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
Related
I have trained a model using sagemaker SDK using script mode. When I am deploying it I am getting this error.
Also, I have tried with the transformer, getting the same error.
It's working fine when I am training using the container.
Need help on this...
I have a model created in Tensorflow that is already trained and accurate. How do I get this to run in a project? I can load the model but I can't figure out how to feed it a single image that my software is generating.
Also if this was a transfer learning project do I have to load and create a model before loading the weights?
All the tutorials are on how to set it up in the cloud or with a local server which I would like to avoid. I am tempted to save the data and then run it but that is a lot slower.
Addition:
My environment I am building this for is a google colab Jupyter notebook. The idea is to have no installation for users which is why it must be self contained.
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
Im using Azure ML Studio to create an automated ML pipeline. I've successfully gotten my model to be trained and tested in Azure, but it fails on model.to_json() and model.save_weights().
I believe these functions do not exist on my model as scikit-multilearn is a wrapper around Keras. However, I want to be able to save my model and weight so I can deploy them to a webservice service. The scikit-multilearn model I'm using is Binary Relevance.
Thanks to anyone who helps.
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.