What is CUDA Memory Usage? CUDA memory refers to the dedicated video memory (VRAM) on NVIDIA GPUs used for: Storing model parameters Holding input/output tensors Caching intermediate computations during training Proper memory…
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…
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…
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…
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: Creating new tensor operations not in standard 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…
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: FP16 (16-bit floats) for faster computations…
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…
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…
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…