Add Nano doc structure and Nano overview (#4504)

* Add Nano doc structrue and nano overview

* add sections

* add unset environment variable instructions

* restructure

* address comments

docs/readthedocs/source/doc/Nano/Overview/nano.md

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+# Nano User Guide
+
+## **1. Overview**
+
+BigDL Nano is a python package to transparently accelerate PyTorch and TensorFlow applications on Intel hardware. It provides a unified and easy-to-use API for several optimization techniques and tools, so that users can only apply a few lines of code changes to make their PyTorch or TensorFlow code run faster.
+
+---
+## **2. Install**
+
+BigDL-Nano can be installed using pip and we recommend installing BigDL-Nano in a conda environment.
+
+For PyTorch Users, you can install bigdl-nano along with some dependencies specific to PyTorch using the following command.
+
+```bash
+conda create -n env
+conda activate env
+pip install bigdl-nano[pytorch]
+```
+
+For TensorFlow users, you can install bigdl-nano along with some dependencies specific to TensorFlow using the following command.
+
+```bash
+conda create -n env
+conda activate env
+pip install bigdl-nano[tensorflow]
+```
+
+After installing bigdl-nano, you can run the following command to setup a few environment variables. 
+
+```bash
+source bigdl-nano-init
+```
+
+The `bigdl-nano-init` scripts will export a few environment variable according to your hardware to maximize performance. 
+
+In a conda environment, this will also add this script to `$CONDA_PREFIX/etc/conda/activate.d/`, which will automaticly run when you activate your current environment.
+
+In a pure pip environment, you need to run `source bigdl-nano-init` every time you open a new shell to get optimal performance and run `source bigdl-nano-unset-env` if you want to unset these environment variables.
+
+---
+
+## **3. Get Started**
+
+#### **3.1 PyTorch**
+
+BigDL-Nano supports both PyTorch and PyTorch Lightning models and most optimizations requires only changing a few "import" lines in your code and adding a few flags.
+
+BigDL-Nano uses a extended version of PyTorch Lightning trainer for integrating our optimizations.
+
+For example, if you are using a LightingModule, you can use the following code enable intel-extension-for-pytorch and multi-instance training.
+
+```python
+from bigdl.nano.pytorch import Trainer
+net = create_lightning_model()
+train_loader = create_training_loader()
+trainer = Trainer(max_epochs=1, use_ipex=True, num_processes=4)
+trainer.fit(net, train_loader)
+```
+
+For more details on the BigDL-Nano's PyTorch usage, please refer to the [PyTorch](../QuickStart/pytorch.md) page.
+
+### **3.2 TensorFlow**
+
+BigDL-Nano supports `tensorflow.keras` API and most optimizations requires only changing a few "import" lines in your code and adding a few flags.
+
+BigDL-Nano uses a extended version of `tf.keras.Model` or `tf.keras.Sequential` for integrating our optimizations.
+
+For example, you can conduct a multi-instance training using the following code:
+
+```python
+import tensorflow as tf
+from bigdl.nano.tf.keras import Sequential
+mnist = tf.keras.datasets.mnist
+
+(x_train, y_train),(x_test, y_test) = mnist.load_data()
+x_train, x_test = x_train / 255.0, x_test / 255.0
+
+model = Sequential([
+  tf.keras.layers.Flatten(input_shape=(28, 28)),
+  tf.keras.layers.Dense(128, activation='relu'),
+  tf.keras.layers.Dropout(0.2),
+  tf.keras.layers.Dense(10, activation='softmax')
+])
+
+model.compile(optimizer='adam',
+              loss='sparse_categorical_crossentropy',
+              metrics=['accuracy'])
+
+model.fit(x_train, y_train, epochs=5, num_processes=4)
+```
+
+For more details on the BigDL-Nano's PyTorch usage, please refer to the [TensorFlow](../QuickStart//tensorflow.md) page.

docs/readthedocs/source/doc/Nano/QuickStart/pytorch.md

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+# BigDL-Nano PyTorch Overview
+
+BigDL-Nano can be used to accelerate PyTorch or PyTorch-Lightning applications on both training and inference workloads. The optimizations in BigDL-Nano are delivered through a extended version of PyTorch-Lightning `Trainer`. These optimizations are either enabled by default, or can be easily turned on by setting a parameter or calling a method.
+
+## PyTorch Training
+
+### Best Known Configurations
+
+### BigDL-Nano PyTorch Trainer
+
+#### Intel® Extension for PyTorch
+
+#### Multi-instance Training
+
+### Optimized Data pipeline
+
+### Optimizers
+
+### Notebooks
+
+## PyTorch Inference
+
+### Runtime Acceleration
+
+### Quantization
+
+### Notebooks

docs/readthedocs/source/doc/Nano/QuickStart/tensorflow.md

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+# Nano TensorFlow Overview
+
+## TensorFlow Training
+
+### Runtime Acceleration
+intel-tensorflow, intel-openmp
+
+### Optimized Layers
+embedding
+
+### Optimizers
+SparseAdam
+
+### Multi-Instance Training
+
+## TensorFlow Inference
+
+### Quantization