Every API here is available over the APIs.io API and to AI agents over MCP.
MCP server
One button, every client — Claude, Cursor, VS Code and the rest.
https://apis.io/mcp
Tools for apis
7 MCP tools reach this
find_apisBrowse and filter every API in the catalog.get_api_artifactsOne API's artifacts, grouped by type.get_openapiThe primary OpenAPI for this API.find_similar_apisAPIs that look like this one.apis_io_searchSTART HERE — APIs, providers and tags for one query, each with its total.resolveTurn a domain, URL or GitHub org into the provider it belongs to.find_cohortsEvery scored population of providers in the catalog.
All 92 tools →
Call it yourself
curl for this page
This API
curl "https://apis.io/api/v1/apis/amazon-sagemaker:amazon-sagemaker-models-api"
All apis
curl "https://apis.io/api/v1/apis?limit=25"
Calls without X-API-Key need no key. Calls with it need a paid plan — get a key.
Get an API key
Free tier, no form to fill in. Signing in shares your email address with us — we
store it to create your key and to recognise you if you sign in with another
provider. See our Privacy Policy and
Terms.
A second provider on the same verified email joins the account you already have.
openapi: 3.1.0
info:
title: Amazon SageMaker Service Models API
version: '2017-07-24'
description: Generated from AWS's published Smithy model for Amazon SageMaker Service. 4 operation(s).
contact:
name: Amazon Web Services
url: https://aws.amazon.com/contact-us/
license:
name: Apache 2.0
identifier: Apache-2.0
url: https://github.com/aws/api-models-aws/blob/main/LICENSE
servers:
- url: https://api.sagemaker.{region}.amazonaws.com
variables:
region:
default: us-east-1
security:
- sigv4: []
paths:
/#CreateModel:
post:
operationId: CreateModel
summary: Create Model
description: '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 '
responses:
'200':
description: Success
content:
application/x-amz-json-1.1:
schema:
$ref: '#/components/schemas/CreateModelOutput'
'400':
description: ResourceLimitExceeded
content:
application/x-amz-json-1.1:
schema:
$ref: '#/components/schemas/ResourceLimitExceeded'
parameters:
- name: X-Amz-Target
in: header
required: true
schema:
type: string
enum:
- SageMaker.CreateModel
requestBody:
required: true
content:
application/x-amz-json-1.1:
schema:
$ref: '#/components/schemas/CreateModelInput'
tags:
- Models
/#DeleteModel:
post:
operationId: DeleteModel
summary: Delete Model
description: 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.
responses:
'200':
description: Success
content:
application/x-amz-json-1.1:
schema:
type: string
parameters:
- name: X-Amz-Target
in: header
required: true
schema:
type: string
enum:
- SageMaker.DeleteModel
requestBody:
required: true
content:
application/x-amz-json-1.1:
schema:
$ref: '#/components/schemas/DeleteModelInput'
tags:
- Models
/#DescribeModel:
post:
operationId: DescribeModel
summary: Describe Model
description: Describes a model that you created using the CreateModel API.
responses:
'200':
description: Success
content:
application/x-amz-json-1.1:
schema:
$ref: '#/components/schemas/DescribeModelOutput'
parameters:
- name: X-Amz-Target
in: header
required: true
schema:
type: string
enum:
- SageMaker.DescribeModel
requestBody:
required: true
content:
application/x-amz-json-1.1:
schema:
$ref: '#/components/schemas/DescribeModelInput'
tags:
- Models
/#ListModels:
post:
operationId: ListModels
summary: List Models
description: Lists models created with the CreateModel API.
responses:
'200':
description: Success
content:
application/x-amz-json-1.1:
schema:
$ref: '#/components/schemas/ListModelsOutput'
parameters:
- name: X-Amz-Target
in: header
required: true
schema:
type: string
enum:
- SageMaker.ListModels
requestBody:
required: true
content:
application/x-amz-json-1.1:
schema:
$ref: '#/components/schemas/ListModelsInput'
tags:
- Models
components:
securitySchemes:
sigv4:
type: apiKey
in: header
name: Authorization
description: AWS Signature Version 4. The Authorization header carries an AWS4-HMAC-SHA256 signature over the canonical
request, with X-Amz-Date (and X-Amz-Security-Token for temporary credentials). Not a bearer token.
x-aws-sigv4:
signingName: sagemaker
schemas:
AdditionalModelDataSource:
type: object
properties:
ChannelName:
type: string
description: A custom name for this AdditionalModelDataSource object.
S3DataSource:
$ref: '#/components/schemas/S3ModelDataSource'
required:
- ChannelName
- S3DataSource
ContainerDefinition:
type: object
properties:
ContainerHostname:
type: string
description: This parameter is ignored for models that contain only a PrimaryContainer. When a ContainerDefinition
is part of an inference pipeline, the value of the parameter uniquely identifies the container for the purposes
of logging and metrics. For information, see Use Logs and Metrics to Monitor an Inference Pipeline. If you don't
specify a value for this parameter for a ContainerDefinition that is part of an inference pipeline, a unique name
is automatically assigned based on the position of the ContainerDefinition in the pipeline. If you specify a value
for the ContainerHostName for any ContainerD
Image:
type: string
description: The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in
a Docker registry that is accessible from the same VPC that you configure for your endpoint. If you are using
your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker
requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats.
For more information, see Using Your Own Algorithms with Amazon SageMaker. The model artifacts in an Amazon S3
bucket and the Docker image for inference container
ImageConfig:
$ref: '#/components/schemas/ImageConfig'
Mode:
type: string
enum:
- SingleModel
- MultiModel
description: Whether the container hosts a single model or multiple models.
ModelDataUrl:
type: string
description: The S3 path where the model artifacts, which result from model training, are stored. This path must
point to a single gzip compressed tar archive (.tar.gz suffix). The S3 path is required for SageMaker built-in
algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters.
The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating.
If you provide a value for this parameter, SageMaker uses Amazon Web Services Security Token Service to download
model artifacts from the S3 path you provi
ModelDataSource:
$ref: '#/components/schemas/ModelDataSource'
AdditionalModelDataSources:
type: array
items:
$ref: '#/components/schemas/AdditionalModelDataSource'
description: Data sources that are available to your model in addition to the one that you specify for ModelDataSource
when you use the CreateModel action.
Environment:
type: object
additionalProperties:
type: string
description: The environment variables to set in the Docker container. Don't include any sensitive data in your
environment variables. The maximum length of each key and value in the Environment map is 1024 bytes. The maximum
length of all keys and values in the map, combined, is 32 KB. If you pass multiple containers to a CreateModel
request, then the maximum length of all of their maps, combined, is also 32 KB.
ModelPackageName:
type: string
description: The name or Amazon Resource Name (ARN) of the model package to use to create the model.
InferenceSpecificationName:
type: string
description: The inference specification name in the model package version.
MultiModelConfig:
$ref: '#/components/schemas/MultiModelConfig'
ContainerMetricsConfig:
$ref: '#/components/schemas/ContainerMetricsConfig'
ContainerMetricsConfig:
type: object
properties:
MetricsEndpoints:
type: array
items:
$ref: '#/components/schemas/MetricsEndpoint'
description: A list of metrics endpoints to scrape from the container. Each endpoint specifies the path where the
container exposes Prometheus-formatted metrics and the frequency at which to publish them. You can specify a maximum
of 1 endpoint.
CreateModelInput:
type: object
properties:
ModelName:
type: string
description: The name of the new model.
PrimaryContainer:
$ref: '#/components/schemas/ContainerDefinition'
Containers:
type: array
items:
$ref: '#/components/schemas/ContainerDefinition'
description: Specifies the containers in the inference pipeline.
InferenceExecutionConfig:
$ref: '#/components/schemas/InferenceExecutionConfig'
ExecutionRoleArn:
type: string
description: The Amazon Resource Name (ARN) of the IAM role that SageMaker can assume to access model artifacts
and docker image for deployment on ML compute instances or for batch transform jobs. Deploying on ML compute instances
is part of model hosting. For more information, see SageMaker Roles. To be able to pass this role to SageMaker,
the caller of this API must have the iam:PassRole permission.
Tags:
type: array
items:
$ref: '#/components/schemas/Tag'
description: An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different
ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
VpcConfig:
$ref: '#/components/schemas/VpcConfig'
EnableNetworkIsolation:
type: boolean
description: Isolates the model container. No inbound or outbound network calls can be made to or from the model
container.
required:
- ModelName
CreateModelOutput:
type: object
properties:
ModelArn:
type: string
description: The ARN of the model created in SageMaker.
required:
- ModelArn
DeleteModelInput:
type: object
properties:
ModelName:
type: string
description: The name of the model to delete.
required:
- ModelName
DeploymentRecommendation:
type: object
properties:
RecommendationStatus:
type: string
enum:
- IN_PROGRESS
- COMPLETED
- FAILED
- NOT_APPLICABLE
description: Status of the deployment recommendation. The status NOT_APPLICABLE means that SageMaker is unable to
provide a default recommendation for the model using the information provided. If the deployment status is IN_PROGRESS,
retry your API call after a few seconds to get a COMPLETED deployment recommendation.
RealTimeInferenceRecommendations:
type: array
items:
$ref: '#/components/schemas/RealTimeInferenceRecommendation'
description: A list of RealTimeInferenceRecommendation items.
required:
- RecommendationStatus
DescribeModelInput:
type: object
properties:
ModelName:
type: string
description: The name of the model.
required:
- ModelName
DescribeModelOutput:
type: object
properties:
ModelName:
type: string
description: Name of the SageMaker model.
PrimaryContainer:
$ref: '#/components/schemas/ContainerDefinition'
Containers:
type: array
items:
$ref: '#/components/schemas/ContainerDefinition'
description: The containers in the inference pipeline.
InferenceExecutionConfig:
$ref: '#/components/schemas/InferenceExecutionConfig'
ExecutionRoleArn:
type: string
description: The Amazon Resource Name (ARN) of the IAM role that you specified for the model.
VpcConfig:
$ref: '#/components/schemas/VpcConfig'
CreationTime:
type: string
format: date-time
description: A timestamp that shows when the model was created.
ModelArn:
type: string
description: The Amazon Resource Name (ARN) of the model.
EnableNetworkIsolation:
type: boolean
description: If True, no inbound or outbound network calls can be made to or from the model container.
DeploymentRecommendation:
$ref: '#/components/schemas/DeploymentRecommendation'
required:
- ModelName
- CreationTime
- ModelArn
ImageConfig:
type: object
properties:
RepositoryAccessMode:
type: string
enum:
- Platform
- Vpc
description: 'Set this to one of the following values: Platform - The model image is hosted in Amazon ECR. Vpc -
The model image is hosted in a private Docker registry in your VPC.'
RepositoryAuthConfig:
$ref: '#/components/schemas/RepositoryAuthConfig'
required:
- RepositoryAccessMode
InferenceExecutionConfig:
type: object
properties:
Mode:
type: string
enum:
- Serial
- Direct
description: How containers in a multi-container are run. The following values are valid. SERIAL - Containers run
as a serial pipeline. DIRECT - Only the individual container that you specify is run.
required:
- Mode
InferenceHubAccessConfig:
type: object
properties:
HubContentArn:
type: string
description: The ARN of the hub content for which deployment access is allowed.
required:
- HubContentArn
ListModelsInput:
type: object
properties:
SortBy:
type: string
enum:
- Name
- CreationTime
description: Sorts the list of results. The default is CreationTime.
SortOrder:
type: string
enum:
- Ascending
- Descending
description: The sort order for results. The default is Descending.
NextToken:
type: string
description: If the response to a previous ListModels request was truncated, the response includes a NextToken.
To retrieve the next set of models, use the token in the next request.
MaxResults:
type: integer
description: The maximum number of models to return in the response.
NameContains:
type: string
description: A string in the model name. This filter returns only models whose name contains the specified string.
CreationTimeBefore:
type: string
format: date-time
description: A filter that returns only models created before the specified time (timestamp).
CreationTimeAfter:
type: string
format: date-time
description: A filter that returns only models with a creation time greater than or equal to the specified time
(timestamp).
ListModelsOutput:
type: object
properties:
Models:
type: array
items:
$ref: '#/components/schemas/ModelSummary'
description: An array of ModelSummary objects, each of which lists a model.
NextToken:
type: string
description: If the response is truncated, SageMaker returns this token. To retrieve the next set of models, use
it in the subsequent request.
required:
- Models
MetricsEndpoint:
type: object
properties:
MetricsEndpointPath:
type: string
description: The path to the metrics endpoint exposed by the container. For example, /metrics or /server/metrics.
The path must start with / and can contain alphanumeric characters, forward slashes, underscores, hyphens, and
periods. Maximum length is 256 characters. If not specified, defaults to /metrics.
MetricPublishFrequencyInSeconds:
type: integer
description: 'The interval, in seconds, at which container metrics scraped from the endpoint are published to Amazon
CloudWatch. Valid values: 10, 30, 60, 120, 180, 240, 300. Defaults to 60.'
required:
- MetricsEndpointPath
ModelAccessConfig:
type: object
properties:
AcceptEula:
type: boolean
description: Specifies agreement to the model end-user license agreement (EULA). The AcceptEula value must be explicitly
defined as True in order to accept the EULA that this model requires. You are responsible for reviewing and complying
with any applicable license terms and making sure they are acceptable for your use case before downloading or
using a model.
required:
- AcceptEula
ModelDataSource:
type: object
properties:
S3DataSource:
$ref: '#/components/schemas/S3ModelDataSource'
ModelSummary:
type: object
properties:
ModelName:
type: string
description: The name of the model that you want a summary for.
ModelArn:
type: string
description: The Amazon Resource Name (ARN) of the model.
CreationTime:
type: string
format: date-time
description: A timestamp that indicates when the model was created.
required:
- ModelName
- ModelArn
- CreationTime
MultiModelConfig:
type: object
properties:
ModelCacheSetting:
type: string
enum:
- Enabled
- Disabled
description: Whether to cache models for a multi-model endpoint. By default, multi-model endpoints cache models
so that a model does not have to be loaded into memory each time it is invoked. Some use cases do not benefit
from model caching. For example, if an endpoint hosts a large number of models that are each invoked infrequently,
the endpoint might perform better if you disable model caching. To disable model caching, set the value of this
parameter to Disabled.
RealTimeInferenceRecommendation:
type: object
properties:
RecommendationId:
type: string
description: The recommendation ID which uniquely identifies each recommendation.
InstanceType:
type: string
enum:
- ml.t2.medium
- ml.t2.large
- ml.t2.xlarge
- ml.t2.2xlarge
- ml.m4.xlarge
- ml.m4.2xlarge
- ml.m4.4xlarge
- ml.m4.10xlarge
- ml.m4.16xlarge
- ml.m5.large
- ml.m5.xlarge
- ml.m5.2xlarge
- ml.m5.4xlarge
- ml.m5.12xlarge
- ml.m5.24xlarge
- ml.m5d.large
- ml.m5d.xlarge
- ml.m5d.2xlarge
- ml.m5d.4xlarge
- ml.m5d.12xlarge
- ml.m5d.24xlarge
- ml.c4.large
- ml.c4.xlarge
- ml.c4.2xlarge
- ml.c4.4xlarge
- ml.c4.8xlarge
- ml.p2.xlarge
- ml.p2.8xlarge
- ml.p2.16xlarge
- ml.p3.2xlarge
- ml.p3.8xlarge
- ml.p3.16xlarge
- ml.c5.large
- ml.c5.xlarge
- ml.c5.2xlarge
- ml.c5.4xlarge
- ml.c5.9xlarge
- ml.c5.18xlarge
- ml.c5d.large
- ml.c5d.xlarge
- ml.c5d.2xlarge
- ml.c5d.4xlarge
- ml.c5d.9xlarge
- ml.c5d.18xlarge
- ml.g4dn.xlarge
- ml.g4dn.2xlarge
- ml.g4dn.4xlarge
- ml.g4dn.8xlarge
- ml.g4dn.12xlarge
- ml.g4dn.16xlarge
- ml.r5.large
- ml.r5.xlarge
- ml.r5.2xlarge
- ml.r5.4xlarge
- ml.r5.12xlarge
- ml.r5.24xlarge
- ml.r5d.large
- ml.r5d.xlarge
- ml.r5d.2xlarge
- ml.r5d.4xlarge
- ml.r5d.12xlarge
- ml.r5d.24xlarge
- ml.inf1.xlarge
- ml.inf1.2xlarge
- ml.inf1.6xlarge
- ml.inf1.24xlarge
- ml.dl1.24xlarge
- ml.c6i.large
- ml.c6i.xlarge
- ml.c6i.2xlarge
- ml.c6i.4xlarge
- ml.c6i.8xlarge
- ml.c6i.12xlarge
- ml.c6i.16xlarge
- ml.c6i.24xlarge
- ml.c6i.32xlarge
- ml.m6i.large
- ml.m6i.xlarge
- ml.m6i.2xlarge
- ml.m6i.4xlarge
- ml.m6i.8xlarge
- ml.m6i.12xlarge
- ml.m6i.16xlarge
- ml.m6i.24xlarge
- ml.m6i.32xlarge
- ml.r6i.large
- ml.r6i.xlarge
- ml.r6i.2xlarge
- ml.r6i.4xlarge
- ml.r6i.8xlarge
- ml.r6i.12xlarge
- ml.r6i.16xlarge
- ml.r6i.24xlarge
- ml.r6i.32xlarge
- ml.g5.xlarge
- ml.g5.2xlarge
- ml.g5.4xlarge
- ml.g5.8xlarge
- ml.g5.12xlarge
- ml.g5.16xlarge
- ml.g5.24xlarge
- ml.g5.48xlarge
- ml.g6.xlarge
- ml.g6.2xlarge
- ml.g6.4xlarge
- ml.g6.8xlarge
- ml.g6.12xlarge
- ml.g6.16xlarge
- ml.g6.24xlarge
- ml.g6.48xlarge
- ml.r8g.medium
- ml.r8g.large
- ml.r8g.xlarge
- ml.r8g.2xlarge
- ml.r8g.4xlarge
- ml.r8g.8xlarge
- ml.r8g.12xlarge
- ml.r8g.16xlarge
- ml.r8g.24xlarge
- ml.r8g.48xlarge
- ml.g6e.xlarge
- ml.g6e.2xlarge
- ml.g6e.4xlarge
- ml.g6e.8xlarge
- ml.g6e.12xlarge
- ml.g6e.16xlarge
- ml.g6e.24xlarge
- ml.g6e.48xlarge
- ml.g7e.2xlarge
- ml.g7e.4xlarge
- ml.g7e.8xlarge
- ml.g7e.12xlarge
- ml.g7e.24xlarge
- ml.g7e.48xlarge
- ml.g7.2xlarge
- ml.g7.4xlarge
- ml.g7.8xlarge
- ml.g7.12xlarge
- ml.g7.24xlarge
- ml.g7.48xlarge
- ml.p4d.24xlarge
- ml.c7g.large
- ml.c7g.xlarge
- ml.c7g.2xlarge
- ml.c7g.4xlarge
- ml.c7g.8xlarge
- ml.c7g.12xlarge
- ml.c7g.16xlarge
- ml.m6g.large
- ml.m6g.xlarge
- ml.m6g.2xlarge
- ml.m6g.4xlarge
- ml.m6g.8xlarge
- ml.m6g.12xlarge
- ml.m6g.16xlarge
- ml.m6gd.large
- ml.m6gd.xlarge
- ml.m6gd.2xlarge
- ml.m6gd.4xlarge
- ml.m6gd.8xlarge
- ml.m6gd.12xlarge
- ml.m6gd.16xlarge
- ml.c6g.large
- ml.c6g.xlarge
- ml.c6g.2xlarge
- ml.c6g.4xlarge
- ml.c6g.8xlarge
- ml.c6g.12xlarge
- ml.c6g.16xlarge
- ml.c6gd.large
- ml.c6gd.xlarge
- ml.c6gd.2xlarge
- ml.c6gd.4xlarge
- ml.c6gd.8xlarge
- ml.c6gd.12xlarge
- ml.c6gd.16xlarge
- ml.c6gn.large
- ml.c6gn.xlarge
- ml.c6gn.2xlarge
- ml.c6gn.4xlarge
- ml.c6gn.8xlarge
- ml.c6gn.12xlarge
- ml.c6gn.16xlarge
- ml.r6g.large
- ml.r6g.xlarge
- ml.r6g.2xlarge
- ml.r6g.4xlarge
- ml.r6g.8xlarge
- ml.r6g.12xlarge
- ml.r6g.16xlarge
- ml.r6gd.large
- ml.r6gd.xlarge
- ml.r6gd.2xlarge
- ml.r6gd.4xlarge
- ml.r6gd.8xlarge
- ml.r6gd.12xlarge
- ml.r6gd.16xlarge
- ml.p4de.24xlarge
- ml.trn1.2xlarge
- ml.trn1.32xlarge
- ml.trn1n.32xlarge
- ml.trn2.48xlarge
- ml.inf2.xlarge
- ml.inf2.8xlarge
- ml.inf2.24xlarge
- ml.inf2.48xlarge
- ml.p5.48xlarge
- ml.p5e.48xlarge
- ml.p5en.48xlarge
- ml.m7i.large
- ml.m7i.xlarge
- ml.m7i.2xlarge
- ml.m7i.4xlarge
- ml.m7i.8xlarge
- ml.m7i.12xlarge
- ml.m7i.16xlarge
- ml.m7i.24xlarge
- ml.m7i.48xlarge
- ml.c7i.large
- ml.c7i.xlarge
- ml.c7i.2xlarge
- ml.c7i.4xlarge
- ml.c7i.8xlarge
- ml.c7i.12xlarge
- ml.c7i.16xlarge
- ml.c7i.24xlarge
- ml.c7i.48xlarge
- ml.r7i.large
- ml.r7i.xlarge
- ml.r7i.2xlarge
- ml.r7i.4xlarge
- ml.r7i.8xlarge
- ml.r7i.12xlarge
- ml.r7i.16xlarge
- ml.r7i.24xlarge
- ml.r7i.48xlarge
- ml.c8g.medium
- ml.c8g.large
- ml.c8g.xlarge
- ml.c8g.2xlarge
- ml.c8g.4xlarge
- ml.c8g.8xlarge
- ml.c8g.12xlarge
- ml.c8g.16xlarge
- ml.c8g.24xlarge
- ml.c8g.48xlarge
- ml.r7gd.medium
- ml.r7gd.large
- ml.r7gd.xlarge
- ml.r7gd.2xlarge
- ml.r7gd.4xlarge
- ml.r7gd.8xlarge
- ml.r7gd.12xlarge
- ml.r7gd.16xlarge
- ml.m8g.medium
- ml.m8g.large
- ml.m8g.xlarge
- ml.m8g.2xlarge
- ml.m8g.4xlarge
- ml.m8g.8xlarge
- ml.m8g.12xlarge
- ml.m8g.16xlarge
- ml.m8g.24xlarge
- ml.m8g.48xlarge
- ml.c6in.large
- ml.c6in.xlarge
- ml.c6in.2xlarge
- ml.c6in.4xlarge
- ml.c6in.8xlarge
- ml.c6in.12xlarge
- ml.c6in.16xlarge
- ml.c6in.24xlarge
- ml.c6in.32xlarge
- ml.p6-b200.48xlarge
- ml.p6-b300.48xlarge
- ml.p6e-gb200.36xlarge
- ml.p5.4xlarge
description: The recommended instance type for Real-Time Inference.
Environment:
type: object
additionalProperties:
type: string
description: The recommended environment variables to set in the model container for Real-Time Inference.
required:
- RecommendationId
- InstanceType
RepositoryAuthConfig:
type: object
properties:
RepositoryCredentialsProviderArn:
type: string
description: The Amazon Resource Name (ARN) of an Amazon Web Services Lambda function that provides credentials
to authenticate to the private Docker registry where your model image is hosted. For information about how to
create an Amazon Web Services Lambda function, see Create a Lambda function with the console in the Amazon Web
Services Lambda Developer Guide.
required:
- RepositoryCredentialsProviderArn
ResourceLimitExceeded:
type: object
properties:
Message:
type: string
S3ModelDataSource:
type: object
properties:
S3Uri:
type: string
description: Specifies the S3 path of ML model data to deploy.
S3DataType:
type: string
enum:
- S3Prefix
- S3Object
description: Specifies the type of ML model data to deploy. If you choose S3Prefix, S3Uri identifies a key name
prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy.
A valid key name prefix identified by S3Uri always ends with a forward slash (/). If you choose S3Object, S3Uri
identifies an object that is the ML model data to deploy.
CompressionType:
type: string
enum:
- None
- Gzip
description: Specifies how the ML model data is prepared. If you choose Gzip and choose S3Object as the value of
S3DataType, S3Uri identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress
and untar the object during model deployment. If you choose None and chooose S3Object as the value of S3DataType,
S3Uri identifies an object that represents an uncompressed ML model to deploy. If you choose None and choose S3Prefix
as the value of S3DataType, S3Uri identifies a key name prefix, under which all objects represents the uncompressed
ML model to deploy. If you choose Non
ModelAccessConfig:
$ref: '#/components/schemas/ModelAccessConfig'
HubAccessConfig:
$ref: '#/components/schemas/InferenceHubAccessConfig'
ManifestS3Uri:
type: string
description: The Amazon S3 URI of the manifest file. The manifest file is a CSV file that stores the artifact locations.
ETag:
type: string
description: The ETag associated with S3 URI.
ManifestEtag:
type: string
description: The ETag associated with Manifest S3 URI.
required:
- S3Uri
- S3DataType
- CompressionType
Tag:
type: object
properties:
Key:
type: string
description: The tag key. Tag keys must be unique per resource.
Value:
type: string
description: The tag value.
required:
- Key
- Value
VpcConfig:
type: object
properties:
SecurityGroupIds:
type: array
items:
type: string
description: The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is
specified in the Subnets field.
Subnets:
type: array
items:
type: string
description: The ID of the subnets in the VPC to which you want to connect your training job or model. For information
about the availability of specific instance types, see Supported Instance Types and Availability Zones.
required:
- SecurityGroupIds
- Subnets
x-provenance:
method: harvested
publisher: Amazon Web Services
source: https://raw.githubusercontent.com/aws/api-models-aws/582e55931c45b944739742a2c1620b6026dd2d13/models/sagemaker/service/2017-07-24/sagemaker-2017-07-24.json
note: converted by smithy-to-openapi.py from AWS's Smithy model (roadmap#1001)