Comments (1)
I study the code and found that bentoml
uses a quite complex method to decide the batch size. It seems that it is the small traffic to cause the problem. When I increase the testing load using go-wrk, the invokation is batched as expected.
go-wrk -c 20 -d 30 -body '{"img":["foo"]}' -H 'content-type: application/json' -M POST http://localhost:3000/infer -T 30000
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Related Issues (20)
- bug: Can't connect to bentoml API using websocket
- Bug: 'bentoml containerize' doesn't include models in the image
- bug: Cannot use pydantic models with namedtuples
- bug: SSL does not work HOT 2
- bug: bentoml.transformers.save_model doesn't work with WhisperFeatureExtractor
- minor bug: Swagger docs.json not correctly specified for list[PydanticModel] Output
- bug: pydantic/pathlib patching causes `LookupError: <ContextVar name='request_directory' at 0x172a18860>` HOT 1
- bug: ModuleNotFoundError: No module named 'bentoml.frameworks'
- bug: Cannot specify Numpy DType and Shape with Pydantic as deletion contains a bug. HOT 1
- bug: Cannot install Development environment using pdm HOT 2
- feature: support bentoml.depends.from_url("http://already.deployed-bento.com:3000")
- feature: Make Adaptative Batching algorithm customizable HOT 2
- bug: memory leak when I am using bentoml>=1.2 HOT 28
- feature: Use tcmalloc in supported platform as default memory allocator
- bug: Cannot get logging to work with monitoring resource
- bug: Excessive time spent parsing pydantic version
- bug: timeout configuration not work HOT 1
- feature: serialization/deserialization with `ormsgpack`
- bug: a lot of missing testing for the `_bento_sdk` package
- bug: Generic exceptions from HTTP client
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