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.
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.
TensorFlow Bite-sized Example#
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)
For more details on the BigDL-Nano’s Tensorflow usage, please refer to the TensorFlow Training and TensorFlow Inference page.