Software Documentation (Version 1.6.0)

Software Release Notes
Documentation Updates

Cerebras Basics

How Cerebras Works
The Cerebras ML Workflow
Cerebras Execution Modes

Getting Started

Software Requirements and Dependencies
Checklist Before You Quickstart
TensorFlow Quickstart
PyTorch Quickstart
Weight Streaming Quickstart

Reference Samples

Cerebras Reference Implementations

General

Data Formats
Dynamic Loss Scaling
Performance Optimization Practices
Multi-Replica Data Parallel Training

Scripts and Templates

The run.py Template
Cerebras Command Line Pattern
The csrun_cpu Script
The csrun_wse Script
The cs_input_analyzer Script

Develop with TensorFlow

Workflow for TensorFlow on CS
Port TensorFlow to Cerebras
Prepare Input
TensorFlow Dynamic Loss Scaling
Compile on CPU
Train, Eval and Predict
Early Stopping
Multiple Models
- Multi-model Inference
TensorFlow Variable Sequence Length
Using TensorBoard
Best Practices
Supported TensorFlow Layers
- tf package

Develop with PyTorch

Workflow for PyTorch on CS
Porting PyTorch Model to CS
PyTorch Runners
PyTorch Variable Tensor Shape
Limitations of PyTorch on Cerebras
Supported PyTorch Ops

Compiler Reports

Input Function Report
Compile Report
Incremental Compile

Extensions

Adding Custom Packages To cbcore Container

Theme by the Executable Book Project

Prepare Input

Prepare Input¶

Multi-worker Input Pipeline
Sharding For the CS system
The CS_AUTOTUNE
- Using CS_AUTOTUNE
Optimize Input Function
- Automatic execution of the analyzer

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Limitations of the CerebrasEstimator

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Multi-worker Input Pipeline

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Last updated on Apr 11, 2023, 11:39:33 PM.