Struct aws_sdk_sagemaker::model::training_job_definition::Builder

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#[non_exhaustive]
pub struct Builder { /* private fields */ }

Expand description

A builder for TrainingJobDefinition

Implementations

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impl Builder

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pub fn training_input_mode(self, input: TrainingInputMode) -> Self

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.

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pub fn set_training_input_mode(self, input: Option<TrainingInputMode>) -> Self

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.

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pub fn hyper_parameters(
 self,
 k: impl Into<String>,
 v: impl Into<String>
) -> Self

Adds a key-value pair to hyper_parameters.

To override the contents of this collection use set_hyper_parameters.

The hyperparameters used for the training job.

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pub fn set_hyper_parameters(
self,
input: Option<HashMap<String, String>>
) -> Self

The hyperparameters used for the training job.

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pub fn input_data_config(self, input: Channel) -> Self

Appends an item to input_data_config.

To override the contents of this collection use set_input_data_config.

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

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pub fn set_input_data_config(self, input: Option<Vec<Channel>>) -> Self

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

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pub fn output_data_config(self, input: OutputDataConfig) -> Self

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

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pub fn set_output_data_config(self, input: Option<OutputDataConfig>) -> Self

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

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pub fn resource_config(self, input: ResourceConfig) -> Self

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

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pub fn set_resource_config(self, input: Option<ResourceConfig>) -> Self

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

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pub fn stopping_condition(self, input: StoppingCondition) -> Self

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, Amazon SageMaker ends the training job. Use this API to cap model training costs.

To stop a job, Amazon 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.

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pub fn set_stopping_condition(self, input: Option<StoppingCondition>) -> Self

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, Amazon SageMaker ends the training job. Use this API to cap model training costs.

To stop a job, Amazon 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.

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