aws-sagemaker-remote ++++++++++++++++++++

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.

Installation

Release

.. code-block:: bash

pip install aws-sagemaker-remote

Development

.. code-block:: bash

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

Documentation

View latest documentation at ReadTheDocs <https://aws-sagemaker-remote.readthedocs.io/>_

Continuous Integration

View continuous integration at TravisCI <https://travis-ci.org/github/bstriner/aws-sagemaker-remote>_

PyPI

View releases on PyPI <https://pypi.org/project/aws-sagemaker-remote/>_

GitHub

View source code on GitHub <https://github.com/bstriner/aws-sagemaker-remote>_

GitHub tags are automatically released on ReadTheDocs, tested on TravisCI, and deployed to PyPI if successful.