Comments (5)
Hi @sgaist Thanks for the details!
We do have this nightly test https://github.com/pytorch/serve/actions/runs/7999859721
If you can add a test to this, then it would easy to understand and expedite your PR
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Hi @agunapal,
Thanks for the pointer !
I ran the tests and while checking how they were going on, I saw that the PROTOCOL_VERSION
environment variable was set properly by the KServe controller (that was the next thing I wanted to fix). Looking at the KServe sources, the fix dates back to September last year.
Seeing that, I verified what I have on my current machine and surprisingly, it's an older version, v0.9.0 to be exact, of the KServe controller that has been deployed. I don't know why that version as I have followed their documentation to deploy the whole system.
This means that the current version of KServe does the right thing when creating the deployment from InferenceService
hence my patch would not be strictly required in that context. However I still think that it serves its purpose of making the wrapper's behaviour in line with the base class implementation.
While writing the test, I saw some things that could be improved such as the configuration default handling. I am wondering whether such improvements should be part of a follow up patch or integrated in this one ?
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Hi @sgaist The nightly tests that we run are on nightly images. Hence, you don't see the issue I guess. We will be be making a new release soon.
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Hi @sgaist We welcome any improvements to the code. You can send a PR for one of the issues you want to fix, include a test and we can take it from there
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Good, then I'll open a ticket about the improvements I have in mind for the wrapper on top of what I have done in this one with a follow up patch.
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Related Issues (20)
- Warning in regression test: test_install_dependencies_to_target_directory_with_requirements
- enable test_install_dependencies_to_venv_with_requirements in docker regression HOT 2
- Cannot run the text_classification example HOT 4
- CPP build failed with errors
- Open Inference Protocol with nightly build not working HOT 14
- CPP backend debugging and troubleshooting
- Update documentation on deprecating mac x86 support
- It seems like `metrics.yaml` doesn't apply HOT 1
- Config to disable gpu system metrics collection HOT 1
- torch.compile benchmark nightlies failing because of dependency of simpy
- '503 Service Unavailable' for url 'http://0.0.0.0:8085/v1/models/mnist:predict' HOT 2
- Update token authentication doc with maven link for downloading prebuilt plugin HOT 1
- KServe nightly tests are failing
- Incomplete example about emitting metrics HOT 3
- Broken example for a custom Counter metrics HOT 2
- Metrics REST API doesn't return custom metric HOT 3
- Model results are inconsistent between preheating and after preheating HOT 1
- Model with name <name> is not ready. HOT 22
- Improve configuration handling with KServe
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