Top 20 Reasons to use Pytorch Framework in Deep Learning?

you’ll  need to use a deep learning framework for studying machine learning, conducting deep learning research, or building AI systems,  The use of a  deep learning framework makes it easy to perform  data loading, preprocessing,
model design, training, and deployment. PyTorch is a  popular Framework among the academic and research communities due to its simplicity, flexibility, and Pythonic nature.

Here are some reasons to learn and use PyTorch:

PyTorch is popular :

Many companies and  organizations use PyTorch as their main deep learning framework. some companies have built their customized machine learning tools using PyTorch. So we can say thay  PyTorch skills are in demand from future prospective.

PyTorch is supported by all major cloud platforms :

Amazon Web Services (AWS), Google Cloud Platform (GCP), Microsoft Azure Etc .You can Configure PyTorch preloaded Virtual Machines
for frictionless development of Deep Learning Models . You can use prebuilt Docker images, perform large-scale training on cloud GPU platforms, and run models at production scale.

PyTorch is supported by Google Colab and Kaggle :

By using Google Colab You can run PyTorch code in a browser with no installation or configuration needed. You can run pytorch directly in Kaggle Kernel  compete in Kaggle competitions.

PyTorch is mature and stable :

PyTorch is regularly get Upgraded and is now beyond release 2.0.

PyTorch supports CPU, GPU, TPU, and parallel processing:

You can accelerate your training using GPUs and TPUs. Tensor processing units (TPUs) are AI accelerated
application-specific integrated circuits (ASIC) chips that were developed by Google to provide an alternative
to GPUs for NN hardware acceleration. With parallel processing, you can apply preprocessing on your CPU
while training a model on the GPU or TPU.

PyTorch supports distributed training :
You can train neural networks over multiple GPUs on multiple machines.

PyTorch supports deployment to production

With the newer TorchScript and TorchServe features, you can easily deploy models to production environments
including cloud servers.

PyTorch is beginning to support mobile deployment
Although it’s currently experimental, you can now deploy models to iOS and Android devices.
PyTorch has a vast ecosystem and set of open source libraries
Libraries such as Torchvision, fastai, and PyTorch Lightning extend capabilities and support specific fields like
natural olanguage processing (NLP) and computer vision.

PyTorch also has a C++ frontend.

Although I will focus on the Python interface in this book,PyTorch also supports a frontend C++ interface. If you
need to build high-performance, low-latency, or baremetal applications, you can write them in C++ using the
same design and architecture as the Python API.

PyTorch supports the Open Neural Network Exchange (ONNX)
format natively.

You can easily export your models to ONNX format and use them with ONNX-compatible platforms, runtimes, or
visualizers.

PyTorch has a large community of developers and user forums

There are more than 38,000 users on the PyTorch forum, and it’s easy to get support or post questions to the community
by visiting the PyTorch Discussion Forum.

Source : https://pytorch.org/docs/stable/index.html