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diff --git a/latest/CODE_OF_CONDUCT.html b/latest/CODE_OF_CONDUCT.html
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diff --git a/latest/_sources/examples/README.md.txt b/latest/_sources/examples/README.md.txt
@@ -15,3 +15,4 @@ A wide variety of examples are provided to demonstrate the usage of Intel® Exte
 |[Stable Diffusion Inference for Text2Image on Intel GPU](./stable_diffussion_inference)|Example for running Stable Diffusion Text2Image inference on Intel GPU with the optimizations from Intel® Extension for TensorFlow*.|GPU|
 |[Accelerate ResNet50 Training by XPUAutoShard on Intel GPU](./train_resnet50_with_autoshard)|Example on running ResNet50 training on Intel GPU with the XPUAutoShard feature.|GPU|
 |[Accelerate BERT-Large Pretraining on Intel GPU](./pretrain_bert)|Example on running BERT-Large pretraining on Intel GPU with the optimizations from Intel® Extension for TensorFlow*.|GPU|
+|[Accelerate 3D-UNet Training w/o horovod for medical image segmentation on Intel GPU](./train_3d_unet)|Example on running 3D-UNet training for medical image segmentation on Intel GPU with the optimizations from Intel® Extension for TensorFlow*.|GPU|
diff --git a/latest/_sources/examples/train_3d_unet/README.md.txt b/latest/_sources/examples/train_3d_unet/README.md.txt
@@ -0,0 +1,125 @@
+# Accelerate 3D-Unet Training w/o horovod for medical image segmentation on Intel GPU
+
+## Introduction
+
+Intel® Extension for TensorFlow* is compatible with stock TensorFlow*. 
+This example shows 3D-UNet Training for medical image segmentation. It contains single-tile training scripts and multi-tile training scripts with horovod.
+
+Install the Intel® Extension for TensorFlow* in legacy running environment, Tensorflow will execute the Training on Intel GPU.
+
+## Hardware Requirements
+
+Verified Hardware Platforms:
+
+ - Intel® Data Center GPU Max Series
+
+## Prerequisites
+
+### Model Code change
+
+To get better performance, instead of installing the official repository, you can apply the patch and install it as shown here. You can choose one patch from single-tile patch `3dunet_itex.patch` and multi-tile patch `3dunet_itex_with_horovod.patch`. 
+
+```
+git clone https://github.com/NVIDIA/DeepLearningExamples.git
+cd DeepLearningExamples/TensorFlow/Segmentation/UNet_3D_Medical/
+git checkout 88eb3cff2f03dad85035621d041e23a14345999e
+git apply patch  # When applying this patch, please move it to the above 3D-UNet dir first.
+```
+
+### Prepare for GPU
+
+Refer to [Prepare](../common_guide_running.md##Prepare).
+
+### Setup Running Environment
+
+You can use `./pip_set_env.sh` to setup for GPU. It contains the following two steps: creating virtual environment and installing python packages.
+
++ Create Virtual Environment
+
+```
+python -m venv env_itex
+source source env_itex/bin/activate
+```
+
++ Install
+
+```
+pip install --upgrade pip
+pip install --upgrade intel-extension-for-tensorflow[gpu]
+pip install intel-optimization-for-horovod
+pip install tfa-nightly
+pip install git+https://github.com/NVIDIA/dllogger.git
+```
+
+### Enable Running Environment
+
+Enable oneAPI running environment (only for GPU) and virtual running environment.
+
+   * For GPU, refer to [Running](../common_guide_running.md##Running)
+
+### Prepare Dataset
+
+We use [Brain Tumor Segmentation 2019 dataset](https://www.med.upenn.edu/cbica/brats-2019/) for 3D-UNet training. Upon registration, the challenge's data is made available through the `https//ipp.cbica.upenn.edu` service.
+
+The training and test datasets are given as 3D `nifti` volumes that can be read using the Nibabel library and NumPy. It can be converted from `nifti` to `tfrecord` using `./dataset/preprocess_data.py` script.
+
+## Execute the Example
+
+Assume current_dir is `examples/train_maskrcnn/DeepLearningExamples/TensorFlow/Segmentation/UNet_3D_Medical/`.
+
+Here we provide single-tile training scripts and multi-tile training scripts with horovod. The datatype can be float32 or bfloat16.
+
+```
+DATASET_DIR=/the/path/to/dataset
+OUTPUT_DIR=/the/path/to/output_dir
+```
+
+### Single Tile
+
+First apply patch.
+
+```
+git apply 3dunet_itex.patch
+```
+
++ float32
+
+```
+python main.py --benchmark --data_dir $DATASET_DIR --model_dir $OUTPUT_DIR --exec_mode train --batch_size $BATCH_SIZE --warmup_steps 150 --max_steps 1000 --log_every 1 
+```
+
++ bfloat16
+
+```
+python main.py --benchmark --data_dir $DATASET_PATH --model_dir $OUTPUT_DIR --exec_mode train --warmup_steps 150 --max_steps 1000 --batch_size=$BATCH_SIZE --log_every 1 --amp
+```
+
+### Multi-tile with horovod
+
+First apply patch.
+
+```
+git apply 3dunet_itex_with_horovod.patch
+```
+
++ float32
+
+```
+mpirun -np 2 -prepend-rank -ppn 2 \
+  python main.py --data_dir=$DATASET_DIR --benchmark --model_dir=$MODEL_DIR --exec_mode train --warmup_steps 150 --max_steps 1000 --batch_size=$BATCH_SIZE
+```
+
++ bf16
+
+```
+mpirun -np 2 -prepend-rank -ppn 2 \
+  python main.py --data_dir=$DATASET_DIR --benchmark --model_dir=$MODEL_DIR --exec_mode train --warmup_steps 150 --max_steps 1000 --batch_size=$BATCH_SIZE --amp
+```
+
+## FAQ
+
+1. If you get the following error log, refer to [Enable Running Environment](#Enable-Running-Environment) to Enable oneAPI running environment.
+
+``` 
+tensorflow.python.framework.errors_impl.NotFoundError: libmkl_sycl.so.2: cannot open shared object file: No such file or directory
+```
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