The term “Generating a Snapshot” can appear in multiple contexts — from deep learning frameworks like PyTorch, to front-end testing libraries like Jest. But regardless of the platform, the concept revolves around capturing the current
What is CUDA Memory Usage? CUDA memory refers to the dedicated video memory (VRAM) on NVIDIA GPUs used for: Proper memory management is crucial because: Code Examples: Monitoring & Managing CUDA Memory 1. Checking Memory Usage import
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
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
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
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
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
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
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
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