Multi-stage pipeline + scoring observability #122
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bump pip requirements versions (microsoft#104)
* Use Azure CLI tasks to remove need for variables SP_APP_ID, SP_APP_SECRET, SUBSCRIPTION_ID, TENANT_ID * Updated gettin_started.md to point out variable path to artifact (microsoft#117) * Made random hyperparameter (ridge regression alpha) a pipeline parameter, setting its value in the DevOps pipeline (microsoft#107) * Changed unit test to test actual training code (partially solves microsoft#74) * Fixed: attach_compute should set exit code after exception (microsoft#113) * Fixed scoring endpoint HTTP behavior (microsoft#110) * Fixed PipelineParameters format in call to Azure ML Extension (microsoft#118) * Fixed Model build environment for Azure Web App for containers (microsoft#119)
Builds on top of PR microsoft#121, and: - Replaces separate build + release pipelines with a single multi-stage pipeline with stages for Build, Train, ACI, AKS and Webapp deployment. - ACI, AKS and Webapp deployment are optionally enabled (through Variables). - Added example of custom traces (logging) and correlating traces with requests with Kusto queries. - Added example of distributed tracing (client passes a trace header, logged by the scoring service). 
dtzar
suggested changes
Dec 7, 2019
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@algattik So close on this after a bit of tweaking it with my commits. The only thing remaining is dynamically getting the MODEL_VERSION variable for when we deploy. Right now this build fails because it's looking for the hard-coded model version 1 in the variable template file while I have version 17 of the file, so it can't find it to deploy. IMO we should use the verify model registered script to return the model version for use in other steps.
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Restored the pre-merge version where MODEL_VERSION is set by the verify_train_pipeline.py task. |
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dtzar
approved these changes
Dec 9, 2019
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Builds on top of PR #121, and: