PyTorch was introduced for the first time in 2016 and it is a deep learning open-source framework. It has become very popular among developers due to its ease of usage and efficiency. PyTorch is getting huge critical acclaim because of its compatibility with a high-level programming language Python which is also favored by data scientists and machine learning developers.
Deep learning models are a type of machine learning model that have multiple applications and usage which include language processing, image recognition, and more. PyTorch is an elegant framework that can help in the construction of deep learning models. This framework has been written using Python and the best part about PyTorch is that it is extremely easy to learn and implement for machine learning developers.
Furthermore, PyTorch is unique in its support of GPUs. Other exclusive features of PyTorch include auto-differentiation, reverse-mode, computational graph, etc. This is also why PyTorch is a popular choice among developers for prototyping and fast experimentation.
Why is PyTorch a popular choice among developers and data scientists?
PyTorch is the product of Meta’s Artificial Intelligence research lab and others. The framework has incorporated the Python programming language in the front end with a resilient and productive backend library from Torch which is also GPU accelerated. The entire framework concentrates on unreadable code, quick prototyping, and assisting multiple categories of deep learning models.
Although PyTorch enables the friendly yet authoritative programming approach for data scientists and developers, simultaneously providing production graphs. The framework was released as open source in the year 2017 and because of its Python roots, it has become fairly popular among machine learning programmers.
Benefits of PyTorch for data scientists
Due to its innovative characteristics, PyTorch is extremely popular in deep learning. For example, PyTorch has implemented a chainer technology known as reverse-mode automatic differentiation. To put it more simply, the method is like a tape recorder that completes each and every operation, then computes the gradients, and finally iterates the entire process.
Due to this particular feature, debugging in PyTorch is very simple and it can also adapt to specific applications such as dynamic neural networks. PyTorch is also well accepted for prototyping because every repetition can provide different results.
Python developers extensively use PyTorch which has been developed using the Python language. The framework utilizes the define-by-run eager execution mode and authoritativeness of the language through which all the operations are executed.
Although Python is fairly popular among developers and other programming languages, a recent survey by Datanami shows there has been a growing focus on machine learning, deep learning, and AI thus paving the way for industry-wide PyTorch implementation.
For existing Python developers and data scientists, PyTorch has become a good choice for its futuristic scope. Moreover, those who are comparatively new to deep learning can already come across an enlarging library of deep learning courses which are specifically based on PyTorch. Since its release, the API of this framework has remained consistent and that is why PyTorch is significantly easy to decipher for experienced Python programmers.
If we look at any particular strength of PyTorch then it is prototyping in smaller projects. It is also beneficial for academics and research communities because of its ease of usage and flexibility. Facebook’s AI research lab is also working tirelessly to ameliorate the productive application of PyTorch.
The latest releases of PyTorch have included multiple enhancements. Moreover, it has also added ONNX, or Open Neural Network Exchange which can help the developers comply with the deep learning models that will be productive for their projects or applications.
Features of PyTorch
Here is a list of important features of PyTorch:
- PyTorch has an excellent and active community of developers that provides brilliant tutorials and documentation. You can visit their forum at PyTorch.org.
- The entire framework has been developed using the popular programming language Python and the developers have also included Python libraries such as NumPy to conduct scientific computing. For the compilation of Python to C and to provide a better performance, SciPy and Cython have been used.
- PyTorch is very easy for data scientists and Python developers because it has similar syntax and utilization.
- Major cloud platform supports PyTorch.
- The scripting language of PyTorch is known as TorchScript and it is very easy to use as well as ductile when used in eager mode (eager mode is a specific mode of this framework where operations are executed instantly as they are derived from Python). You can also change to the graph mode if you require better optimization and more speed in C++ runtime settings.
- PyTorch can effectively support parallel processing, GPU, distributed training, and CPU, which means any computational work can be allocated among various GPU and CPU cores. Furthermore, you can also conduct training on multiple machines using multiple GPUs.
- Dynamic computational graphs are supported by PyTorch which enables the network behavior to be transitioned during the runtime. This flexible characteristic is a major feature that sets apart PyTorch from the existing deep learning frameworks (because the rest of them require neural networks to be delineated as a static object before runtime.)
- PyTorch also has a storage of pre-trained models that can be replicated using a single code line.
- PyTorch as a deep learning framework has both the eager mode (for experiments) and graph mode (for the execution of performance).
- You can extend the core functionality of your applications using the brilliant APIs of PyTorch.
- The libraries and tools of PyTorch range from reinforcement learning to computer vision.
- The pure C++ frontend interface which the python developers are accustomed to is supported by PyTorch and you can also create high-performance C++ applications using the same.
- In PyTorch, you will be easily able to construct a brand-new custom component as a subclass under the standard Python class.
- You can easily import the libraries and parameters which can further be efficiently dispensed with the help of TensorBoard (which is an external toolkit.)
Practical use case of PyTorch for data scientists
Due to the PyTorch framework being convenient and flexible, it is being used in multiple projects and applications such as natural language processing, reinforcement learning, image classification, etc. Let us discuss them in brief:
Natural Language Processing (NLP)
If we look at software or virtual assistants, we will be able to understand how machine learning has made significant breakthroughs in understanding natural languages.
Most of these models utilize a flat sequence of characters or words in the form of recurrent neural networks or RNN to process the sequences. Yet, a lot of linguistics think that language can be comprehended most efficiently if we use a stratified tree of phrases.
That is why a lot of research has been done on the deep learning models which are termed as recursive neural networks that undertake this approach recommended by linguistics. Although these models do have a complex nature and are hard to implement, PyTorch smoothens these difficult natural language processing models to make them much easier and more efficient. Right now, Salesforce is utilizing PyTorch for multi-task learning and NLP.
You can utilize computer neural networks to reinforce the development of image classification, object detection, and generative application. The framework also helps the programmers to process images and videos through which they will be able to construct a detailed and unambiguous computer vision model.
You can easily control the motion of robots, create business development plans and reinforce robotic processes with the help of PyTorch.
How data scientists can work with reinforcement learning with the help of PyTorch
For data scientists, there are multiple use cases of PyTorch in the deep learning field. Moreover, you can experience better results with the implementation of PyTorch in multiple projects regarding style transfer, image classification for identifying fake goods, etc.
Currently, tech giants are also using PyTorch for natural language processing. If we carefully look at the progress and implementation of PyTorch in the field of deep learning and artificial intelligence, learning this framework as one of your technical abilities can open up lots of future opportunities for you.