Add model saving warning at end of training #171
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* Add warning when no model artifact is found * Add warning if model is not saved in the SavedModel bundle format
| .format(direct_parent_dir)) | ||
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| if not file_exists: | ||
| logger.warn('No model artifact is saved under path {}.' |
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Shouldn't we toss an exception here?
Are there any use cases of users not utilizing /opt/ml/model for packaging their models to S3?
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They could have saved models or checkpoints to s3 during training. saving it to '/opt/ml/model' just to make sure ease will upload it at end of training.
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Can you provide any links or references to users expecting to have had their training artifacts in /opt/ml/model, however they didn't know?
jesterhazy
approved these changes
Feb 28, 2019
ChoiByungWook
approved these changes
Feb 28, 2019
Elizaaaaa
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Nov 4, 2019
* Add model saving warning at end of training * Add warning when no model artifact is found * Add warning if model is not saved in the SavedModel bundle format * Combine logging messages * Enforce psutil version * Remove pinned version of sagemaker-containers and install this package last
Elizaaaaa
pushed a commit
to Elizaaaaa/sagemaker-tensorflow-container
that referenced
this pull request
Nov 4, 2019
* Add model saving warning at end of training * Add warning when no model artifact is found * Add warning if model is not saved in the SavedModel bundle format * Combine logging messages * Enforce psutil version * Remove pinned version of sagemaker-containers and install this package last
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