FlexFlow
latest

Getting Started

  • Overview
  • Building from source
  • Docker
  • Multinode tutorial

FlexFlow Serve

  • Serving Overview
  • Serving Usecases
    • Chatbot
    • Prompt Template
    • RAG Q&A
  • FlexFlow Serve Python API
    • FlexFlow Serve FastAPI
    • FlexFlow Serve Gradio API

FlexFlow Train

  • Training Overview
  • Training Interface
    • Keras Interface
    • PyTorch Interface
    • ONNX Support
  • Training Examples
    • mT5 Model
      • mT5 in PyTorch
      • mT5 in FlexFlow
  • Python API
    • Models API
      • Model Creation
        • Model Creation
        • Tensor Creation
      • Model Initialization
        • Compile
        • Initialization
      • Model Training and Testing
        • Fit
        • Evaluate
        • Customized Training
    • Layers API
      • Conv2D
      • Pool2D
      • Dense
      • Embedding
      • Transpose
      • Reverse
      • Concatenate
      • Split
      • Reshape
      • Flat
      • BatchNorm
      • BatchMatMul
      • Add
      • Subtract
      • Multiply
      • Divide
      • Exponential
      • ReLU
      • ELU
      • Sigmoid
      • Tanh
      • Softmax
      • Dropout
      • MultiheadAttention
    • Dataloader API
      • Dataloader Creation
      • Use Dataloader for Training

FlexFlow Backend

  • C++ API

Developers Guide

  • Developers Guide
    • Code Organization
      • AlexNet example (C++)
        • Tensor creation
        • Adding layers to a DNN model
        • Optimizer and training metrics
        • Model compilation
    • Continuous Integration
      • Github Workflow syntax
    • Pip packages
      • Packaging
      • Source VS Wheel distribution
      • Versioning
      • Test PyPI
      • Build vs install dependencies
    • Contributing to FlexFlow
      • Formatting
      • Documenting the code
      • Pull Requests
      • Issues
      • License
FlexFlow
  • Training Interface
  • Edit on GitHub

Training Interface

  • Keras Interface
  • PyTorch Interface
  • ONNX Support
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© Copyright 2023 CMU, Facebook, LANL, MIT, NVIDIA, and Stanford (alphabetical). Revision ca3dabf7.

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