fd1da60251
* Add Nano doc structrue and nano overview * add sections * add unset environment variable instructions * restructure * address comments
92 lines
3.4 KiB
Markdown
92 lines
3.4 KiB
Markdown
# 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.
|