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docs: clarify gRPC quickstart example #322

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Feb 3, 2023
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docs: clarify gRPC quickstart example #322

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7 changes: 3 additions & 4 deletions docs/quickstart.md
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Expand Up @@ -191,7 +191,7 @@ since a normal Service has issues load balancing gRPC requests. See more info

### gRPC request

To test out **gRPC** inference requests, you can port-forward the headless service _in a separate terminal window_:
To test out gRPC inference requests, you can port-forward the headless service **in a separate terminal window**:

```shell
kubectl port-forward --address 0.0.0.0 service/modelmesh-serving 8033 -n modelmesh-serving
Expand All @@ -200,10 +200,9 @@ kubectl port-forward --address 0.0.0.0 service/modelmesh-serving 8033 -n modelme
Then a gRPC client generated from the KServe [grpc_predict_v2.proto](https://github.com/kserve/kserve/blob/master/docs/predict-api/v2/grpc_predict_v2.proto)
file can be used with `localhost:8033`. A ready-to-use Python example of this can be found [here](https://github.com/pvaneck/model-serving-sandbox/tree/main/grpc-predict).

Alternatively, you can test inference with [grpcurl](https://github.com/fullstorydev/grpcurl). This can easily be installed with `brew install grpcurl` if on macOS.
Alternatively, you can test inferences using [grpcurl](https://github.com/fullstorydev/grpcurl). This can be installed easily with `brew install grpcurl` if on macOS.

With `grpcurl`, a request can be sent to the SKLearn MNIST model like the following. Make sure that the `MODEL_NAME`
variable below is set to the name of your `InferenceService`.
An example that uses `grpcurl` to send a request to the SKLearn MNIST model is provided below. The example should be run from `modelmesh-serving`'s root directory and `MODEL_NAME` should be set to the name of the deployed `InferenceService`.

```shell
MODEL_NAME=example-sklearn-isvc
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