Case Summary: Inefficient Data Transfer on SageMaker Studio

Description: The user is encountering slow data transfer speeds when running a Python script (training.prepare_dataset) within a SageMaker Studio environment. The purpose of the script is to copy a large dataset from a network-mounted directory (127.0.0.1:/200020) to a local NVMe SSD storage (/dev/nvme0n1p1). The process is slower than expected.

Technical Environment:

AWS SageMaker Studio TensorFlow Docker container running as root within SageMaker Studio Source Data: Network file system (NFS) mounted on SageMaker Destination Data: Local NVMe SSD on SageMaker instance Operating System: Likely a variant of Amazon Linux Python version: 3.9

Observed Behavior:

The data transfer is much slower than anticipated, which disrupts the dataset preparation process. A TensorFlow warning regarding CPU optimization suggests potential computational inefficiencies, though this is distinct from the file transfer speed issue.

Impact:

The reduced data transfer speed significantly affects the machine learning workflow in SageMaker Studio, leading to longer preparation times and potentially delaying subsequent model training and experimentation.