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¶
Note: For windows users, we recommend using Windows Subsystem for Linux 2 (WSL2) to run BigDL-Nano. Please refer here for instructions.
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 commands.
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 commands.
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.
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, source bigdl-nano-init
will also be added 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 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.
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.
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 and PyTorch Inference page.
3.2 TensorFlow¶
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:
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 Training and TensorFlow Inference page.