Deploying FastAI Models to TorchServer: The State_dict Hurdle скачать в хорошем качестве

Deploying FastAI Models to TorchServer: The State_dict Hurdle

A guide on how to deploy models trained with FastAI to TorchServer, addressing the common `state_dict` loading issues. Learn how to fix missing keys errors in PyTorch. --- This video is based on the question https://stackoverflow.com/q/67804598/ asked by the user 'JoseM LM' ( https://stackoverflow.com/u/3918511/ ) and on the answer https://stackoverflow.com/a/70147144/ provided by the user 'Benjamin Kolber' ( https://stackoverflow.com/u/8958984/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions. Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: fastaiv2 to pytorch for torchserver Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l... The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license. If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com. --- Deploying FastAI Models to TorchServer: The State_dict Hurdle When working with machine learning frameworks, fast prototyping is often key to success. FastAI is a popular choice for quickly developing models, but deploying these models can sometimes present challenges, especially when transitioning to TorchServer. One common issue many developers face is related to loading the model's state_dict. In this post, we'll explore a specific issue when deploying FastAI models to TorchServer and provide a straightforward solution. The Problem: RuntimeError with state_dict Imagine you have trained a simple convolutional neural network (CNN) model using FastAI, and you're eager to deploy it to TorchServer. After some experimentation, you decide to save and load the model’s state using PyTorch. However, you encounter the following error: [[See Video to Reveal this Text or Code Snippet]] This message indicates that certain keys are missing from the state_dict when you attempt to load it into your model. Source of the Problem The root cause of this problem stems from how the FastAI framework saves the state_dict. When you save the model's parameters directly from FastAI's Learner, the keys in the saved state_dict may be prefixed with "module.". This prefix is often a result of how data is wrapped during training in multi-GPU settings or when using FastAI’s Learner class to encapsulate your models. The Solution: Clean Up the state_dict Fortunately, this issue can be easily resolved by cleaning up the keys in your state_dict before loading them into your model. Below, we offer a step-by-step breakdown of how to do this: Step 1: Save Your Model First, ensure you save your model correctly using FastAI’s functions: [[See Video to Reveal this Text or Code Snippet]] Step 2: Load and Fix the State Dictionary Next, when you load the state_dict, you'll need to address the module. prefix. Here’s how: [[See Video to Reveal this Text or Code Snippet]] Step 3: Verify Your Model Once you've loaded the fixed state_dict, it's important to ensure that your model runs as expected. You can do this by making a few predictions or testing on a validation set. Conclusion Deploying models trained with FastAI to TorchServer doesn't have to be a frustrating experience. By understanding the state_dict issue that can arise due to the "module." prefix, you can take simple steps to resolve it. Remember to clean the keys of your state_dict before loading them into the model, and you'll be well on your way to successful deployment! If you've faced similar challenges or have additional tips for transitioning FastAI models to TorchServer, feel free to share your thoughts in the comments below!