Is there a way to deploy my machine learning model(which is currently a pickle file) into Pycharm through Tkinter GUI. I have seen methods to deploy models in python but they all have to do with integrating through flask or Django. The gui allows users to input data but I need the input to be passed through the trained model to produce a prediction.
This Tutorial can give you some insight into how to use a machine learning model with Tkinter.
Machine Learning model is like any other python package you can load your model anywhere in the code and use it with the native API, without a need for an HTTP request like with Django and Flask
Related
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.
I'm trying to deploy a .tflite to Firebase ML so that I can distribute it from there.
I used transfer learning on this TF Hub model. I then followed this tutorial to convert the model .tflite format.
This model gives good results in the python TFLite interpreter and can be used on Android if I package with the app.
However I want to serve the model via Firebase, so I use this tutorial to deploy the .tflite file to Firebase. Using this tutorial, I get an error firebase_admin.exceptions.FailedPreconditionError: Cannot publish a model that is not verified..
I can't find any information about this error anywhere, and given the model works on both Android and Python, I'm at a loss as to what could be causing this
Did you solve this issue? I had the same one, and it turned out the model size should be < 40mb. That caused the error, and the detailed error is only reported when uploading a model manually through web Firebase dashboard
I have made an image recognition system using python and I am trying to implement that in website. I have have a system that recognizes the fruit images built using CNN. Now, I have built a website which is of a Fruit-blog. I want a section or a page in in that website in which a user can upload an image and the website can recognize which fruit is the given image of but I have no idea how to implement my recognition system in the website. Can you please help me explaining what to do and how can I implement the AI from python to Django website?
The main part comes from freezing your model and serving it using a web framework like flask or django. Then you can communicate with it by passing images and receiving prediction objects. An example is at this github repo written in Django for deploy in Heroku https://github.com/marcogdepinto/Deep-learning-model-deploy-with-django
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!
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.