is it possible for me to create a website that uses ONNX.js to serve the model and create a frontend using React? If yes, please point toward some resources.
Also, what other ways can a Pytorch model be served into production in conjunction with a react frontend, any suggestions are welcome.
Thanks!
I have done my final year Engineering project using FastAi and PyTorch and I created a website for deployment using Flask Framework. It was straightforward and simple to create. I suggest looking into this and as for deployment, you can use Heroku or GitHub pages for deploying your model.
You can refer to this https://medium.com/unpackai/how-to-deploy-fast-ai-models-8704ea711ad2
You can also create a notebook app if you are using Jupyter Notebooks. For reference https://www.analyticsvidhya.com/blog/2020/10/develop-and-deploy-an-image-classifier-app-using-fastai/
It was not easy to understand in the beginning but by watching various YouTube videos, I was able to successfully deploy my model online for anyone to use.
Related
I am currently trying to serve into a webapp build in Django however I am facing some difficulties.
1. I have succesfully trained my model and I have the precious frozen_inference_graph.
2. I am creating the webapp on django.
I would like to call my model directly from my webapp folder. However, when using the inference method from my webapp folder, no inference is done: there is not bug and the script does nothing . In my tf folder, the script does the proper inference.
Do you have any clues? Also I have not found any tutorial for serving tensorflow on Django, are you aware of some?
Thanks,
Have a nice day !
You could serving your model.
TensorFlow Serving is a flexible, high-performance serving system for machine learning models, designed for production environments. TensorFlow Serving makes it easy to deploy new algorithms and experiments, while keeping the same server architecture and APIs. TensorFlow Serving provides out-of-the-box integration with TensorFlow models, but can be easily extended to serve other types of models and data.
https://www.tensorflow.org/tfx/guide/serving
good luck!
I have a Machine learning module which uses Google Colab's free GPU for NLP tasks, and I want to make a web app out of it. I've been thinking of using React js for frontend and spring boot for the back end and was wondering whether there is a way to connect the code at Google Colab with the backend.
Want to know other alternative suggestions to building a web app incorporating the ML module in Colab as well. Any sort of help is appriciated.
It's possible. But there's no good examples of it.
There's an example of serving local resources, using http-server. Flask examples used to work too. But not much else.
I am developing a Django app to run on every client computer separately. The question is, which technologies should I use to distribute such as docker, virtual machine etc.? How can I protect the Django app's code? How can I prevent to distribute without licenses?
I suggest you look into Heroku. They have a free plan too so you can test it without having to pay first. Their guide with django after you set up an app is good too. You will find everything in their documentation but if you get stuck, I recommend this video by Corey Schafer. Good luck
Edit: Heroku also supports Docker but I'm not too familiar with it. Might be useful to you
I am building am application to process user's photo on server. Basically, user upload a photo to the server and do some filtering processing using deep learning model. Once it's done filter, user can download the new photo. The filter program is based on the deep learning algorithm, using torch framework, it runs on python/lua. I currently run this filter code on my local ubuntu machine. Just wonder how to turn this into a web service. I have 0 server side knowledge, I did some research, maybe I should use flask or tornado, or other architecture?
I'd recommend using Django if you are comfortable with python and working with an AWS serviced database! It is pretty intuitive, and a lot of resources and examples are available on the web. For example, here is the file upload documentation.
It does make sense to look at the whole task and how it fits to your actual server, Nginx or Lighttpd or Apache since you are serving static content. If you are going to call a library to create the static content, the integration of your library to your web framework would be simpler if you use Flask but it might be a fit for AWS S3 and Lambda services.
It may be worth it to roughly design the whole site and match your content to the tools at hand.
From google tutorial we know how to train a model in TensorFlow. But what is the best way to save a trained model, then serve the prediction using a basic minimal python api in production server.
My question is basically for TensorFlow best practices to save the model and serve prediction on live server without compromising speed and memory issue. Since the API server will be running on the background for forever.
A small snippet of python code will be appreciated.
TensorFlow Serving is a high performance, open source serving system for machine learning models, designed for production environments and optimized for TensorFlow. The initial release contains C++ server and Python client examples based on gRPC. The basic architecture is shown in the diagram below.
To get started quickly, check out the tutorial.