sagemaker_create_ai_workload_config: Creates a reusable AI workload configuration that defines...
In paws.machine.learning: 'Amazon Web Services' Machine Learning Services
View source: R/sagemaker_operations.R
sagemaker_create_ai_workload_config R Documentation
Creates a reusable AI workload configuration that defines datasets, data
sources, and benchmark tool settings for consistent performance testing
of generative AI inference deployments on Amazon SageMaker AI
Description
Creates a reusable AI workload configuration that defines datasets, data sources, and benchmark tool settings for consistent performance testing of generative AI inference deployments on Amazon SageMaker AI.
See https://www.paws-r-sdk.com/docs/sagemaker_create_ai_workload_config/ for full documentation.
Usage
sagemaker_create_ai_workload_config(
AIWorkloadConfigName,
DatasetConfig = NULL,
AIWorkloadConfigs = NULL,
Tags = NULL
)
Arguments
AIWorkloadConfigName
[required] The name of the AI workload configuration. The name must be unique within your Amazon Web Services account in the current Amazon Web Services Region.
DatasetConfig
The dataset configuration for the workload. Specify input data channels with their data sources for benchmark workloads.
AIWorkloadConfigs
The benchmark tool configuration and workload specification. Provide the specification as an inline YAML or JSON string.
Tags
The metadata that you apply to Amazon Web Services resources to help you categorize and organize them. Each tag consists of a key and a value, both of which you define. For more information, see Tagging Amazon Web Services Resources in the Amazon Web Services General Reference.
paws.machine.learning documentation built on May 31, 2026, 1:07 a.m.
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View source: R/sagemaker_operations.R
| sagemaker_create_ai_workload_config | R Documentation |
Creates a reusable AI workload configuration that defines datasets, data sources, and benchmark tool settings for consistent performance testing of generative AI inference deployments on Amazon SageMaker AI
Description
Creates a reusable AI workload configuration that defines datasets, data sources, and benchmark tool settings for consistent performance testing of generative AI inference deployments on Amazon SageMaker AI.
See https://www.paws-r-sdk.com/docs/sagemaker_create_ai_workload_config/ for full documentation.
Usage
sagemaker_create_ai_workload_config(
AIWorkloadConfigName,
DatasetConfig = NULL,
AIWorkloadConfigs = NULL,
Tags = NULL
)
Arguments
AIWorkloadConfigName |
[required] The name of the AI workload configuration. The name must be unique within your Amazon Web Services account in the current Amazon Web Services Region. |
DatasetConfig |
The dataset configuration for the workload. Specify input data channels with their data sources for benchmark workloads. |
AIWorkloadConfigs |
The benchmark tool configuration and workload specification. Provide the specification as an inline YAML or JSON string. |
Tags |
The metadata that you apply to Amazon Web Services resources to help you categorize and organize them. Each tag consists of a key and a value, both of which you define. For more information, see Tagging Amazon Web Services Resources in the Amazon Web Services General Reference. |
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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