Dec 11, 2024 · The source for these code samples can be found inside the csl-extras-{build id}.tar.gz tarball within the release, or at the CSL examples GitHub repository (request access from developer @ cerebras. net).

This section presents a conceptual view, a mental model, of computing with the Cerebras architecture. Read this before you get into the details of how to write a kernel with CSL.

Dec 11, 2024 · This section presents a conceptual view of computing with the Cerebras architecture. 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).

How Cerebras Works¶ This section presents a big picture view of how CS systems are deployed and used. See ML Workflow on Cerebras for more details on how a Machine Learning (ML) developer runs ML jobs on CS systems.

Dec 11, 2024 · Now that we’ve shown the basic syntax of writing a GEMV in CSL, let’s create a complete program which you can compile and run on the fabric simulator or a real Cerebras system.

Run Cerebras Model Zoo on a GPU. Learn how to run models in the Cerebras Model Zoo on GPUs and which packages to install

Run training, eval or prediction on the Cerebras system. Without modifying your code, run training, eval or prediction on a CPU or a GPU. You will use the csrun_wse script to accomplish this.

Jan 30, 2024 · Whether your preferred framework is PyTorch or TensorFlow, start by first porting your ML code to Cerebras. For TensorFlow, you use CerebrasEstimator, and for PyTorch, you use cerebras.framework.torch.

Limitations of PyTorch on Cerebras (Early access) Port your code using Cerebras PyTorch API cerebras_pytorch.experimental package Optimizer package in PyTorch API 2.0 Sample Training and Eval Scripts for Cerebras PyTorch API Cerebras Model Zoo Supported Operations API

Bases: cerebras.pytorch.sparse.utils.HyperParameterSchedule. Invoke a user’s lambda function of step to obtain the hyper parameter. Parameters. fn (Callable[[torch.Tensor], torch.Tensor]) – A lambda function that takes a step and returns a hyperparameter. cerebras.pytorch.sparse.utils. make_hyperparam_schedule (schedule) [source] #