You are developing a TensorFlow project that uses Amazon SageMaker for training but will be offline without Wi‑Fi for an extended period. Which approach allows you to continue working locally with minimal changes?

The correct answer is to download the TensorFlow Docker container used by Amazon SageMaker from GitHub and use the Amazon SageMaker Python SDK locally. This approach ensures environment consistency with SageMaker, minimizing changes when you return online. The SageMaker Python SDK allows you to test your TensorFlow code locally using the same API calls you'd use with SageMaker, making the transition seamless. Installing Python 3 and boto3 is insufficient because it doesn't replicate the specific TensorFlow environment or the SageMaker SDK functionality. Downloading TensorFlow from tensorflow.org and emulating the SageMaker environment is possible but requires significant manual configuration to match SageMaker's specific TensorFlow version and dependencies, which is not "minimal changes." Downloading the SageMaker notebook and installing Jupyter locally doesn't provide the SageMaker-specific TensorFlow environment or the SDK for local testing, only the notebook interface.