We performed a comparison between Hugging Face and PyTorch based on real PeerSpot user reviews.
Find out in this report how the two AI Development Platforms solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."My preferred aspects are natural language processing and question-answering."
"What I find the most valuable about Hugging Face is that I can check all the models on it and see which ones have the best performance without using another platform."
"I like that PyTorch actually follows the pythonic way, and I feel that it's quite easy. It's easy to find compared to others who require us to type a long paragraph of code."
"The framework of the solution is valuable."
"The tool is very user-friendly."
"Its interface is the most valuable. The ability to have an interface to train machine learning models and construct them with the high-level interface, without excess busting and reconstructing the same technical elements, is very useful."
"It's been pretty scalable in terms of using multiple GPUs."
"yTorch is gaining credibility in the research space, it's becoming easier to find examples of papers that use PyTorch. This is an advantage for someone who uses PyTorch primarily."
"The area that needs improvement would be the organization of the materials. It could be clearer and more systematic. It would be good if the layout was clear and we could search the models easily."
"Implementing a cloud system to showcase historical data would be beneficial."
"PyTorch could make certain things more obvious. Even though it does make things like defining loss functions and calculating gradients in backward propagation clear, these concepts may confuse beginners. We find that it's kind of problematic. Despite having methods called on loss functions during backward passes, the oral documentation for beginners is quite complex."
"There is not enough documentation about some methods and parameters. It is sometimes difficult to find information."
"I would like a model to be available. I think Google recently released a new version of EfficientNet. It's a really good classifier, and a PyTorch implementation would be nice."
"The training of the models could be faster."
"I've had issues with stability when I use a lot of data and try out different combinations of modeling techniques."
"On the production side of things, having more frameworks would be helpful."
Hugging Face is ranked 8th in AI Development Platforms with 3 reviews while PyTorch is ranked 11th in AI Development Platforms with 6 reviews. Hugging Face is rated 9.0, while PyTorch is rated 8.6. The top reviewer of Hugging Face writes "A comprehensive natural language processing ecosystem offering a diverse range of pre-trained models and a collaborative platform". On the other hand, the top reviewer of PyTorch writes "Offers good backward compatible and simple to use". Hugging Face is most compared with Google Vertex AI, Azure OpenAI, Replicate and Google Cloud AI Platform, whereas PyTorch is most compared with OpenVINO, MXNet, Microsoft Azure Machine Learning Studio, Google Cloud AI Platform and TensorFlow. See our Hugging Face vs. PyTorch report.
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