Skip to main content

Amazon SageMaker Debugger is an offering from AWS which help you automate the debugging of machine learning training jobs.

Project description

This library powers Amazon SageMaker Debugger, and helps you develop better, faster and cheaper models by catching common errors quickly. It allows you to save tensors from training jobs and makes these tensors available for analysis, all through a flexible and powerful API. It supports TensorFlow, PyTorch, MXNet, and XGBoost on Python 3.6+.

  • Zero Script Change experience on SageMaker when using supported versions of SageMaker Framework containers or AWS Deep Learning containers
  • Full visibility into any tensor part of the training process
  • Real-time training job monitoring through Rules
  • Automated anomaly detection and state assertions
  • Interactive exploration of saved tensors
  • Distributed training support
  • TensorBoard support

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for smdebug, version 0.4.14
Filename, size File type Python version Upload date Hashes
Filename, size smdebug-0.4.14-py2.py3-none-any.whl (159.1 kB) File type Wheel Python version py2.py3 Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page