Tensorflow protocol is only available in version >=1.1.
Seldon Core supports the following data planes:
REST and gRPC Seldon Protocol¶
Seldon is the default protocol for SeldonDeployment resources. You can specify the gRPC protocol by setting
transport: grpc in your SeldonDeployment resource or ensuring all components in the graph have endpoint.tranport set ot grpc.
See example notebook.
REST and gRPC Tensorflow Protocol¶
Activate this protocol by speicfying
protocol: tensorflow and
transport: rest or
transport: grpc in your Seldon Deployment. See example notebook.
For Seldon graphs the protocol will work as expected for single model graphs for Tensorflow Serving servers running as the single model in the graph. For more complex graphs you can chain models:
Sending the response from the first as a request to the second. This will be done automatically when you defined a chain of models as a Seldon graph. It is up to the user to ensure the response of each changed model can be fed a request to the next in the chain.
Only Predict calls can be handled in multiple model chaining.
Seldon components marked as MODELS, INPUT_TRANSFORMER and OUTPUT_TRANSFORMERS will allow a PredictionService Predict method to be called.
GetModelStatus for any model in the graph is available.
GetModelMetadata for any model in the graph is available.
Combining and Routing with the Tensorflow protocol is not presently supported.
metadatacalls can be asked for any model in the graph
a non-standard Seldon extension is available to call predict on the graph as a whole:
The name of the model in the
graphsection of the SeldonDeployment spec must match the name of the model loaded onto the Tensorflow Server.
V2 KFServing Protocol¶
Seldon has collaborated with the NVIDIA Triton Server Project and the KFServing Project to create a new ML inference protocol. The core idea behind this joint effort is that this new protocol will become the standard inference protocol and will be used across multiple inference services.
In Seldon Core, this protocol can be used by specifing
protocol: kfserving on
apiVersion: machinelearning.seldon.io/v1alpha2 kind: SeldonDeployment metadata: name: sklearn spec: name: iris-predict protocol: kfserving predictors: - graph: children:  implementation: SKLEARN_SERVER modelUri: gs://seldon-models/sklearn/iris name: classifier parameters: - name: method type: STRING value: predict name: default
At present, the
kfserving protocol is only supported in a subset of
pre-packaged inference servers.
You can try out the
kfserving in this example notebook.