Add hyperparameter tuning support #207
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owen-t
previously approved these changes
May 31, 2018
| based on the training image name and current timestamp. | ||
| **kwargs: Other arguments | ||
| """ | ||
| if isinstance(inputs, list) or isinstance(inputs, RecordSet): |
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This is much better:
kwargs = dict(kwargs)
kwargs['job_name'] = job_name
self._prepare_for_training(**kwargs)There was a problem hiding this comment.
basically replace line 140 to 145 with that block.
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_prepare_for_training() still needs records for 1P estimators but not for the others, though. Instead, it'd end up looking like:
kwargs = dict(kwargs)
kwargs['job_name'] = job_name
if isinstance(inputs, list) or isinstance(inputs, RecordSet):
kwargs['records'] = inputs
self.estimator._prepare_for_training(**kwargs)
Codecov Report
@@ Coverage Diff @@
## master #207 +/- ##
==========================================
+ Coverage 90.76% 91.49% +0.73%
==========================================
Files 42 45 +3
Lines 2717 3162 +445
==========================================
+ Hits 2466 2893 +427
- Misses 251 269 +18
Continue to review full report at Codecov.
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owen-t
approved these changes
Jun 5, 2018
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Description of changes:
Add support for hyperparameter tuning jobs.
This introduces a few key features:
HyperparameterTuner, which looks/acts like an estimator withfit(),deploy(), andattach()except that it creates hyperparameter tuning jobs instead of regular training jobs_prepare_for_training(), which should set all values needed before trainingThis PR also bumps the SDK version to 1.4.0.
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