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StartTrainingRequest

edgefirst_client

Struct StartTrainingRequest 

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pub struct StartTrainingRequest {
Show 15 fields
    pub project_id: ProjectID,
    pub name: String,
    pub experiment_id: ExperimentID,
    pub trainer_type: String,
    pub dataset_id: DatasetID,
    pub annotation_set_id: AnnotationSetID,
    pub tag_name: Option<String>,
    pub train_group: Option<String>,
    pub val_group: Option<String>,
    pub session_name: Option<String>,
    pub session_description: Option<String>,
    pub weights_session: Option<TrainingSessionID>,
    pub params: HashMap<String, Parameter>,
    pub is_local: bool,
    pub is_kubernetes: bool,

}
Expand description

Request payload for client::Client::start_training_session.

Launches a new training session against a single dataset using group-based train/validation splits. When train_group / val_group are None, the dataset’s default split groups ("train" / "val") are used. When tag_name is None, the dataset’s most recent tag is used.

The hyperparameters in params are trainer-specific; query the trainer’s parameter schema with Client::trainer_schema (using a schema_type from Client::trainer_schemas) to discover the accepted parameter names, defaults, and ranges.

Set is_local: true for a user-managed session: the session row is created and fully usable for artifact/metric uploads, but no cloud instance is provisioned — the caller runs the training loop themselves. is_kubernetes: true schedules onto the organization’s Kubernetes runner; with both flags false the server provisions a cloud (AWS EC2) instance.

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§project_id: ProjectID

Project owning the experiment and dataset.

§name: String

Name for the session’s background task.

§experiment_id: ExperimentID

Experiment (trainer) the session belongs to.

§trainer_type: String

Trainer schema type (e.g. "modelpack"), from Client::trainer_schemas.

§dataset_id: DatasetID

Dataset to train on.

§annotation_set_id: AnnotationSetID

Annotation set providing the ground-truth labels.

§tag_name: Option<String>

Dataset tag to train against; None selects the latest tag (resolved via the legacy Tag/Client::dataset_tags list, not VersionTag/Client::version_tag_list).

§train_group: Option<String>

Training split group name; None uses the default "train".

§val_group: Option<String>

Validation split group name; None uses the default "val".

§session_name: Option<String>

Display name for the training session itself; None uses the task name.

§session_description: Option<String>

Optional description for the training session.

§weights_session: Option<TrainingSessionID>

Optional source session for transfer-learning weights.

§params: HashMap<String, Parameter>

Trainer hyperparameters, keyed by schema parameter name.

§is_local: bool

Create a user-managed session (no cloud instance).

§is_kubernetes: bool

Schedule onto the organization’s Kubernetes runner.

Trait Implementations§

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impl Clone for StartTrainingRequest

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fn clone(&self) -> StartTrainingRequest

Returns a duplicate of the value. Read more
1.0.0 (const: unstable) · Source§

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source. Read more
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impl Debug for StartTrainingRequest

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fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter. Read more

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