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BentoML — model serving and deployment. Build prediction services from any ML framework with OpenAPI/Swagger. Containerize, deploy to Kubernetes, AWS, GCP, Azure. Adaptive batching and GPU support.
Passed 17 of 22 test cases with the skill, and 15 of 22 without it.
3 fixed14 passed either way4 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 3 test cases the skill fixed. Same model, same prompt, with and without the skill.
I manage Python environments using uv and want to install the model serving framework. Standard pip is slow in my workflow. What command installs the package via uv?
| Checked | Without skill | With skill |
|---|---|---|
| Executes uv pip install bentoml | Missed | Passed |
The instructions your model reads when the skill loads.
BentoML packages ML models with service definitions, dependencies, environment config, and deployment targets into a portable "Bento." Deploy to Kubernetes (Kserve, Seldon), AWS SageMaker, GCP Vertex AI, or as a standalone Docker container.
uv pip install bentoml
import bentoml
from bentoml.io import JSON
import numpy as np
iris_clf = bentoml.sklearn.get("iris_model:latest")
@bentoml.service
class IrisClassifier:
def __init__(self):
self.model = iris_clf.to_runner()
self.model.init_local()
@bentoml.api(input=JSON(), output=JSON())
def classify(self, input_data):
result = self.model.run(np.array([input_data["features"]]))
return {"class": int(result[0]), "probabilities": result[1].tolist()}
bentoml build # creates a Bento
bentoml containerize iris_classifier:latest # Docker image
docker run -p 3000:3000 iris_classifier:latest
test cases passed, vs 15 / 22 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.