SageMakerClient Class

Member Function Documentation

SageMakerClient::SageMakerClient(const QtAws::Core::AwsRegion::Region region = QtAws::Core::AwsRegion::InvalidRegion, QtAws::Core::AwsAbstractCredentials *credentials = NULL, QNetworkAccessManager * const manager = NULL, QObject * const parent = 0)

Constructs a SageMakerClient object.

The new client object will region, credentials, and manager for network operations.

The new object will be owned by parent, if set.

SageMakerClient::SageMakerClient(const QUrl &endpoint, QtAws::Core::AwsAbstractCredentials *credentials = NULL, QNetworkAccessManager * const manager = NULL, QObject * const parent = 0)

This function overloads SageMakerClient().

This overload allows the caller to specify the specific endpoint to send requests to. Typically, it is easier to use the alternative constructor, which allows the caller to specify an AWS region instead, in which case this client will determine the correct endpoint for the given region automatically (via AwsEndpoint::getEndpoint).

See also QtAws::Core::AwsEndpoint::getEndpoint.

[slot] AddTagsResponse *SageMakerClient::addTags(const AddTagsRequest &request)

Sends request to the SageMakerClient service, and returns a pointer to an AddTagsResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

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.

</p

Each tag consists of a key and an optional value. Tag keys must be unique per resource. For more information about tags, see <a href="http://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-what">Using Cost Allocation Tags</a> in the <i>AWS Billing and Cost Management User Guide</i>.

[slot] CreateEndpointResponse *SageMakerClient::createEndpoint(const CreateEndpointRequest &request)

Sends request to the SageMakerClient service, and returns a pointer to an CreateEndpointResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

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 <a href="http://docs.aws.amazon.com/sagemaker/latest/dg/API_CreateEndpointConfig.html">CreateEndpointConfig</a> API.

</p <note>

Use this API only for hosting models using Amazon SageMaker hosting services.

</p </note>

The endpoint name must be unique within an AWS Region in your AWS account.

</p

When it receives the request, Amazon SageMaker creates the endpoint, launches the resources (ML compute instances), and deploys the model(s) on them.

</p

When Amazon SageMaker receives the request, it sets the endpoint status to <code>Creating</code>. After it creates the endpoint, it sets the status to <code>InService</code>. Amazon SageMaker can then process incoming requests for inferences. To check the status of an endpoint, use the <a href="http://docs.aws.amazon.com/sagemaker/latest/dg/API_DescribeEndpoint.html">DescribeEndpoint</a>

API>

For an example, see <a href="http://docs.aws.amazon.com/sagemaker/latest/dg/ex1.html">Exercise 1: Using the K-Means Algorithm Provided by Amazon SageMaker</a>.

[slot] CreateEndpointConfigResponse *SageMakerClient::createEndpointConfig(const CreateEndpointConfigRequest &request)

Sends request to the SageMakerClient service, and returns a pointer to an CreateEndpointConfigResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

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 <code>CreateModel</code> API, to deploy and the resources that you want Amazon SageMaker to provision. Then you call the <a href="http://docs.aws.amazon.com/sagemaker/latest/dg/API_CreateEndpoint.html">CreateEndpoint</a>

API> <note>

Use this API only if you want to use Amazon SageMaker hosting services to deploy models into production.

</p </note>

In the request, you define one or more <code>ProductionVariant</code>s, each of which identifies a model. Each <code>ProductionVariant</code> parameter also describes the resources that you want Amazon SageMaker to provision. This includes the number and type of ML compute instances to deploy.

</p

If you are hosting multiple models, you also assign a <code>VariantWeight</code> 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.

[slot] CreateModelResponse *SageMakerClient::createModel(const CreateModelRequest &request)

Sends request to the SageMakerClient service, and returns a pointer to an CreateModelResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

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.

</p

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 <code>CreateEndpointConfig</code> API, and then create an endpoint with the <code>CreateEndpoint</code> API.

</p

Amazon SageMaker then deploys all of the containers that you defined for the model in the hosting environment.

</p

In the <code>CreateModel</code> request, you must define a container with the <code>PrimaryContainer</code> parameter.

</p

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

[slot] CreateNotebookInstanceResponse *SageMakerClient::createNotebookInstance(const CreateNotebookInstanceRequest &request)

Sends request to the SageMakerClient service, and returns a pointer to an CreateNotebookInstanceResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Creates an Amazon SageMaker notebook instance. A notebook instance is a machine learning (ML) compute instance running on a Jupyter notebook.

</p

In a <code>CreateNotebookInstance</code> request, 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.

</p

Amazon SageMaker also provides a set of example notebooks. Each notebook demonstrates how to use Amazon SageMaker with a specific algorithm or with a machine learning framework.

</p

After receiving the request, Amazon SageMaker does the

following> <ol> <li>

Creates a network interface in the Amazon SageMaker

VPC> </li> <li>

(Option) If you specified <code>SubnetId</code>, Amazon SageMaker 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> </li> <li>

Launches an EC2 instance of the type specified in the request in the Amazon SageMaker VPC. If you specified <code>SubnetId</code> 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> </li> </ol>

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.

</p

For more information, see <a href="http://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works.html">How It Works</a>.

[slot] CreateNotebookInstanceLifecycleConfigResponse *SageMakerClient::createNotebookInstanceLifecycleConfig(const CreateNotebookInstanceLifecycleConfigRequest &request)

Sends request to the SageMakerClient service, and returns a pointer to an CreateNotebookInstanceLifecycleConfigResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Creates a lifecycle configuration that you can associate with a notebook instance. A <i>lifecycle configuration</i> is a collection of shell scripts that run when you create or start a notebook

instance>

Each lifecycle configuration script has a limit of 16384

characters>

The value of the <code>$PATH</code> environment variable that is available to both scripts is

<code>/sbin:bin:/usr/sbin:/usr/bin</code>>

View CloudWatch Logs for notebook instance lifecycle configurations in log group <code>/aws/sagemaker/NotebookInstances</code> in log stream

<code>[notebook-instance-name]/[LifecycleConfigHook]</code>>

Lifecycle configuration scripts cannot run for longer than 5 minutes. If a script runs for longer than 5 minutes, it fails and the notebook instance is not created or

started>

For information about notebook instance lifestyle configurations, see

[slot] CreatePresignedNotebookInstanceUrlResponse *SageMakerClient::createPresignedNotebookInstanceUrl(const CreatePresignedNotebookInstanceUrlRequest &request)

Sends request to the SageMakerClient service, and returns a pointer to an CreatePresignedNotebookInstanceUrlResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

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 <code>Open</code> 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.

[slot] CreateTrainingJobResponse *SageMakerClient::createTrainingJob(const CreateTrainingJobRequest &request)

Sends request to the SageMakerClient service, and returns a pointer to an CreateTrainingJobResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Starts a model training job. After training completes, Amazon SageMaker saves the resulting model artifacts to an Amazon S3 location that you specify.

</p

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.

</p

In the request body, you provide the following:

</p <ul> <li>

<code>AlgorithmSpecification</code> - Identifies the training algorithm to use.

</p </li> <li>

<code>HyperParameters</code> - 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 <a href="http://docs.aws.amazon.com/sagemaker/latest/dg/algos.html">Algorithms</a>.

</p </li> <li>

<code>InputDataConfig</code> - Describes the training dataset and the Amazon S3 location where it is

stored> </li> <li>

<code>OutputDataConfig</code> - Identifies the Amazon S3 location where you want Amazon SageMaker to save the results of model training.

</p <p/> </li> <li>

<code>ResourceConfig</code> - Identifies the resources, ML compute instances, and ML storage volumes to deploy for model training. In distributed training, you specify more than one instance.

</p </li> <li>

<code>RoleARN</code> - 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.

</p </li> <li>

<code>StoppingCondition</code> - Sets a duration for training. Use this parameter to cap model training costs.

</p </li> </ul>

For more information about Amazon SageMaker, see <a href="http://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works.html">How It Works</a>.

[slot] DeleteEndpointResponse *SageMakerClient::deleteEndpoint(const DeleteEndpointRequest &request)

Sends request to the SageMakerClient service, and returns a pointer to an DeleteEndpointResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Deletes an endpoint. Amazon SageMaker frees up all of the resources that were deployed when the endpoint was created.

[slot] DeleteEndpointConfigResponse *SageMakerClient::deleteEndpointConfig(const DeleteEndpointConfigRequest &request)

Sends request to the SageMakerClient service, and returns a pointer to an DeleteEndpointConfigResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Deletes an endpoint configuration. The <code>DeleteEndpoingConfig</code> API deletes only the specified configuration. It does not delete endpoints created using the configuration.

[slot] DeleteModelResponse *SageMakerClient::deleteModel(const DeleteModelRequest &request)

Sends request to the SageMakerClient service, and returns a pointer to an DeleteModelResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Deletes a model. The <code>DeleteModel</code> API deletes only the model entry that was created in Amazon SageMaker when you called the <a href="http://docs.aws.amazon.com/sagemaker/latest/dg/API_CreateModel.html">CreateModel</a> API. It does not delete model artifacts, inference code, or the IAM role that you specified when creating the model.

[slot] DeleteNotebookInstanceResponse *SageMakerClient::deleteNotebookInstance(const DeleteNotebookInstanceRequest &request)

Sends request to the SageMakerClient service, and returns a pointer to an DeleteNotebookInstanceResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Deletes an Amazon SageMaker notebook instance. Before you can delete a notebook instance, you must call the <code>StopNotebookInstance</code> API.

</p <b>

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.

[slot] DeleteNotebookInstanceLifecycleConfigResponse *SageMakerClient::deleteNotebookInstanceLifecycleConfig(const DeleteNotebookInstanceLifecycleConfigRequest &request)

Sends request to the SageMakerClient service, and returns a pointer to an DeleteNotebookInstanceLifecycleConfigResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Deletes a notebook instance lifecycle

[slot] DeleteTagsResponse *SageMakerClient::deleteTags(const DeleteTagsRequest &request)

Sends request to the SageMakerClient service, and returns a pointer to an DeleteTagsResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Deletes the specified tags from an Amazon SageMaker

resource>

To list a resource's tags, use the <code>ListTags</code> API.

[slot] DescribeEndpointResponse *SageMakerClient::describeEndpoint(const DescribeEndpointRequest &request)

Sends request to the SageMakerClient service, and returns a pointer to an DescribeEndpointResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Returns the description of an

[slot] DescribeEndpointConfigResponse *SageMakerClient::describeEndpointConfig(const DescribeEndpointConfigRequest &request)

Sends request to the SageMakerClient service, and returns a pointer to an DescribeEndpointConfigResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Returns the description of an endpoint configuration created using the <code>CreateEndpointConfig</code>

[slot] DescribeModelResponse *SageMakerClient::describeModel(const DescribeModelRequest &request)

Sends request to the SageMakerClient service, and returns a pointer to an DescribeModelResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Describes a model that you created using the <code>CreateModel</code>

[slot] DescribeNotebookInstanceResponse *SageMakerClient::describeNotebookInstance(const DescribeNotebookInstanceRequest &request)

Sends request to the SageMakerClient service, and returns a pointer to an DescribeNotebookInstanceResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Returns information about a notebook

[slot] DescribeNotebookInstanceLifecycleConfigResponse *SageMakerClient::describeNotebookInstanceLifecycleConfig(const DescribeNotebookInstanceLifecycleConfigRequest &request)

Sends request to the SageMakerClient service, and returns a pointer to an DescribeNotebookInstanceLifecycleConfigResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Returns a description of a notebook instance lifecycle

configuration>

For information about notebook instance lifestyle configurations, see

[slot] DescribeTrainingJobResponse *SageMakerClient::describeTrainingJob(const DescribeTrainingJobRequest &request)

Sends request to the SageMakerClient service, and returns a pointer to an DescribeTrainingJobResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Returns information about a training

[slot] ListEndpointConfigsResponse *SageMakerClient::listEndpointConfigs(const ListEndpointConfigsRequest &request)

Sends request to the SageMakerClient service, and returns a pointer to an ListEndpointConfigsResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Lists endpoint

[slot] ListEndpointsResponse *SageMakerClient::listEndpoints(const ListEndpointsRequest &request)

Sends request to the SageMakerClient service, and returns a pointer to an ListEndpointsResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Lists

[slot] ListModelsResponse *SageMakerClient::listModels(const ListModelsRequest &request)

Sends request to the SageMakerClient service, and returns a pointer to an ListModelsResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Lists models created with the <a href="http://docs.aws.amazon.com/sagemaker/latest/dg/API_CreateModel.html">CreateModel</a>

[slot] ListNotebookInstanceLifecycleConfigsResponse *SageMakerClient::listNotebookInstanceLifecycleConfigs(const ListNotebookInstanceLifecycleConfigsRequest &request)

Sends request to the SageMakerClient service, and returns a pointer to an ListNotebookInstanceLifecycleConfigsResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Lists notebook instance lifestyle configurations created with the

[slot] ListNotebookInstancesResponse *SageMakerClient::listNotebookInstances(const ListNotebookInstancesRequest &request)

Sends request to the SageMakerClient service, and returns a pointer to an ListNotebookInstancesResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Returns a list of the Amazon SageMaker notebook instances in the requester's account in an AWS Region.

[slot] ListTagsResponse *SageMakerClient::listTags(const ListTagsRequest &request)

Sends request to the SageMakerClient service, and returns a pointer to an ListTagsResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Returns the tags for the specified Amazon SageMaker

[slot] ListTrainingJobsResponse *SageMakerClient::listTrainingJobs(const ListTrainingJobsRequest &request)

Sends request to the SageMakerClient service, and returns a pointer to an ListTrainingJobsResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Lists training

[slot] StartNotebookInstanceResponse *SageMakerClient::startNotebookInstance(const StartNotebookInstanceRequest &request)

Sends request to the SageMakerClient service, and returns a pointer to an StartNotebookInstanceResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

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 <code>InService</code>. A notebook instance's status must be <code>InService</code> before you can connect to your Jupyter notebook.

[slot] StopNotebookInstanceResponse *SageMakerClient::stopNotebookInstance(const StopNotebookInstanceRequest &request)

Sends request to the SageMakerClient service, and returns a pointer to an StopNotebookInstanceResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

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.

</p

To access data on the ML storage volume for a notebook instance that has been terminated, call the <code>StartNotebookInstance</code> API. <code>StartNotebookInstance</code> launches another ML compute instance, configures it, and attaches the preserved ML storage volume so you can continue your work.

[slot] StopTrainingJobResponse *SageMakerClient::stopTrainingJob(const StopTrainingJobRequest &request)

Sends request to the SageMakerClient service, and returns a pointer to an StopTrainingJobResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Stops a training job. To stop a job, Amazon SageMaker sends the algorithm the <code>SIGTERM</code> 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.

</p

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.

</p

When it receives a <code>StopTrainingJob</code> request, Amazon SageMaker changes the status of the job to <code>Stopping</code>. After Amazon SageMaker stops the job, it sets the status to

[slot] UpdateEndpointResponse *SageMakerClient::updateEndpoint(const UpdateEndpointRequest &request)

Sends request to the SageMakerClient service, and returns a pointer to an UpdateEndpointResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Deploys the new <code>EndpointConfig</code> specified in the request, switches to using newly created endpoint, and then deletes resources provisioned for the endpoint using the previous <code>EndpointConfig</code> (there is no availability loss).

</p

When Amazon SageMaker receives the request, it sets the endpoint status to <code>Updating</code>. After updating the endpoint, it sets the status to <code>InService</code>. To check the status of an endpoint, use the <a href="http://docs.aws.amazon.com/sagemaker/latest/dg/API_DescribeEndpoint.html">DescribeEndpoint</a> API.

[slot] UpdateEndpointWeightsAndCapacitiesResponse *SageMakerClient::updateEndpointWeightsAndCapacities(const UpdateEndpointWeightsAndCapacitiesRequest &request)

Sends request to the SageMakerClient service, and returns a pointer to an UpdateEndpointWeightsAndCapacitiesResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Updates variant weight of one or more variants associated with an existing endpoint, or capacity of one variant associated with an existing endpoint. When it receives the request, Amazon SageMaker sets the endpoint status to <code>Updating</code>. After updating the endpoint, it sets the status to <code>InService</code>. To check the status of an endpoint, use the <a href="http://docs.aws.amazon.com/sagemaker/latest/dg/API_DescribeEndpoint.html">DescribeEndpoint</a> API.

[slot] UpdateNotebookInstanceResponse *SageMakerClient::updateNotebookInstance(const UpdateNotebookInstanceRequest &request)

Sends request to the SageMakerClient service, and returns a pointer to an UpdateNotebookInstanceResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

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

[slot] UpdateNotebookInstanceLifecycleConfigResponse *SageMakerClient::updateNotebookInstanceLifecycleConfig(const UpdateNotebookInstanceLifecycleConfigRequest &request)

Sends request to the SageMakerClient service, and returns a pointer to an UpdateNotebookInstanceLifecycleConfigResponse object to track the result.

Note: The caller is to take responsbility for the resulting pointer.

Updates a notebook instance lifecycle configuration created with the