Uncompressed model output

SageMaker AI stores your model in /opt/ml/model and your data in /opt/ml/output/data. After the model and data are written to those locations, they're uploaded to your Amazon S3 bucket as compressed files by default.

You can save time on large data file compression by uploading model and data outputs to your S3 bucket as uncompressed files. To do this, create a training job in uncompressed upload mode by using either the AWS Command Line Interface (AWS CLI) or the SageMaker Python SDK.

The following code example shows how to create a training job in uncompressed upload mode when using the AWS CLI. To enable uncompressed upload mode, set CompressionType field in the OutputDataConfig API to NONE.

{
   "TrainingJobName": "uncompressed_model_upload",
   ...
   "OutputDataConfig": { 
      "S3OutputPath": "s3://amzn-s3-demo-bucket/uncompressed_upload/output",
      "CompressionType": "NONE"
   },
   ...
}

The following code example shows you how to create a training job in uncompressed upload mode using the SageMaker Python SDK.

from sagemaker.train import ModelTrainer
from sagemaker.train.configs import Compute
from sagemaker.core.helper.session_helper import Session, get_execution_role
from sagemaker.core.shapes import OutputDataConfig

model_trainer = ModelTrainer(
    training_image="your-own-image-uri",
    role=get_execution_role(),
    compute=Compute(
        instance_count=1,
        instance_type='ml.c4.xlarge',
    ),
    output_data_config=OutputDataConfig(compression_type="NONE")
)