Struct TrainingJobDefinition

#[non_exhaustive]
pub struct TrainingJobDefinition {
    pub training_input_mode: Option<TrainingInputMode>,
    pub hyper_parameters: Option<HashMap<String, String>>,
    pub input_data_config: Option<Vec<Channel>>,
    pub output_data_config: Option<OutputDataConfig>,
    pub resource_config: Option<ResourceConfig>,
    pub stopping_condition: Option<StoppingCondition>,
}

Defines the input needed to run a training job using the algorithm.

Fields (Non-exhaustive)

Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.

training_input_mode: Option<TrainingInputMode>

The training input mode that the algorithm supports. For more information about input modes, see Algorithms.

Pipe mode

If an algorithm supports Pipe mode, Amazon SageMaker streams data directly from Amazon S3 to the container.

File mode

If an algorithm supports File mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.

You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.

For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.

FastFile mode

If an algorithm supports FastFile mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.

FastFile mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided.

hyper_parameters: Option<HashMap<String, String>>

The hyperparameters used for the training job.

input_data_config: Option<Vec<Channel>>

An array of Channel objects, each of which specifies an input source.

output_data_config: Option<OutputDataConfig>

the path to the S3 bucket where you want to store model artifacts. SageMaker creates subfolders for the artifacts.

resource_config: Option<ResourceConfig>

The resources, including the ML compute instances and ML storage volumes, to use for model training.

stopping_condition: Option<StoppingCondition>

Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.

To stop a job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts.

Implementations

impl TrainingJobDefinition

pub fn training_input_mode(&self) -> Option<&TrainingInputMode>

The training input mode that the algorithm supports. For more information about input modes, see Algorithms.

Pipe mode

If an algorithm supports Pipe mode, Amazon SageMaker streams data directly from Amazon S3 to the container.

File mode

If an algorithm supports File mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.

You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.

For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.

FastFile mode

If an algorithm supports FastFile mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.

FastFile mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided.

pub fn hyper_parameters(&self) -> Option<&HashMap<String, String>>

The hyperparameters used for the training job.

pub fn input_data_config(&self) -> &[Channel]

An array of Channel objects, each of which specifies an input source.

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .input_data_config.is_none().

pub fn output_data_config(&self) -> Option<&OutputDataConfig>

the path to the S3 bucket where you want to store model artifacts. SageMaker creates subfolders for the artifacts.

pub fn resource_config(&self) -> Option<&ResourceConfig>

The resources, including the ML compute instances and ML storage volumes, to use for model training.

pub fn stopping_condition(&self) -> Option<&StoppingCondition>

Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.

To stop a job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts.

impl TrainingJobDefinition

pub fn builder() -> TrainingJobDefinitionBuilder

Creates a new builder-style object to manufacture TrainingJobDefinition.

Trait Implementations

impl Clone for TrainingJobDefinition

fn clone(&self) -> TrainingJobDefinition

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for TrainingJobDefinition

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

Formats the value using the given formatter.

impl PartialEq for TrainingJobDefinition

fn eq(&self, other: &TrainingJobDefinition) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for TrainingJobDefinition