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Create wrapper for PyTorch models #82
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For the Kubeflow 0.3.0 release I think we'd like to flush out our PyTorch story which includes finding a good way to serve PyTorch models. |
We have an IBM FfDL Example. We could close this or work with Kubeflow on a focused example. |
FYI - I am working on a Kubeflow Pytorch example leveraging Seldon. |
@dsdinter That's great - we'll follow your updates. Feel free to reach out if there are any questions. |
Kubeflow example exists. So closing. |
* initial wiring for http reverse proxy * server config updates * lint * remove enable proxy * docker compose updates * Update envoy processing so each route has a unqiue name and is not set to model name. * Update set state method to take an expected state to transition from * add missing Docker compose files * lint * Add model lock/unlock * lint * Add test for unexpected state change * lint * Add cluster based weighting to split traffic * review comments * remove extract model from path method * remove debug stmt * Add prometheus metrics to http reverse proxy * add grpc metrics * Make namespace name safe for prometheus metric prefix * Update server configs for metrocs * install prometheus and monitors * Fix ports - always need to direct at revere proxy ports as envoy will notuse svc ports but direct pod ports from headless svc * rename metrics and add a latency sum metric * review comment fixes * small typo - space * update metrics * review fixes * Review fixes * review fix
There are issues to create wrappers for R and Spark, so adding PyTorch here too as it was raised the community Slack earlier today. This would be a great first PR for anyone looking to contribute!
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