We performed a comparison between PyTorch and TensorFlow 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."The framework of the solution is valuable."
"It's been pretty scalable in terms of using multiple GPUs."
"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."
"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 tool is very user-friendly."
"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."
"Optimization is very good in TensorFlow. There are many opportunities to do hyper-parameter training."
"TensorFlow improves my organization because our clients get a lot of investment from their investors and we are progressively improving the products. Every six months we release new features."
"The most valuable feature of TensorFlow is deep learning. It is the best tool for deep learning in the market."
"It's got quite a big community, which is useful."
"The most valuable features are the frameworks and the functionality to work with different data, even when we have a certain quantity of data flowing."
"It is also totally Open-Source and free. Open-source applications are not good usually. but TensorFlow actually changed my view about it and I thought, "Look, Oh my God. This is an open-source application and it's as good as it could be." I learned that TensorFlow, by sharing their own knowledge and their own platform with other developers, it improved the lives of many people around the globe."
"It empowers us to seamlessly create and deploy machine learning models, offering a versatile solution for implementing sophisticated environments and various types of AI solutions."
"It is open-source, and it is being worked on all the time. You don't have to pay all the big bucks like Azure and Databricks. You can just use your local machine with the open-source TensorFlow and create pretty good models."
"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."
"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."
"There is not enough documentation about some methods and parameters. It is sometimes difficult to find information."
"The training of the models could be faster."
"On the production side of things, having more frameworks would be helpful."
"I've had issues with stability when I use a lot of data and try out different combinations of modeling techniques."
"There are connection issues that interrupt the download needed for the data sets. We need to prepare them ourselves."
"It would be nice to have more pre-trained models that we can utilize within layers. I utilize a Mac, and I am unable to utilize AMD GPUs. That's something that I would definitely be like to be able to access within TensorFlow since most of it is with CUDA ML. This only matters for local machines because, in Azure, you can just access any GPU you want from the cloud. It doesn't really matter, but the clients that I work with don't have cloud accounts, or they don't want to utilize that or spend the money. They all see it as too expensive and want to know what they can do on their local machines."
"In terms of improvement, we always look for ways they can optimize the model, accelerate the speed and the accuracy, and how can we optimize with our different techniques. There are various techniques available in TensorFlow. Maintaining accuracy is an area they should work on."
"I would love to have a user interface like a programming interface. You need to have a set of menus where you can put things together in a graphical interface. The complete automation of the integration of the modules would also be interesting. It’s more like plumbing as opposed to a fully automated environment."
"JavaScript is a different thing and all the websites and web apps and all the mobile apps are built-in JavaScript. JavaScript is the core of that. However, TensorFlow is like a machine learning item. What can be improved with TensorFlow is how it can mix in how the JavaScript developers can use TensorFlow."
"It would be cool if TensorFlow could make it easier for companies like us to program for running it across different hyperscalers."
"It would be nice if the solution was in Hungarian. I would like more Hungarian NAT models."
"The solution is hard to integrate with the GPUs."
PyTorch is ranked 11th in AI Development Platforms with 6 reviews while TensorFlow is ranked 4th in AI Development Platforms with 16 reviews. PyTorch is rated 8.6, while TensorFlow is rated 9.0. The top reviewer of PyTorch writes "Offers good backward compatible and simple to use". On the other hand, the top reviewer of TensorFlow writes "Effective deep learning, free to use, and highly stable". PyTorch is most compared with OpenVINO, MXNet, Microsoft Azure Machine Learning Studio and Google Cloud AI Platform, whereas TensorFlow is most compared with Microsoft Azure Machine Learning Studio, Google Vertex AI, OpenVINO, IBM Watson Machine Learning and MXNet. See our PyTorch vs. TensorFlow report.
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