class Aws::SageMaker::Types::TrainingJobDefinition
Defines the input needed to run a training job using the algorithm.
@note When making an API call, you may pass TrainingJobDefinition
data as a hash: { training_input_mode: "Pipe", # required, accepts Pipe, File hyper_parameters: { "HyperParameterKey" => "HyperParameterValue", }, input_data_config: [ # required { channel_name: "ChannelName", # required data_source: { # required s3_data_source: { s3_data_type: "ManifestFile", # required, accepts ManifestFile, S3Prefix, AugmentedManifestFile s3_uri: "S3Uri", # required s3_data_distribution_type: "FullyReplicated", # accepts FullyReplicated, ShardedByS3Key attribute_names: ["AttributeName"], }, file_system_data_source: { file_system_id: "FileSystemId", # required file_system_access_mode: "rw", # required, accepts rw, ro file_system_type: "EFS", # required, accepts EFS, FSxLustre directory_path: "DirectoryPath", # required }, }, content_type: "ContentType", compression_type: "None", # accepts None, Gzip record_wrapper_type: "None", # accepts None, RecordIO input_mode: "Pipe", # accepts Pipe, File shuffle_config: { seed: 1, # required }, }, ], output_data_config: { # required kms_key_id: "KmsKeyId", s3_output_path: "S3Uri", # required }, resource_config: { # required instance_type: "ml.m4.xlarge", # required, accepts ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.p4d.24xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.c5n.xlarge, ml.c5n.2xlarge, ml.c5n.4xlarge, ml.c5n.9xlarge, ml.c5n.18xlarge instance_count: 1, # required volume_size_in_gb: 1, # required volume_kms_key_id: "KmsKeyId", }, stopping_condition: { # required max_runtime_in_seconds: 1, max_wait_time_in_seconds: 1, }, }
@!attribute [rw] training_input_mode
The input mode used by the algorithm for the training job. For the input modes that Amazon SageMaker algorithms support, see [Algorithms][1]. If an algorithm supports the `File` input mode, Amazon SageMaker downloads the training data from S3 to the provisioned ML storage Volume, and mounts the directory to docker volume for training container. If an algorithm supports the `Pipe` input mode, Amazon SageMaker streams data directly from S3 to the container. [1]: https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html @return [String]
@!attribute [rw] hyper_parameters
The hyperparameters used for the training job. @return [Hash<String,String>]
@!attribute [rw] input_data_config
An array of `Channel` objects, each of which specifies an input source. @return [Array<Types::Channel>]
@!attribute [rw] output_data_config
the path to the S3 bucket where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts. @return [Types::OutputDataConfig]
@!attribute [rw] resource_config
The resources, including the ML compute instances and ML storage volumes, to use for model training. @return [Types::ResourceConfig]
@!attribute [rw] stopping_condition
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. @return [Types::StoppingCondition]
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/TrainingJobDefinition AWS API Documentation
Constants
- SENSITIVE