I want to hand write a framework to perform inference of a given neural network. The network is so complicated, so to make sure my implementation is correct, I need to know how exactly the inference process is done on device.
I tried to use torchviz to visualize the network, but what I got seems to be the back propagation compute graph, which is really hard to understand.
Then I tried to convert the pytorch model to ONNX format, following the instruction enter link description here, but when I tried to visualize it, it seems that the original layers of the model had been seperated into very small operators.
I just want to get the result like this
How can I get this? Thanks!
Have you tried saving the model with torch.save (https://pytorch.org/tutorials/beginner/saving_loading_models.html) and opening it with Netron? The last view you showed is a view of the Netron app.
You can try also the package torchview, which provides several features (useful especially for large models). For instance you can set the display depth (depth in nested hierarchy of moduls).
It is also based on forward prop
github repo
Disclaimer: I am the author of the package
Note: The accepted format for tool is pytorch model
I need to train on someone else's GPU because they don't want to provide the data。
Is there any way to make it less easy for them to get my source code ?
I have built and trained neural network in Pytorch and is ready for production to a website but how do I deploy it?
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 !
save the trained model however you want (HD5 or with pickle)
write the program to handle in production by loading the trained model
deploy the program on distributed system for real time computation like on Apache storm, Flink, Alink, Apache Samoa etc..
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)
Now I have 10GB of data set to train the model in sklearn, but my computer only has 8GB of memory, so I have other ways to go besides incremental classifier.
I think sklearn can be used for larger data if the technique is right. If your chosen algorithms support partial_fit or an online learning approach then you're on track. The chunk_size may influence your success
This link may be useful( Working with big data in python and numpy, not enough ram, how to save partial results on the disc?)
Another thing you can do is to randomly pick whether or not to keep a row in your csv file...and save the result to a .npy file so it loads quicker. That way you get a sampling of your data that will allow you to start playing with it with all algorithms...and deal with the bigger data issue along the way(or not at all! sometimes a sample with a good approach is good enough depending on what you want).
I am using a Convolutional Neural Network and I am saving it and loading it via the model serializer class.
What I want to do is to be able to come back at a later time and continue training the model on new data provided to it.
What I am doing is I load it using
ComputationGraph net = ModelSerializer.restoreComputationGraph(modelFileName);
and then I give it the data like before with
net.train(dataSetIterator);
This seems to work, but it makes my accuracy really bad. It was about 89% before I did this, and, using the same data, it gets to be around 50% accurate after a few iterations (using the same data it just trained itself on, so if anything it should be getting stupidly more accurate right?).
Am I missing a step?
I think it'll be difficult to answer based on the information given, but I'll give you an example.
I had this exact problem. I had based my app on the GravesLSTMCharModellingExample (which is LSTM). I had saved my model after running for a couple of epochs (at which point it generated legible sentences), but when loading it, it produced garbage.
I thought everything was the same, but in the end it turned out I didn't initialize the CharacterIterator the same. When I fixed it, it worked as expected.
So to cut a long story short;
Check your values when initializing the auxiliary classes.