A summary of the main contributions to the Seldon Core release 0.4.0.
Seldon provides several prepacked servers you can use to deploy trained models:
For these servers you only need the location of the saved model in a local filestore, Google bucket or S3 bucket. An example manifest with an sklearn server is shown below:
apiVersion: machinelearning.seldon.io/v1alpha2
kind: SeldonDeployment
metadata:
name: sklearn
spec:
name: iris
predictors:
- graph:
children: []
implementation: SKLEARN_SERVER
modelUri: gs://seldon-models/sklearn/iris
name: classifier
name: default
replicas: 1
The modelUri
specifies the bucket containing the saved model, in this case gs://seldon-models/sklearn/iris
.
modeluri
supports the following three object storage providers:
- Google Cloud Storage (using
gs://
) - S3-comptaible (using
s3://
) - Azure Blob storage (using
https://(.+?).blob.core.windows.net/(.+)
)
We have provided an early alpha release for the python language wrapper to run under gunicorn rather than Flask. For further details see our gunicorn documentation.
We have a kustomize resource you can use and extend for your own particular setup for installing Seldon Core.
Our range of example has expanded to include: