torch.cuda in PyTorch
torch.cuda in PyTorch: Complete Guide to GPU Acceleration If you’re diving into deep learning with PyTorch, harnessing the power of your GPU is key to speeding up model training. That’s where torch.cuda comes in. This
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understanding torch.cpu in PyTorch
What is torch.cpu in PyTorch? torch.cpu refers to PyTorch’s CPU backend that executes tensor operations on central processing units (CPUs) rather than GPUs. This is PyTorch’s default computation mode when CUDA is unavailable or when explicitly specified. Key
torch.accelerator in PyTorch
If you’re working with deep learning models in PyTorch, speed and efficiency matter. Whether you’re training models on CPUs, GPUs (CUDA), or Apple Silicon (MPS), the torch.accelerator API is designed to simplify and optimize how
torch.library in PyTorch
What is torch.library in PyTorch? torch.library is PyTorch’s powerful system for defining custom operations that integrate natively with PyTorch’s autograd and JIT compilation. It enables: Key capabilities: Code Examples: Creating Custom Operations 1. Basic Custom Operation Registration import
torch.autograd in PyTorch
If you’re diving into machine learning or deep learning with PyTorch, you’ll often hear the term automatic differentiation. Behind the scenes, PyTorch handles this functionality using the torch.autograd module. In this tutorial, we’ll explore what
torch.amp in PyTorch
What is torch.amp in PyTorch? torch.amp (Automatic Mixed Precision) is a PyTorch module that speeds up neural network training while maintaining accuracy by strategically using different numerical precisions: Key benefits: Code Examples: Using torch.amp 1. Basic Autocast Usage
Tensor Views in PyTorch
In PyTorch, understanding how to reshape and manipulate tensors is essential. One powerful feature that helps with this is Tensor Views. If you’re working with neural networks, image data, or any kind of machine learning
Understanding Tensor Attributes in PyTorch
What Are Tensor Attributes? In PyTorch, a tensor is a multi-dimensional array that stores numerical data. Each tensor has attributes—properties that define its structure, data type, storage location, and other metadata. Understanding these attributes is crucial for debugging
torch.Tensor Explained: Basics, Examples, and Common Methods in PyTorch
If you’re diving into deep learning with PyTorch, there’s one core building block you must understand — the torch.Tensor. This object is the heart of all computations in PyTorch, similar to arrays in NumPy, but
Understanding torch.nn in PyTorch
If you’re learning deep learning with PyTorch, one of the most important modules you’ll encounter is torch.nn. This module forms the backbone of neural network creation in PyTorch. Whether you’re building a simple linear regression