Remotely run and track ML research using AWS SageMaker.

• Standardized command line flags
• Remotely run scripts with minimal changes
• Automatically manage AWS resources
• All code, inputs, outputs, arguments, and settings are tracked in one place
• Reproducible batch processing jobs to prepare datasets
• Reproducible training jobs that track hyperparameters and metrics

Track three types of objects in a standard way:

• Processing jobs consume file inputs and produce file outputs. Useful for data conversion, extraction, etc.
• Training jobs train models while tracking metrics and hyperparameters.
• Inference models provide predictions and can be deployed on endpoints. Can be automatically created from and linked to training jobs for tracking purposes or can deploy externally-created models.

pip install aws-sagemaker-remote

git clone https://github.com/bstriner/aws-sagemaker-remote
cd aws-sagemaker-remote
python setup.py develop

View latest documentation at ReadTheDocs

View continuous integration at TravisCI

View releases on PyPI

View source code on GitHub

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