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+# Introduction
+
+ONNX Runtime Trainer is a test feature introduced in the ONNX Runtime engine. This trainer can be used to accelerate the computation of the ops used to train transformer class models.
+
+The ONNX Runtime trainer can be used with your existing Pytorch training code to accelerate execution on NVIDIA GPU clusters.
+
+## Build on Linux
+
+Build the ONNX Runtime Training engine from source to use with NVIDIA GPUs for accelerating the computations.
+
+### Dependencies
+
+This default NVIDIA GPU build requires CUDA runtime libraries installed on the system:
+
+* The GPU-accelerated CUDA libraries [CUDA 10.1](https://developer.nvidia.com/cuda-downloads?target_os=Linux&target_arch=x86_64&target_distro=Ubuntu&target_version=1604&target_type=debnetwork)
+
+* The GPU-accelerated library of primitives for deep neural networks [cuDNN 7.6.2](https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#install-linux)
+
+* The NVIDIA Collective Communications Library (NCCL) multi-GPU and multi-node communication primitives library [NCCL v2.4.8](https://docs.nvidia.com/deeplearning/sdk/nccl-install-guide/index.html) (*download v2.4.8 from the Legacy downloads page*)
+
+* OpenMPI 4.0.0.0
+
+```
+wget https://download.open-mpi.org/release/open-mpi/v4.0/openmpi-4.0.0.tar.gz
+tar zxf openmpi-4.0.0.tar.gz
+cd openmpi-4.0.0
+./configure --enable-orterun-prefix-by-default
+make -j $(nproc) all
+sudo make install
+sudo ldconfig
+```
+
+### Get the code and setup the environment
+
+* Checkout this code repo with `git clone https://github.com/microsoft/onnxruntime`
+
+* Set the environment variables: __adjust the path for location your build machine__
+
+```
+export CUDA_HOME=<location for CUDA libs> # e.g. /usr/local/cuda
+export CUDNN_HOME=<location for cuDNN libs> # e.g. /usr/local/cuda
+export CUDACXX=<location for NVCC> #e.g. /usr/local/cuda/bin/nvcc
+export PATH=<location for openmpi/bin/>:$PATH
+export LD_LIBRARY_PATH=<location for openmpi/lib/>:$LD_LIBRARY_PATH
+export MPI_CXX_INCLUDE_PATH=<location for openmpi/include/>
+source <location of the mpivars script> # e.g. /data/intel/impi/2018.3.222/intel64/bin/mpivars.sh
+```
+
+### 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 will produce the `.whl` file in `./build/Linux/RelWithDebInfo/dist` for ONNX Runtime Trainer.
+
+## Use with PyTorch training
+
+You can use the ONNX Runtime Training wheel as the *trainer* in your PyTorch pre-training script. Here is a high-level code fragment to include in your pre-training code:
+
+```
+import torch
+import onnxruntime.training.pytorch as ort
+
+# Model definition
+class Net(torch.nn.Module):
+    def __init__(self, D_in, H, D_out):
+        ...
+    def forward(self, x): 
+        ...
+
+model = Net(D_in, H, H_out)
+criterion = torch.nn.MSELoss()
+optimizer = torch.optim.SGD(model.parameters(), lr=1e-4)
+trainer = ort.trainer(model, criterion, optimizer, ...)
+
+# Training Loop
+for t in range(1000):
+    # forward + backward + weight update 
+    loss, y_pred = trainer.step(x, y)
+    ...
+    
+```
+
+A sample for end-to-end training with ONNX Runtime trainer is *coming soon*.