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Gluon vs TensorFlow: What are the differences?
- Execution Model: Gluon uses dynamic computational graphs which allow for easier debugging and flexibility in model changes during runtime, while TensorFlow relies on static computational graphs which optimize performance by compiling the entire graph upfront.
- API Design: Gluon provides a more user-friendly API that makes deep learning models easier to build and understand, geared towards beginners and rapid prototyping. In contrast, TensorFlow's API design is more low-level and can be intimidating for newcomers, but offers more control and customization for advanced users.
- Eager Execution: Gluon adopts eager execution, where operations are evaluated immediately, making it easier to debug and learn, while TensorFlow requires the use of sessions and graph definitions for execution, leading to a more complex workflow.
- Community Support: TensorFlow has a larger community and ecosystem with more resources, models, and tutorials available, making it more suitable for industrial-strength applications and research projects, while Gluon, being relatively newer, has a smaller but growing community.
- Deployment Flexibility: TensorFlow supports a wider range of deployment options including mobile, web, and embedded devices through TensorFlow Lite and TensorFlow.js, providing more flexibility for deploying models beyond traditional server environments compared to Gluon.
- Integration with Other Libraries: TensorFlow has seamless integration with other popular libraries like Keras, allowing users to leverage a variety of tools and resources readily available, whereas Gluon may require additional work for integration with other frameworks and libraries. In Summary, Gluon emphasizes simplicity and ease-of-use, catering to beginners and rapid prototyping, while TensorFlow offers more control, optimization, and deployment options, suitable for production-grade applications and research projects.
Pytorch is a famous tool in the realm of machine learning and it has already set up its own ecosystem. Tutorial documentation is really detailed on the official website. It can help us to create our deep learning model and allowed us to use GPU as the hardware support.
I have plenty of projects based on Pytorch and I am familiar with building deep learning models with this tool. I have used TensorFlow too but it is not dynamic. Tensorflow works on a static graph concept that means the user first has to define the computation graph of the model and then run the ML model, whereas PyTorch believes in a dynamic graph that allows defining/manipulating the graph on the go. PyTorch offers an advantage with its dynamic nature of creating graphs.
For data analysis, we choose a Python-based framework because of Python's simplicity as well as its large community and available supporting tools. We choose PyTorch over TensorFlow for our machine learning library because it has a flatter learning curve and it is easy to debug, in addition to the fact that our team has some existing experience with PyTorch. Numpy is used for data processing because of its user-friendliness, efficiency, and integration with other tools we have chosen. Finally, we decide to include Anaconda in our dev process because of its simple setup process to provide sufficient data science environment for our purposes. The trained model then gets deployed to the back end as a pickle.
Pros of Gluon
- Good learning materials3
Pros of TensorFlow
- High Performance32
- Connect Research and Production19
- Deep Flexibility16
- Auto-Differentiation12
- True Portability11
- Easy to use6
- High level abstraction5
- Powerful5
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Cons of Gluon
Cons of TensorFlow
- Hard9
- Hard to debug6
- Documentation not very helpful2
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