0407ebf5c3
* Remove manually-added bold style for titles in [User guide] section * Fix failed relative links in windows user guide * Remove manually-added bold style for titles in [Orca] section * Fix failed relative links & title bold fix in Nano 5 min * Remove manually-added bold style for titles in [Nano] section * Remove manually-added bold style for titles in [DLlib] section * Remove manually-added bold style for titles in [Chronos] section * Remove manually-added bold style for titles in Developer guide * Remove manually-added bold title style for all other not-included md files in docs/readthedocs/source/doc folder * Fix based on comments
70 lines
2.7 KiB
Markdown
70 lines
2.7 KiB
Markdown
# Nano in 5 minutes
|
|
|
|
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.
|
|
|
|
----
|
|
|
|
|
|
### PyTorch Bite-sized Example
|
|
|
|
BigDL-Nano supports both PyTorch and PyTorch Lightning models and most optimizations require 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 LightningModule, you can use the following code snippet to 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)
|
|
```
|
|
|
|
If you are using custom training loop, you can use the following code to enable intel-extension-for-pytorch, multi-instance training and other nano's optimizations.
|
|
|
|
```python
|
|
from bigdl.nano.pytorch import TorchNano
|
|
|
|
class MyNano(TorchNano):
|
|
def train(...):
|
|
# copy your train loop here and make a few changes
|
|
...
|
|
|
|
MyNano(use_ipex=True, num_processes=2).train()
|
|
```
|
|
|
|
For more details on the BigDL-Nano's PyTorch usage, please refer to the [PyTorch Training](./pytorch_train.md) and [PyTorch Inference](./pytorch_inference.md) page.
|
|
|
|
|
|
### TensorFlow Bite-sized Example
|
|
|
|
BigDL-Nano supports `tensorflow.keras` API and most optimizations require 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 lines of 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 Tensorflow usage, please refer to the [TensorFlow Training](./tensorflow_train.md) and [TensorFlow Inference](./tensorflow_inference.md) page.
|