Set up a S3 bucket to upload training datasets and save training output data for your hyperparameter tuning job.
To use a default S3 bucket
Use the following code to specify the default S3 bucket allocated for your SageMaker AI session.
prefix is the path within the bucket where SageMaker AI stores the data for the
current training job.
sess = sagemaker.Session()
bucket = sess.default_bucket() # Set a default S3 bucket
prefix = 'DEMO-automatic-model-tuning-xgboost-dm'To use a specific S3 bucket (Optional)
If you want to use a specific S3 bucket, use the following code and replace the strings
to the exact name of the S3 bucket. The name of the bucket must contain
sagemaker, and be globally unique. The bucket must be in the same
AWS Region as the notebook instance that you use for this example.
bucket = "sagemaker-your-preferred-s3-bucket"
sess = sagemaker.Session(
default_bucket = bucket
)Note
The name of the bucket doesn't need to contain sagemaker if the
IAM role that you use to run the hyperparameter tuning job has a policy that gives the
S3FullAccess permission.
Next Step
Download, Prepare, and Upload Training Data
