What is PyTorch?
PyTorch is a scientific computing package based on Python that uses the power of graphics processing units. It is also one of the preferred deep learning research platforms, designed to provide maximum flexibility and speed. It is known for providing two of the highest level features; Tensor calculations with strong support for GPU acceleration and the construction of deep neural networks in tape-based auto-grading systems. There are many existing Python libraries that have the potential to alter the performance of deep learning and artificial intelligence, and this is one of those libraries. One of the main reasons behind the success of PyTorch is that it is completely Pythonic and it is possible to create neural network models effortlessly. He is still a young player compared to other competitors, however, he is rapidly gaining strength.
An Overview of PyTorch
The creators of PyTorch say they have a philosophy: they want to be essential. This means that we perform our calculations immediately. This fits perfectly with Python’s programming methodology since we don’t have to wait for all the code to be written before we know if it works or not. We can easily execute a code snippet and inspect it in real-time.
For me, as a neural network debugger, it’s a blessing! PyTorch is a Python-based library designed to provide flexibility as a deep learning development platform. The PyTorch workflow is as close as possible to the Python scientific computing library: numpy. Now you may wonder, why would we use PyTorch to create deep learning models? I can list three things that can help answer this:
- Easy to use API: it’s as simple as Python can be
- Python support: As mentioned earlier, PyTorch integrates seamlessly with the Python data science stack. It is so similar to the numbness that you don’t even notice the difference.
- Dynamic computing graphics: instead of predefined graphics with specific functionality, PyTorch provides a framework for creating
computer graphics as you go along and even change them at runtime. This is valuable for situations where we don’t know how much memory it will take to create a neural network. Some other advantages of using PyTorch are multi GPU support, custom data loaders, and simplified preprocessors. Since its launch in early January As of 2016, many researchers have adopted it as an essential library due to its ease in creating new and even extremely complex graphics. That said, it was a long time before PyTorch was adopted by most data science professionals because of its new and “under construction” status.
History of PyTorch
PyTorch was launched in 2016. Many researchers are willing to adopt PyTorch more and more. It was operated by Facebook. Facebook also operates Caffe2 (Convolutional architecture for the rapid incorporation of resources). It is a challenge to transform a model defined by PyTorch into Caffe2. To this end, Facebook and Microsoft invented an Open Neural Network Exchange (ONNX) in September 2017. Simply put, ONNX was developed to convert models between frames. Caffe2 merged in March 2018 in PyTorch, which facilitates the construction of an extremely complex neural network.
This feature quickly made a library accessible. In the research work, she offers strong competition to TensorFlow. The inventors of PyTorch want to create a highly imperative library that can easily perform all numerical calculations and finally invented PyTorch. There was a great challenge for the Deep Learning scientist, the Machine Learning developer and the Neural Network debuggers to execute and test some of the code in real-time. PyTorch completes this challenge and allows them to execute and test their code in real-time. Therefore, they don’t have to wait to see if it works or not.
Overview of the PyTorch Library
Data Loading and Handling
The first step of any deep learning project is to load and manage data. PyTorch provides utilities for him in torch.utils.data. The two important classes in this module are Dataset and DataLoader.
The data set is built on the Tensor data type and is mainly used for custom data sets. DataLoader is used when you have a large data set and want to load data from a background data set so that you are ready and waiting for the training cycle. We can also use torch.nn.DataParallel and torch.distributed if we have access to multiple machines or GPUs.
Building Neural Network
The torch.nn module is used to create neural networks. It provides all layers of common neural networks, such as fully connected layers, convolutional layers, activation and loss functions, etc. Once the network architecture is created and the data is ready to be fed to the network, we need techniques to update the weights and deviations so that the network begins to learn. These utilities are provided in the torch.optim module. Similarly, for the automatic differentiation required during the backward step, we use the torch.autograd module.
Model Inference & Compatibility
After model training, it can be used to predict the output of test cases or even new data sets. This process is called model inference. PyTorch also provides TorchScript, which can be used to run models regardless of Python runtime. This can be considered as a virtual machine with mainly tensor specific instructions. You can also convert the trained model using PyTorch into formats such as ONNX, which allow the use of these models in other DL frames such as MXNet, CNTK, Caffe2. You can also convert onnx templates to Tensorflow.
The Future Of Pytorch
Given its advantages, PyTorch represents a significant advance in the evolution of machine learning development tools. Even if it never goes beyond what it is now, it should inspire other machine learning frameworks to add features to simplify and shorten the design cycle of the neural network. Finally, by making the design of the neural network more accessible to more developers, it will bring us closer to one or more “killer applications” of machine learning that lack artificial intelligence for so long. Other machine learning development tools and buckle up for an exciting trip. At AndPlus, we look forward to it and hope to take you to see how machine learning can meet your automation needs.