At Pranon, we are excited to introduce our latest deep-learning framework: PyTorch. As a leading AI and machine learning software company, we are always looking for the most effective and efficient tools to power our products. PyTorch has proven to be the perfect choice for us, and we’re excited to share why.

PyTorch is an open-source deep learning framework that is built on the popular Python programming language. Developed by the Facebook Artificial Intelligence Research (FAIR) team, it provides a set of tools and libraries for building, training, and deploying machine learning models.

One of the key features of PyTorch is its dynamic computational graph, which allows for flexibility and ease of use in building and training neural networks. Unlike other deep learning frameworks like TensorFlow, which use a static computational graph, PyTorch allows for the modification of the graph during runtime. This is particularly useful for debugging and advanced research.

Another advantage of PyTorch is its built-in support for CUDA and other hardware acceleration. This allows for much faster training of models, which is particularly important when dealing with large and complex models.

In addition, PyTorch has a large and growing community of users and developers, which provides a wealth of resources such as tutorials, pre-trained models, and code examples. This makes it easier for our team to learn and work with the framework, as well as find pre-built models to speed up development.

At Pranon, we have already started to use PyTorch in several of our AI products, such as computer vision and natural language processing. We have found that it has significantly improved the speed and accuracy of our models. We are confident that PyTorch will continue to be a valuable tool for us as we continue to innovate and push the boundaries of what is possible with AI.

The reasons behind we at Pranon choice of PyTorch for AI and Machine Learning Development are:

  • Easy to use: PyTorch has a simple and intuitive API, which makes it easy to learn and use, even for developers with little experience in deep learning.
  • Dynamic Computational Graph: PyTorch allows for building dynamic computational graphs, which means that the user can change the graph on the fly, during runtime. This can be useful for debugging, or for more advanced research. This feature provides more flexibility for developers and researchers.
  • Support for CUDA and other hardware acceleration: PyTorch has built-in support for CUDA and other hardware accelerators, which allows models to be trained much faster. This is important for training large and complex models.
  • Good community support: PyTorch has a large and growing community of users and developers, which provides a wealth of resources such as tutorials, pre-trained models, and code examples.
  • Saves development time: PyTorch comes with a lot of pre-built libraries and models that can be used to speed up the development process
  • Popularity in the industry: PyTorch has become popular in industry and research, so finding experienced engineers and pre-trained models is easy.
  • Interoperability with other tools: Pytorch is well-integrated with other popular libraries and frameworks like TensorFlow, NumPy, and more.

In conclusion, Pytorch is a powerful and easy-to-use deep learning framework that provides a high degree of flexibility and performance. It is gaining popularity in the industry and research, which is why we at Pranon have chosen Pytorch as our go-to framework for our AI products.


Written By
Tihasha Rafa
Junior Software Engineer
Pranon Global Limited