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Deploy PyTorch model with TorchServe InferenceService

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

Creating model storage with model archive file

TorchServe provides a utility to package all the model artifacts into a single Torchserve Model Archive Files (MAR).

You can store your model and dependent files on remote storage or local persistent volume, the mnist model and dependent files can be obtained from here.

The KServe/TorchServe integration expects following model store layout.

├── config
│   ├── config.properties
├── model-store
│   ├── densenet_161.mar
│   ├── mnist.mar

Note

For remote storage you can choose to start the example using the prebuilt mnist MAR file stored on KServe example GCS bucket gs://kfserving-examples/models/torchserve/image_classifier, you can also generate the MAR file with torch-model-archiver and create the model store on remote storage according to the above layout.

torch-model-archiver --model-name mnist --version 1.0 \
--model-file model-archiver/model-store/mnist/mnist.py \
--serialized-file model-archiver/model-store/mnist/mnist_cnn.pt \
--handler model-archiver/model-store/mnist/mnist_handler.py \

For PVC user please refer to model archive file generation for auto generation of MAR files from the model and dependent files.

TorchServe with KServe inference protocol

The KServe/TorchServe integration supports KServe v1 protocol and we are working on to support v2 protocol.

API Verb Path Payload
Predict POST /v1/models/:predict Request:{"instances": []} Response:{"predictions": []}
Explain POST /v1/models/:explain Request:{"instances": []} Response:{"predictions": [], "explainations": []}

Note

The config.properties file includes the flag service_envelope=kfserving to enable the KServe inference protocol. The requests are converted from KServe inference request format to torchserve request format and sent to the inference_address configured via local socket.

Sample requests for text and image classification

Create the InferenceService

For deploying the InferenceService on CPU

apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
  name: "torchserve"
spec:
  predictor:
    pytorch:
      storageUri: gs://kfserving-examples/models/torchserve/image_classifier
kubectl apply -f torchserve.yaml

For deploying the InferenceService on GPU

apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
  name: "torchserve"
spec:
  predictor:
    pytorch:
      storageUri: gs://kfserving-examples/models/torchserve/image_classifier
      resources:
        limits:
          memory: 4Gi
          nvidia.com/gpu: "1"
kubectl apply -f gpu.yaml

Expected Output

$inferenceservice.serving.kserve.io/torchserve created

Inference

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"]}

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 KServe 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.

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
{"explanations": [[[[0.0005394675730469475, -0.0022280013123036043, -0.003416480100841055, -0.0051329881112415965, -0.009973864160829985, -0.004112560908882716, -0.009223458030656112, -0.0006676354577291628, -0.005249806664413386, -0.0009790519227372953, -0.0026914653993121195, -0.0069470097151383995, -0.00693530415962956, -0.005973878697847718, -0.00425042437288857, 0.0032867281838150977, -0.004297780258633562, -0.005643196661192014, -0.00653025019738562, -0.0047062916121001185, -0.0018656628277792628, -0.0016757477204072532, -0.0010410417081844845, -0.0019093520822156726, -0.004451403461006374, -0.0008552767257773671, -0.0027638888169885267, -0.0], [0.006971297052106784, 0.007316855222185687, 0.012144494329150574, 0.011477799383288441, 0.006846725347670252, 0.01149386176451476, 0.0045351987881190655, 0.007038361889638708, 0.0035855377023272157, 0.003031419502053957, -0.0008611575226775316, -0.0011085224745969223, -0.0050840743637658534, 0.009855491784340777, 0.007220680811043034, 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Autoscaling

One of the main serverless inference features is to automatically scale the replicas of an InferenceService matching the incoming workload. KServe by default enables Knative Pod Autoscaler which watches traffic flow and scales up and down based on the configured metrics.

Autoscaling Example

Canary Rollout

Canary rollout is a deployment strategy when you release a new version of model to a small percent of the production traffic.

Canary Deployment

Monitoring

Expose metrics and setup grafana dashboards