Search Results for "high"

Megatron-LM is a GPU-optimized deep learning framework from NVIDIA designed to train extremely large transformer-based language models efficiently at scale. The repository provides both a reference training implementation and Megatron Core, a composable library of high-performance building blocks for custom large-model pipelines. It supports advanced parallelism strategies including tensor, pipeline, data, expert, and context parallelism, enabling training across massive multi-GPU and multi-node clusters. The framework includes mixed-precision training options such as FP16, BF16, FP8, and FP4 to maximize performance and memory efficiency on modern hardware. ...

AllenNLP makes it easy to design and evaluate new deep learning models for nearly any NLP problem, along with the infrastructure to easily run them in the cloud or on your laptop. AllenNLP includes reference implementations of high quality models for both core NLP problems (e.g. semantic role labeling) and NLP applications (e.g. textual entailment). AllenNLP supports loading "plugins" dynamically. A plugin is just a Python package that provides custom registered classes or additional allennlp subcommands. There is ecosystem of open source plugins, some of which are maintained by the AllenNLP team here at AI2, and some of which are maintained by the broader community. ...

The Library is for Finite Transforms such as the Number Theoretic Transform (NTT) and Finite Radon Transform (FRT). Current modules include NTTW for NTTs with high resolution (microsecond) timing, basic array and imaging. The transforms are optimised for performance.

The project contains a matlab package for generating poised lattices and coarse-fine interpolation matrices for high-order adaptive mesh refinement. It accompanies the paper by Qinghai Zhang submitted to Comput. Methods Appl. Mech. Engrg.

Java-based Image Server engine to organise high resolution images. The intermediate images will be generated transparently and available through defined URLs. The aim is to extended to Digital Asset Management Server

Integration routine to be employed in evaluation of high dimensional integrals arising from problems in statistical mechanics, more specifically the partition function of classical Heisenberg spin-chains, and its resulting derivatives.