Design decisions for training #1
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mvsusp
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Jul 26, 2018
* add container support code (#1) * Add missing test dependencies to setup.py (#2) * Add entry.py as script in setup.py so it can be invoked from our images (#3) * Add data_files to setup.py to copy nginx config correctly + fix install_requires (#4) * Move dependencies from extras_require to install_requires * Copy in change from internal repo: support pip install requirements.txt (#5) * Use package_data instead of data_files to package config files, refer to (#6) * Add missing conf files (accidentally left out of previous commit) (aws#8) * pip install requirements in training container (aws#11) * Use container exit code to exit Trainer process. (aws#9) * Add gunicorn and gevent as setup.py dependencies (aws#13) * Specify version ranges for install_requires dependencies * Add Apache license headers (aws#14) * Add changelog (aws#15) * Bump version to 1.0.0 (aws#16) * Fix year in license headers (aws#18) * Add README description (aws#20) * Update version to 1.0.1 (aws#21) * Read new TRAINING_JOB_NAME environment variable during training (aws#34) * Bump version to 1.0.2 and update Changelog (aws#37) * Move processing of requirements file out to the specific container. (aws#39) * Bump version to 1.0.3 and update Changelog (aws#42) * Change module name to string in __all__ (aws#50) * Hardcode special case for tuning job hyperparameter (aws#54) * Use strip() for more readability (aws#55) This change is a small improvement upon aws#54 * Bump version to 1.0.4 (aws#56)
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These PR contain a minimal implementation of the sagemaker-containers for training so we can discuss the design.