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Gaudi Documentation 1.20.1 documentation
  • Welcome to Intel® Gaudi® v1.20 Documentation

Getting Started

  • Gaudi Architecture and Software Overview
    • Gaudi Architecture
    • Intel Gaudi Software Suite
  • Support Matrix
  • Release Notes
  • Installation
    • Hardware and Network Requirements
    • Driver and Software Installation
    • Firmware Upgrade
    • Additional Installation
      • Bare Metal Installation
      • Docker Installation
      • Kubernetes Installation
        • Intel Gaudi Base Operator for Kubernetes
        • Intel Gaudi Device Plugin for Kubernetes
      • OpenShift Installation
    • System Verifications and Final Tests
  • Quick Start Guides
    • Intel Tiber AI Cloud Quick Start Guide
    • IBM Cloud Quick Start Guide
    • AWS DL1 Quick Start Guide
    • Running Workloads on Bare Metal
    • Running Workloads on Docker
    • Running Workloads on Kubernetes

PyTorch

  • Training
    • Getting Started with Training on Intel Gaudi
    • PyTorch Model Porting
      • GPU Migration Toolkit
      • Importing PyTorch Models Manually
    • Mixed Precision Training with PyTorch Autocast
    • Intel Gaudi Media Loader
    • FP8 Training with Intel Gaudi Transformer Engine
    • Distributed Training with PyTorch
      • Scale-out Topology
      • Distributed Backend Initialization
      • Gaudi-to-process Assignment
      • DDP-based Scaling of Gaudi on PyTorch
      • Theory of Distributed Training
    • Using Fully Sharded Data Parallel (FSDP) with Intel Gaudi
    • Using DistributedTensor with Intel Gaudi
  • Inference
    • Getting Started with Inference on Intel Gaudi
    • AI Model Serving with Intel Gaudi
    • Run Inference Using HPU Graphs
    • Inference with Quantization
      • Run Inference Using FP8
      • Run Inference Using UINT4
    • Optimize Inference on PyTorch
    • Using Gaudi Trained Checkpoints on Xeon
    • vLLM Inference Server with Intel Gaudi
      • Getting Started with vLLM
      • FP8 Calibration and Inference with vLLM
      • vLLM with Intel Gaudi FAQs
    • Triton Inference Server with Gaudi
    • TorchServe Inference Server with Gaudi
  • DeepSpeed
    • Getting Started with DeepSpeed
    • DeepSpeed Training
    • Optimizing Large Language Models
    • Inference Using DeepSpeed
  • Optimization
    • Model Optimization Checklist
    • Optimizations of PyTorch Models
    • Inference Optimizations
    • Handling Dynamic Shapes
    • Fused Optimizers and Custom Ops for Intel Gaudi
    • HPU Graphs for Training
    • Optimizing Training Platform
  • Reference
    • Debugging and Troubleshooting
      • Debugging with Intel Gaudi Logs
      • Debugging Model Divergence
      • Debugging Slow Convergence
      • Troubleshooting PyTorch Model
    • Runtime Environment Variables
    • Intel Gaudi PyTorch Python API (habana_frameworks.torch)
    • PyTorch Operators
    • PyTorch CustomOp API
    • PyTorch Support Matrix
    • PyTorch Gaudi Theory of Operations
  • Hugging Face Optimum for Intel Gaudi
  • PyTorch Lightning

Guides

  • MediaPipe
    • Creating and Executing Media Pipeline
    • MediaPipe for PyTorch ResNet
    • MediaPipe for PyTorch ResNet3d
    • Operators
      • fn.Add
      • fn.BasicCrop
      • fn.BitwiseAnd
      • fn.BitwiseOr
      • fn.BitwiseXor
      • fn.Brightness
      • fn.Cast
      • fn.Clamp
      • fn.CocoReader
      • fn.CoinFlip
      • fn.ColorSpaceConversion
      • fn.Concat
      • fn.Constant
      • fn.Contrast
      • fn.Crop
      • fn.CropMirrorNorm
      • fn.ExtCpuOp
      • fn.ExtHpuOp
      • fn.Flip
      • fn.GatherND
      • fn.GaussianBlur
      • fn.Hue
      • fn.ImageDecoder
      • fn.MediaConst
      • fn.MediaExtReaderOp
      • fn.MediaFunc
      • fn.MemCpy
      • fn.Mult
      • fn.Neg
      • fn.Normalize
      • fn.Pad
      • fn.RandomBiasedCrop
      • fn.RandomFlip
      • fn.RandomNormal
      • fn.RandomUniform
      • fn.ReadImageDatasetFromDir
      • fn.ReadNumpyDatasetFromDir
      • fn.ReadVideoDatasetFromDir
      • fn.ReduceMax
      • fn.ReduceMin
      • fn.Reshape
      • fn.Resize
      • fn.Saturation
      • fn.Slice
      • fn.Split
      • fn.SSDBBoxFlip
      • fn.SSDCropWindowGen
      • fn.SSDEncode
      • fn.SSDMetadata
      • fn.Sub
      • fn.Transpose
      • fn.VideoDecoder
      • fn.Where
      • fn.Zoom
  • Profiling
    • Profiling Workflow
    • Profiling Real-World Examples
    • Profiling with PyTorch
    • Profiling with Intel Gaudi Software
      • Getting Started with Intel Gaudi Profiler
      • Configuration
      • Analysis
      • Remote Trace Viewer Tool
      • Offline Trace Parser Tool
      • Tips and Tricks to Accelerate the Training
  • Management and Monitoring
    • Qualification Tool Library Guide (hl_qual)
      • hl_qual Common Plugin Switches and Parameters
      • hl_qual Report Structure
      • hl_qual Expected Output and Failure Debug
      • Memory Stress Test Plugins Design, Switches and Parameters
      • Power Stress and EDP Tests Plugins Design, Switches and Parameters
      • Connectivity Serdes Test Plugins Design, Switches and Parameters
      • Functional Test Plugins Design, Switches and Parameters
      • Bandwidth Test Plugins Design, Switches and Parameters
      • hl_qual Monitor Textual UI
      • Package Content
      • hl_qual Design
      • Diagnostic Tool
        • Test Plan Automation
        • Log Analysis
        • Qual Package Installation Validator
    • Embedded System Tools User Guide
      • Firmware Update Tool
      • System Management Interface Tool ( hl-smi )
      • Intel Gaudi Secure Firmware Flow
      • Intel Gaudi Secure Boot Flow
      • Disable/Enable NICs
    • Hypervisor Tools Installation and Usage
      • Installing Hypervisor Tools Package
      • Memory Scrub Verification Tool
      • hl_smi_async Tool
    • Intel Gaudi RDMA PerfTest Tool
    • Intel Gaudi Network Configuration
      • Configure E2E Test in L3 Switching Environment
      • Expected Switch Configuration
      • Monitoring Switch and Gaudi 3 Accelerator
      • Collectives Performance
      • Congestion Test
      • How to Pick Good Nodes in the Datacenter
      • Arista Switch Configuration Example
    • Habana Labs Management Library (HLML) C API Reference
      • C APIs
      • Common APIs
      • Per Device APIs
      • Linkage HLML
    • Habana Labs Management Library (PYHLML) Python API Reference
      • Python APIs
      • Common APIs
      • Per Device APIs
  • Orchestration
    • Running Workloads on Kubernetes
    • VMware Tanzu User Guide
    • Enabling Multiple Tenants on PyTorch
      • Multiple Workloads on a Single Docker
      • Multiple Dockers Each with a Single Workload
    • BMC Exporter User Guide
    • Prometheus Metric Exporter
    • Using Slurm Workload Manager with Intel Gaudi
    • Amazon ECS with Gaudi User Guide
      • Setting up EFA-Enabled Security Group
      • Creating a Multi-Node Parallel (MNP) Compatible Docker Image
      • Create AWS Batch Compute Environment
      • Create and Submit AWS Batch Job
      • Advanced Model Training Batch Example: ResNet50
    • Amazon EKS with Gaudi User Guide
      • Creating Cluster and Node Group
      • Enabling Plugins
      • Running a Job on the Cluster
      • MNIST Model Training Example: Run MPIJob on Multi-node Cluster
      • Advanced Model Training Example: Run ResNet Multi-node Cluster
  • Virtualization
  • AWS User Guides
    • Habana Deep Learning Base AMI Installation
    • AWS Base OS AMI Installation
    • Distributed Training across Multiple AWS DL1 Instances
    • Amazon ECS with Gaudi User Guide
      • Setting up EFA-Enabled Security Group
      • Creating a Multi-Node Parallel (MNP) Compatible Docker Image
      • Create AWS Batch Compute Environment
      • Create and Submit AWS Batch Job
      • Advanced Model Training Batch Example: ResNet50
    • Amazon EKS with Gaudi User Guide
      • Creating Cluster and Node Group
      • Enabling Plugins
      • Running a Job on the Cluster
      • MNIST Model Training Example: Run MPIJob on Multi-node Cluster
      • Advanced Model Training Example: Run ResNet Multi-node Cluster
  • APIs
    • Habana Collective Communications Library (HCCL) API Reference
      • Supported Collective Primitives
      • Using HCCL
      • Scale-out via Host NIC
      • C APIs
      • Testing and Benchmarking
    • Habana Labs Management Library (HLML) C API Reference
      • C APIs
      • Common APIs
      • Per Device APIs
      • Linkage HLML
    • Habana Labs Management Library (PYHLML) Python API Reference
      • Python APIs
      • Common APIs
      • Per Device APIs
    • Intel Gaudi PyTorch Python API
  • TPC Programming
    • TPC Getting Started Guide
    • TPC Tools Installation Guide
    • TPC User Guide
      • TPC Programming Language
      • Processor Architectural Overview
      • TPC Programming Model
      • TPC-C Language
      • Built-in Functions
      • Implementing and Integrating New lib
      • TPC Coherency
      • Multiple Kernel Libraries
      • Abbreviations
    • TPC Tools Debugger
      • Installation
      • Starting a Debug Session
      • TPC-C Source or Disassembly Level Debugging
      • Debug Session Views and Operations
    • TPC-C Language Specification
      • Supported Data Types
      • Conversions and Type Casting
      • Operators
      • Vector Operations
      • Address Space Qualifiers
      • Storage-Class Specifiers
      • Exceptions to C99 standard
      • Exceptions to C++ 11 Standard
      • Preprocessor Directives and Macros
      • Functions
      • Built-in Special Functions
    • TPC Intrinsics Guide
      • Arithmetic
      • Bitwise
      • Cache
      • Convert
      • IRF
      • LUT
      • Load
      • Logical
      • Move
      • Pack/Unpack
      • Select
      • Store
      • Miscellaneous
    • TPC I64 Built-ins Guide
      • Arithmetic
      • Load
      • Move
      • Select
      • Store

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