As graph data becomes increasingly prevalent in mobile computing scenarios, deploying Graph Convolutional Network-based self-supervised learning (GCN-SSL) models on mobile devices provides a powerful ...

GRAPH_MODE, PYNATIVE_MODE MindSpore offers two execution modes: GRAPH_MODE: Compiles the computation graph in advance, for better performance and hardware utilisation. PYNATIVE_MODE: Executes ...

Unlike frameworks that use static computation graphs, PyTorch uses a dynamic computation graph, allowing for real-time model changes, easier debugging, and faster prototyping, making PyTorch ...

The visualization of complex mathematical graphs once required high-end software and expensive equipment. Now, it’s at your fingertips. With Apple and Android apps, math enthusiasts, educators ...

The benefits of FlashAttention-3 The faster attention computation offered by FlashAttention-3 has several implications for LLM development and applications.

Graph similarity measurement is a fundamental task in various graph-related applications. However, recent learning-based approaches lack interpretability as they directly transform interaction ...

A new project to improve the processing speed of neural networks on Apple Silicon is potentially able to speed up training on large datasets by up to ten times.

Torch Dynamo is the graph capture mechanism of PyTorch introduced in the PyTorch 2.0 release. Torch Dynamo will take a PyTorch Model and emit the TrochFX IR representing the computation. Currently ...