torch.export in PyTorch
With the rise of AI deployment in production environments, exporting a trained model is a crucial step in any machine learning pipeline. In PyTorch, this is where torch.export comes in—a modern, robust API that lets
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torch.backends in PyTorch
PyTorch is known for its flexibility and dynamic computation graph, but what powers its performance under the hood is something called “backends.” In PyTorch, the torch.backends module plays a vital role in fine-tuning how low-level
Meta Device in PyTorch
What is the Meta Device in PyTorch? The meta device (device='meta') is PyTorch’s virtual tensor backend that: Key benefits: Code Examples: Using Meta Device 1. Creating Meta Tensors import torch # Create meta tensor (no memory allocated)
torch.mtia.memory in PyTorch: Memory Management on Meta’s MTIA AI Hardware
As deep learning models become larger and more complex, efficient memory management is crucial. When working with specialized hardware like Meta’s Meta Training and Inference Accelerator (MTIA), PyTorch provides built-in utilities to track and manage
torch.mtia in PyTorch: Powering AI Workloads with Meta’s AI Accelerator
As artificial intelligence workloads grow in complexity and scale, the demand for high-performance, domain-specific hardware accelerators continues to rise. Meta (formerly Facebook) has entered the custom chip race with its Meta Training and Inference Accelerator
torch.xpu: Intel GPU Acceleration for PyTorch
What is torch.xpu in PyTorch? torch.xpu is PyTorch’s backend for Intel GPU acceleration, providing: Key benefits: Code Examples: Using torch.xpu 1. Basic Tensor Operations import torch # Create XPU tensor x = torch.randn(1000, 1000).xpu() # Move to
torch.mps in PyTorch
With the rise of Apple Silicon chips like the M1, M2, and M3, developers using macOS for deep learning have long desired access to GPU acceleration. PyTorch answered that call with the torch.mps backend, allowing
Using the visualizer in PyTorch
What is a Visualizer in PyTorch? A visualizer in PyTorch refers to tools and techniques for graphically representing: Key visualization benefits: Code Examples: Visualization Techniques 1. Tensor Visualization with Matplotlib import matplotlib.pyplot as plt import torch
Generating a Snapshot
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
Understanding CUDA Memory Usage in PyTorch
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