Awso_sagemaker.Values_1SourceSpecifies which training algorithm to use for training jobs that a hyperparameter tuning job launches and the metrics to monitor.
Defines the training jobs launched by a hyperparameter tuning job.
Starts a hyperparameter tuning job. A hyperparameter tuning job finds the best version of a model by running many training jobs on your dataset using the algorithm you choose and values for hyperparameters within ranges that you specify. It then chooses the hyperparameter values that result in a model that performs the best, as measured by an objective metric that you choose. A hyperparameter tuning job automatically creates Amazon SageMaker experiments, trials, and trial components for each training job that it runs. You can view these entities in Amazon SageMaker Studio. For more information, see View Experiments, Trials, and Trial Components. Do not include any security-sensitive information including account access IDs, secrets, or tokens in any hyperparameter fields. As part of the shared responsibility model, you are responsible for any potential exposure, unauthorized access, or compromise of your sensitive data if caused by any security-sensitive information included in the request hyperparameter variable or plain text fields..
Starts a hyperparameter tuning job. A hyperparameter tuning job finds the best version of a model by running many training jobs on your dataset using the algorithm you choose and values for hyperparameters within ranges that you specify. It then chooses the hyperparameter values that result in a model that performs the best, as measured by an objective metric that you choose. A hyperparameter tuning job automatically creates Amazon SageMaker experiments, trials, and trial components for each training job that it runs. You can view these entities in Amazon SageMaker Studio. For more information, see View Experiments, Trials, and Trial Components. Do not include any security-sensitive information including account access IDs, secrets, or tokens in any hyperparameter fields. As part of the shared responsibility model, you are responsible for any potential exposure, unauthorized access, or compromise of your sensitive data if caused by any security-sensitive information included in the request hyperparameter variable or plain text fields..
Creates a custom SageMaker AI image. A SageMaker AI image is a set of image versions. Each image version represents a container image stored in Amazon ECR. For more information, see Bring your own SageMaker AI image.
Creates a custom SageMaker AI image. A SageMaker AI image is a set of image versions. Each image version represents a container image stored in Amazon ECR. For more information, see Bring your own SageMaker AI image.
Creates a version of the SageMaker AI image specified by ImageName. The version represents the Amazon ECR container image specified by BaseImage.
Creates a version of the SageMaker AI image specified by ImageName. The version represents the Amazon ECR container image specified by BaseImage.
Settings that take effect while the model container starts up.
Configuration for balancing inference component copies across Availability Zones.
The scheduling configuration that determines how inference component copies are placed across available instances when copies are added or removed.
Settings that affect how the inference component caches data.
Defines a container that provides the runtime environment for a model that you deploy with an inference component.
Defines the compute resources to allocate to run a model, plus any adapter models, that you assign to an inference component. These resources include CPU cores, accelerators, and memory.
Details about the resources to deploy with this inference component, including the model, container, and compute resources.
Runtime settings for a model that is deployed with an inference component.
Creates an inference component, which is a SageMaker AI hosting object that you can use to deploy a model to an endpoint. In the inference component settings, you specify the model, the endpoint, and how the model utilizes the resources that the endpoint hosts. You can optimize resource utilization by tailoring how the required CPU cores, accelerators, and memory are allocated. You can deploy multiple inference components to an endpoint, where each inference component contains one model and the resource utilization needs for that individual model. After you deploy an inference component, you can directly invoke the associated model when you use the InvokeEndpoint API action.
Creates an inference component, which is a SageMaker AI hosting object that you can use to deploy a model to an endpoint. In the inference component settings, you specify the model, the endpoint, and how the model utilizes the resources that the endpoint hosts. You can optimize resource utilization by tailoring how the required CPU cores, accelerators, and memory are allocated. You can deploy multiple inference components to an endpoint, where each inference component contains one model and the resource utilization needs for that individual model. After you deploy an inference component, you can directly invoke the associated model when you use the InvokeEndpoint API action.
The name and sampling percentage of a shadow variant.
The configuration of ShadowMode inference experiment type, which specifies a production variant to take all the inference requests, and a shadow variant to which Amazon SageMaker replicates a percentage of the inference requests. For the shadow variant it also specifies the percentage of requests that Amazon SageMaker replicates.
The infrastructure configuration for deploying the model to a real-time inference endpoint.
The configuration for the infrastructure that the model will be deployed to.
Contains information about the deployment options of a model.
The start and end times of an inference experiment. The maximum duration that you can set for an inference experiment is 30 days.
The Amazon S3 location and configuration for storing inference request and response data.
Creates an inference experiment using the configurations specified in the request. Use this API to setup and schedule an experiment to compare model variants on a Amazon SageMaker inference endpoint. For more information about inference experiments, see Shadow tests. Amazon SageMaker begins your experiment at the scheduled time and routes traffic to your endpoint's model variants based on your specified configuration. While the experiment is in progress or after it has concluded, you can view metrics that compare your model variants. For more information, see View, monitor, and edit shadow tests.
Creates an inference experiment using the configurations specified in the request. Use this API to setup and schedule an experiment to compare model variants on a Amazon SageMaker inference endpoint. For more information about inference experiments, see Shadow tests. Amazon SageMaker begins your experiment at the scheduled time and routes traffic to your endpoint's model variants based on your specified configuration. While the experiment is in progress or after it has concluded, you can view metrics that compare your model variants. For more information, see View, monitor, and edit shadow tests.
The model latency threshold.
Specifies conditions for stopping a job. When a job reaches a stopping condition limit, SageMaker ends the job.
Provides information about the output configuration for the compiled model.
Provides information about the output configuration for the compiled model.
Defines the stairs traffic pattern for an Inference Recommender load test. This pattern type consists of multiple steps where the number of users increases at each step. Specify either the stairs or phases traffic pattern.
Defines the traffic pattern of the load test.
Inference Recommender provisions SageMaker endpoints with access to VPC in the inference recommendation job.
Specifies the maximum number of jobs that can run in parallel and the maximum number of jobs that can run.
The configuration for the payload for a recommendation job.
Specifies mandatory fields for running an Inference Recommender job directly in the CreateInferenceRecommendationsJob API. The fields specified in ContainerConfig override the corresponding fields in the model package. Use ContainerConfig if you want to specify these fields for the recommendation job but don't want to edit them in your model package.
Details about a customer endpoint that was compared in an Inference Recommender job.
Specifies the range of environment parameters
The endpoint configuration for the load test.
The input configuration of the recommendation job.
Starts a recommendation job. You can create either an instance recommendation or load test job.
Starts a recommendation job. You can create either an instance recommendation or load test job.
A set of conditions for stopping a labeling job. If any of the conditions are met, the job is automatically stopped. You can use these conditions to control the cost of data labeling. Labeling jobs fail after 30 days with an appropriate client error message.
Output configuration information for a labeling job.
An Amazon SNS data source used for streaming labeling jobs.
The Amazon S3 location of the input data objects.
Provides information about the location of input data. You must specify at least one of the following: S3DataSource or SnsDataSource. Use SnsDataSource to specify an SNS input topic for a streaming labeling job. If you do not specify and SNS input topic ARN, Ground Truth will create a one-time labeling job. Use S3DataSource to specify an input manifest file for both streaming and one-time labeling jobs. Adding an S3DataSource is optional if you use SnsDataSource to create a streaming labeling job.
Attributes of the data specified by the customer. Use these to describe the data to be labeled.
Input configuration information for a labeling job.
Configure encryption on the storage volume attached to the ML compute instance used to run automated data labeling model training and inference.
Provides configuration information for auto-labeling of your data objects. A LabelingJobAlgorithmsConfig object must be supplied in order to use auto-labeling.
Provided configuration information for the worker UI for a labeling job. Provide either HumanTaskUiArn or UiTemplateS3Uri. For named entity recognition, 3D point cloud and video frame labeling jobs, use HumanTaskUiArn. For all other Ground Truth built-in task types and custom task types, use UiTemplateS3Uri to specify the location of a worker task template in Amazon S3.
Information required for human workers to complete a labeling task.
Creates a job that uses workers to label the data objects in your input dataset. You can use the labeled data to train machine learning models. You can select your workforce from one of three providers: A private workforce that you create. It can include employees, contractors, and outside experts. Use a private workforce when want the data to stay within your organization or when a specific set of skills is required. One or more vendors that you select from the Amazon Web Services Marketplace. Vendors provide expertise in specific areas. The Amazon Mechanical Turk workforce. This is the largest workforce, but it should only be used for public data or data that has been stripped of any personally identifiable information. You can also use automated data labeling to reduce the number of data objects that need to be labeled by a human. Automated data labeling uses active learning to determine if a data object can be labeled by machine or if it needs to be sent to a human worker. For more information, see Using Automated Data Labeling. The data objects to be labeled are contained in an Amazon S3 bucket. You create a manifest file that describes the location of each object. For more information, see Using Input and Output Data. The output can be used as the manifest file for another labeling job or as training data for your machine learning models. You can use this operation to create a static labeling job or a streaming labeling job. A static labeling job stops if all data objects in the input manifest file identified in ManifestS3Uri have been labeled. A streaming labeling job runs perpetually until it is manually stopped, or remains idle for 10 days. You can send new data objects to an active (InProgress) streaming labeling job in real time. To learn how to create a static labeling job, see Create a Labeling Job (API) in the Amazon SageMaker Developer Guide. To learn how to create a streaming labeling job, see Create a Streaming Labeling Job.
Creates a job that uses workers to label the data objects in your input dataset. You can use the labeled data to train machine learning models. You can select your workforce from one of three providers: A private workforce that you create. It can include employees, contractors, and outside experts. Use a private workforce when want the data to stay within your organization or when a specific set of skills is required. One or more vendors that you select from the Amazon Web Services Marketplace. Vendors provide expertise in specific areas. The Amazon Mechanical Turk workforce. This is the largest workforce, but it should only be used for public data or data that has been stripped of any personally identifiable information. You can also use automated data labeling to reduce the number of data objects that need to be labeled by a human. Automated data labeling uses active learning to determine if a data object can be labeled by machine or if it needs to be sent to a human worker. For more information, see Using Automated Data Labeling. The data objects to be labeled are contained in an Amazon S3 bucket. You create a manifest file that describes the location of each object. For more information, see Using Input and Output Data. The output can be used as the manifest file for another labeling job or as training data for your machine learning models. You can use this operation to create a static labeling job or a streaming labeling job. A static labeling job stops if all data objects in the input manifest file identified in ManifestS3Uri have been labeled. A streaming labeling job runs perpetually until it is manually stopped, or remains idle for 10 days. You can send new data objects to an active (InProgress) streaming labeling job in real time. To learn how to create a static labeling job, see Create a Labeling Job (API) in the Amazon SageMaker Developer Guide. To learn how to create a streaming labeling job, see Create a Streaming Labeling Job.
Creates an MLflow Tracking Server using a general purpose Amazon S3 bucket as the artifact store.
Creates an MLflow Tracking Server using a general purpose Amazon S3 bucket as the artifact store.
Creates an MLflow Tracking Server using a general purpose Amazon S3 bucket as the artifact store. For more information, see Create an MLflow Tracking Server.
Creates an MLflow Tracking Server using a general purpose Amazon S3 bucket as the artifact store. For more information, see Create an MLflow Tracking Server.
The ground truth labels for the dataset used for the monitoring job.
Inputs for the model bias job.
The configuration for a baseline model bias job.
Docker container image configuration object for the model bias job.
Creates the definition for a model bias job.
Creates the definition for a model bias job.
Configure the export output details for an Amazon SageMaker Model Card.
Creates an Amazon SageMaker Model Card export job.
Creates an Amazon SageMaker Model Card export job.
Configure the security settings to protect model card data.
Creates an Amazon SageMaker Model Card. For information about how to use model cards, see Amazon SageMaker Model Card.
Creates an Amazon SageMaker Model Card. For information about how to use model cards, see Amazon SageMaker Model Card.
Inputs for the model explainability job.
The configuration for a baseline model explainability job.
Docker container image configuration object for the model explainability job.
Creates the definition for a model explainability job.
Creates the definition for a model explainability job.
Specifies details about how containers in a multi-container endpoint are run.
Creates a model in SageMaker. In the request, you name the model and describe a primary container. For the primary container, you specify the Docker image that contains inference code, artifacts (from prior training), and a custom environment map that the inference code uses when you deploy the model for predictions. Use this API to create a model if you want to use SageMaker hosting services or run a batch transform job. To host your model, you create an endpoint configuration with the CreateEndpointConfig API, and then create an endpoint with the CreateEndpoint API. SageMaker then deploys all of the containers that you defined for the model in the hosting environment. To run a batch transform using your model, you start a job with the CreateTransformJob API. SageMaker uses your model and your dataset to get inferences which are then saved to a specified S3 location. In the request, you also provide an IAM role that SageMaker can assume to access model artifacts and docker image for deployment on ML compute hosting instances or for batch transform jobs. In addition, you also use the IAM role to manage permissions the inference code needs. For example, if the inference code access any other Amazon Web Services resources, you grant necessary permissions via this role.
Creates a model in SageMaker. In the request, you name the model and describe a primary container. For the primary container, you specify the Docker image that contains inference code, artifacts (from prior training), and a custom environment map that the inference code uses when you deploy the model for predictions. Use this API to create a model if you want to use SageMaker hosting services or run a batch transform job. To host your model, you create an endpoint configuration with the CreateEndpointConfig API, and then create an endpoint with the CreateEndpoint API. SageMaker then deploys all of the containers that you defined for the model in the hosting environment. To run a batch transform using your model, you start a job with the CreateTransformJob API. SageMaker uses your model and your dataset to get inferences which are then saved to a specified S3 location. In the request, you also provide an IAM role that SageMaker can assume to access model artifacts and docker image for deployment on ML compute hosting instances or for batch transform jobs. In addition, you also use the IAM role to manage permissions the inference code needs. For example, if the inference code access any other Amazon Web Services resources, you grant necessary permissions via this role.
The managed configuration of a model package group.
Creates a model group. A model group contains a group of model versions.
Creates a model group. A model group contains a group of model versions.
Specifies an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your SageMaker account or an algorithm in Amazon Web Services Marketplace that you are subscribed to.
A list of algorithms that were used to create a model package.
Contains data, such as the inputs and targeted instance types that are used in the process of validating the model package. The data provided in the validation profile is made available to your buyers on Amazon Web Services Marketplace.
Specifies batch transform jobs that SageMaker runs to validate your model package.
An optional Key Management Service key to encrypt, decrypt, and re-encrypt model package information for regulated workloads with highly sensitive data.
The model card associated with the model package. Since ModelPackageModelCard is tied to a model package, it is a specific usage of a model card and its schema is simplified compared to the schema of ModelCard. The ModelPackageModelCard schema does not include model_package_details, and model_overview is composed of the model_creator and model_artifact properties. For more information about the model package model card schema, see Model package model card schema. For more information about the model card associated with the model package, see View the Details of a Model Version.
Model quality statistics and constraints.
Data quality constraints and statistics for a model.
Contains explainability metrics for a model.
Contains metrics captured from a model.
A structure describing the current state of the model in its life cycle.
Represents the drift check model quality baselines that can be used when the model monitor is set using the model package.
Represents the drift check data quality baselines that can be used when the model monitor is set using the model package.
Contains details regarding the file source.
Represents the drift check explainability baselines that can be used when the model monitor is set using the model package.
Represents the drift check bias baselines that can be used when the model monitor is set using the model package.
Represents the drift check baselines that can be used when the model monitor is set using the model package.
Creates a model package that you can use to create SageMaker models or list on Amazon Web Services Marketplace, or a versioned model that is part of a model group. Buyers can subscribe to model packages listed on Amazon Web Services Marketplace to create models in SageMaker. To create a model package by specifying a Docker container that contains your inference code and the Amazon S3 location of your model artifacts, provide values for InferenceSpecification. To create a model from an algorithm resource that you created or subscribed to in Amazon Web Services Marketplace, provide a value for SourceAlgorithmSpecification. There are two types of model packages: Versioned - a model that is part of a model group in the model registry. Unversioned - a model package that is not part of a model group.
Creates a model package that you can use to create SageMaker models or list on Amazon Web Services Marketplace, or a versioned model that is part of a model group. Buyers can subscribe to model packages listed on Amazon Web Services Marketplace to create models in SageMaker. To create a model package by specifying a Docker container that contains your inference code and the Amazon S3 location of your model artifacts, provide values for InferenceSpecification. To create a model from an algorithm resource that you created or subscribed to in Amazon Web Services Marketplace, provide a value for SourceAlgorithmSpecification. There are two types of model packages: Versioned - a model that is part of a model group in the model registry. Unversioned - a model package that is not part of a model group.
The input for the model quality monitoring job. Currently endpoints are supported for input for model quality monitoring jobs.
Configuration for monitoring constraints and monitoring statistics. These baseline resources are compared against the results of the current job from the series of jobs scheduled to collect data periodically.
Container image configuration object for the monitoring job.
Creates a definition for a job that monitors model quality and drift. For information about model monitor, see Amazon SageMaker AI Model Monitor.
Creates a definition for a job that monitors model quality and drift. For information about model monitor, see Amazon SageMaker AI Model Monitor.
Configuration details about the monitoring schedule.
Networking options for a job, such as network traffic encryption between containers, whether to allow inbound and outbound network calls to and from containers, and the VPC subnets and security groups to use for VPC-enabled jobs.
The inputs for a monitoring job.
Configuration for monitoring constraints and monitoring statistics. These baseline resources are compared against the results of the current job from the series of jobs scheduled to collect data periodically.
Container image configuration object for the monitoring job.
Defines the monitoring job.
Configures the monitoring schedule and defines the monitoring job.
Creates a schedule that regularly starts Amazon SageMaker AI Processing Jobs to monitor the data captured for an Amazon SageMaker AI Endpoint.
Creates a schedule that regularly starts Amazon SageMaker AI Processing Jobs to monitor the data captured for an Amazon SageMaker AI Endpoint.
Information on the IMDS configuration of the notebook instance
Creates an SageMaker AI notebook instance. A notebook instance is a machine learning (ML) compute instance running on a Jupyter notebook. In a CreateNotebookInstance request, specify the type of ML compute instance that you want to run. SageMaker AI 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. SageMaker AI also provides a set of example notebooks. Each notebook demonstrates how to use SageMaker AI with a specific algorithm or with a machine learning framework. After receiving the request, SageMaker AI does the following: Creates a network interface in the SageMaker AI VPC. (Option) If you specified SubnetId, SageMaker AI 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, SageMaker AI 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 SageMaker AI VPC. If you specified SubnetId of your VPC, SageMaker AI 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, SageMaker AI returns its Amazon Resource Name (ARN). You can't change the name of a notebook instance after you create it. After SageMaker AI 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 SageMaker AI endpoints, and validate hosted models. For more information, see How It Works.
Contains the notebook instance lifecycle configuration script. Each lifecycle configuration script has a limit of 16384 characters. The value of the $PATH environment variable that is available to both scripts is /sbin:bin:/usr/sbin:/usr/bin. View Amazon CloudWatch Logs for notebook instance lifecycle configurations in log group /aws/sagemaker/NotebookInstances in log stream [notebook-instance-name]/[LifecycleConfigHook]. 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 Step 2.1: (Optional) Customize a Notebook Instance.
Creates a lifecycle configuration that you can associate with a notebook instance. A lifecycle configuration 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 $PATH environment variable that is available to both scripts is /sbin:bin:/usr/sbin:/usr/bin. View Amazon CloudWatch Logs for notebook instance lifecycle configurations in log group /aws/sagemaker/NotebookInstances in log stream [notebook-instance-name]/[LifecycleConfigHook]. 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 Step 2.1: (Optional) Customize a Notebook Instance. Lifecycle configuration scripts execute with root access and the notebook instance's IAM execution role privileges. Grant this permission only to trusted principals. See Customize a Notebook Instance Using a Lifecycle Configuration Script for security best practices.
Creates a lifecycle configuration that you can associate with a notebook instance. A lifecycle configuration 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 $PATH environment variable that is available to both scripts is /sbin:bin:/usr/sbin:/usr/bin. View Amazon CloudWatch Logs for notebook instance lifecycle configurations in log group /aws/sagemaker/NotebookInstances in log stream [notebook-instance-name]/[LifecycleConfigHook]. 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 Step 2.1: (Optional) Customize a Notebook Instance. Lifecycle configuration scripts execute with root access and the notebook instance's IAM execution role privileges. Grant this permission only to trusted principals. See Customize a Notebook Instance Using a Lifecycle Configuration Script for security best practices.
Creates an SageMaker AI notebook instance. A notebook instance is a machine learning (ML) compute instance running on a Jupyter notebook. In a CreateNotebookInstance request, specify the type of ML compute instance that you want to run. SageMaker AI 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. SageMaker AI also provides a set of example notebooks. Each notebook demonstrates how to use SageMaker AI with a specific algorithm or with a machine learning framework. After receiving the request, SageMaker AI does the following: Creates a network interface in the SageMaker AI VPC. (Option) If you specified SubnetId, SageMaker AI 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, SageMaker AI 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 SageMaker AI VPC. If you specified SubnetId of your VPC, SageMaker AI 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, SageMaker AI returns its Amazon Resource Name (ARN). You can't change the name of a notebook instance after you create it. After SageMaker AI 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 SageMaker AI endpoints, and validate hosted models. For more information, see How It Works.
A VPC in Amazon VPC that's accessible to an optimized that you create with an optimization job. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC.
A SageMaker model to use as the source or destination for an optimization job.
Details for where to store the optimized model that you create with the optimization job.
The access configuration settings for the source ML model for an optimization job, where you can accept the model end-user license agreement (EULA).
The Amazon S3 location of a source model to optimize with an optimization job.
The location of the source model to optimize with an optimization job.
Contains information about the training data source for speculative decoding.
Settings for the model speculative decoding technique that's applied by a model optimization job.
Settings for the model sharding technique that's applied by a model optimization job.
Settings for the model quantization technique that's applied by a model optimization job.
Settings for the model compilation technique that's applied by a model optimization job.
Settings for an optimization technique that you apply with a model optimization job.
Creates a job that optimizes a model for inference performance. To create the job, you provide the location of a source model, and you provide the settings for the optimization techniques that you want the job to apply. When the job completes successfully, SageMaker uploads the new optimized model to the output destination that you specify. For more information about how to use this action, and about the supported optimization techniques, see Optimize model inference with Amazon SageMaker.
Creates a job that optimizes a model for inference performance. To create the job, you provide the location of a source model, and you provide the settings for the optimization techniques that you want the job to apply. When the job completes successfully, SageMaker uploads the new optimized model to the output destination that you specify. For more information about how to use this action, and about the supported optimization techniques, see Optimize model inference with Amazon SageMaker.
Creates a presigned URL to access an Amazon SageMaker Partner AI App.
Creates a presigned URL to access an Amazon SageMaker Partner AI App.
Maintenance configuration settings for the SageMaker Partner AI App.
Defines the mapping between an in-app role and the Amazon Web Services IAM Identity Center group patterns that should be assigned to that role within the SageMaker Partner AI App.
Configuration settings for the SageMaker Partner AI App.
Creates an Amazon SageMaker Partner AI App.
Creates an Amazon SageMaker Partner AI App.
The location of the pipeline definition stored in Amazon S3.
Configuration that controls the parallelism of the pipeline. By default, the parallelism configuration specified applies to all executions of the pipeline unless overridden.
Creates a pipeline using a JSON pipeline definition.
Creates a pipeline using a JSON pipeline definition.
Creates a URL for a specified UserProfile in a Domain. When accessed in a web browser, the user will be automatically signed in to the domain, and granted access to all of the Apps and files associated with the Domain's Amazon Elastic File System volume. This operation can only be called when the authentication mode equals IAM. The IAM role or user passed to this API defines the permissions to access the app. Once the presigned URL is created, no additional permission is required to access this URL. IAM authorization policies for this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the app. You can restrict access to this API and to the URL that it returns to a list of IP addresses, Amazon VPCs or Amazon VPC Endpoints that you specify. For more information, see Connect to Amazon SageMaker AI Studio Through an Interface VPC Endpoint . The URL that you get from a call to CreatePresignedDomainUrl has a default timeout of 5 minutes. You can configure this value using ExpiresInSeconds. If you try to use the URL after the timeout limit expires, you are directed to the Amazon Web Services console sign-in page. The JupyterLab session default expiration time is 12 hours. You can configure this value using SessionExpirationDurationInSeconds.
Creates a URL for a specified UserProfile in a Domain. When accessed in a web browser, the user will be automatically signed in to the domain, and granted access to all of the Apps and files associated with the Domain's Amazon Elastic File System volume. This operation can only be called when the authentication mode equals IAM. The IAM role or user passed to this API defines the permissions to access the app. Once the presigned URL is created, no additional permission is required to access this URL. IAM authorization policies for this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the app. You can restrict access to this API and to the URL that it returns to a list of IP addresses, Amazon VPCs or Amazon VPC Endpoints that you specify. For more information, see Connect to Amazon SageMaker AI Studio Through an Interface VPC Endpoint . The URL that you get from a call to CreatePresignedDomainUrl has a default timeout of 5 minutes. You can configure this value using ExpiresInSeconds. If you try to use the URL after the timeout limit expires, you are directed to the Amazon Web Services console sign-in page. The JupyterLab session default expiration time is 12 hours. You can configure this value using SessionExpirationDurationInSeconds.
Returns a presigned URL that you can use to connect to the MLflow UI attached to your MLflow App. For more information, see Launch the MLflow UI using a presigned URL.
Returns a presigned URL that you can use to connect to the MLflow UI attached to your MLflow App. For more information, see Launch the MLflow UI using a presigned URL.
Returns a presigned URL that you can use to connect to the MLflow UI attached to your tracking server. For more information, see Launch the MLflow UI using a presigned URL.
Returns a presigned URL that you can use to connect to the MLflow UI attached to your tracking server. For more information, see Launch the MLflow UI using a presigned URL.
Returns a URL that you can use to connect to the Jupyter server from a notebook instance. In the SageMaker AI console, when you choose Open next to a notebook instance, SageMaker AI 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. The IAM role or user used to call this API defines the permissions to access the notebook instance. Once the presigned URL is created, no additional permission is required to access this URL. IAM authorization policies for this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the notebook instance. You can restrict access to this API and to the URL that it returns to a list of IP addresses that you specify. Use the NotIpAddress condition operator and the aws:SourceIP condition context key to specify the list of IP addresses that you want to have access to the notebook instance. For more information, see Limit Access to a Notebook Instance by IP Address. The URL that you get from a call to CreatePresignedNotebookInstanceUrl is valid only for 5 minutes. If you try to use the URL after the 5-minute limit expires, you are directed to the Amazon Web Services console sign-in page.
Returns a URL that you can use to connect to the Jupyter server from a notebook instance. In the SageMaker AI console, when you choose Open next to a notebook instance, SageMaker AI 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. The IAM role or user used to call this API defines the permissions to access the notebook instance. Once the presigned URL is created, no additional permission is required to access this URL. IAM authorization policies for this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the notebook instance. You can restrict access to this API and to the URL that it returns to a list of IP addresses that you specify. Use the NotIpAddress condition operator and the aws:SourceIP condition context key to specify the list of IP addresses that you want to have access to the notebook instance. For more information, see Limit Access to a Notebook Instance by IP Address. The URL that you get from a call to CreatePresignedNotebookInstanceUrl is valid only for 5 minutes. If you try to use the URL after the 5-minute limit expires, you are directed to the Amazon Web Services console sign-in page.
Configures conditions under which the processing job should be stopped, such as how long the processing job has been running. After the condition is met, the processing job is stopped.
Configuration for the cluster used to run a processing job.
Identifies the resources, ML compute instances, and ML storage volumes to deploy for a processing job. In distributed training, you specify more than one instance.
Configuration for uploading output data to Amazon S3 from the processing container.
Configuration for processing job outputs in Amazon SageMaker Feature Store.
Describes the results of a processing job. The processing output must specify exactly one of either S3Output or FeatureStoreOutput types.
Configuration for uploading output from the processing container.
Configuration for downloading input data from Amazon S3 into the processing container.
The database user name used in Redshift query execution.
The SQL query statements to be executed.
The name of the Redshift database used in Redshift query execution.
The Redshift cluster Identifier.
Configuration for Redshift Dataset Definition input.
Configuration for Dataset Definition inputs. The Dataset Definition input must specify exactly one of either AthenaDatasetDefinition or RedshiftDatasetDefinition types.
The inputs for a processing job. The processing input must specify exactly one of either S3Input or DatasetDefinition types.
Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs: CreateProcessingJob CreateTrainingJob CreateTransformJob
Creates a processing job.
Creates a processing job.
A key value pair used when you provision a project as a service catalog product. For information, see What is Amazon Web Services Service Catalog.
Details that you specify to provision a service catalog product. For information about service catalog, see What is Amazon Web Services Service Catalog.
Contains configuration details for a template provider. Only one type of template provider can be specified.
Creates a machine learning (ML) project that can contain one or more templates that set up an ML pipeline from training to deploying an approved model.
Creates a machine learning (ML) project that can contain one or more templates that set up an ML pipeline from training to deploying an approved model.
A collection of space sharing settings.
A collection of EBS storage settings that apply to both private and shared spaces.
The storage settings for a space.
Settings related to idle shutdown of Studio applications in a space.
Settings that are used to configure and manage the lifecycle of Amazon SageMaker Studio applications in a space.
The settings for the JupyterLab application within a space.
The application settings for a Code Editor space.
A custom file system in Amazon S3. This is only supported in Amazon SageMaker Unified Studio.
A custom file system in Amazon FSx for Lustre.
A file system, created by you in Amazon EFS, that you assign to a user profile or space for an Amazon SageMaker AI Domain. Permitted users can access this file system in Amazon SageMaker AI Studio.
A file system, created by you, that you assign to a user profile or space for an Amazon SageMaker AI Domain. Permitted users can access this file system in Amazon SageMaker AI Studio.
A collection of space settings.
The collection of ownership settings for a space.
Creates a private space or a space used for real time collaboration in a domain.
Creates a private space or a space used for real time collaboration in a domain.
Creates a new Amazon SageMaker AI Studio Lifecycle Configuration.
Creates a new Amazon SageMaker AI Studio Lifecycle Configuration.
Configuration of storage locations for the Amazon SageMaker Debugger TensorBoard output data.
Contains information about attribute-based access control (ABAC) for a training job. The session chaining configuration uses Amazon Security Token Service (STS) for your training job to request temporary, limited-privilege credentials to tenants. For more information, see Attribute-based access control (ABAC) for multi-tenancy training.
ServerlessJobConfig relevant fields
The configuration for the serverless training job.
Configuration for remote debugging for the CreateTrainingJob API. To learn more about the remote debugging functionality of SageMaker, see Access a training container through Amazon Web Services Systems Manager (SSM) for remote debugging.
Configuration information for profiling rules.
Configuration information for Amazon SageMaker Debugger system monitoring, framework profiling, and storage paths.
The configuration for the Model package.
MlflowConfig relevant fields
MlflowDetails relevant fields
The MLflow configuration using SageMaker managed MLflow.
Configuration information for the infrastructure health check of a training job. A SageMaker-provided health check tests the health of instance hardware and cluster network connectivity.
Configuration information for SageMaker Debugger rules for debugging. To learn more about how to configure the DebugRuleConfiguration parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.
Configuration information for the Amazon SageMaker Debugger hook parameters, metric and tensor collections, and storage paths. To learn more about how to configure the DebugHookConfig parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.
Starts a model training job. After training completes, SageMaker saves the resulting model artifacts to an Amazon S3 location that you specify. If you choose to host your model using SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts in a machine learning service other than SageMaker, provided that you know how to use them for inference. In the request body, you provide the following: AlgorithmSpecification - Identifies the training algorithm to use. HyperParameters - Specify these algorithm-specific parameters to enable the estimation of model parameters during training. Hyperparameters can be tuned to optimize this learning process. For a list of hyperparameters for each training algorithm provided by SageMaker, see Algorithms. Do not include any security-sensitive information including account access IDs, secrets, or tokens in any hyperparameter fields. As part of the shared responsibility model, you are responsible for any potential exposure, unauthorized access, or compromise of your sensitive data if caused by security-sensitive information included in the request hyperparameter variable or plain text fields. InputDataConfig - Describes the input required by the training job and the Amazon S3, EFS, or FSx location where it is stored. OutputDataConfig - Identifies the Amazon S3 bucket where you want 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. EnableManagedSpotTraining - Optimize the cost of training machine learning models by up to 80% by using Amazon EC2 Spot instances. For more information, see Managed Spot Training. RoleArn - The Amazon Resource Name (ARN) that SageMaker assumes to perform tasks on your behalf during model training. You must grant this role the necessary permissions so that SageMaker can successfully complete model training. StoppingCondition - To help cap training costs, use MaxRuntimeInSeconds to set a time limit for training. Use MaxWaitTimeInSeconds to specify how long a managed spot training job has to complete. Environment - The environment variables to set in the Docker container. Do not include any security-sensitive information including account access IDs, secrets, or tokens in any environment fields. As part of the shared responsibility model, you are responsible for any potential exposure, unauthorized access, or compromise of your sensitive data if caused by security-sensitive information included in the request environment variable or plain text fields. RetryStrategy - The number of times to retry the job when the job fails due to an InternalServerError. For more information about SageMaker, see How It Works.
Starts a model training job. After training completes, SageMaker saves the resulting model artifacts to an Amazon S3 location that you specify. If you choose to host your model using SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts in a machine learning service other than SageMaker, provided that you know how to use them for inference. In the request body, you provide the following: AlgorithmSpecification - Identifies the training algorithm to use. HyperParameters - Specify these algorithm-specific parameters to enable the estimation of model parameters during training. Hyperparameters can be tuned to optimize this learning process. For a list of hyperparameters for each training algorithm provided by SageMaker, see Algorithms. Do not include any security-sensitive information including account access IDs, secrets, or tokens in any hyperparameter fields. As part of the shared responsibility model, you are responsible for any potential exposure, unauthorized access, or compromise of your sensitive data if caused by security-sensitive information included in the request hyperparameter variable or plain text fields. InputDataConfig - Describes the input required by the training job and the Amazon S3, EFS, or FSx location where it is stored. OutputDataConfig - Identifies the Amazon S3 bucket where you want 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. EnableManagedSpotTraining - Optimize the cost of training machine learning models by up to 80% by using Amazon EC2 Spot instances. For more information, see Managed Spot Training. RoleArn - The Amazon Resource Name (ARN) that SageMaker assumes to perform tasks on your behalf during model training. You must grant this role the necessary permissions so that SageMaker can successfully complete model training. StoppingCondition - To help cap training costs, use MaxRuntimeInSeconds to set a time limit for training. Use MaxWaitTimeInSeconds to specify how long a managed spot training job has to complete. Environment - The environment variables to set in the Docker container. Do not include any security-sensitive information including account access IDs, secrets, or tokens in any environment fields. As part of the shared responsibility model, you are responsible for any potential exposure, unauthorized access, or compromise of your sensitive data if caused by security-sensitive information included in the request environment variable or plain text fields. RetryStrategy - The number of times to retry the job when the job fails due to an InternalServerError. For more information about SageMaker, see How It Works.
Creates a new training plan in SageMaker to reserve compute capacity. Amazon SageMaker Training Plan is a capability within SageMaker that allows customers to reserve and manage GPU capacity for large-scale AI model training. It provides a way to secure predictable access to computational resources within specific timelines and budgets, without the need to manage underlying infrastructure. How it works Plans can be created for specific resources such as SageMaker Training Jobs or SageMaker HyperPod clusters, automatically provisioning resources, setting up infrastructure, executing workloads, and handling infrastructure failures. Plan creation workflow Users search for available plan offerings based on their requirements (e.g., instance type, count, start time, duration) using the SearchTrainingPlanOfferings API operation. They create a plan that best matches their needs using the ID of the plan offering they want to use. After successful upfront payment, the plan's status becomes Scheduled. The plan can be used to: Queue training jobs. Allocate to an instance group of a SageMaker HyperPod cluster. When the plan start date arrives, it becomes Active. Based on available reserved capacity: Training jobs are launched. Instance groups are provisioned. Plan composition A plan can consist of one or more Reserved Capacities, each defined by a specific instance type, quantity, Availability Zone, duration, and start and end times. For more information about Reserved Capacity, see ReservedCapacitySummary .
Creates a new training plan in SageMaker to reserve compute capacity. Amazon SageMaker Training Plan is a capability within SageMaker that allows customers to reserve and manage GPU capacity for large-scale AI model training. It provides a way to secure predictable access to computational resources within specific timelines and budgets, without the need to manage underlying infrastructure. How it works Plans can be created for specific resources such as SageMaker Training Jobs or SageMaker HyperPod clusters, automatically provisioning resources, setting up infrastructure, executing workloads, and handling infrastructure failures. Plan creation workflow Users search for available plan offerings based on their requirements (e.g., instance type, count, start time, duration) using the SearchTrainingPlanOfferings API operation. They create a plan that best matches their needs using the ID of the plan offering they want to use. After successful upfront payment, the plan's status becomes Scheduled. The plan can be used to: Queue training jobs. Allocate to an instance group of a SageMaker HyperPod cluster. When the plan start date arrives, it becomes Active. Based on available reserved capacity: Training jobs are launched. Instance groups are provisioned. Plan composition A plan can consist of one or more Reserved Capacities, each defined by a specific instance type, quantity, Availability Zone, duration, and start and end times. For more information about Reserved Capacity, see ReservedCapacitySummary .
Configures the timeout and maximum number of retries for processing a transform job invocation.
The data structure used to specify the data to be used for inference in a batch transform job and to associate the data that is relevant to the prediction results in the output. The input filter provided allows you to exclude input data that is not needed for inference in a batch transform job. The output filter provided allows you to include input data relevant to interpreting the predictions in the output from the job. For more information, see Associate Prediction Results with their Corresponding Input Records.
Starts a transform job. A transform job uses a trained model to get inferences on a dataset and saves these results to an Amazon S3 location that you specify. To perform batch transformations, you create a transform job and use the data that you have readily available. In the request body, you provide the following: TransformJobName - Identifies the transform job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account. ModelName - Identifies the model to use. ModelName must be the name of an existing Amazon SageMaker model in the same Amazon Web Services Region and Amazon Web Services account. For information on creating a model, see CreateModel. TransformInput - Describes the dataset to be transformed and the Amazon S3 location where it is stored. TransformOutput - Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job. TransformResources - Identifies the ML compute instances and AMI image versions for the transform job. For more information about how batch transformation works, see Batch Transform.
Starts a transform job. A transform job uses a trained model to get inferences on a dataset and saves these results to an Amazon S3 location that you specify. To perform batch transformations, you create a transform job and use the data that you have readily available. In the request body, you provide the following: TransformJobName - Identifies the transform job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account. ModelName - Identifies the model to use. ModelName must be the name of an existing Amazon SageMaker model in the same Amazon Web Services Region and Amazon Web Services account. For information on creating a model, see CreateModel. TransformInput - Describes the dataset to be transformed and the Amazon S3 location where it is stored. TransformOutput - Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job. TransformResources - Identifies the ML compute instances and AMI image versions for the transform job. For more information about how batch transformation works, see Batch Transform.
The status of the trial component.
The value of a hyperparameter. Only one of NumberValue or StringValue can be specified. This object is specified in the CreateTrialComponent request.
Represents an input or output artifact of a trial component. You specify TrialComponentArtifact as part of the InputArtifacts and OutputArtifacts parameters in the CreateTrialComponent request. Examples of input artifacts are datasets, algorithms, hyperparameters, source code, and instance types. Examples of output artifacts are metrics, snapshots, logs, and images.
Creates a trial component, which is a stage of a machine learning trial. A trial is composed of one or more trial components. A trial component can be used in multiple trials. Trial components include pre-processing jobs, training jobs, and batch transform jobs. When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK. You can add tags to a trial component and then use the Search API to search for the tags.
Creates a trial component, which is a stage of a machine learning trial. A trial is composed of one or more trial components. A trial component can be used in multiple trials. Trial components include pre-processing jobs, training jobs, and batch transform jobs. When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK. You can add tags to a trial component and then use the Search API to search for the tags.
Creates an SageMaker trial. A trial is a set of steps called trial components that produce a machine learning model. A trial is part of a single SageMaker experiment. When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK. You can add tags to a trial and then use the Search API to search for the tags. To get a list of all your trials, call the ListTrials API. To view a trial's properties, call the DescribeTrial API. To create a trial component, call the CreateTrialComponent API.
Creates an SageMaker trial. A trial is a set of steps called trial components that produce a machine learning model. A trial is part of a single SageMaker experiment. When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK. You can add tags to a trial and then use the Search API to search for the tags. To get a list of all your trials, call the ListTrials API. To view a trial's properties, call the DescribeTrial API. To create a trial component, call the CreateTrialComponent API.
Creates a user profile. A user profile represents a single user within a domain, and is the main way to reference a "person" for the purposes of sharing, reporting, and other user-oriented features. This entity is created when a user onboards to a domain. If an administrator invites a person by email or imports them from IAM Identity Center, a user profile is automatically created. A user profile is the primary holder of settings for an individual user and has a reference to the user's private Amazon Elastic File System home directory.
Creates a user profile. A user profile represents a single user within a domain, and is the main way to reference a "person" for the purposes of sharing, reporting, and other user-oriented features. This entity is created when a user onboards to a domain. If an administrator invites a person by email or imports them from IAM Identity Center, a user profile is automatically created. A user profile is the primary holder of settings for an individual user and has a reference to the user's private Amazon Elastic File System home directory.
The VPC object you use to create or update a workforce.
A list of IP address ranges (CIDRs). Used to create an allow list of IP addresses for a private workforce. Workers will only be able to log in to their worker portal from an IP address within this range. By default, a workforce isn't restricted to specific IP addresses.
Use this parameter to configure your OIDC Identity Provider (IdP).
Use this operation to create a workforce. This operation will return an error if a workforce already exists in the Amazon Web Services Region that you specify. You can only create one workforce in each Amazon Web Services Region per Amazon Web Services account. If you want to create a new workforce in an Amazon Web Services Region where a workforce already exists, use the DeleteWorkforce API operation to delete the existing workforce and then use CreateWorkforce to create a new workforce. To create a private workforce using Amazon Cognito, you must specify a Cognito user pool in CognitoConfig. You can also create an Amazon Cognito workforce using the Amazon SageMaker console. For more information, see Create a Private Workforce (Amazon Cognito). To create a private workforce using your own OIDC Identity Provider (IdP), specify your IdP configuration in OidcConfig. Your OIDC IdP must support groups because groups are used by Ground Truth and Amazon A2I to create work teams. For more information, see Create a Private Workforce (OIDC IdP).
Use this operation to create a workforce. This operation will return an error if a workforce already exists in the Amazon Web Services Region that you specify. You can only create one workforce in each Amazon Web Services Region per Amazon Web Services account. If you want to create a new workforce in an Amazon Web Services Region where a workforce already exists, use the DeleteWorkforce API operation to delete the existing workforce and then use CreateWorkforce to create a new workforce. To create a private workforce using Amazon Cognito, you must specify a Cognito user pool in CognitoConfig. You can also create an Amazon Cognito workforce using the Amazon SageMaker console. For more information, see Create a Private Workforce (Amazon Cognito). To create a private workforce using your own OIDC Identity Provider (IdP), specify your IdP configuration in OidcConfig. Your OIDC IdP must support groups because groups are used by Ground Truth and Amazon A2I to create work teams. For more information, see Create a Private Workforce (OIDC IdP).
Use this parameter to specify a supported global condition key that is added to the IAM policy.
This object defines the access restrictions to Amazon S3 resources that are included in custom worker task templates using the Liquid filter, grant_read_access. To learn more about how custom templates are created, see Create custom worker task templates.
Use this optional parameter to constrain access to an Amazon S3 resource based on the IP address using supported IAM global condition keys. The Amazon S3 resource is accessed in the worker portal using a Amazon S3 presigned URL.
Configures Amazon SNS notifications of available or expiring work items for work teams.
A list of user groups that exist in your OIDC Identity Provider (IdP). One to ten groups can be used to create a single private work team. When you add a user group to the list of Groups, you can add that user group to one or more private work teams. If you add a user group to a private work team, all workers in that user group are added to the work team.
Defines an Amazon Cognito or your own OIDC IdP user group that is part of a work team.
Creates a new work team for labeling your data. A work team is defined by one or more Amazon Cognito user pools. You must first create the user pools before you can create a work team. You cannot create more than 25 work teams in an account and region.
Creates a new work team for labeling your data. A work team is defined by one or more Amazon Cognito user pools. You must first create the user pools before you can create a work team. You cannot create more than 25 work teams in an account and region.
A customized metric.
The currently active data capture configuration used by your Endpoint.
Information about the status of the rule evaluation.
The configuration of deep health checks for an instance group. Overlapping deep health check configurations will be merged into a single operation.
Deletes the specified AI benchmark job.
Deletes the specified AI benchmark job.
Deletes the specified AI recommendation job.
Deletes the specified AI recommendation job.
Deletes the specified AI workload configuration. You cannot delete a configuration that is referenced by an active benchmark job.
Deletes the specified AI workload configuration. You cannot delete a configuration that is referenced by an active benchmark job.
Deletes an action.
Deletes an action.
Removes the specified algorithm from your account.
Deletes an AppImageConfig.
Used to stop and delete an app.
Deletes an artifact. Either ArtifactArn or Source must be specified.
Deletes an artifact. Either ArtifactArn or Source must be specified.
Deletes an association.
Deletes an association.
Delete a SageMaker HyperPod cluster.
Delete a SageMaker HyperPod cluster.
Deletes the cluster policy of the cluster.
Deletes the specified Git repository from your account.
Deletes the specified compilation job. This action deletes only the compilation job resource in Amazon SageMaker AI. It doesn't delete other resources that are related to that job, such as the model artifacts that the job creates, the compilation logs in CloudWatch, the compiled model, or the IAM role. You can delete a compilation job only if its current status is COMPLETED, FAILED, or STOPPED. If the job status is STARTING or INPROGRESS, stop the job, and then delete it after its status becomes STOPPED.
Deletes the compute allocation from the cluster.
Deletes an context.
Deletes an context.
Deletes a data quality monitoring job definition.
Deletes a fleet.
The retention policy for data stored on an Amazon Elastic File System volume.
Used to delete a domain. If you onboarded with IAM mode, you will need to delete your domain to onboard again using IAM Identity Center. Use with caution. All of the members of the domain will lose access to their EFS volume, including data, notebooks, and other artifacts.
Deletes an edge deployment plan if (and only if) all the stages in the plan are inactive or there are no stages in the plan.
Delete a stage in an edge deployment plan if (and only if) the stage is inactive.
Deletes an endpoint configuration. The DeleteEndpointConfig API deletes only the specified configuration. It does not delete endpoints created using the configuration. You must not delete an EndpointConfig in use by an endpoint that is live or while the UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. If you delete the EndpointConfig of an endpoint that is active or being created or updated you may lose visibility into the instance type the endpoint is using. The endpoint must be deleted in order to stop incurring charges.
Deletes an endpoint. SageMaker frees up all of the resources that were deployed when the endpoint was created. SageMaker retires any custom KMS key grants associated with the endpoint, meaning you don't need to use the RevokeGrant API call. When you delete your endpoint, SageMaker asynchronously deletes associated endpoint resources such as KMS key grants. You might still see these resources in your account for a few minutes after deleting your endpoint. Do not delete or revoke the permissions for your ExecutionRoleArn , otherwise SageMaker cannot delete these resources.
Deletes an SageMaker experiment. All trials associated with the experiment must be deleted first. Use the ListTrials API to get a list of the trials associated with the experiment.
Deletes an SageMaker experiment. All trials associated with the experiment must be deleted first. Use the ListTrials API to get a list of the trials associated with the experiment.
Delete the FeatureGroup and any data that was written to the OnlineStore of the FeatureGroup. Data cannot be accessed from the OnlineStore immediately after DeleteFeatureGroup is called. Data written into the OfflineStore will not be deleted. The Amazon Web Services Glue database and tables that are automatically created for your OfflineStore are not deleted. Note that it can take approximately 10-15 minutes to delete an OnlineStore FeatureGroup with the InMemory StorageType.
Deletes the specified flow definition.
Deletes the specified flow definition.
Delete a hub content reference in order to remove a model from a private hub.
Delete the contents of a hub.
Delete a hub.
Use this operation to delete a human task user interface (worker task template). To see a list of human task user interfaces (work task templates) in your account, use ListHumanTaskUis. When you delete a worker task template, it no longer appears when you call ListHumanTaskUis.
Use this operation to delete a human task user interface (worker task template). To see a list of human task user interfaces (work task templates) in your account, use ListHumanTaskUis. When you delete a worker task template, it no longer appears when you call ListHumanTaskUis.
Deletes a hyperparameter tuning job. The DeleteHyperParameterTuningJob API deletes only the tuning job entry that was created in SageMaker when you called the CreateHyperParameterTuningJob API. It does not delete training jobs, artifacts, or the IAM role that you specified when creating the model.
Deletes a SageMaker AI image and all versions of the image. The container images aren't deleted.
Deletes a SageMaker AI image and all versions of the image. The container images aren't deleted.
Deletes a version of a SageMaker AI image. The container image the version represents isn't deleted.
Deletes a version of a SageMaker AI image. The container image the version represents isn't deleted.
Deletes an inference component.
Deletes an inference experiment. This operation does not delete your endpoint, variants, or any underlying resources. This operation only deletes the metadata of your experiment.
Deletes an inference experiment. This operation does not delete your endpoint, variants, or any underlying resources. This operation only deletes the metadata of your experiment.
Deletes an MLflow App.
Deletes an MLflow App.
Deletes an MLflow Tracking Server. For more information, see Clean up MLflow resources.
Deletes an MLflow Tracking Server. For more information, see Clean up MLflow resources.
Deletes an Amazon SageMaker AI model bias job definition.
Deletes an Amazon SageMaker Model Card.
Deletes an Amazon SageMaker AI model explainability job definition.
Deletes a model. The DeleteModel API deletes only the model entry that was created in 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.
Deletes the specified model group.
Deletes a model group resource policy.
Deletes a model package. A model package is used to create SageMaker models or list on Amazon Web Services Marketplace. Buyers can subscribe to model packages listed on Amazon Web Services Marketplace to create models in SageMaker.
Deletes the secified model quality monitoring job definition.
Deletes a monitoring schedule. Also stops the schedule had not already been stopped. This does not delete the job execution history of the monitoring schedule.
Deletes an SageMaker AI 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. SageMaker AI removes the ML compute instance, and deletes the ML storage volume and the network interface associated with the notebook instance.
Deletes a notebook instance lifecycle configuration.
Deletes an optimization job.
Deletes a SageMaker Partner AI App.
Deletes a SageMaker Partner AI App.
Deletes a pipeline if there are no running instances of the pipeline. To delete a pipeline, you must stop all running instances of the pipeline using the StopPipelineExecution API. When you delete a pipeline, all instances of the pipeline are deleted.
Deletes a pipeline if there are no running instances of the pipeline. To delete a pipeline, you must stop all running instances of the pipeline using the StopPipelineExecution API. When you delete a pipeline, all instances of the pipeline are deleted.
Deletes a processing job. After Amazon SageMaker deletes a processing job, all of the metadata for the processing job is lost. You can delete only processing jobs that are in a terminal state (Stopped, Failed, or Completed). You cannot delete a job that is in the InProgress or Stopping state. After deleting the job, you can reuse its name to create another processing job.
Delete the specified project.
Used to delete a space.
Deletes the Amazon SageMaker AI Studio Lifecycle Configuration. In order to delete the Lifecycle Configuration, there must be no running apps using the Lifecycle Configuration. You must also remove the Lifecycle Configuration from UserSettings in all Domains and UserProfiles.
Deletes the specified tags from an SageMaker resource. To list a resource's tags, use the ListTags API. When you call this API to delete tags from a hyperparameter tuning job, the deleted tags are not removed from training jobs that the hyperparameter tuning job launched before you called this API. When you call this API to delete tags from a SageMaker Domain or User Profile, the deleted tags are not removed from Apps that the SageMaker Domain or User Profile launched before you called this API.
Deletes the specified tags from an SageMaker resource. To list a resource's tags, use the ListTags API. When you call this API to delete tags from a hyperparameter tuning job, the deleted tags are not removed from training jobs that the hyperparameter tuning job launched before you called this API. When you call this API to delete tags from a SageMaker Domain or User Profile, the deleted tags are not removed from Apps that the SageMaker Domain or User Profile launched before you called this API.
Deletes a training job. After SageMaker deletes a training job, all of the metadata for the training job is lost. You can delete only training jobs that are in a terminal state (Stopped, Failed, or Completed) and don't retain an Available managed warm pool. You cannot delete a job that is in the InProgress or Stopping state. After deleting the job, you can reuse its name to create another training job.
Deletes the specified trial component. A trial component must be disassociated from all trials before the trial component can be deleted. To disassociate a trial component from a trial, call the DisassociateTrialComponent API.
Deletes the specified trial component. A trial component must be disassociated from all trials before the trial component can be deleted. To disassociate a trial component from a trial, call the DisassociateTrialComponent API.
Deletes the specified trial. All trial components that make up the trial must be deleted first. Use the DescribeTrialComponent API to get the list of trial components.
Deletes the specified trial. All trial components that make up the trial must be deleted first. Use the DescribeTrialComponent API to get the list of trial components.
Deletes a user profile. When a user profile is deleted, the user loses access to their EFS volume, including data, notebooks, and other artifacts.
Use this operation to delete a workforce. If you want to create a new workforce in an Amazon Web Services Region where a workforce already exists, use this operation to delete the existing workforce and then use CreateWorkforce to create a new workforce. If a private workforce contains one or more work teams, you must use the DeleteWorkteam operation to delete all work teams before you delete the workforce. If you try to delete a workforce that contains one or more work teams, you will receive a ResourceInUse error.
Use this operation to delete a workforce. If you want to create a new workforce in an Amazon Web Services Region where a workforce already exists, use this operation to delete the existing workforce and then use CreateWorkforce to create a new workforce. If a private workforce contains one or more work teams, you must use the DeleteWorkteam operation to delete all work teams before you delete the workforce. If you try to delete a workforce that contains one or more work teams, you will receive a ResourceInUse error.
Deletes an existing work team. This operation can't be undone.
Deletes an existing work team. This operation can't be undone.
Gets the Amazon EC2 Container Registry path of the docker image of the model that is hosted in this ProductionVariant. If you used the registry/repository[:tag] form to specify the image path of the primary container when you created the model hosted in this ProductionVariant, the path resolves to a path of the form registry/repository[@digest]. A digest is a hash value that identifies a specific version of an image. For information about Amazon ECR paths, see Pulling an Image in the Amazon ECR User Guide.
The recommended configuration to use for Real-Time Inference.
A set of recommended deployment configurations for the model. To get more advanced recommendations, see CreateInferenceRecommendationsJob to create an inference recommendation job.
Contains information summarizing the deployment stage results.
Contains information summarizing the deployment stage results.
Deregisters the specified devices. After you deregister a device, you will need to re-register the devices.
Information that SageMaker Neo automatically derived about the model.
Returns details of an AI benchmark job, including its status, configuration, target endpoint, and timing information.
Returns details of an AI benchmark job, including its status, configuration, target endpoint, and timing information.
Returns details of an AI recommendation job, including its status, model source, performance targets, optimization recommendations, and deployment configurations.
Returns details of an AI recommendation job, including its status, model source, performance targets, optimization recommendations, and deployment configurations.
Returns details of an AI workload configuration, including the dataset configuration, benchmark tool settings, tags, and creation time.
Returns details of an AI workload configuration, including the dataset configuration, benchmark tool settings, tags, and creation time.
Describes an action.
Describes an action.
Returns a description of the specified algorithm that is in your account.
Returns a description of the specified algorithm that is in your account.
Describes an AppImageConfig.
Describes an AppImageConfig.
Describes the app.
Describes the app.
Describes an artifact.
Describes an artifact.
Returns information about an AutoML job created by calling CreateAutoMLJob. AutoML jobs created by calling CreateAutoMLJobV2 cannot be described by DescribeAutoMLJob.
The resolved attributes.
Provides information about the endpoint of the model deployment.
Returns information about an AutoML job created by calling CreateAutoMLJob. AutoML jobs created by calling CreateAutoMLJobV2 cannot be described by DescribeAutoMLJob.
Returns information about an AutoML job created by calling CreateAutoMLJobV2 or CreateAutoMLJob.
Returns information about an AutoML job created by calling CreateAutoMLJobV2 or CreateAutoMLJob.
Retrieves detailed information about a specific event for a given HyperPod cluster. This functionality is only supported when the NodeProvisioningMode is set to Continuous.
Retrieves detailed information about a specific event for a given HyperPod cluster. This functionality is only supported when the NodeProvisioningMode is set to Continuous.
Retrieves information of a node (also called a instance interchangeably) of a SageMaker HyperPod cluster.
Retrieves information of a node (also called a instance interchangeably) of a SageMaker HyperPod cluster.
Retrieves information of a SageMaker HyperPod cluster.
Retrieves information of a SageMaker HyperPod cluster.
Description of the cluster policy. This policy is used for task prioritization and fair-share allocation. This helps prioritize critical workloads and distributes idle compute across entities.
Description of the cluster policy. This policy is used for task prioritization and fair-share allocation. This helps prioritize critical workloads and distributes idle compute across entities.
Gets details about the specified Git repository.
Gets details about the specified Git repository.
Returns information about a model compilation job. To create a model compilation job, use CreateCompilationJob. To get information about multiple model compilation jobs, use ListCompilationJobs.
Provides information to verify the integrity of stored model artifacts.
Provides information about the location that is configured for storing model artifacts. Model artifacts are outputs that result from training a model. They typically consist of trained parameters, a model definition that describes how to compute inferences, and other metadata. A SageMaker container stores your trained model artifacts in the /opt/ml/model directory. After training has completed, by default, these artifacts are uploaded to your Amazon S3 bucket as compressed files.
Returns information about a model compilation job. To create a model compilation job, use CreateCompilationJob. To get information about multiple model compilation jobs, use ListCompilationJobs.
Description of the compute allocation definition.
Description of the compute allocation definition.
Describes a context.
Describes a context.
Gets the details of a data quality monitoring job definition.
Gets the details of a data quality monitoring job definition.
A description of the fleet the device belongs to.
A description of the fleet the device belongs to.
Describes the device.
Describes the device.
The description of the domain.
The description of the domain.
Describes an edge deployment plan with deployment status per stage.
Describes an edge deployment plan with deployment status per stage.
A description of edge packaging jobs.
The output of a SageMaker Edge Manager deployable resource.
A description of edge packaging jobs.
Returns the description of an endpoint configuration created using the CreateEndpointConfig API.
Returns the description of an endpoint configuration created using the CreateEndpointConfig API.
Returns the description of an endpoint.
Describes the status of the production variant.
The EC2 capacity reservations that are shared to an ML capacity reservation.
Details about an ML capacity reservation.
A summary of an instance pool for a production variant, including the instance type and the current number of instances.
Describes weight and capacities for a production variant associated with an endpoint. If you sent a request to the UpdateEndpointWeightsAndCapacities API and the endpoint status is Updating, you get different desired and current values.
The production variant summary for a deployment when an endpoint is creating or updating with the CreateEndpoint or UpdateEndpoint operations. Describes the VariantStatus , weight and capacity for a production variant associated with an endpoint.
The summary of an in-progress deployment when an endpoint is creating or updating with a new endpoint configuration.
Returns the description of an endpoint.
Provides a list of an experiment's properties.
The source of the experiment.
Provides a list of an experiment's properties.
Use this operation to describe a FeatureGroup. The response includes information on the creation time, FeatureGroup name, the unique identifier for each FeatureGroup, and more.
Active throughput configuration of the feature group. There are two modes: ON_DEMAND and PROVISIONED. With on-demand mode, you are charged for data reads and writes that your application performs on your feature group. You do not need to specify read and write throughput because Feature Store accommodates your workloads as they ramp up and down. You can switch a feature group to on-demand only once in a 24 hour period. With provisioned throughput mode, you specify the read and write capacity per second that you expect your application to require, and you are billed based on those limits. Exceeding provisioned throughput will result in your requests being throttled. Note: PROVISIONED throughput mode is supported only for feature groups that are offline-only, or use the Standard tier online store.
The status of OfflineStore.
A value that indicates whether the update was successful.
Use this operation to describe a FeatureGroup. The response includes information on the creation time, FeatureGroup name, the unique identifier for each FeatureGroup, and more.
Shows the metadata for a feature within a feature group.
A key-value pair that you specify to describe the feature.
Shows the metadata for a feature within a feature group.
Returns information about the specified flow definition.
Returns information about the specified flow definition.
Describe the content of a hub.
Any dependencies related to hub content, such as scripts, model artifacts, datasets, or notebooks.
Describe the content of a hub.
Describes a hub.
Describes a hub.
Returns information about the requested human task user interface (worker task template).
Container for user interface template information.
Returns information about the requested human task user interface (worker task template).
Returns a description of a hyperparameter tuning job, depending on the fields selected. These fields can include the name, Amazon Resource Name (ARN), job status of your tuning job and more.
The numbers of training jobs launched by a hyperparameter tuning job, categorized by status.
Specifies the number of training jobs that this hyperparameter tuning job launched, categorized by the status of their objective metric. The objective metric status shows whether the final objective metric for the training job has been evaluated by the tuning job and used in the hyperparameter tuning process.
The total resources consumed by your hyperparameter tuning job.
A structure that contains runtime information about both current and completed hyperparameter tuning jobs.
Shows the latest objective metric emitted by a training job that was launched by a hyperparameter tuning job. You define the objective metric in the HyperParameterTuningJobObjective parameter of HyperParameterTuningJobConfig.
The container for the summary information about a training job.
Returns a description of a hyperparameter tuning job, depending on the fields selected. These fields can include the name, Amazon Resource Name (ARN), job status of your tuning job and more.
Describes a SageMaker AI image.
Describes a SageMaker AI image.
Describes a version of a SageMaker AI image.
Describes a version of a SageMaker AI image.
Returns information about an inference component.
Settings that affect how the inference component caches data.
Details about the resources that are deployed with this inference component.
Details about the resources that are deployed with this inference component.
The placement status of an inference component on a specific instance type. Shows the number of inference component copies currently placed on instances of a given type.
Details about the runtime settings for the model that is deployed with the inference component.
Specifies the type and size of the endpoint capacity to activate for a rolling deployment or a rollback strategy. You can specify your batches as either of the following: A count of inference component copies The overall percentage or your fleet For a rollback strategy, if you don't specify the fields in this object, or if you set the Value parameter to 100%, then SageMaker AI uses a blue/green rollback strategy and rolls all traffic back to the blue fleet.
Specifies a rolling deployment strategy for updating a SageMaker AI inference component.
The deployment configuration for an endpoint that hosts inference components. The configuration includes the desired deployment strategy and rollback settings.
Returns information about an inference component.
Returns details about an inference experiment.
Summary of the deployment configuration of a model.
The metadata of the endpoint.
Returns details about an inference experiment.
Provides the results of the Inference Recommender job. One or more recommendation jobs are returned.
The metrics of recommendations.
A list of environment parameters suggested by the Amazon SageMaker Inference Recommender.
Defines the model configuration. Includes the specification name and environment parameters.
The endpoint configuration made by Inference Recommender during a recommendation job.
A list of recommendations made by Amazon SageMaker Inference Recommender.
The metrics for an existing endpoint compared in an Inference Recommender job.
The performance results from running an Inference Recommender job on an existing endpoint.
Provides the results of the Inference Recommender job. One or more recommendation jobs are returned.
Gets information about a labeling job.
Specifies the location of the output produced by the labeling job.
Provides a breakdown of the number of objects labeled.
Gets information about a labeling job.
Provides a list of properties for the requested lineage group. For more information, see Cross-Account Lineage Tracking in the Amazon SageMaker Developer Guide.
Provides a list of properties for the requested lineage group. For more information, see Cross-Account Lineage Tracking in the Amazon SageMaker Developer Guide.
Returns information about an MLflow App.
Returns information about an MLflow App.
Returns information about an MLflow Tracking Server.
Returns information about an MLflow Tracking Server.
Returns a description of a model bias job definition.
Returns a description of a model bias job definition.
Describes an Amazon SageMaker Model Card export job.
The artifacts of the model card export job.
Describes an Amazon SageMaker Model Card export job.
Describes the content, creation time, and security configuration of an Amazon SageMaker Model Card.
Describes the content, creation time, and security configuration of an Amazon SageMaker Model Card.
Returns a description of a model explainability job definition.
Returns a description of a model explainability job definition.
Describes a model that you created using the CreateModel API.
Describes a model that you created using the CreateModel API.
Gets a description for the specified model group.
Gets a description for the specified model group.
Returns a description of the specified model package, which is used to create SageMaker models or list them on Amazon Web Services Marketplace. If you provided a KMS Key ID when you created your model package, you will see the KMS Decrypt API call in your CloudTrail logs when you use this API. To create models in SageMaker, buyers can subscribe to model packages listed on Amazon Web Services Marketplace.
Represents the overall status of a model package.
Specifies the validation and image scan statuses of the model package.
Returns a description of the specified model package, which is used to create SageMaker models or list them on Amazon Web Services Marketplace. If you provided a KMS Key ID when you created your model package, you will see the KMS Decrypt API call in your CloudTrail logs when you use this API. To create models in SageMaker, buyers can subscribe to model packages listed on Amazon Web Services Marketplace.
Returns a description of a model quality job definition.
Returns a description of a model quality job definition.
Describes the schedule for a monitoring job.
Summary of information about the last monitoring job to run.
Describes the schedule for a monitoring job.
Returns information about a notebook instance.
Returns a description of a notebook instance lifecycle configuration. For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.
Returns a description of a notebook instance lifecycle configuration. For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.
Returns information about a notebook instance.
Provides the properties of the specified optimization job.
Output values produced by an optimization job.
Provides the properties of the specified optimization job.
Gets information about a SageMaker Partner AI App.
This is an error field object that contains the error code and the reason for an operation failure.
Gets information about a SageMaker Partner AI App.
Describes the details of an execution's pipeline definition.
Describes the details of an execution's pipeline definition.
Describes the details of a pipeline execution.
A step selected to run in selective execution mode.
The selective execution configuration applied to the pipeline run.
Specifies the names of the experiment and trial created by a pipeline.
The MLflow configuration.
Describes the details of a pipeline execution.
Describes the details of a pipeline.
Describes the details of a pipeline.
Returns a description of a processing job.
Returns a description of a processing job.
Describes the details of a project.
Details about a template provider configuration and associated provisioning information.
Details of a provisioned service catalog product. For information about service catalog, see What is Amazon Web Services Service Catalog.
Describes the details of a project.
Retrieves details about a reserved capacity.
A summary of UltraServer resources and their current status.
Retrieves details about a reserved capacity.
Describes the space.
Describes the space.
Describes the Amazon SageMaker AI Studio Lifecycle Configuration.
Describes the Amazon SageMaker AI Studio Lifecycle Configuration.
Gets information about a work team provided by a vendor. It returns details about the subscription with a vendor in the Amazon Web Services Marketplace.
Describes a work team of a vendor that does the labelling job.
Gets information about a work team provided by a vendor. It returns details about the subscription with a vendor in the Amazon Web Services Marketplace.
Returns information about a training job. Some of the attributes below only appear if the training job successfully starts. If the training job fails, TrainingJobStatus is Failed and, depending on the FailureReason, attributes like TrainingStartTime, TrainingTimeInSeconds, TrainingEndTime, and BillableTimeInSeconds may not be present in the response.
Optional. Indicates how many seconds the resource stayed in ResourceRetained state. Populated only after resource reaches ResourceReused or ResourceReleased state.
Status and billing information about the warm pool.
TrainingProgressInfo relevant fields
The serverless training job progress information.
An array element of SecondaryStatusTransitions for DescribeTrainingJob. It provides additional details about a status that the training job has transitioned through. A training job can be in one of several states, for example, starting, downloading, training, or uploading. Within each state, there are a number of intermediate states. For example, within the starting state, SageMaker could be starting the training job or launching the ML instances. These transitional states are referred to as the job's secondary status.
Information about the status of the rule evaluation.
The MLflow details of this job.
The name, value, and date and time of a metric that was emitted to Amazon CloudWatch.