This repo is a sample illustrating how to do TensorFlow browser based and server side inference.
In backend, is the server side inference code written in Python and served with FastApi. In frontend, is the browser based inference code written in Typescript / React
The TensorFlow artifacts should be served at here. A sample artifact can be found in the repo releases
Server Side Inference : Heroku Browse Based Inference: Github Pages
It is strongly recommended to create a separate environment for tesnorflowjs
Installing tensorflowjs
pip install tensorflowjs==2.1.0
Converting keras model located at artifacts/model_tf_keras.h5 and saving to artifacts/model_tfjs
The 99999999 indicates that the model file should be split to 100 MB partitions
tensorflowjs_converter \
--input_format=keras \
--output_format=tfjs_graph_model \
--split_weights_by_layer \
--weight_shard_size_bytes=99999999 \
artifacts/model_tf_keras.h5 artifacts/model_tfjs
docker build -t app .
docker run -p 8000:8000 -t app
If you want to develop outside docker, python (backend)
conda create -n dl_env python=3.7
pip install -r backend/requirements.txt
frontend
yarn
This app is deployed at Heroku. Here are the steps for mac
Setup
brew tap heroku/brew && brew install heroku
heroku login
heroku container:login
Replace APP_NAME with something unique
APP_NAME="manning-deploy-imagenet"
heroku create $APP_NAME
heroku container:push web --app ${APP_NAME}
heroku container:release web --app ${APP_NAME}
heroku open --app $APP_NAME
heroku logs --tail --app ${APP_NAME}
In order to prevent most frontend changes, most of the text and options are configured in this config.yml.