Platform for deploying your Machine Learning to production
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Hydrosphere Serving is a cluster for deploying and versioning your machine learning models in production.
- Framework Agnostic. Serve machine learning models developed in any language or framework. Hydrosphere Serving will wrap them in a Docker image and deploy on your production cluster, exposing HTTP, gRPC and Kafka interfaces.
- Traffic shadowing. Shadow your traffic between different model versions to examine how different model versions behave on the same traffic.
- Model Version Control. Version control your models and pipelines as they are deployed.
You can refer to our documentation to see tutorials, check out example projects, and learn about all features of Hydrosphere.
There are two main ways of installing Hydropshere:
Before installing Hydrosphere Serving, please install its prerequisites:
To install Hydrosphere Serving, follow the instructions below:
- Download the latest release version from the releases page;
export HYDROSPHERE_RELEASE=2.4.3 wget -O hydro-serving-${HYDROSPHERE_RELEASE}.tar.gz https://github.com/Hydrospheredata/hydro-serving/archive/${HYDROSPHERE_RELEASE}.tar.gz
- Unpack the tar ball;
tar -xvf hydro-serving-${HYDROSPHERE_RELEASE}.tar.gz
- Set up an environment.
cd hydro-serving-${HYDROSPHERE_RELEASE} docker-compose up
To check installation, open http://localhost/. By default Hydrosphere UI is available at port 80.
Note, other installation options are described in the documentation.
Before installing Hydrosphere Serving, please install its prerequisites:
- Helm 2 starting from 2.9+ with the tiller installed on the cluster;
- Kubernetes 1.8 and above with beta APIs enabled.
To install Hydrosphere Serving, follow the instructions below:
helm repo add hydrosphere https://hydrospheredata.github.io/hydro-serving/helm/
helm install --name serving --namespace hydrosphere hydrosphere/serving
To reach the cluster, port-forward ui
service locally.
kubectl port-forward -n hydrosphere svc/serving-ui 8080:9090
To check installation, open http://localhost:8080/.
Note, other installation options are described in the documentation.
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or contact us directly at [email protected]
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Check out our issues for anything labeled with help-wanted
, they will be the perfect starting point! If you don't see any, just let us know, we would be happy to hear from you.