PyTorch Landscape
Summary
An overview of the PyTorch ecosystem, covering key tools, libraries, and community resources.
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@PyTorch: PyTorch releases include thousands of changes, and our Release Live Q&A gives you direct access to the maintainers and …
PyTorch 2.12 release with major updates is highlighted, and a live Q&A event on May 20 with maintainers is announced to discuss compilation, distributed systems, quantization, and more.
We’re proud to open-source LIDARLearn [R] [D] [P]
LIDARLearn is an open-source PyTorch library for 3D point cloud deep learning that unifies 56 pre-configured models with built-in cross-validation and automatic publication-ready LaTeX report generation. The framework supports supervised, self-supervised, and parameter-efficient fine-tuning methods across datasets like ModelNet40, ShapeNet, and remote sensing benchmarks.
@ManningBooks: PyTorch gets you pretty far, but when performance becomes the problem, understanding what's happening at the GPU level …
Promotional post for the book 'CUDA for Deep Learning' by Elliot Arledge, offering a first chapter summary video that explains GPU performance, the CUDA programming model, and when to write custom CUDA kernels.
PyTorch 2.12 Release Highlights (7 minute read)
PyTorch 2.12 introduces significant performance improvements including up to 100x faster batched eigendecomposition on CUDA, a new device-agnostic torch.accelerator.Graph API, and support for Microscaling quantization in torch.export, continuing the framework's evolution into a unified production platform.
@_rohit_tiwari_: Builds GPT-like LLMs from scratch in PyTorch > Breaks the LLM architecture into simple parts. > Beginner friendly. > Fu…
A beginner-friendly, hands-on GitHub repository that breaks down GPT-like LLM architecture into simple parts, with 10 Jupyter notebooks covering tokenization, attention, transformer blocks, and a mini GPT implementation in PyTorch.