0f78a568c7
* add tutorial, notebook and API doc for LightningLite * increase the timeout when running tutorial notebook * change docs and tutorial to use new API
107 lines
4 KiB
Markdown
107 lines
4 KiB
Markdown
# Nano User Guide
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## **1. Overview**
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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.
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---
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## **2. Install**
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Note: For windows Users, we recommend using Windows Subsystem for Linux 2 (WSL2) to run BigDL-Nano. Please refer [here](./windows_guide.md) for instructions.
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BigDL-Nano can be installed using pip and we recommend installing BigDL-Nano in a conda environment.
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For PyTorch Users, you can install bigdl-nano along with some dependencies specific to PyTorch using the following command.
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```bash
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conda create -n env
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conda activate env
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pip install bigdl-nano[pytorch]
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```
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For TensorFlow users, you can install bigdl-nano along with some dependencies specific to TensorFlow using the following command.
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```bash
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conda create -n env
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conda activate env
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pip install bigdl-nano[tensorflow]
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```
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After installing bigdl-nano, you can run the following command to setup a few environment variables.
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```bash
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source bigdl-nano-init
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```
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The `bigdl-nano-init` scripts will export a few environment variable according to your hardware to maximize performance.
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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.
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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.
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---
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## **3. Get Started**
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#### **3.1 PyTorch**
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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.
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BigDL-Nano uses a extended version of PyTorch Lightning trainer for integrating our optimizations.
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For example, if you are using a LightingModule, you can use the following code to enable intel-extension-for-pytorch and multi-instance training.
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```python
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from bigdl.nano.pytorch import Trainer
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net = create_lightning_model()
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train_loader = create_training_loader()
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trainer = Trainer(max_epochs=1, use_ipex=True, num_processes=4)
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trainer.fit(net, train_loader)
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```
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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.
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```python
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from bigdl.nano.pytorch import TorchNano
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class MyNano(TorchNano):
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def train(...):
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# copy your train loop here and make a few changes
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...
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MyNano(use_ipex=True, num_processes=2).train()
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```
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For more details on the BigDL-Nano's PyTorch usage, please refer to the [PyTorch Training](../QuickStart/pytorch_train.md) and [PyTorch Inference](../QuickStart/pytorch_inference.md) page.
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### **3.2 TensorFlow**
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BigDL-Nano supports `tensorflow.keras` API and most optimizations requires only changing a few "import" lines in your code and adding a few flags.
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BigDL-Nano uses a extended version of `tf.keras.Model` or `tf.keras.Sequential` for integrating our optimizations.
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For example, you can conduct a multi-instance training using the following code:
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```python
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import tensorflow as tf
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from bigdl.nano.tf.keras import Sequential
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mnist = tf.keras.datasets.mnist
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(x_train, y_train),(x_test, y_test) = mnist.load_data()
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x_train, x_test = x_train / 255.0, x_test / 255.0
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model = Sequential([
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tf.keras.layers.Flatten(input_shape=(28, 28)),
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tf.keras.layers.Dense(128, activation='relu'),
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tf.keras.layers.Dropout(0.2),
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tf.keras.layers.Dense(10, activation='softmax')
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])
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model.compile(optimizer='adam',
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loss='sparse_categorical_crossentropy',
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metrics=['accuracy'])
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model.fit(x_train, y_train, epochs=5, num_processes=4)
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```
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For more details on the BigDL-Nano's PyTorch usage, please refer to the [TensorFlow Training](../QuickStart/tensorflow_train.md) and [TensorFlow Inference](../QuickStart/tensorflow_inference.md) page.
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