If you’re reading this, you’ve most likely already begun your deep learning adventure. If you’re unfamiliar with the area, deep learning is a feature that allows developers to create human-like computers that solve real-world issues using artificial neural networks, which are brain-like designs. Both PyTorch and Tensorflow are widely used frameworks for neural network applications. When it comes to the development of breakthrough deep learning applications or even research, project managers and data scientists frequently regard them to be the go-to libraries.
PyTorch vs TensorFlow is a hot subject among AI and machine learning experts and students. The reason for this is that both are among the most widely used machine learning libraries. TensorFlow is a curated machine learning project from the Google Brain Team, whereas PyTorch is the Pythonic successor to the now-unsupported Torch framework.
Moving further let’s explore more about the differences between PyTorch and Tensor Flow.
Overview of Pytorch
PyTorch was released for the first time in 2016. Before PyTorch, deep learning frameworks tended to prioritize one speed or usability above the other. By combining an emphasis on usability with thorough performance considerations, PyTorch has become a popular tool in the deep learning research community. It has an imperative Python programming language that supports code as a model, simplifies debugging, and is compatible with other widely used scientific computing libraries, all while staying efficient and enabling hardware accelerators like GPUs.
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PyTorch is an open-source machine learning library based on the Torch framework. PyTorch is imperative, which implies that computations are performed instantly, and the user does not have to wait until the entire code has been written to see if it works. We can execute a portion of the code quickly and inspect it in real-time. The library is written in Python and is intended to be used as a deep learning development platform. The features that make PyTorch a deep learning model are given below:
- API that is simple to use
- Python support: PyTorch works seamlessly with Python’s data science stack. It’s similar to NumPy, so if you’ve used it before, you’ll be right at home.
- Dynamic computation graphs: Instead of pre-configured graphs with certain features, PyTorch provides a framework to create computational graphs as we go, and even alter them during runtime. This service is useful in circumstances where we don’t know how much RAM we’ll need to build a neural network.
It integrates well with Python’s data science stack. Furthermore, PyTorch is comparable to NumPy, so if you’ve used NumPy before, you’ll have no trouble learning PyTorch. Genentech, Microsoft, OpenAI, and the Toyota Research Institute are just a few of the prominent names that use PyTorch.
Overview of Tensorflow
Google’s TensorFlow is a well-known machine learning package. The Google Brain Team built the machine learning library, which was released in November 2015. TensorFlow is one of the most popular deep learning libraries because it emphasizes deep neural network training. TensorFlow has taken the position of Google’s DistBelief framework and is compatible with practically all execution platforms (CPU, GPU, TPU, Mobile, etc.). Several worldwide corporations, including Google, Uber, Microsoft, and several colleges, are now using TensorFlow.
TensorFlow is a well-known open-source deep-learning toolkit from Google that may be used for data flow and differentiable programming across a variety of activities. It’s also a symbolic math library, and it’s used by machine learning applications like neural networks. The library’s principal functions are research and production. TensorFlow’s characteristics are enumerated below:
Secure Model Building: The package lets us design and train ML models using straightforward high-level APIs like Keras, allowing for efficient model iteration and debugging.
ML Production Anywhere: Regardless of the user’s language, trains and deploys models in the cloud, on-premise, in the browser, or on-device.
Robust Experimentation for Research: A flexible and simple design that allows new ideas to move more quickly from concept to code, state-of-the-art models, and publishing.
Pros & Cons of Pytorch
Pros:
- Coding in the Python style.
- Graph that is always changing.
- Editing is simple and quick.
- Documentation and community support are both excellent.
- It’s free and open source.
- PyTorch is used in a lot of applications.
Cons:
- For visualization, a third party is required.
- For production, an API server is required.
Pros & Cons of Tensorflow
Pros:
- A high-level API is already built-in.
- Tensorboard is used to visualize training.
- TensorFlow serving has made this project production-ready.
- Support for mobile devices is simple.
- It’s free and open source.
- Documentation and community support are both excellent.
Cons :
- The graph is static.
- Method of debugging
- It’s difficult to make fast modifications.
Pytorch vs Tensorflow
TensorFlow is a sophisticated and mature deep learning toolkit with great visualization capabilities and a variety of choices for developing high-level models. It supports mobile platforms and provides production-ready deployment options. PyTorch, on the other hand, is a relatively new framework with a more active community and is more Python-friendly.
PyTorch is great for research, making dynamic changes to the machine learning model, working in a Python-based environment, and improving development and runtime debugging. TensorFlow, on the other hand, is perfect for developing production-ready machine learning models, deploying machine learning models on mobile devices, and doing large-scale distributed model training.
PyTorch has a visualization library, Visdom. Its features are minimal when compared to TensorFlow. But, the latest version of PyTorch, i.e. 1.2.0 has made it possible to integrate Tensorboard as well. Tensorboard has a set of applications that help the users understand the deep learning model through five visualizations.
Both PyTorch and Tensorflow are widely used frameworks for neural network applications. When it comes to the development of breakthrough deep learning applications or even research, project managers and data scientists frequently regard them to be the go-to libraries.
Conclusion
TensorFlow and PyTorch both offer valuable abstractions for reducing boilerplate code and speeding up model development. The key difference between them is that PyTorch is more “pythonic” and uses an object-oriented approach, whilst TensorFlow offers a variety of alternatives. If you wish to deepen your knowledge about these frameworks, then you can sign up for some good online courses that help you understand the concepts.
