Crate fakecloud_sagemaker 

Amazon SageMaker (sagemaker) awsJson1.1 control-plane service for fakecloud.

The full ~403-operation Amazon SageMaker Smithy model (SDK id SageMaker, SigV4 signing name sagemaker, awsJson1.1 protocol). Every request is a POST / whose operation is selected by the X-Amz-Target: SageMaker.<Op> header, with all inputs carried in the JSON body (no HTTP path / label / query bindings). The operation table, per-operation input constraints, output member shapes, list element shapes, resource families and identifier members are generated from the Smithy model (see src/generated.rs, produced by scripts/generate-sagemaker-tables.py), so the control plane tracks the model exactly.

What is real. Every named resource family — models, endpoints, endpoint configs, training / processing / transform / labeling / compilation / AutoML / hyper-parameter-tuning jobs, notebook instances (+ lifecycle configs), model packages (+ groups), pipelines, feature groups, domains, user profiles, spaces, apps, images, experiments, trials, actions, artifacts, contexts, clusters, inference components, monitoring schedules, workteams, workforces, and the rest — mints a proper ARN, persists its accepted input attributes plus creation / last-modified timestamps, and echoes them back on Describe* / List*. Create is conflict-checked (ResourceInUse), Describe / Update / Delete round-trip by the resource’s identifier (name, id, or ARN), and AddTags / ListTags / DeleteTags persist tags keyed by ARN. State is account-partitioned and persisted across restarts. Input validation is model-derived: required members, string @length, numeric @range, and @enum constraints are enforced, returning SageMaker’s ValidationException / ResourceNotFound / ResourceInUse.

Honest emulation choices (documented, not stubbed):

• This is the SageMaker control plane only. There is no ML execution plane: training / processing / transform / AutoML / tuning jobs are created, persisted and described, but no container is scheduled, no model is trained, and no inference endpoint serves traffic. Jobs and endpoints are not advanced through a live lifecycle by a background scheduler.
• Timestamps are emitted as awsJson1.1 epoch-second JSON numbers.
• A handful of resources whose Describe* identifier is a service-minted Id / ARN distinct from the create-time Name (e.g. Domain, ImageVersion, ModelCardExportJob) resolve by scanning the family’s minted identifiers, so a describe by the returned Id / ARN still round-trips.

Re-exports

pub use service::SageMakerService;

pub use service::SAGEMAKER_ACTIONS;

pub use state::SageMakerData;

pub use state::SageMakerSnapshot;

pub use state::SharedSageMakerState;

pub use state::SAGEMAKER_SNAPSHOT_SCHEMA_VERSION;

Modules

generated

persistence

Snapshot save/load for Amazon SageMaker control-plane state.

service

Amazon SageMaker (sagemaker) awsJson1.1 dispatch.

state

Account-partitioned, serializable state for the Amazon SageMaker control plane.

validate

Model-derived input validation for Amazon SageMaker operations.

Functions

start_pipeline_execution_from_delivery

Start a SageMaker pipeline execution from a cross-service delivery (an EventBridge Scheduler sagemaker:pipeline target). Persists a PipelineExecution record keyed by its minted ARN so DescribePipelineExecution / ListPipelineExecutions resolve it — the same shape the StartPipelineExecution API produces. pipeline_arn is arn:aws:sagemaker:<region>:<account>:pipeline/<name>; parameters is the target’s PipelineParameterList (a JSON array, echoed onto the record).