Bug fix for getting dataframes in TrainingJobAnalytics. #441
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laurenyu
reviewed
Oct 25, 2018
src/sagemaker/analytics.py
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| @@ -246,7 +246,12 @@ def _determine_timeinterval(self): | |||
| """ | |||
| description = self._sage_client.describe_training_job(TrainingJobName=self.name) | |||
| start_time = description[u'TrainingStartTime'] # datetime object | |||
| end_time = description.get(u'TrainingEndTime', datetime.datetime.utcnow()) | |||
| # Incrementing end time by 1 min since cloud watch drops seconds before finding the logs. | |||
src/sagemaker/analytics.py
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| # Incrementing end time by 1 min since cloud watch drops seconds before finding the logs. | ||
| # This results in logs being searched in the time range in which the correct log line was not present. | ||
| # Example - Log time - 2018-10-22 08:25:55 | ||
| # Here calculated end time would also be 2018-10-22 08:25:55 (without 1 min addition) |
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nitpick: there's an extra space after "be"
Codecov Report
@@ Coverage Diff @@
## master #441 +/- ##
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+ Coverage 93.75% 94.02% +0.27%
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Files 55 57 +2
Lines 4034 4269 +235
==========================================
+ Hits 3782 4014 +232
- Misses 252 255 +3
Continue to review full report at Codecov.
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Unit test for this are already in place. We are in all cases mocking the calls to CW client and that mock is independent of the namespace. |
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given the change in logic for calculating end time, it would be good to have a unit test for that |
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UT's added. |
laurenyu
previously approved these changes
Oct 26, 2018
mvsusp
previously requested changes
Oct 26, 2018
4 tasks
…#450) * Add incremental training model parameters to Estimator, and bump library version to 1.13.0 * Update README.rst Removed unnecessary comma from sentence. * Update estimator.py Removed duplicated line
output_path is now honored: Can be either file:// or s3:// - This also changes the default behavior of local mode to use the SDK provided default S3 bucket if nothing is passed. This makes it easier for customers to create models in SageMaker too since their Model Artifacts will already be a tarfile in S3. input_channel content_type is now honored in the same way as SageMaker. If it is not provided it is not passed to the container. Before we were always passing 'application/octet-stream'
* Make InputDataConfig optional for training. * Update boto3 dependency to make sure boto support no InputDataConfig. * Update changelog. * Add missing assertion for chainer failure script test.
* add tensorflow serving container support
* add failure test case * fix flaky assert
Add Pylint checking. Fixed all the current errors and warnings, any future PRs will fail if they introduce any pylint error/warnings.
docker-compose 1.23 has moved to a newer version of requests which allows us to remove the upper bound on urllib3 that was causing a lot of problems for everyone.
* add tensorflow serving docs * add content_type to tensorflow.serving.Predictor * support CustomAttributes in local mode
* Better documentation comment on DeferredError. * Using Napolean-style docstring formatting for example code. * Fixup flake8 trailing whitespace.
This is in accordance with our new strategy around not making framework_version completely mandatory.
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can you check the unit tests? four are failing with: |
laurenyu
approved these changes
Nov 14, 2018
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Issue #, if available:
Description of changes:
Bug fix for getting dataframes in TrainingJobAnalytics.
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