Release notes for profiling capabilities of Amazon SageMaker - Amazon SageMaker
March 21, 2024December 14, 2023August 24, 2023

Release notes for profiling capabilities of Amazon SageMaker

See the following release notes to track the latest updates for profiling capabilities of Amazon SageMaker.

March 21, 2024

Currency updates

SageMaker Profiler has added support for PyTorch v2.2.0, v2.1.0, and v2.0.1.

AWS Deep Learning Containers pre-installed with SageMaker Profiler

SageMaker Profiler is packaged in the following AWS Deep Learning Containers.

  • SageMaker Framework Container for PyTorch v2.2.0

  • SageMaker Framework Container for PyTorch v2.1.0

  • SageMaker Framework Container for PyTorch v2.0.1

December 14, 2023

Currency updates

SageMaker Profiler has added support for TensorFlow v2.13.0.

Breaking changes

This release involves a breaking change. The SageMaker Profiler Python package name is changed from smppy to smprof. If you have been using the previous version of the package while you have started using the latest SageMaker Framework Containers for TensorFlow listed in the following section, make sure that you update the package name from smppy to smprof in the import statement in your training script.

AWS Deep Learning Containers pre-installed with SageMaker Profiler

SageMaker Profiler is packaged in the following AWS Deep Learning Containers.

  • SageMaker Framework Container for TensorFlow v2.13.0

  • SageMaker Framework Container for TensorFlow v2.12.0

If you use the previous versions of the framework containers such TensorFlow v2.11.0, the SageMaker Profiler Python package is still available as smppy. If you are uncertain which version or the package name you should use, replace the import statement of the SageMaker Profiler package with the following code snippet.

try:
    import smprof 
except ImportError:
    # backward-compatability for TF 2.11 and PT 1.13.1 images
    import smppy as smprof

August 24, 2023

New features

Released Amazon SageMaker Profiler, a profiling and visualization capability of SageMaker to deep dive into compute resources provisioned while training deep learning models and gain visibility into operation-level details. SageMaker Profiler provides Python modules (smppy) for adding annotations throughout PyTorch or TensorFlow training scripts and activating SageMaker Profiler. You can access the modules through the SageMaker Python SDK and AWS Deep Learning Containers. For any jobs run with the SageMaker Profiler Python modules, you can load the profile data in the SageMaker Profiler UI application that provides a summary dashboard and a detailed timeline. To learn more, see Amazon SageMaker Profiler.

This release of the SageMaker Profiler Python package is integrated into the following SageMaker Framework Containers for PyTorch and TensorFlow.

  • PyTorch v2.0.0

  • PyTorch v1.13.1

  • TensorFlow v2.12.0

  • TensorFlow v2.11.0