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Comment by yubozhao | Hacker News Reader

hi Aaron, We did exactly what works for you into a open source python library, github.com/bentoml/bentoml.

It packages your model for you into a standardized format, that you can use it in multiply serving scenarios online serving with api endpoint, offline serving with spark udf, CLI access or import it as python module. It also helps you deploy to different platform such as lambda, sagemaker and others.

Our value is from model in notebook to production service in 5 mins. Love to hear your feedback on this. You can try out our quick start on Google colab (https://colab.research.google.com/github/bentoml/BentoML/blo...)

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It's been great seeing this space fill out with solutions in the last year. MLFlow[1] is another open source solution I have my eyes on.

BentoML looks more cohesive than our homegrown solution because it targets a more general case. One of the things I would miss switching to BentoML would be automatic requirements generation. We use pipreqs[2] to generate a requirements.txt given a model instance. Any thoughts on the difficulty as a user in extending BentoML as to integrate pipreqs?

Again another difficulty question: we have a few statsmodels[3] predictors and it isn't clear how much work would be involved extending BentoML to accept those too.

Thanks for pointing out BentoML. I'll keep an eye on it as a migration target as this space develops.

[1] https://mlflow.org/docs/latest/index.html

[2] https://github.com/bndr/pipreqs

[3] https://www.statsmodels.org/stable/index.html

hi Aaron, I'm one of the BentoML aurthors - great suggestion on pipreqs, will look into incorparating that into BentoML!

It should be very straightforward adding support for saving/loading Statsmodels in BentoML. In fact you should also be able to just use the existing "PickleArtifact" in BentoML for statsmodel predictors too. We will add an example notebook for working with Statsmodels library soon!

Hi Aaron! We in Kubeflow[1] would love if you took a look at us as well. We're always open to feedback!

[1] https://kubeflow.org

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