https://github.com/abhirockzz/fn-hello-tensorflow
Image classification as a serverless Function using TensorFlow
https://github.com/abhirockzz/fn-hello-tensorflow
faas fnproject image-classification machine-learning orafunc-la serverless tensorflow
Last synced: about 1 year ago
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Image classification as a serverless Function using TensorFlow
- Host: GitHub
- URL: https://github.com/abhirockzz/fn-hello-tensorflow
- Owner: abhirockzz
- Created: 2018-11-29T10:19:12.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2019-01-10T05:25:03.000Z (over 7 years ago)
- Last Synced: 2025-03-24T00:25:09.915Z (about 1 year ago)
- Topics: faas, fnproject, image-classification, machine-learning, orafunc-la, serverless, tensorflow
- Language: Java
- Homepage: https://medium.com/@abhishek1987/serverless-image-classification-with-oracle-functions-and-tensorflow-849395786110
- Size: 46.6 MB
- Stars: 3
- Watchers: 2
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Serverless image classification with functions & TensorFlow
Demonstrates a serverless function capable of simple image classification using TensorFlow
Throw random images at your function and expect it to (hopefully) return something sane !
Just like this image (of what looks like a pizza) when passed on to our classification function returns - `This is a 'pizza' Accuracy - 96%`
## Pre-requisites
- Get latest FN CLI - `curl -LSs https://raw.githubusercontent.com/fnproject/cli/master/install | sh`
- Configure your environment i.e. setup Fn context as well as OCI configuration for Oracle Functions
This is what a context file looks like:
api-url: https://functions.us-phoenix-1.oraclecloud.com oracle.compartment-id:
oracle.profile:
provider: oracle
registry:
.. and here is an example configuration file:
[ORACLE_FUNCTIONS_USER]
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
- Switch to the correct context according to your Functions development environment: `fn use context `
## Create the Application
- `git clone https://github.com/abhirockzz/fn-hello-tensorflow`
- `cd fn-hello-tensorflow`
- `fn create app --annotation oracle.com/oci/subnetIds='[""]'`
## Deploy the function
If you want to use TensorFlow version 1.12.0 (for Java SDK and corresponding native libraries), use the following command: `fn -v deploy --app fn-tensorflow-app`
You can also choose a specific version. Ensure that you specify it in `pom.xml` file before you build the function. For example, if you want to use version 1.11.0:
`fn -v deploy --app fn-tensorflow-app --build-arg TENSORFLOW_VERSION=1.11.0`
When the deployment completes successfully, your function is ready to use. Use the `fn ls apps` command to list down the applications currently deployed. `fn-tensorflow-app` should be listed
## Test
All you need to do is pass the image to the function while invoking it:
`cat | fn invoke fn-tensorflow-app classify`
For the fun part, check out the `Time to Classify Images!` section in the corresponding blog post - https://medium.com/@abhishek1987/serverless-image-classification-with-oracle-functions-and-tensorflow-849395786110