# Nano Tutorial - [**BigDL-Nano PyTorch Trainer Quickstart**](./pytorch_train_quickstart.html) > ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub][Nano_pytorch_training] In this guide we will describe how to scale out PyTorch programs using Nano Trainer --------------------------- - [**BigDL-Nano PyTorch TorchNano Quickstart**](./pytorch_nano.html) > ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub][Nano_pytorch_nano] In this guide we will describe how to use BigDL-Nano to accelerate custom training loop easily with very few changes --------------------------- - [**BigDL-Nano TensorFlow Training Quickstart**](./tensorflow_train_quickstart.html) > ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub][Nano_tensorflow_training] In this guide we will describe how to accelerate TensorFlow Keras applications on training workloads with BigDL-Nano --------------------------- - [**BigDL-Nano PyTorch ONNXRuntime Acceleration Quickstart**](./pytorch_onnxruntime.html) > ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub][Nano_pytorch_onnxruntime] In this guide we will describe how to apply ONNXRuntime Acceleration on inference pipeline with the APIs delivered by BigDL-Nano --------------------------- - [**BigDL-Nano PyTorch OpenVINO Acceleration Quickstart**](./pytorch_openvino.html) > ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub][Nano_pytorch_openvino] In this guide we will describe how to apply OpenVINO Acceleration on inference pipeline with the APIs delivered by BigDL-Nano --------------------------- - [**BigDL-Nano PyTorch Quantization with INC Quickstart**](./pytorch_quantization_inc.html) > ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub][Nano_pytorch_Quantization_inc] In this guide we will describe how to obtain a quantized model with the APIs delivered by BigDL-Nano --------------------------- - [**BigDL-Nano PyTorch Quantization with ONNXRuntime accelerator Quickstart**](./pytorch_quantization_inc_onnx.html) > ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub][Nano_pytorch_quantization_inc_onnx] In this guide we will describe how to obtain a quantized model running inference in the ONNXRuntime engine with the APIs delivered by BigDL-Nano --------------------------- - [**BigDL-Nano PyTorch Quantization with POT Quickstart**](./pytorch_quantization_openvino.html) > ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub][Nano_pytorch_quantization_openvino] In this guide we will describe how to obtain a quantized model with the APIs delivered by BigDL-Nano --------------------------- - [**BigDL-Nano TensorFlow Quantization with INC Quickstart**](./tensorflow_quantization_quickstart.html) > ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub][Nano_tensorflow_quantization_inc] In this guide we will demonstrates how to apply Post-training quantization on a keras model with BigDL-Nano. --------------------------- - [**BigDL-Nano TensorFlow SparseEmbedding and SparseAdam**](./tensorflow_embedding.html) > ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub][Nano_tensorflow_embedding] In this guide we demonstrates how to use SparseEmbedding and SparseAdam to obtain stroger performance with sparse gradient ------------------------- - [**BigDL-Nano Hyperparameter Tuning (Tensorflow Sequential/Functional API) Quickstart**](../Tutorials/seq_and_func.html) > ![](../../../../image/colab_logo_32px.png)[Run in Google Colab][Nano_hpo_tf_seq_func_colab]  ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub][Nano_hpo_tf_seq_func] In this guide we will describe how to use Nano's built-in HPO utils to do hyperparameter tuning for models defined using Tensorflow Sequential or Functional API. --------------------------- - [**BigDL-Nano Hyperparameter Tuning (Tensorflow Subclassing Model) Quickstart**](../Tutorials/custom.html) > ![](../../../../image/colab_logo_32px.png)[Run in Google Colab][Nano_hpo_tf_subclassing_colab]  ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub][Nano_hpo_tf_subclassing] In this guide we will describe how to use Nano's built-in HPO utils to do hyperparameter tuning for models defined by subclassing tf.keras.Model. [Nano_pytorch_training]: [Nano_pytorch_nano]: [Nano_tensorflow_training]: [Nano_pytorch_onnxruntime]: [Nano_pytorch_openvino]: [Nano_pytorch_Quantization_inc]: [Nano_pytorch_quantization_inc_onnx]: [Nano_pytorch_quantization_openvino]: [Nano_tensorflow_quantization_inc]: [Nano_tensorflow_embedding]: [Nano_hpo_tf_seq_func]: [Nano_hpo_tf_seq_func_colab]: [Nano_hpo_tf_subclassing]: [Nano_hpo_tf_subclassing_colab]: