create-training-plan¶
Description¶
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 `` .
See also: AWS API Documentation
Synopsis¶
create-training-plan
--training-plan-name <value>
--training-plan-offering-id <value>
[--tags <value>]
[--cli-input-json | --cli-input-yaml]
[--generate-cli-skeleton <value>]
[--debug]
[--endpoint-url <value>]
[--no-verify-ssl]
[--no-paginate]
[--output <value>]
[--query <value>]
[--profile <value>]
[--region <value>]
[--version <value>]
[--color <value>]
[--no-sign-request]
[--ca-bundle <value>]
[--cli-read-timeout <value>]
[--cli-connect-timeout <value>]
[--cli-binary-format <value>]
[--no-cli-pager]
[--cli-auto-prompt]
[--no-cli-auto-prompt]
Options¶
--training-plan-name (string)
The name of the training plan to create.
--training-plan-offering-id (string)
The unique identifier of the training plan offering to use for creating this plan.
--tags (list)
An array of key-value pairs to apply to this training plan.
(structure)
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key -> (string)
The tag key. Tag keys must be unique per resource.Value -> (string)
The tag value.
Shorthand Syntax:
Key=string,Value=string ...
JSON Syntax:
[
{
"Key": "string",
"Value": "string"
}
...
]
--cli-input-json | --cli-input-yaml (string)
Reads arguments from the JSON string provided. The JSON string follows the format provided by --generate-cli-skeleton. If other arguments are provided on the command line, those values will override the JSON-provided values. It is not possible to pass arbitrary binary values using a JSON-provided value as the string will be taken literally. This may not be specified along with --cli-input-yaml.
--generate-cli-skeleton (string)
Prints a JSON skeleton to standard output without sending an API request. If provided with no value or the value input, prints a sample input JSON that can be used as an argument for --cli-input-json. Similarly, if provided yaml-input it will print a sample input YAML that can be used with --cli-input-yaml. If provided with the value output, it validates the command inputs and returns a sample output JSON for that command. The generated JSON skeleton is not stable between versions of the AWS CLI and there are no backwards compatibility guarantees in the JSON skeleton generated.
Global Options¶
--debug (boolean)
Turn on debug logging.
--endpoint-url (string)
Override command’s default URL with the given URL.
--no-verify-ssl (boolean)
By default, the AWS CLI uses SSL when communicating with AWS services. For each SSL connection, the AWS CLI will verify SSL certificates. This option overrides the default behavior of verifying SSL certificates.
--no-paginate (boolean)
Disable automatic pagination. If automatic pagination is disabled, the AWS CLI will only make one call, for the first page of results.
--output (string)
The formatting style for command output.
- json
- text
- table
- yaml
- yaml-stream
--query (string)
A JMESPath query to use in filtering the response data.
--profile (string)
Use a specific profile from your credential file.
--region (string)
The region to use. Overrides config/env settings.
--version (string)
Display the version of this tool.
--color (string)
Turn on/off color output.
- on
- off
- auto
--no-sign-request (boolean)
Do not sign requests. Credentials will not be loaded if this argument is provided.
--ca-bundle (string)
The CA certificate bundle to use when verifying SSL certificates. Overrides config/env settings.
--cli-read-timeout (int)
The maximum socket read time in seconds. If the value is set to 0, the socket read will be blocking and not timeout. The default value is 60 seconds.
--cli-connect-timeout (int)
The maximum socket connect time in seconds. If the value is set to 0, the socket connect will be blocking and not timeout. The default value is 60 seconds.
--cli-binary-format (string)
The formatting style to be used for binary blobs. The default format is base64. The base64 format expects binary blobs to be provided as a base64 encoded string. The raw-in-base64-out format preserves compatibility with AWS CLI V1 behavior and binary values must be passed literally. When providing contents from a file that map to a binary blob fileb:// will always be treated as binary and use the file contents directly regardless of the cli-binary-format setting. When using file:// the file contents will need to properly formatted for the configured cli-binary-format.
- base64
- raw-in-base64-out
--no-cli-pager (boolean)
Disable cli pager for output.
--cli-auto-prompt (boolean)
Automatically prompt for CLI input parameters.
--no-cli-auto-prompt (boolean)
Disable automatically prompt for CLI input parameters.