Add Nano TensorFlow Training QuickStart Doc (#4591)

* Add Nano TensorFlow Training QuickStart Doc

* fix typo

* break training and inference to two files

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

@@ -91,4 +91,4 @@ model.compile(optimizer='adam',
 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.
+For more details on the BigDL-Nano's PyTorch usage, please refer to the [TensorFlow Training](../QuickStart//tensorflow.md) and [TensorFlow Inference](../QuickStart/tensorflow_inference.md) page.

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

@@ -1,18 +0,0 @@
-# Nano TensorFlow Overview
-
-## TensorFlow Training
-
-### Runtime Acceleration
-intel-tensorflow, intel-openmp
-
-### Optimized Layers
-embedding
-
-### Optimizers
-SparseAdam
-
-### Multi-Instance Training
-
-## TensorFlow Inference
-
-### Quantization

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

@@ -0,0 +1,3 @@
+# Nano TensorFlow Inference Overview
+
+### Quantization

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

@@ -0,0 +1,47 @@
+# Nano TensorFlow Training Overview
+
+BigDL-Nano can be used to accelerate TensorFlow Keras applications on training workloads. The optimizations in BigDL-Nano are delivered through BigDL-Nano's `Model` and `Sequential` classes, which have identical APIs with `tf.keras.Model` and `tf.keras.Sequential`. For most cases, you can just replace your `tf.keras.Model` to `bigdl.nano.tf.keras.Model` and `tf.keras.Sequential` to `bigdl.nano.tf.keras.Sequential` to benefits from BigDL-Nano.
+
+We will briefly describe here the major features in BigDL-Nano for TensorFlow training. You can find complete examples here [links to be added]().
+
+### Best Known Configurations
+When you install BigDL-Nano by `pip install bigdl-nano[tensorflow]`, intel-tensorflow will be installed in your environment, which has intel's oneDNN optimizations enabled by default; and when you run `source bigdl-nano-init`, it will export a few environment variables, such as OMP_NUM_THREADS and KMP_AFFINITY, according to your current hardware. Empirically, these environment variables work best for most TensorFlow applications. After setting these environment variables, you can just run your applications as usual (`python app.py`) and no additional changes are required.
+
+### Multi-Instance Training
+
+When training on a server with dozens of CPU cores, it is often beneficial to use multiple training instances in a data-parallel fashion to make full use of the CPU cores. However, naively using TensorFlow's `MultiWorkerMirroredStrategy` can cause conflict in CPU cores and often cannot provide performance benefits.
+
+BigDL-Nano makes it very easy to conduct multi-instance training correctly. You can just set the `num_processes` parameter in the `fit` method in your `Model` or `Sequential` object and BigDL-Nano will launch the specific number of processes to perform data-parallel training. Each process will be automatically pinned to a different subset of CPU cores to avoid conflict and maximize training throughput.
+
+```python
+import tensorflow as tf
+from tensorflow.keras import layers
+from bigdl.nano.tf.keras import Sequential
+
+model = Sequential([
+    layers.Rescaling(1. / 255, input_shape=(img_height, img_width, 3)),
+    layers.Conv2D(16, 3, padding='same', activation='relu'),
+    layers.MaxPooling2D(),
+    layers.Conv2D(32, 3, padding='same', activation='relu'),
+    layers.MaxPooling2D(),
+    layers.Conv2D(64, 3, padding='same', activation='relu'),
+    layers.MaxPooling2D(),
+    layers.Flatten(),
+    layers.Dense(128, activation='relu'),
+    layers.Dense(num_classes)
+])
+
+model.compile(optimizer='adam',
+              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+              metrics=['accuracy'])
+
+model.fit(train_ds, epochs=3, validation_data=val_ds, num_processes=2)
+```
+
+Note that, different from the conventions in PyTorch, the effective batch size will not change in TensorFlow multi-instance training, which means it is still the batch size you specify in your dataset. This is because TensorFlow's `MultiWorkerMirroredStrategy` will try to split the batch into multiple sub-batches for different workers. We chose this behavior to match the semantics of TensorFlow distributed training. 
+
+When you do want to increase your effective batch_size, you can do so by directly changing it in your dataset definition and you may also want to gradually increase the learning rate linearly to the batch_size, as described in the [Facebook paper](https://arxiv.org/abs/1706.02677).
+
+## TensorFlow Inference
+
+### Quantization