def _s3_region()

in src/sagemaker_tensorflow_container/s3_utils.py [0:0]

• 9 lines of code
• 4 McCabe index (conditional complexity)

def _s3_region(job_region, model_dir):
    if model_dir and model_dir.startswith("s3://"):
        s3 = boto3.client("s3", region_name=job_region)

        # We get the AWS region of the checkpoint bucket, which may be different from
        # the region this container is currently running in.
        parsed_url = urlparse(model_dir)
        bucket_name = parsed_url.netloc

        bucket_location = s3.get_bucket_location(Bucket=bucket_name)["LocationConstraint"]

        return bucket_location or job_region
    else:
        return job_region