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Software Documentation (Version 1.6.1)

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: Quick-Start Guide
Pytorch: Quick-Start Guide

Reference Samples

Cerebras Model Zoo

General

DataLoader Overview
- PyTorch DataLoader
- Basics of TFRecords
Data Formats
Dynamic Loss Scaling
Performance Optimization Practices
Multi-Replica Data Parallel Training

Scripts and Templates

The run.py Template
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
Cerebras PyTorch Layer API

Compiler Reports

Input Function Report
Compile Report
Incremental Compile

Extensions

Adding Custom Packages To cbcore Container

Theme by the Executable Book Project

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Supported TensorFlow Layers

Supported TensorFlow Layers#

This section describes the TensorFlow layers supported by the CS system.

tf package
- Submodules

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Best Practices

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tf package

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Last updated on Jan 30, 2024, 9:20:44 PM.