PyTorch is an open-source deep learning framework primarily developed by Facebook's AI Research (FAIR) lab. It provides a Python-based scientific computing package that serves as a replacement for NumPy and a deep learning research platform that supports dynamic computational graphs.
PyTorch is widely used for various applications in machine learning and artificial intelligence, particularly in the field of deep learning. It offers a flexible and intuitive interface that allows researchers and developers to build and train neural networks efficiently.
Key features of PyTorch include:
Dynamic computational graphs: Unlike some other frameworks, PyTorch utilizes dynamic computational graphs. This means that the graph is constructed on the fly during runtime, which enables more flexibility and easier debugging.
Automatic differentiation: PyTorch provides automatic differentiation capabilities. It can compute gradients automatically, making it convenient for implementing and training complex neural network architectures.
GPU acceleration: PyTorch leverages the power of GPUs (Graphics Processing Units) for accelerated computation. It provides seamless integration with CUDA, a parallel computing platform, allowing users to perform high-speed computations on GPUs.
TorchScript: PyTorch supports TorchScript, which allows developers to serialize and optimize models for deployment in production environments. It provides a just-in-time (JIT) compiler that converts PyTorch models into a more efficient representation, improving performance.
Extensive library ecosystem: PyTorch has a rich ecosystem of libraries and tools that extend its functionality. It includes torchvision for computer vision tasks, torchaudio for audio processing, and torchtext for natural language processing (NLP), among others.
Overall, PyTorch offers a user-friendly and efficient platform for deep learning research, making it a popular choice among researchers, practitioners, and enthusiasts in the field of artificial intelligence.
Pytorch official site has lot of resource and tutorials- https://pytorch.org/tutorials/ . Even there is extensive work on Pytorch mobile to run ml models on edge devices.
Main packages for building DL models are-
Computation Graph- useful in auto differentiation, dynamic operations, visualization & debugging.
Basic Operations on tensors-
All the operation of numpy can be performed on tensors. Both can be transformed into ech-other.
torch.nn package
see the types of the variables below-
Developing a Basic pytroch model-
1. Defining Model: it can be sequential-
It can be defined as sub-class of nn.Module sth like what we need in ResNet ( skip connections) etc.
The class should have init and forward method. forward method would have definitions.
torch.nn is base class for all neural networks. For any model definition this should be inherited.
2. Creating DataLoader for train ( also for test), create loss function and optimizer's object.
3. Training the model with 100 epochs.
now net object is trained and has all parameters, it can be used for further prediction. Complete code is also available here-