onnxruntime/docs/build/training.md
Cassie a0f3e30de6
Docs update: updated nav, get started sections, home page, apis (#9060)
* initial setup and rename "how to" to "setup"

* move API to main nav

* move api to main nav

* add get starated, rework nav order

* rename to install move mds out of install section

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* add install docs and python qs updates

* python get started work

* remove c and obj c for now

* move java, python, and obj-c docs under api folder

* move java api html to iframe (ugh)

* remove api docs w/o details, move api text getstar

* remove api docs wo detail updates get started

* remvoe iframes

* move eco system to main nav

* fix api buttons

* added more examples moved intro to ORT

* fix links

* fix get started titles

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* fix nav remove inferencing and training subnav

* fix top nav remove inference and training nav

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* update nav and python get started page

* move ort training example, add coming soon for iot

* update C# get started

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* Add some js get started content

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* removed onnx-pytorch and onnx-tf

* updated pip install torch and added links iot page

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* updated web to docs soon added release blog link

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2021-09-15 16:23:42 -05:00

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Build for training Build ORT 2

Build ONNX Runtime for training

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Contents

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  • TOC placeholder {:toc}

CPU

Build Instructions

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To build ORT with training support add --enable_training build instruction.

All other build options are the same for inferencing as they are for training.

Windows

.\build.bat --config RelWithDebInfo --build_shared_lib --parallel --enable_training

The default Windows CMake Generator is Visual Studio 2017, but you can also use the newer Visual Studio 2019 by passing --cmake_generator "Visual Studio 16 2019" to .\build.bat

Linux/macOS

./build.sh --config RelWithDebInfo --build_shared_lib --parallel --enable_training

GPU / CUDA

Prerequisites

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The default NVIDIA GPU build requires CUDA runtime libraries installed on the system:

These dependency versions should reflect what is in Dockerfile.training.

Build instructions

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  1. Checkout this code repo with git clone https://github.com/microsoft/onnxruntime

  2. Set the environment variables: adjust the path for location your build machine

    export CUDA_HOME=<location for CUDA libs> # e.g. /usr/local/cuda
    export CUDNN_HOME=<location for cuDNN libs> # e.g. /usr/local/cuda
    export CUDACXX=<location for NVCC> #e.g. /usr/local/cuda/bin/nvcc
    export PATH=<location for openmpi/bin/>:$PATH
    export LD_LIBRARY_PATH=<location for openmpi/lib/>:$LD_LIBRARY_PATH
    export MPI_CXX_INCLUDE_PATH=<location for openmpi/include/>
    
  3. Create the ONNX Runtime wheel

    • Change to the ONNX Runtime repo base folder: cd onnxruntime
    • Run ./build.sh --enable_training --use_cuda --config=RelWithDebInfo --build_wheel

    This produces the .whl file in ./build/Linux/RelWithDebInfo/dist for ONNX Runtime Training.

GPU / ROCM

Prerequisites

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The default AMD GPU build requires ROCM software toolkit installed on the system:

These dependency versions should reflect what is in Dockerfile.training.

Build instructions

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  1. Checkout this code repo with git clone https://github.com/microsoft/onnxruntime

  2. Create the ONNX Runtime wheel

    • Change to the ONNX Runtime repo base folder: cd onnxruntime
    • Run ./build.sh --config RelWithDebInfo --enable_training --build_wheel --use_rocm --rocm_home /opt/rocm --nccl_home /opt/rocm --mpi_home <location for openmpi>

    This produces the .whl file in ./build/Linux/RelWithDebInfo/dist for ONNX Runtime Training.

DNNL and MKLML

Build Instructions

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Linux

./build.sh --enable_training --use_dnnl

Windows

.\build.bat --enable_training --use_dnnl

Add --build_wheel to build the ONNX Runtime wheel

This will produce a .whl file in build/Linux/RelWithDebInfo/dist for ONNX Runtime Training