Read model name from request body #290
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This was referenced Oct 10, 2024
Jeffwan
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Oct 11, 2024
Jeffwan
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Oct 22, 2024
* Update manifests version to v0.1.0-rc.3 (#287) * [Misc] Add sync images step and scripts in release process (#283) Add sync images step and scripts in release process * [batch] E2E works with driver and request proxy (#272) * e2e driver and test * comment functions * check job status in test * format update * update copyright * add examples with instructions and interfaces * move batch tutorial --------- Co-authored-by: xin.chen <[email protected]> * Fix address already in use when AIRuntime start in pod (#289) add uvicorn startup into file entrypoint * Read model name from request body (#290) * Use model name from request body * rename dummy to reserved router * Fix redis bootstrap flaky connection issue (#293) * skip docs CI if no changes in /docs dir (#294) * skip docs CI if no changes in /docs dir * test docs build * Improve Rayclusterreplicaset Status (#295) * improve rayclusterreplicaset status * nit * fix lint error * improve isClusterActive logic * fix lint error * remove redundant isRayPodCreateOrDeleteFailed check --------- Signed-off-by: Yicheng-Lu-llll <[email protected]> * Add request trace for profiling (#291) * Add request trace for profiling * add to redis at 10 second interval * nit * round to nearest 10s interval * round timestamp to nearest 10s interval and aggregate data by model * add go routine to add request trace * Update the crd definiton due to runtime upgrade (#298) #295 introduce the latest kuberay api and the dependencies bumps sigs.k8s.io/controller-runtime from v0.17.3 to v0.17.5. Due to that change, make manifest update the CRD definitions * Push images to Github registry in release pipeline (#301) * Disable docker build github workflow to cut CI cost * Push images to Github registry in release pipeline * Build autoscaler abstractions like fetcher, client and scaler (#300) * minor clean up on the autoscaler controller * Extract the algorithm package algorithm is extracted to distinguish with the scaler. * Refactor scaler interface 1. Split the Scaler interface and BaseAutoscaler implementation 2. Create APA/KPA scaler separately and adopt the corresponding algorithms * Introduce the scalingContext in algorithm * Introduce k8s.io/metrics for resource & custom metrics fetching * Extract metric fetcher to cover the fetching logic * Optimize the scaler workflow to adopt fetch and client interface * Further refactor the code structure * Support pod autoscaler periodically check (#306) * Support pod autoscaler periodically check * Fix the error case * Add timeout in nc check for redis bootstrap (#309) * Refactor AutoScaler: metricClient, context, reconcile (#308) * Refactor AutoScaler: optimize metric client, context, and reconcile processes. * fix make lint-all * fix typos --------- Signed-off-by: Yicheng-Lu-llll <[email protected]> Co-authored-by: xinchen384 <[email protected]> Co-authored-by: xin.chen <[email protected]> Co-authored-by: brosoul <[email protected]> Co-authored-by: Varun Gupta <[email protected]> Co-authored-by: Yicheng-Lu-llll <[email protected]> Co-authored-by: Rong-Kang <[email protected]>
gangmuk
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Jan 25, 2025
* Use model name from request body * rename dummy to reserved router
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