Cerebras
Nov 12, 2024 · This is the documentation for developing kernels for Cerebras system. Here you will find getting started guides, quickstarts, tutorials, code samples, release notes, and more.
Spotlight - National Energy Technology Laboratory - Cerebras
Cerebras Systems and National Energy Technology Laboratory Set New Milestones for High-Performance, Energy-Efficient Field Equation Modeling Using Simple Python Interface
Concepts and Guides — Cerebras Developer Documentation
You’ll find detailed information on our core concepts, workflows, features, and the Cerebras PyTorch API. Explore the Cerebras Model Zoo with its pre-configured Large Language Models (LLM), Computer Vision (CV) models, and multimodal support for models like LLaVA, or bring your own PyTorch ML model and dataset.
A Conceptual View — SDK Documentation (1.2.0) - Cerebras
Nov 12, 2024 · Read this before you get into the details of how to write programs with the Cerebras SDK. The Cerebras Wafer-Scale Engine (WSE) is a wafer-parallel compute accelerator, containing hundreds of thousands of independent processing elements (PEs).
Cerebras Wafer-Scale Cluster
The Cerebras Wafer-Scale Cluster is meticulously engineered to enable the training of neural networks with remarkably efficient linear scaling across millions of cores, all without the complexities of traditional distributed computing.
Overview — Cerebras Developer Documentation
The Cerebras Wafer-Scale cluster is meticulously engineered to enable the training of neural networks with remarkably efficient linear scaling across millions of cores, all without the complexities of traditional distributed computing.
Cerebras AI Model Studio Documentation
The Cerebras AI Model Studio is a computing service powered by dedicated clusters of Cerebras CS-2s and hosted by Cirrascale Cloud Services. It is a purpose-built platform, optimized for training large language models on dedicated clusters of millions of cores.
Software Release Notes — Cerebras Developer Documentation
The following are the release notes for the Cerebras software. Release 1.6.1# New features and enhancements# Weight Streaming models in PyTorch# Early access support for GPT model variants, up to 1.5B parameters in PyTorch on single CS-2 …
Incremental Compile — Cerebras Developer Documentation
After you compile your model the first time, the incremental compile feature of CGC will automatically speed up the subsequent compile runs of your model by reusing, wherever possible, the optimizations already performed. See the following high-level CGC flow diagram.
Profiling the executor — Cerebras Developer Documentation
Profiling the executor. #. We provide tools through the DataExecutor to profile its performance during the run. Currently, the supported activities can be profiled as follows: Non-smoothed samples/second since the beginning of when the executor context was entered. For a more detailed explanation see Measure throughput of your model. Non ...