(Advanced) Model Serving by Seldon

In this section, we want to post our MNIST classifier online, thus allowing everyone to try our model via a URL.

To do this, you must have:

Full control of your Kubernetes cluster
Full control of your PrimeHub account
A Google Cloud account

Install Seldon (https://docs.seldon.io/projects/seldon-core/en/latest/).

 helm install seldon-core-operator --name seldon-core --repo https://storage.googleapis.com/seldon-charts --set usageMetrics.enabled=true --namespace seldon-system --version 0.5.1

Create a new python file train_mnist_serving.py in JupyterLab.

 import tensorflow as tf
 import argparse
 import os
 import shutil
 
 parser = argparse.ArgumentParser(description='Process some integers.')
 parser.add_argument('--dropout', type=float, default=0.2)
 args = parser.parse_args()
 
 mnist = tf.keras.datasets.mnist
 
 (x_train, y_train),(x_test, y_test) = mnist.load_data()
 x_train, x_test = x_train / 255.0, x_test / 255.0
 
 model = tf.keras.models.Sequential([
   tf.keras.layers.Flatten(input_shape=(28, 28)),
   tf.keras.layers.Dense(512, activation=tf.nn.relu),
   tf.keras.layers.Dropout(args.dropout),
   tf.keras.layers.Dense(10, activation=tf.nn.softmax)
 ])
 
 model.compile(optimizer='adam',
               loss='sparse_categorical_crossentropy',
               metrics=['accuracy'])
 
 model.fit(x_train, y_train, epochs=5)
 model.evaluate(x_test, y_test)
 
 export_path = "1"
 if os.path.isdir(export_path):
     print('Cleaning up\n')
     shutil.rmtree(export_path)
 
 tf.saved_model.simple_save(
     tf.keras.backend.get_session(),
     export_path,
     inputs={'input_image': model.input},
     outputs={t.name:t for t in model.outputs})

Run this file in job submission with command.

 cd /project/<group name>/
 python -u train_mnist_serving.py

Upload the outputted 1 folder into your Google Bucket in <your_bucket_name>/mnist
Create a service account and a JSON key.
Download the key in Google Cloud.

Create a secret in your cluster. <LOCALFILE> is the key file you just downloaded.

 kubectl create secret generic user-gcp-sa --from-file=gcloud-application-credentials.json=<LOCALFILE>

Create a service account in your cluster.

 apiVersion: v1
 kind: ServiceAccount
 metadata:
   name: user-gcp-sa
 secrets:
   - name: user-gcp-sa

Create a Seldon deployment in your cluster with your bucket. Replace gs://<your_bucket_name>/mnist.

 apiVersion: machinelearning.seldon.io/v1alpha2
 kind: SeldonDeployment
 metadata:
   name: tfserving
 spec:
   name: mnist
   predictors:
   - graph:
       children: []
       implementation: TENSORFLOW_SERVER
       modelUri: gs://<your_bucket_name>/mnist
       serviceAccountName: user-gcp-sa
       name: mnist-model
       parameters:
         - name: signature_name
           type: STRING
           value: serving_default
         - name: model_name
           type: STRING
           value: mnist-model
     name: default
     replicas: 1

Create an ingress rule with your service. Find out your newly created service name starting with mnist-xxx by kubectl get svc. Replae <check_svc_name_which_created_by_seldondeployment> and *.<your_host>'.

apiVersion: extensions/v1beta1
kind: Ingress
metadata:
  name: mnist-classifier
  annotations:
    certmanager.k8s.io/acme-challenge-type: dns01
    certmanager.k8s.io/acme-dns01-provider: clouddns
    certmanager.k8s.io/cluster-issuer: letsencrypt-prod-dns
    kubernetes.io/ingress.allow-http: "true"
    kubernetes.io/ingress.class: "nginx"
    kubernetes.io/tls-acme: "true"
  namespace: default
spec:
  rules:
  - host: mnist.<your_host>
    http:
      paths:
      - backend:
          serviceName: <check_svc_name_which_created_by_seldondeployment>
          servicePort: 9000
        path: /
  tls:
  - hosts:
    - '*.<your_host>'
    secretName: dns01-tls

Make a prediction through curl. Replace https://mnist.<your_host>/predict.

curl -X POST https://mnist.<your_host>/predict -d '{"data":{"tensor":{"shape":[1,784],"values":[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 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The data is normalized vector of this image:

You will get output like the screenshot below. The values field has a total of ten values (separated by commas). The first value represents the probability that the image is a '0', the second value represents the probability that the image is a '1', and so on. Thus, the eighth value represents the highest probability that this picture is '7', which is in line with our expectations (since the image is surely a '7'!). Our model makes a correct prediction!

In the future, we will provide even better model serving functions. Stay tuned!

Previous(Advanced) Use Job Submission to Tune Hyperparameters NextJob Artifacts Simple Usecase

Last updated 1 year ago

import tensorflow as tf import argparse import os import shutil parser = argparse.ArgumentParser(description='Process some integers.') parser.add_argument('--dropout', type=float, default=0.2) args = parser.parse_args() mnist = tf.keras.datasets.mnist (x_train, y_train),(x_test, y_test) = mnist.load_data() x_train, x_test = x_train / 255.0, x_test / 255.0 model = tf.keras.models.Sequential([ tf.keras.layers.Flatten(input_shape=(28, 28)), tf.keras.layers.Dense(512, activation=tf.nn.relu), tf.keras.layers.Dropout(args.dropout), tf.keras.layers.Dense(10, activation=tf.nn.softmax) ]) model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) model.fit(x_train, y_train, epochs=5) model.evaluate(x_test, y_test) export_path = "1" if os.path.isdir(export_path): print('Cleaning up\n') shutil.rmtree(export_path) tf.saved_model.simple_save( tf.keras.backend.get_session(), export_path, inputs={'input_image': model.input}, outputs={t.name:t for t in model.outputs})

apiVersion: machinelearning.seldon.io/v1alpha2 kind: SeldonDeployment metadata: name: tfserving spec: name: mnist predictors: - graph: children: [] implementation: TENSORFLOW_SERVER modelUri: gs://<your_bucket_name>/mnist serviceAccountName: user-gcp-sa name: mnist-model parameters: - name: signature_name type: STRING value: serving_default - name: model_name type: STRING value: mnist-model name: default replicas: 1

apiVersion: extensions/v1beta1 kind: Ingress metadata: name: mnist-classifier annotations: certmanager.k8s.io/acme-challenge-type: dns01 certmanager.k8s.io/acme-dns01-provider: clouddns certmanager.k8s.io/cluster-issuer: letsencrypt-prod-dns kubernetes.io/ingress.allow-http: "true" kubernetes.io/ingress.class: "nginx" kubernetes.io/tls-acme: "true" namespace: default spec: rules: - host: mnist.<your_host> http: paths: - backend: serviceName: <check_svc_name_which_created_by_seldondeployment> servicePort: 9000 path: / tls: - hosts: - '*.<your_host>' secretName: dns01-tls

curl -X POST https://mnist.<your_host>/predict -d '{"data":{"tensor":{"shape":[1,784],"values":[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 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