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¶
pip install aws-sagemaker-remote
Development¶
git clone https://github.com/bstriner/aws-sagemaker-remote
cd aws-sagemaker-remote
python setup.py develop
Documentation¶
View latest documentation at ReadTheDocs