2.
Note: If you prefer to have control on service calls, use the
listEndpoints(software.amazon.awssdk.services.sagemaker.model.ListEndpointsRequest)
operation.
This is a convenience which creates an instance of the ListEndpointsRequest.Builder
avoiding the need to
create one manually via ListEndpointsRequest.builder()
listEndpointsRequest
- A Consumer
that will call methods on ListEndpointsInput.Builder
to create a request.default CompletableFuture<ListModelsResponse> listModels(ListModelsRequest listModelsRequest)
Lists models created with the CreateModel API.
listModelsRequest
- default CompletableFuture<ListModelsResponse> listModels(Consumer<ListModelsRequest.Builder> listModelsRequest)
Lists models created with the CreateModel API.
This is a convenience which creates an instance of the ListModelsRequest.Builder
avoiding the need to
create one manually via ListModelsRequest.builder()
listModelsRequest
- A Consumer
that will call methods on ListModelsInput.Builder
to create a request.default CompletableFuture<ListModelsResponse> listModels()
Lists models created with the CreateModel API.
default ListModelsPublisher listModelsPaginator()
Lists models created with the CreateModel API.
This is a variant of listModels(software.amazon.awssdk.services.sagemaker.model.ListModelsRequest)
operation. The return type is a custom publisher that can be subscribed to request a stream of response pages.
SDK will internally handle making service calls for you.
When the operation is called, an instance of this class is returned. At this point, no service calls are made yet
and so there is no guarantee that the request is valid. If there are errors in your request, you will see the
failures only after you start streaming the data. The subscribe method should be called as a request to start
streaming data. For more info, see
Publisher.subscribe(org.reactivestreams.Subscriber)
. Each call to the subscribe
method will result in a new Subscription
i.e., a new contract to stream data from the
starting request.
The following are few ways to use the response class:
1) Using the forEach helper method
software.amazon.awssdk.services.sagemaker.paginators.ListModelsPublisher publisher = client.listModelsPaginator(request);
CompletableFuture<Void> future = publisher.forEach(res -> { // Do something with the response });
future.get();
2) Using a custom subscriber
software.amazon.awssdk.services.sagemaker.paginators.ListModelsPublisher publisher = client.listModelsPaginator(request);
publisher.subscribe(new Subscriber<software.amazon.awssdk.services.sagemaker.model.ListModelsResponse>() {
public void onSubscribe(org.reactivestreams.Subscriber subscription) { //... };
public void onNext(software.amazon.awssdk.services.sagemaker.model.ListModelsResponse response) { //... };
});
As the response is a publisher, it can work well with third party reactive streams implementations like RxJava2.
Note: If you prefer to have control on service calls, use the
listModels(software.amazon.awssdk.services.sagemaker.model.ListModelsRequest)
operation.
default ListModelsPublisher listModelsPaginator(ListModelsRequest listModelsRequest)
Lists models created with the CreateModel API.
This is a variant of listModels(software.amazon.awssdk.services.sagemaker.model.ListModelsRequest)
operation. The return type is a custom publisher that can be subscribed to request a stream of response pages.
SDK will internally handle making service calls for you.
When the operation is called, an instance of this class is returned. At this point, no service calls are made yet
and so there is no guarantee that the request is valid. If there are errors in your request, you will see the
failures only after you start streaming the data. The subscribe method should be called as a request to start
streaming data. For more info, see
Publisher.subscribe(org.reactivestreams.Subscriber)
. Each call to the subscribe
method will result in a new Subscription
i.e., a new contract to stream data from the
starting request.
The following are few ways to use the response class:
1) Using the forEach helper method
software.amazon.awssdk.services.sagemaker.paginators.ListModelsPublisher publisher = client.listModelsPaginator(request);
CompletableFuture<Void> future = publisher.forEach(res -> { // Do something with the response });
future.get();
2) Using a custom subscriber
software.amazon.awssdk.services.sagemaker.paginators.ListModelsPublisher publisher = client.listModelsPaginator(request);
publisher.subscribe(new Subscriber<software.amazon.awssdk.services.sagemaker.model.ListModelsResponse>() {
public void onSubscribe(org.reactivestreams.Subscriber subscription) { //... };
public void onNext(software.amazon.awssdk.services.sagemaker.model.ListModelsResponse response) { //... };
});
As the response is a publisher, it can work well with third party reactive streams implementations like RxJava2.
Note: If you prefer to have control on service calls, use the
listModels(software.amazon.awssdk.services.sagemaker.model.ListModelsRequest)
operation.
listModelsRequest
- default ListModelsPublisher listModelsPaginator(Consumer<ListModelsRequest.Builder> listModelsRequest)
Lists models created with the CreateModel API.
This is a variant of listModels(software.amazon.awssdk.services.sagemaker.model.ListModelsRequest)
operation. The return type is a custom publisher that can be subscribed to request a stream of response pages.
SDK will internally handle making service calls for you.
When the operation is called, an instance of this class is returned. At this point, no service calls are made yet
and so there is no guarantee that the request is valid. If there are errors in your request, you will see the
failures only after you start streaming the data. The subscribe method should be called as a request to start
streaming data. For more info, see
Publisher.subscribe(org.reactivestreams.Subscriber)
. Each call to the subscribe
method will result in a new Subscription
i.e., a new contract to stream data from the
starting request.
The following are few ways to use the response class:
1) Using the forEach helper method
software.amazon.awssdk.services.sagemaker.paginators.ListModelsPublisher publisher = client.listModelsPaginator(request);
CompletableFuture<Void> future = publisher.forEach(res -> { // Do something with the response });
future.get();
2) Using a custom subscriber
software.amazon.awssdk.services.sagemaker.paginators.ListModelsPublisher publisher = client.listModelsPaginator(request);
publisher.subscribe(new Subscriber<software.amazon.awssdk.services.sagemaker.model.ListModelsResponse>() {
public void onSubscribe(org.reactivestreams.Subscriber subscription) { //... };
public void onNext(software.amazon.awssdk.services.sagemaker.model.ListModelsResponse response) { //... };
});
As the response is a publisher, it can work well with third party reactive streams implementations like RxJava2.
Note: If you prefer to have control on service calls, use the
listModels(software.amazon.awssdk.services.sagemaker.model.ListModelsRequest)
operation.
This is a convenience which creates an instance of the ListModelsRequest.Builder
avoiding the need to
create one manually via ListModelsRequest.builder()
listModelsRequest
- A Consumer
that will call methods on ListModelsInput.Builder
to create a request.default CompletableFuture<ListNotebookInstancesResponse> listNotebookInstances(ListNotebookInstancesRequest listNotebookInstancesRequest)
Returns a list of the Amazon SageMaker notebook instances in the requester's account in an AWS Region.
listNotebookInstancesRequest
- default CompletableFuture<ListNotebookInstancesResponse> listNotebookInstances(Consumer<ListNotebookInstancesRequest.Builder> listNotebookInstancesRequest)
Returns a list of the Amazon SageMaker notebook instances in the requester's account in an AWS Region.
This is a convenience which creates an instance of the ListNotebookInstancesRequest.Builder
avoiding the
need to create one manually via ListNotebookInstancesRequest.builder()
listNotebookInstancesRequest
- A Consumer
that will call methods on ListNotebookInstancesInput.Builder
to create a
request.default CompletableFuture<ListNotebookInstancesResponse> listNotebookInstances()
Returns a list of the Amazon SageMaker notebook instances in the requester's account in an AWS Region.
default ListNotebookInstancesPublisher listNotebookInstancesPaginator()
Returns a list of the Amazon SageMaker notebook instances in the requester's account in an AWS Region.
This is a variant of
listNotebookInstances(software.amazon.awssdk.services.sagemaker.model.ListNotebookInstancesRequest)
operation. The return type is a custom publisher that can be subscribed to request a stream of response pages.
SDK will internally handle making service calls for you.
When the operation is called, an instance of this class is returned. At this point, no service calls are made yet
and so there is no guarantee that the request is valid. If there are errors in your request, you will see the
failures only after you start streaming the data. The subscribe method should be called as a request to start
streaming data. For more info, see
Publisher.subscribe(org.reactivestreams.Subscriber)
. Each call to the subscribe
method will result in a new Subscription
i.e., a new contract to stream data from the
starting request.
The following are few ways to use the response class:
1) Using the forEach helper method
software.amazon.awssdk.services.sagemaker.paginators.ListNotebookInstancesPublisher publisher = client.listNotebookInstancesPaginator(request);
CompletableFuture<Void> future = publisher.forEach(res -> { // Do something with the response });
future.get();
2) Using a custom subscriber
software.amazon.awssdk.services.sagemaker.paginators.ListNotebookInstancesPublisher publisher = client.listNotebookInstancesPaginator(request);
publisher.subscribe(new Subscriber<software.amazon.awssdk.services.sagemaker.model.ListNotebookInstancesResponse>() {
public void onSubscribe(org.reactivestreams.Subscriber subscription) { //... };
public void onNext(software.amazon.awssdk.services.sagemaker.model.ListNotebookInstancesResponse response) { //... };
});
As the response is a publisher, it can work well with third party reactive streams implementations like RxJava2.
Note: If you prefer to have control on service calls, use the
listNotebookInstances(software.amazon.awssdk.services.sagemaker.model.ListNotebookInstancesRequest)
operation.
default ListNotebookInstancesPublisher listNotebookInstancesPaginator(ListNotebookInstancesRequest listNotebookInstancesRequest)
Returns a list of the Amazon SageMaker notebook instances in the requester's account in an AWS Region.
This is a variant of
listNotebookInstances(software.amazon.awssdk.services.sagemaker.model.ListNotebookInstancesRequest)
operation. The return type is a custom publisher that can be subscribed to request a stream of response pages.
SDK will internally handle making service calls for you.
When the operation is called, an instance of this class is returned. At this point, no service calls are made yet
and so there is no guarantee that the request is valid. If there are errors in your request, you will see the
failures only after you start streaming the data. The subscribe method should be called as a request to start
streaming data. For more info, see
Publisher.subscribe(org.reactivestreams.Subscriber)
. Each call to the subscribe
method will result in a new Subscription
i.e., a new contract to stream data from the
starting request.
The following are few ways to use the response class:
1) Using the forEach helper method
software.amazon.awssdk.services.sagemaker.paginators.ListNotebookInstancesPublisher publisher = client.listNotebookInstancesPaginator(request);
CompletableFuture<Void> future = publisher.forEach(res -> { // Do something with the response });
future.get();
2) Using a custom subscriber
software.amazon.awssdk.services.sagemaker.paginators.ListNotebookInstancesPublisher publisher = client.listNotebookInstancesPaginator(request);
publisher.subscribe(new Subscriber<software.amazon.awssdk.services.sagemaker.model.ListNotebookInstancesResponse>() {
public void onSubscribe(org.reactivestreams.Subscriber subscription) { //... };
public void onNext(software.amazon.awssdk.services.sagemaker.model.ListNotebookInstancesResponse response) { //... };
});
As the response is a publisher, it can work well with third party reactive streams implementations like RxJava2.
Note: If you prefer to have control on service calls, use the
listNotebookInstances(software.amazon.awssdk.services.sagemaker.model.ListNotebookInstancesRequest)
operation.
listNotebookInstancesRequest
- default ListNotebookInstancesPublisher listNotebookInstancesPaginator(Consumer<ListNotebookInstancesRequest.Builder> listNotebookInstancesRequest)
Returns a list of the Amazon SageMaker notebook instances in the requester's account in an AWS Region.
This is a variant of
listNotebookInstances(software.amazon.awssdk.services.sagemaker.model.ListNotebookInstancesRequest)
operation. The return type is a custom publisher that can be subscribed to request a stream of response pages.
SDK will internally handle making service calls for you.
When the operation is called, an instance of this class is returned. At this point, no service calls are made yet
and so there is no guarantee that the request is valid. If there are errors in your request, you will see the
failures only after you start streaming the data. The subscribe method should be called as a request to start
streaming data. For more info, see
Publisher.subscribe(org.reactivestreams.Subscriber)
. Each call to the subscribe
method will result in a new Subscription
i.e., a new contract to stream data from the
starting request.
The following are few ways to use the response class:
1) Using the forEach helper method
software.amazon.awssdk.services.sagemaker.paginators.ListNotebookInstancesPublisher publisher = client.listNotebookInstancesPaginator(request);
CompletableFuture<Void> future = publisher.forEach(res -> { // Do something with the response });
future.get();
2) Using a custom subscriber
software.amazon.awssdk.services.sagemaker.paginators.ListNotebookInstancesPublisher publisher = client.listNotebookInstancesPaginator(request);
publisher.subscribe(new Subscriber<software.amazon.awssdk.services.sagemaker.model.ListNotebookInstancesResponse>() {
public void onSubscribe(org.reactivestreams.Subscriber subscription) { //... };
public void onNext(software.amazon.awssdk.services.sagemaker.model.ListNotebookInstancesResponse response) { //... };
});
As the response is a publisher, it can work well with third party reactive streams implementations like RxJava2.
Note: If you prefer to have control on service calls, use the
listNotebookInstances(software.amazon.awssdk.services.sagemaker.model.ListNotebookInstancesRequest)
operation.
This is a convenience which creates an instance of the ListNotebookInstancesRequest.Builder
avoiding the
need to create one manually via ListNotebookInstancesRequest.builder()
listNotebookInstancesRequest
- A Consumer
that will call methods on ListNotebookInstancesInput.Builder
to create a
request.default CompletableFuture<ListTagsResponse> listTags(ListTagsRequest listTagsRequest)
Returns the tags for the specified Amazon SageMaker resource.
listTagsRequest
- default CompletableFuture<ListTagsResponse> listTags(Consumer<ListTagsRequest.Builder> listTagsRequest)
Returns the tags for the specified Amazon SageMaker resource.
This is a convenience which creates an instance of the ListTagsRequest.Builder
avoiding the need to
create one manually via ListTagsRequest.builder()
listTagsRequest
- A Consumer
that will call methods on ListTagsInput.Builder
to create a request.default ListTagsPublisher listTagsPaginator(ListTagsRequest listTagsRequest)
Returns the tags for the specified Amazon SageMaker resource.
This is a variant of listTags(software.amazon.awssdk.services.sagemaker.model.ListTagsRequest)
operation. The return type is a custom publisher that can be subscribed to request a stream of response pages.
SDK will internally handle making service calls for you.
When the operation is called, an instance of this class is returned. At this point, no service calls are made yet
and so there is no guarantee that the request is valid. If there are errors in your request, you will see the
failures only after you start streaming the data. The subscribe method should be called as a request to start
streaming data. For more info, see
Publisher.subscribe(org.reactivestreams.Subscriber)
. Each call to the subscribe
method will result in a new Subscription
i.e., a new contract to stream data from the
starting request.
The following are few ways to use the response class:
1) Using the forEach helper method
software.amazon.awssdk.services.sagemaker.paginators.ListTagsPublisher publisher = client.listTagsPaginator(request);
CompletableFuture<Void> future = publisher.forEach(res -> { // Do something with the response });
future.get();
2) Using a custom subscriber
software.amazon.awssdk.services.sagemaker.paginators.ListTagsPublisher publisher = client.listTagsPaginator(request);
publisher.subscribe(new Subscriber<software.amazon.awssdk.services.sagemaker.model.ListTagsResponse>() {
public void onSubscribe(org.reactivestreams.Subscriber subscription) { //... };
public void onNext(software.amazon.awssdk.services.sagemaker.model.ListTagsResponse response) { //... };
});
As the response is a publisher, it can work well with third party reactive streams implementations like RxJava2.
Note: If you prefer to have control on service calls, use the
listTags(software.amazon.awssdk.services.sagemaker.model.ListTagsRequest)
operation.
listTagsRequest
- default ListTagsPublisher listTagsPaginator(Consumer<ListTagsRequest.Builder> listTagsRequest)
Returns the tags for the specified Amazon SageMaker resource.
This is a variant of listTags(software.amazon.awssdk.services.sagemaker.model.ListTagsRequest)
operation. The return type is a custom publisher that can be subscribed to request a stream of response pages.
SDK will internally handle making service calls for you.
When the operation is called, an instance of this class is returned. At this point, no service calls are made yet
and so there is no guarantee that the request is valid. If there are errors in your request, you will see the
failures only after you start streaming the data. The subscribe method should be called as a request to start
streaming data. For more info, see
Publisher.subscribe(org.reactivestreams.Subscriber)
. Each call to the subscribe
method will result in a new Subscription
i.e., a new contract to stream data from the
starting request.
The following are few ways to use the response class:
1) Using the forEach helper method
software.amazon.awssdk.services.sagemaker.paginators.ListTagsPublisher publisher = client.listTagsPaginator(request);
CompletableFuture<Void> future = publisher.forEach(res -> { // Do something with the response });
future.get();
2) Using a custom subscriber
software.amazon.awssdk.services.sagemaker.paginators.ListTagsPublisher publisher = client.listTagsPaginator(request);
publisher.subscribe(new Subscriber<software.amazon.awssdk.services.sagemaker.model.ListTagsResponse>() {
public void onSubscribe(org.reactivestreams.Subscriber subscription) { //... };
public void onNext(software.amazon.awssdk.services.sagemaker.model.ListTagsResponse response) { //... };
});
As the response is a publisher, it can work well with third party reactive streams implementations like RxJava2.
Note: If you prefer to have control on service calls, use the
listTags(software.amazon.awssdk.services.sagemaker.model.ListTagsRequest)
operation.
This is a convenience which creates an instance of the ListTagsRequest.Builder
avoiding the need to
create one manually via ListTagsRequest.builder()
listTagsRequest
- A Consumer
that will call methods on ListTagsInput.Builder
to create a request.default CompletableFuture<ListTrainingJobsResponse> listTrainingJobs(ListTrainingJobsRequest listTrainingJobsRequest)
Lists training jobs.
listTrainingJobsRequest
- default CompletableFuture<ListTrainingJobsResponse> listTrainingJobs(Consumer<ListTrainingJobsRequest.Builder> listTrainingJobsRequest)
Lists training jobs.
This is a convenience which creates an instance of the ListTrainingJobsRequest.Builder
avoiding the need
to create one manually via ListTrainingJobsRequest.builder()
listTrainingJobsRequest
- A Consumer
that will call methods on ListTrainingJobsRequest.Builder
to create a request.default CompletableFuture<ListTrainingJobsResponse> listTrainingJobs()
Lists training jobs.
default ListTrainingJobsPublisher listTrainingJobsPaginator()
Lists training jobs.
This is a variant of
listTrainingJobs(software.amazon.awssdk.services.sagemaker.model.ListTrainingJobsRequest)
operation. The
return type is a custom publisher that can be subscribed to request a stream of response pages. SDK will
internally handle making service calls for you.
When the operation is called, an instance of this class is returned. At this point, no service calls are made yet
and so there is no guarantee that the request is valid. If there are errors in your request, you will see the
failures only after you start streaming the data. The subscribe method should be called as a request to start
streaming data. For more info, see
Publisher.subscribe(org.reactivestreams.Subscriber)
. Each call to the subscribe
method will result in a new Subscription
i.e., a new contract to stream data from the
starting request.
The following are few ways to use the response class:
1) Using the forEach helper method
software.amazon.awssdk.services.sagemaker.paginators.ListTrainingJobsPublisher publisher = client.listTrainingJobsPaginator(request);
CompletableFuture<Void> future = publisher.forEach(res -> { // Do something with the response });
future.get();
2) Using a custom subscriber
software.amazon.awssdk.services.sagemaker.paginators.ListTrainingJobsPublisher publisher = client.listTrainingJobsPaginator(request);
publisher.subscribe(new Subscriber<software.amazon.awssdk.services.sagemaker.model.ListTrainingJobsResponse>() {
public void onSubscribe(org.reactivestreams.Subscriber subscription) { //... };
public void onNext(software.amazon.awssdk.services.sagemaker.model.ListTrainingJobsResponse response) { //... };
});
As the response is a publisher, it can work well with third party reactive streams implementations like RxJava2.
Note: If you prefer to have control on service calls, use the
listTrainingJobs(software.amazon.awssdk.services.sagemaker.model.ListTrainingJobsRequest)
operation.
default ListTrainingJobsPublisher listTrainingJobsPaginator(ListTrainingJobsRequest listTrainingJobsRequest)
Lists training jobs.
This is a variant of
listTrainingJobs(software.amazon.awssdk.services.sagemaker.model.ListTrainingJobsRequest)
operation. The
return type is a custom publisher that can be subscribed to request a stream of response pages. SDK will
internally handle making service calls for you.
When the operation is called, an instance of this class is returned. At this point, no service calls are made yet
and so there is no guarantee that the request is valid. If there are errors in your request, you will see the
failures only after you start streaming the data. The subscribe method should be called as a request to start
streaming data. For more info, see
Publisher.subscribe(org.reactivestreams.Subscriber)
. Each call to the subscribe
method will result in a new Subscription
i.e., a new contract to stream data from the
starting request.
The following are few ways to use the response class:
1) Using the forEach helper method
software.amazon.awssdk.services.sagemaker.paginators.ListTrainingJobsPublisher publisher = client.listTrainingJobsPaginator(request);
CompletableFuture<Void> future = publisher.forEach(res -> { // Do something with the response });
future.get();
2) Using a custom subscriber
software.amazon.awssdk.services.sagemaker.paginators.ListTrainingJobsPublisher publisher = client.listTrainingJobsPaginator(request);
publisher.subscribe(new Subscriber<software.amazon.awssdk.services.sagemaker.model.ListTrainingJobsResponse>() {
public void onSubscribe(org.reactivestreams.Subscriber subscription) { //... };
public void onNext(software.amazon.awssdk.services.sagemaker.model.ListTrainingJobsResponse response) { //... };
});
As the response is a publisher, it can work well with third party reactive streams implementations like RxJava2.
Note: If you prefer to have control on service calls, use the
listTrainingJobs(software.amazon.awssdk.services.sagemaker.model.ListTrainingJobsRequest)
operation.
listTrainingJobsRequest
- default ListTrainingJobsPublisher listTrainingJobsPaginator(Consumer<ListTrainingJobsRequest.Builder> listTrainingJobsRequest)
Lists training jobs.
This is a variant of
listTrainingJobs(software.amazon.awssdk.services.sagemaker.model.ListTrainingJobsRequest)
operation. The
return type is a custom publisher that can be subscribed to request a stream of response pages. SDK will
internally handle making service calls for you.
When the operation is called, an instance of this class is returned. At this point, no service calls are made yet
and so there is no guarantee that the request is valid. If there are errors in your request, you will see the
failures only after you start streaming the data. The subscribe method should be called as a request to start
streaming data. For more info, see
Publisher.subscribe(org.reactivestreams.Subscriber)
. Each call to the subscribe
method will result in a new Subscription
i.e., a new contract to stream data from the
starting request.
The following are few ways to use the response class:
1) Using the forEach helper method
software.amazon.awssdk.services.sagemaker.paginators.ListTrainingJobsPublisher publisher = client.listTrainingJobsPaginator(request);
CompletableFuture<Void> future = publisher.forEach(res -> { // Do something with the response });
future.get();
2) Using a custom subscriber
software.amazon.awssdk.services.sagemaker.paginators.ListTrainingJobsPublisher publisher = client.listTrainingJobsPaginator(request);
publisher.subscribe(new Subscriber<software.amazon.awssdk.services.sagemaker.model.ListTrainingJobsResponse>() {
public void onSubscribe(org.reactivestreams.Subscriber subscription) { //... };
public void onNext(software.amazon.awssdk.services.sagemaker.model.ListTrainingJobsResponse response) { //... };
});
As the response is a publisher, it can work well with third party reactive streams implementations like RxJava2.
Note: If you prefer to have control on service calls, use the
listTrainingJobs(software.amazon.awssdk.services.sagemaker.model.ListTrainingJobsRequest)
operation.
This is a convenience which creates an instance of the ListTrainingJobsRequest.Builder
avoiding the need
to create one manually via ListTrainingJobsRequest.builder()
listTrainingJobsRequest
- A Consumer
that will call methods on ListTrainingJobsRequest.Builder
to create a request.default CompletableFuture<StartNotebookInstanceResponse> startNotebookInstance(StartNotebookInstanceRequest startNotebookInstanceRequest)
Launches an ML compute instance with the latest version of the libraries and attaches your ML storage volume.
After configuring the notebook instance, Amazon SageMaker sets the notebook instance status to
InService
. A notebook instance's status must be InService
before you can connect to
your Jupyter notebook.
startNotebookInstanceRequest
- default CompletableFuture<StartNotebookInstanceResponse> startNotebookInstance(Consumer<StartNotebookInstanceRequest.Builder> startNotebookInstanceRequest)
Launches an ML compute instance with the latest version of the libraries and attaches your ML storage volume.
After configuring the notebook instance, Amazon SageMaker sets the notebook instance status to
InService
. A notebook instance's status must be InService
before you can connect to
your Jupyter notebook.
This is a convenience which creates an instance of the StartNotebookInstanceRequest.Builder
avoiding the
need to create one manually via StartNotebookInstanceRequest.builder()
startNotebookInstanceRequest
- A Consumer
that will call methods on StartNotebookInstanceInput.Builder
to create a
request.default CompletableFuture<StopNotebookInstanceResponse> stopNotebookInstance(StopNotebookInstanceRequest stopNotebookInstanceRequest)
Terminates the ML compute instance. Before terminating the instance, Amazon SageMaker disconnects the ML storage volume from it. Amazon SageMaker preserves the ML storage volume.
To access data on the ML storage volume for a notebook instance that has been terminated, call the
StartNotebookInstance
API. StartNotebookInstance
launches another ML compute instance,
configures it, and attaches the preserved ML storage volume so you can continue your work.
stopNotebookInstanceRequest
- default CompletableFuture<StopNotebookInstanceResponse> stopNotebookInstance(Consumer<StopNotebookInstanceRequest.Builder> stopNotebookInstanceRequest)
Terminates the ML compute instance. Before terminating the instance, Amazon SageMaker disconnects the ML storage volume from it. Amazon SageMaker preserves the ML storage volume.
To access data on the ML storage volume for a notebook instance that has been terminated, call the
StartNotebookInstance
API. StartNotebookInstance
launches another ML compute instance,
configures it, and attaches the preserved ML storage volume so you can continue your work.
This is a convenience which creates an instance of the StopNotebookInstanceRequest.Builder
avoiding the
need to create one manually via StopNotebookInstanceRequest.builder()
stopNotebookInstanceRequest
- A Consumer
that will call methods on StopNotebookInstanceInput.Builder
to create a
request.default CompletableFuture<StopTrainingJobResponse> stopTrainingJob(StopTrainingJobRequest stopTrainingJobRequest)
Stops a training job. To stop a job, Amazon SageMaker sends the algorithm the SIGTERM
signal, which
delays job termination for 120 seconds. Algorithms might use this 120-second window to save the model artifacts,
so the results of the training is not lost.
Training algorithms provided by Amazon SageMaker save the intermediate results of a model training job. This intermediate data is a valid model artifact. You can use the model artifacts that are saved when Amazon SageMaker stops a training job to create a model.
When it receives a StopTrainingJob
request, Amazon SageMaker changes the status of the job to
Stopping
. After Amazon SageMaker stops the job, it sets the status to Stopped
.
stopTrainingJobRequest
- default CompletableFuture<StopTrainingJobResponse> stopTrainingJob(Consumer<StopTrainingJobRequest.Builder> stopTrainingJobRequest)
Stops a training job. To stop a job, Amazon SageMaker sends the algorithm the SIGTERM
signal, which
delays job termination for 120 seconds. Algorithms might use this 120-second window to save the model artifacts,
so the results of the training is not lost.
Training algorithms provided by Amazon SageMaker save the intermediate results of a model training job. This intermediate data is a valid model artifact. You can use the model artifacts that are saved when Amazon SageMaker stops a training job to create a model.
When it receives a StopTrainingJob
request, Amazon SageMaker changes the status of the job to
Stopping
. After Amazon SageMaker stops the job, it sets the status to Stopped
.
This is a convenience which creates an instance of the StopTrainingJobRequest.Builder
avoiding the need
to create one manually via StopTrainingJobRequest.builder()
stopTrainingJobRequest
- A Consumer
that will call methods on StopTrainingJobRequest.Builder
to create a request.default CompletableFuture<UpdateEndpointResponse> updateEndpoint(UpdateEndpointRequest updateEndpointRequest)
Deploys the new EndpointConfig
specified in the request, switches to using newly created endpoint,
and then deletes resources provisioned for the endpoint using the previous EndpointConfig
(there is
no availability loss).
When Amazon SageMaker receives the request, it sets the endpoint status to Updating
. After updating
the endpoint, it sets the status to InService
. To check the status of an endpoint, use the DescribeEndpoint API.
updateEndpointRequest
- default CompletableFuture<UpdateEndpointResponse> updateEndpoint(Consumer<UpdateEndpointRequest.Builder> updateEndpointRequest)
Deploys the new EndpointConfig
specified in the request, switches to using newly created endpoint,
and then deletes resources provisioned for the endpoint using the previous EndpointConfig
(there is
no availability loss).
When Amazon SageMaker receives the request, it sets the endpoint status to Updating
. After updating
the endpoint, it sets the status to InService
. To check the status of an endpoint, use the DescribeEndpoint API.
This is a convenience which creates an instance of the UpdateEndpointRequest.Builder
avoiding the need to
create one manually via UpdateEndpointRequest.builder()
updateEndpointRequest
- A Consumer
that will call methods on UpdateEndpointInput.Builder
to create a request.default CompletableFuture<UpdateEndpointWeightsAndCapacitiesResponse> updateEndpointWeightsAndCapacities(UpdateEndpointWeightsAndCapacitiesRequest updateEndpointWeightsAndCapacitiesRequest)
Updates variant weight, capacity, or both of one or more variants associated with an endpoint. This operation
updates weight, capacity, or both for the previously provisioned endpoint. When it receives the request, Amazon
SageMaker sets the endpoint status to Updating
. After updating the endpoint, it sets the status to
InService
. To check the status of an endpoint, use the DescribeEndpoint API.
updateEndpointWeightsAndCapacitiesRequest
- default CompletableFuture<UpdateEndpointWeightsAndCapacitiesResponse> updateEndpointWeightsAndCapacities(Consumer<UpdateEndpointWeightsAndCapacitiesRequest.Builder> updateEndpointWeightsAndCapacitiesRequest)
Updates variant weight, capacity, or both of one or more variants associated with an endpoint. This operation
updates weight, capacity, or both for the previously provisioned endpoint. When it receives the request, Amazon
SageMaker sets the endpoint status to Updating
. After updating the endpoint, it sets the status to
InService
. To check the status of an endpoint, use the DescribeEndpoint API.
This is a convenience which creates an instance of the UpdateEndpointWeightsAndCapacitiesRequest.Builder
avoiding the need to create one manually via UpdateEndpointWeightsAndCapacitiesRequest.builder()
updateEndpointWeightsAndCapacitiesRequest
- A Consumer
that will call methods on UpdateEndpointWeightsAndCapacitiesInput.Builder
to
create a request.default CompletableFuture<UpdateNotebookInstanceResponse> updateNotebookInstance(UpdateNotebookInstanceRequest updateNotebookInstanceRequest)
Updates a notebook instance. NotebookInstance updates include upgrading or downgrading the ML compute instance used for your notebook instance to accommodate changes in your workload requirements. You can also update the VPC security groups.
updateNotebookInstanceRequest
- default CompletableFuture<UpdateNotebookInstanceResponse> updateNotebookInstance(Consumer<UpdateNotebookInstanceRequest.Builder> updateNotebookInstanceRequest)
Updates a notebook instance. NotebookInstance updates include upgrading or downgrading the ML compute instance used for your notebook instance to accommodate changes in your workload requirements. You can also update the VPC security groups.
This is a convenience which creates an instance of the UpdateNotebookInstanceRequest.Builder
avoiding the
need to create one manually via UpdateNotebookInstanceRequest.builder()
updateNotebookInstanceRequest
- A Consumer
that will call methods on UpdateNotebookInstanceInput.Builder
to create a
request.Copyright © 2017 Amazon Web Services, Inc. All Rights Reserved.