Serverless Image Classification with Oracle Functions and TensorFlow

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Oracle Functions

What to Expect

Photo by Alan Hardman on Unsplash

The Code

Function Image Input

  • Simple data binding, like handling string input.
  • Binding JSON data types to POJOs. You can customize this because it’s internally implemented using Jackson.
  • Working with raw inputs, enabled by an abstraction of the raw Fn Java FDK events received or returned by the function.
public class LabelImageFunction { 
public String classify(byte[] image) { ...
Tensor<String> input = Tensors.create(image); ...

Machine Learning Model

  • Training: An algorithm is fed with past (historical) data in order to learn from it (derive patterns) and build a model. Very often, this process is ongoing.
  • Predicting: The generated model is then used to generate predictions or outputs in response to new inputs based on the facts that were learned during the training phase

Function Metadata

schema_version: 20180708 
name: classify
version: 0.0.1
runtime: java
memory: 1024
timeout: 120
- name: classify
type: http
source: /classify
  • schema_version represents the version of the specification for this file.
  • name is the name and tag to which this function is pushed.
  • version represents the current version of the function. When deploying, it is appended to the image as a tag.
  • runtime represents the programming language runtime, which is java in this case.
  • memory (optional) is the maximum memory threshold for this function. If this function exceeds this limit during execution, it’s stopped and an error message is logged.
  • timeout (optional) is the maximum time that a function is allowed to run.
  • triggers (optional) is an array of trigger entities that specify triggers for the function. In this case, we’re using an HTTP trigger.

Function Dockerfile

FROM fnproject/fn-java-fdk-build:jdk9-1.0.75 as build-stage 
WORKDIR /function
ENV MAVEN_OPTS -Dhttp.proxyHost= -Dhttp.proxyPort= -Dhttps.proxyHost= -Dhttps.proxyPort= -Dhttp.nonProxyHosts= -Dmaven.repo.local=/usr/share/maven/ref/repository
ADD pom.xml /function/pom.xml
RUN ["mvn", "package", "dependency:copy-dependencies", "-DincludeScope=runtime", "-DskipTests=true", "-Dmdep.prependGroupId=true", "-DoutputDirectory=target", "--fail-never"]
ADD src /function/src
RUN ["mvn", "package"]
FROM fnproject/fn-java-fdk:jdk9-1.0.75
WORKDIR /function
COPY --from=build-stage /function/target/*.jar /function/app/
CMD ["com.example.fn.HelloFunction::handleRequest"]
  • Maven package and build
  • Copying (using COPY) the function JAR and dependencies to the runtime image
  • Setting the command to be executed (using CMD) when the function container is spawned
FROM fnproject/fn-java-fdk-build:jdk9-1.0.75 as build-stage 
WORKDIR /function
ENV MAVEN_OPTS -Dhttp.proxyHost= -Dhttp.proxyPort= -Dhttps.proxyHost= -Dhttps.proxyPort= -Dhttp.nonProxyHosts= -Dmaven.repo.local=/usr/share/maven/ref/repository ADD pom.xml /function/pom.xml
RUN ["mvn", "package", "dependency:copy-dependencies", "-DincludeScope=runtime", "-DskipTests=true", "-Dmdep.prependGroupId=true", "-DoutputDirectory=target", "--fail-never"]'
RUN echo "using tensorflow version " $TENSORFLOW_VERSION RUN curl -LJO$TENSORFLOW_VERSION.jar
RUN curl -LJO$TENSORFLOW_VERSION.tar.gz RUN tar -xvzf libtensorflow_jni-cpu-linux-x86_64-$TENSORFLOW_VERSION.tar.gz
ADD src /function/src
RUN ["mvn", "package"]
FROM fnproject/fn-java-fdk:jdk9-1.0.75
WORKDIR /function
COPY --from=build-stage /function/ /function/runtime/lib
COPY --from=build-stage /function/ /function/runtime/lib
COPY --from=build-stage /function/libtensorflow-$TENSORFLOW_VERSION.jar /function/app/
COPY --from=build-stage /function/target/*.jar /function/app/
CMD ["com.example.fn.LabelImageFunction::classify"]
  • Automates TensorFlow setup (per the instructions), extracts the TensorFlow Java SDK and the native JNI (.so) libraries
  • (as part of the second stage of the Docker build) Copies the JNI libraries to /function/runtime/lib and the SDK JAR to /function/app so that they are available to the function at runtime

Deploying to Oracle Functions

curl -LSs | sh

Oracle Functions Context

api-url: oracle.compartment-id: <OCI_compartment_OCID> 
oracle.profile: <profile_name_in_OCI_config>
provider: oracle
registry: <OCI_docker_registry>

Oracle Cloud Infrastructure Configuration

user=ocid1.user.oc1..exampleuniqueID fingerprint=20:3b:97:13:55:1c:5b:0d:d3:37:d8:50:4e:c5:3a:34 key_file=~/.oci/oci_api_key.pem tenancy=ocid1.tenancy.oc1..exampleuniqueID pass_phrase=tops3cr3t region=us-ashburn-1
user=ocid1.user.oc1..exampleuniqueID fingerprint=72:00:22:7f:d3:8b:47:a4:58:05:b8:95:84:31:dd:0e key_file=/.oci/admin_key.pem tenancy=ocid1.tenancy.oc1..exampleuniqueID pass_phrase=s3cr3t region=us-phoenix-1
fn use context <context_name>

Create the Application

git clone
fn create app <app_name> --annotation'["<subnet_ocid>"]'
  • <app_name> is the name of the new application.
  • <subnet_ocid> is the OCID of the subnet in which to run your function.
fn create app fn-tensorflow-app --annotation'["ocid1.subnet.oc1.phx.exampleuniqueID","ocid1.subnet.oc1.phx.exampleuniqueID","ocid1.subnet.oc1.phx.exampleuniqueID"]'

Deploy the Function

fn deploy --app <app_name>
fn -v deploy --app fn-tensorflow-app
<groupId>org.tensorflow</groupId> <artifactId>tensorflow</artifactId>
fn -v deploy --app fn-tensorflow-app --build-arg TENSORFLOW_VERSION=<version>
fn -v deploy --app fn-tensorflow-app --build-arg TENSORFLOW_VERSION=1.11.0

Time to Classify Images!

cat <path to image> | fn invoke fn-tensorflow-app classify
cat /Users/abhishek/manwithhat.jpg | fn invoke fn-tensorflow-app classify
“366 • 9 • Gringo” (CC BY-NC-ND 2.0) by Pragmagraphr
This is a ‘sombrero’ Accuracy — 92%
cat /Users/abhishek/terrier.jpg | fn invoke fn-tensorflow-app classify
“Terrier” (CC BY-NC 2.0) by No_Water
This is a 'West Highland white terrier' Accuracy - 88%


Principal Developer Advocate at AWS | I ❤️ Databases, Go, Kubernetes

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Abhishek Gupta

Abhishek Gupta

Principal Developer Advocate at AWS | I ❤️ Databases, Go, Kubernetes

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