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Class: Aws::SageMaker::Types::TrainingJobDefinition
- Inherits:
-
Struct
- Object
- Struct
- Aws::SageMaker::Types::TrainingJobDefinition
- Defined in:
- (unknown)
Overview
When passing TrainingJobDefinition as input to an Aws::Client method, you can use a vanilla 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,
},
}
Defines the input needed to run a training job using the algorithm.
Returned by:
Instance Attribute Summary collapse
-
#hyper_parameters ⇒ Hash<String,String>
The hyperparameters used for the training job.
-
#input_data_config ⇒ Array<Types::Channel>
An array of
Channel
objects, each of which specifies an input source. -
#output_data_config ⇒ Types::OutputDataConfig
the path to the S3 bucket where you want to store model artifacts.
-
#resource_config ⇒ Types::ResourceConfig
The resources, including the ML compute instances and ML storage volumes, to use for model training.
-
#stopping_condition ⇒ Types::StoppingCondition
Specifies a limit to how long a model training job can run.
-
#training_input_mode ⇒ String
The input mode used by the algorithm for the training job.
Instance Attribute Details
#hyper_parameters ⇒ Hash<String,String>
The hyperparameters used for the training job.
#input_data_config ⇒ Array<Types::Channel>
An array of Channel
objects, each of which specifies an input source.
#output_data_config ⇒ Types::OutputDataConfig
the path to the S3 bucket where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.
#resource_config ⇒ Types::ResourceConfig
The resources, including the ML compute instances and ML storage volumes, to use for model training.
#stopping_condition ⇒ Types::StoppingCondition
Specifies a limit to how long a model training job can run. 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.
#training_input_mode ⇒ String
The input mode used by the algorithm for the training job. For the input modes that Amazon SageMaker algorithms support, see Algorithms.
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.