Category: Tutorials

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 state of a system for reuse, debugging, or validation. In this guide, we’ll focus on what snapshot generation means, its…

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 # Current allocated memory allocated = torch.cuda.memory_allocated(0) / 1024**2 # MB # Total reserved memory reserved = torch.cuda.memory_reserved(0) /…

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 guide is your all-in-one reference to understand and effectively use torch.cuda in PyTorch. You’ll learn what it does, how to…

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 Characteristics of CPU Tensors Code Examples: Working with CPU Tensors 1. Creating CPU Tensors (Explicit vs Implicit) import torch #…

If you’re working with deep learning models in PyTorch, speed, flexibility, and hardware efficiency are crucial. Whether you’re training models on CPU, CUDA-enabled GPUs, or Apple Silicon (MPS), Accelerator PyTorch tools help streamline device management. In this blog, we’ll explore torch.device, the Accelerator from Hugging Face, and how they simplify training across different hardware setups.…

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 import torch.library # Define a custom “square” operation mylib = torch.library.Library(“mylib”, “DEF”) mylib.define(“square(Tensor x) -> Tensor”) @torch.library.impl(mylib, “square”, “CPU”)…

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.autograd is, why it’s essential for training neural networks, and how you can use it to compute gradients efficiently. You’ll…

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 import torch from torch.cuda.amp import autocast # Create model and optimizer model = torch.nn.Linear(100, 50).cuda() optimizer = torch.optim.Adam(model.parameters()) # Training…

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 pipeline, you’ll often need to reshape tensors without copying the underlying data. That’s exactly what tensor.view() enables. In this guide,…

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 and efficient tensor manipulation. Key Tensor Attributes Attribute Description Example shape Dimensions of the tensor torch.Size([3, 4]) dtype Data type…