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Structured logging and other bug fixes. #91
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* worker.py should format logs as json entries. This will make it easier to query the data in BigQuery and stackdriver to measure performance. * Related to kubeflow#79 * To deal with workload identity flakiness (kubeflow#88) test that we can get application default credentials on startup and if not exit. * As a hack to deal with multi-threading issues with Keras models (kubeflow#89) have the predict function load a new model on each call * It looks like the way pubsub works there is actually a thread pool so predict calls won't be handled in the same thread even though we throttle it to handle one item at a time.
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/assign @hamelsmu |
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/lgtm |
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[APPROVALNOTIFIER] This PR is APPROVED This pull-request has been approved by: hamelsmu The full list of commands accepted by this bot can be found here. The pull request process is described here
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hamelsmu
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Jan 4, 2020
| #transform raw text into array of ints | ||
| vec_body = self.body_pp.transform([body]) | ||
| vec_title = self.title_pp.transform([title]) | ||
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| # make predictions with the model | ||
| # TODO(jlewi): Is this right? Do we want to get the default graph or |
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I think getting the default graph works because it makes it easy to refer to that graph lazily and we are only using this one graph at the moment
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worker.py should format logs as json entries. This will make it easier
to query the data in BigQuery and stackdriver to measure performance.
To deal with workload identity flakiness (label microservice show 500's contacting metadata server when they first start #88) test that we can get
application default credentials on startup and if not exit.
As a hack to deal with multi-threading issues with Keras models (FailedPreconditionError op not initialized #89)
have the predict function load a new model on each call
so predict calls won't be handled in the same thread even though
we throttle it to handle one item at a time.
Related to #70: Ensemble models
This change is