kserve/docs/samples/v1beta1/torchserve/v1/README.md

Predict on a InferenceService using TorchServe

In this example, we use a trained pytorch mnist model to predict handwritten digits by running an inference service with TorchServe predictor.

Setup

1. Your ~/.kube/config should point to a cluster with KServe installed.
2. Your cluster's Istio Ingress gateway must be network accessible.

Inference with REST protocol

Create the InferenceService

For deploying the InferenceService on CPU

kubectl apply -f torchserve.yaml

For deploying the InferenceService on GPU

kubectl apply -f gpu.yaml

Expected Output

$inferenceservice.serving.kserve.io/torchserve created

The first step is to determine the ingress IP and ports and set INGRESS_HOST and INGRESS_PORT

MODEL_NAME=mnist
SERVICE_HOSTNAME=$(kubectl get inferenceservice torchserve -o jsonpath='{.status.url}' | cut -d "/" -f 3)

Use image converter to create input request for mnist. For other models please refer to input request

curl -v -H "Host: ${SERVICE_HOSTNAME}" http://${INGRESS_HOST}:${INGRESS_PORT}/v1/models/${MODEL_NAME}:predict -d @./mnist.json

Expected Output

*   Trying 52.89.19.61...
* Connected to a881f5a8c676a41edbccdb0a394a80d6-2069247558.us-west-2.elb.amazonaws.com (52.89.19.61) port 80 (#0)
> PUT /v1/models/mnist HTTP/1.1
> Host: torchserve.kserve-test.example.com
> User-Agent: curl/7.47.0
> Accept: */*
> Content-Length: 167
> Expect: 100-continue
>
< HTTP/1.1 100 Continue
* We are completely uploaded and fine
< HTTP/1.1 200 OK
< cache-control: no-cache; no-store, must-revalidate, private
< content-length: 1
< date: Tue, 27 Oct 2020 08:26:19 GMT
< expires: Thu, 01 Jan 1970 00:00:00 UTC
< pragma: no-cache
< x-request-id: b10cfc9f-cd0f-4cda-9c6c-194c2cdaa517
< x-envoy-upstream-service-time: 6
< server: istio-envoy
<
* Connection #0 to host a881f5a8c676a41edbccdb0a394a80d6-2069247558.us-west-2.elb.amazonaws.com left intact
{"predictions": ["2"]}

Inference with gRPC protocol

Create the InferenceService

For deploying the InferenceService with gRPC protocol you need to expose the gRPC port on InferenceService.

kubectl apply -f grpc.yaml

Expected Output

$inferenceservice.serving.kserve.io/torchserve-grpc created

Run Inference with gRPC protocol

Clone TorchServe Repo

git clone https://github.com/pytorch/serve

Install gRPC python dependencies

pip install -U grpcio protobuf grpcio-tools

Generate python gRPC client stub using the proto files

python -m grpc_tools.protoc --proto_path=serve/frontend/server/src/main/resources/proto/ --python_out=. --grpc_python_out=. serve/frontend/server/src/main/resources/proto/inference.proto serve/frontend/server/src/main/resources/proto/management.proto

Run gRPC Inference

python torchserve_gprc_client.py infer <model_name> <input json>

Explanation

Model interpretability is an important aspect which help to understand which of the input features were important for a particular classification. Captum is a model interpretability library, the Explain Endpoint uses Captum's state-of-the-art algorithm, including integrated gradients to provide user with an easy way to understand which features are contributing to the model output.

Your can refer to Captum Tutorial for more examples.

Explain Request

curl -v -H "Host: ${SERVICE_HOSTNAME}" http://${INGRESS_HOST}:${INGRESS_PORT}/v1/models/mnist:explain -d @./mnist.json

Expected Output

*   Trying 52.89.19.61...
* Connected to a881f5a8c676a41edbccdb0a394a80d6-2069247558.us-west-2.elb.amazonaws.com (52.89.19.61) port 80 (#0)
> PUT /v1/models/mnist:explain HTTP/1.1
> Host: torchserve.kserve-test.example.com
> User-Agent: curl/7.47.0
> Accept: */*
> Content-Length: 167
> Expect: 100-continue
>
< HTTP/1.1 100 Continue
* We are completely uploaded and fine
< HTTP/1.1 200 OK
< cache-control: no-cache; no-store, must-revalidate, private
< content-length: 1
< date: Tue, 27 Oct 2020 08:26:19 GMT
< expires: Thu, 01 Jan 1970 00:00:00 UTC
< pragma: no-cache
< x-request-id: b10cfc9f-cd0f-4cda-9c6c-194c2cdaa517
< x-envoy-upstream-service-time: 6
< server: istio-envoy
<
* Connection #0 to host a881f5a8c676a41edbccdb0a394a80d6-2069247558.us-west-2.elb.amazonaws.com left intact
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