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.
