How do I deploy deep reinforcement learning neural network I coded in Pytorch to my website?

Question

I have built and trained neural network in Pytorch and is ready for production to a website but how do I deploy it?

Answer 1

There is multiple ways to do it.

Yet first, I add a PS : I noticed that you were asking specifically about reinforcement learning after having posted my answer. Know that even though I have written this answer with a static neural network model in mind, I offer at the end of the post a solution to apply the ideas of this answer to reinforcement learning.

The different options :

From what I know, using PyTorch in production is not especially recommended for big scale production. It is more common to convert the PyTorch model to the ONNX format (a format to make ai models interchangeable between frameworks). Here is a tutorial if you want to operate this way : https://github.com/onnx/tutorials/blob/master/tutorials/PytorchOnnxExport.ipynb . Then run it using the ONNX runtime, Caffe2 (by Facebook) or with TensorFlow (by Google).

My answer is not going to explore those solutions (and i did not include tutorials to those options), because i recently did the same as you are trying to do (building a neural network architecture and wanting to deploy it, and also allowing users to train their neural network with the architecture), yet i did not converted my neural network for the following reasons :

• ONNX is evolving quickly, yet is currently not supporting all the operations you can possibly do in a PyTorch model. So if you have a highly custom or specific neural network (like in my case), you might not be able to convert it to ONNX with ease. You might need to change your architecture, or maybe have to re-write a big part of it so that it can be converted to ONNX.
• You will need to use one or two additional tools, where most tutorials are not going really deep, or not explaining the logic behind what they are doing.

Note that you might want to convert your neural network if you call your network billions or trillions of times a day, otherwise i think you can stick with PyTorch without issues even for production, and avoid the fallback of converting to ONNX.

First let's see how we can save a trained neural network, load it back trough the architecture of the network, and re-run the trained network. Second how we can deploy a network to a website, and also how you can allow users to train their networks. It is likely not the best or most efficient way, yet it sure works.

Saving the network :

First, you clearly need to have imported pyTorch with "import torch". Inside your neural network file you should save the stateDict (basically a dictionary of the operations and weights of your network) of the network you want to re-use. You could for example only save the stateDict of the model with the smallest loss of your epoch.

# network is the variable containing your neural network class
network_stateDict = network.state_dict()
# Saving network stateDict to a variable

Then when you want to save the stateDict to a file that you can re-use later, use :

torch.save(network_stateDict, "folderPath/myStateDict.pt)
# Saving the stateDict variable to a file
# The pt extension is just a convention in the PyTorch community, ptr is also used a lot

Finally when you will want to re-use your trained network later on, you will need to :

network = myNetwork(1, 2, 3)
# Load the architecture of the network in a variable (use the same architecture
# and the same network parameters as the ones used to create the stateDict)

network.load_state_dict(torch.load(folderPath/myStateDict.pt))
# Loading the file containing the stateDict of the trained network into a format
# pyTorch can read with the torch.load function. Then load the stateDict inside the 
# network architecture with the load_state_dict function, applied to your network
# object with network.load_state_dict .

network.eval()
# To make sure that the stateDict has correctly been loaded.

output = network(input_data)
# You should now be able to get output data from your
# trained network, by feeding it a single set of input data.

For more infos on saving models and the stateDicts : https://pytorch.org/tutorials/beginner/saving_loading_models.html

Deploying the network:

Now that we know how to save, restore and feed input data to a network, all that there is left to do is to deploy it so that this process is done trough the website.

You first need to get (likely from your user) the inputs that your neural network will use. I am not going to include any link, since there is so many different web frameworks.

You would then need to either use a framework (like Django) that allow you to do in Python the logic of :

import torch

network = myNetwork(1, 2, 3)
network.load_state_dict(torch.load(folderPath/myStateDict.pt))
network.eval()

input_data = data_fromMyUser
output = network(input_data)

Then you would collect the output to display it, or do whatever you want it.

If your framework is not giving you the ability to use Python, i think it would be a good idea to have a tiny Python script, to which you would give the input data, and which would return the output.

If you would like to give the possibility to the user to train networks, you should just give them the possibility to start the training of one, and then use torch.save on a stateDict object to save the stateDict to a file. You or they could later use the trained networks (you should also need to create a little function to make sure that you do not override previous stateDict files).

How to apply it to reinforcement learning :

I did not deploy a reinforcement learning model, yet i can offer you some ideas and leads to explore to deploy one.

You could store and add the inputs that you get from your user to a file or a database, and write a little program, that say every 24 hours or every hour, re-run the neural network with the now bigger dataset.

You could then totally apply the suggestions in this answer, of running the network, saving the stateDict of the model and then changing the stateDict that your network is using in production.

This is a bit hacky, yet would allow you to save in a "static way" your trained networks, and still have them evolving and changing their stateDicts.

Conclusion

This is clearly not the most mass-scale production approach that you could employ, yet it is in my opinion the easiest to put in place. You also know that the output that you will get, will be the actual output of your neural network, without any distorsions or errors in the values.

Have a great day !

Answer 2

1. save the trained model however you want (HD5 or with pickle)
2. write the program to handle in production by loading the trained model
3. deploy the program on distributed system for real time computation like on Apache storm, Flink, Alink, Apache Samoa etc..
4. if you feel you need to retrain the model depending on feedback then retrain the model on different cluster or parallel environment and observe the model accuracy if looks good then move the model to production (initial days you need to retrain multiple times and it will decrease time goes on if your model is designed in a good way)