PyTorch is an open-source deep learning framework primarily used for building and training neural networks. Developed by Facebook's AI Research lab (FAIR), it provides a flexible and intuitive way to perform tensor computations, automatic differentiation, and GPU-accelerated deep learning workflows. PyTorch is widely used for research, prototyping, and production-level AI applications. At its core, PyTorch operates on tensors, which are multi-dimensional arrays similar to NumPy arrays but with built-in support for GPU acceleration. PyTorch’s autograd module enables automatic differentiation, allowing for efficient computation of gradients required for backpropagation. This is crucial for optimizing deep learning models. PyTorch adopts a dynamic computation graph (as opposed to TensorFlow’s static graph approach in its earlier versions). This means that computational graphs are built on-the-fly, allowing for more flexibility when defining and modifying neural networks. This feature is particularly useful for research and experimentation, as it enables easier debugging and real-time modifications to models. PyTorch provides a high-level module called torch.nn, which contains predefined layers, activation functions, and loss functions to simplify neural network creation. Additionally, torch.optim offers various optimization algorithms such as SGD, Adam, and RMSprop for model training. Data handling is streamlined through torch.utils.data, which provides tools like DataLoader for efficient batching and loading of datasets. A major advantage of PyTorch is its seamless integration with CUDA, enabling GPU acceleration to significantly speed up deep learning tasks. This makes it highly suitable for training large-scale models, including convolutional neural networks (CNNs) for image processing and transformers for natural language processing (NLP).

With the introduction of TorchScript, PyTorch also supports deployment in production environments by converting models into a static graph representation, allowing for optimizations and cross-platform execution.