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10/16/2023, 12:33 PMJaydeep Samanta
10/16/2023, 12:34 PMbentoml serve analytics:latest 1 ↵ 1348 13:30:16
Error: [bentoml-cli] `serve` failed: Failed to load bento or import service 'analytics:latest'.
If you are attempting to import bento in local store: 'Attribute "latest" not found in module "analytics".'.
If you are importing by python module path: 'no Models with name 'cpu_utilization_model_arima' exist in BentoML store <osfs '/Users/jaydeepsamanta/bentoml/bentos/analytics/ghdwc5tmd2dhvury/models'>'.Vivien Robert
10/16/2023, 12:36 PMbentoml models list ?Jaydeep Samanta
10/16/2023, 12:52 PMTag Module Size Creation Time
cpu_utilization_model_xgb:4wgjc4dmdoomtury bentoml.xgboost 1.50 MiB 2023-10-16 13:02:36
cpu_utilization_model_arima:4wamujtmdkk7pury bentoml.picklable_model 1.65 MiB 2023-10-16 12:55:27Jaydeep Samanta
10/16/2023, 12:53 PMChaoyu
10/18/2023, 5:56 AMChaoyu
10/18/2023, 5:57 AMmodels field to your bentofile.yaml? see: https://docs.bentoml.com/en/latest/concepts/bento.html#modelsJaydeep Samanta
10/18/2023, 10:47 AMChaoyu
10/18/2023, 4:16 PMembedded runner option, or add the runner instance to the bentoml.Service’s runners listChaoyu
10/18/2023, 4:16 PMJaydeep Samanta
10/19/2023, 8:26 AMembedded runner Cheers!Jaydeep Samanta
10/19/2023, 3:47 PMembedded runner , if you could point me one that would be really helpful. Also I tried the other option adding my custom runner instance to bentoml.Service's runner list but then I get the same error before, I do not see that model appearing in the bentoJaydeep Samanta
10/19/2023, 3:47 PMBuilding BentoML service "analytics:kspkrjtos2izjury" from build context
Packing model "cpu_utilization_model_xgb:ebpiutdmh6l65ury"Jaydeep Samanta
10/19/2023, 3:49 PMI expect something like below:Jaydeep Samanta
10/19/2023, 3:49 PMPacking model "cpu_utilization_model_arima:gumpddtmh6m5lury"
Packing model "cpu_utilization_model_xgb:ebpiutdmh6l65ury"Jian Shen Yap
10/23/2023, 5:53 PMJaydeep Samanta
10/23/2023, 5:54 PMJian Shen Yap
10/23/2023, 5:56 PMclass SimpleRunnable(bentoml.Runnable):
SUPPORTED_RESOURCES = ("cpu",)
SUPPORTS_CPU_MULTI_THREADING = True
@bentoml.Runnable.method()
def predict(self, inputs: str) -> tuple[np.array, np.array, np.array]:
return np.array([1,2,3]), np.array([4,5,6]), np.array([7,8,9])
# return f"{inputs}"
simple_runner = bentoml.Runner(SimpleRunnable, embedded=True)Jaydeep Samanta
04/08/2024, 9:47 AM