Nano User Guide¶
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.
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.
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¶
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()
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.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)