# 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

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- [**BigDL-Nano PyTorch TorchNano Quickstart**](./pytorch_nano.html)

    > ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub][Nano_pytorch_nano]

    In this guide we'll describe how to use BigDL-Nano to accelerate custom training loop easily with very few changes

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- [**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

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- [**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

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- [**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

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- [**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

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- [**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

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- [**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


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- [**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.


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- [**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


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- [**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] &nbsp;![](../../../../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.


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- [**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] &nbsp;![](../../../../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]: <https://github.com/intel-analytics/BigDL/blob/main/python/nano/notebooks/pytorch/tutorial/pytorch_train.ipynb>
[Nano_pytorch_nano]: <https://github.com/intel-analytics/BigDL/blob/main/python/nano/notebooks/pytorch/tutorial/pytorch_nano.ipynb>
[Nano_tensorflow_training]: <https://github.com/intel-analytics/BigDL/blob/main/python/nano/notebooks/tensorflow/tutorial/tensorflow_fit.ipynb>
[Nano_pytorch_onnxruntime]: <https://github.com/intel-analytics/BigDL/blob/main/python/nano/notebooks/pytorch/tutorial/pytorch_inference_onnx.ipynb>
[Nano_pytorch_openvino]: <https://github.com/intel-analytics/BigDL/blob/main/python/nano/notebooks/pytorch/tutorial/pytorch_inference_openvino.ipynb>
[Nano_pytorch_Quantization_inc]: <https://github.com/intel-analytics/BigDL/blob/main/python/nano/notebooks/pytorch/tutorial/pytorch_quantization_inc.ipynb>
[Nano_pytorch_quantization_inc_onnx]: <https://github.com/intel-analytics/BigDL/blob/main/python/nano/notebooks/pytorch/tutorial/pytorch_quantization_inc.ipynb>
[Nano_pytorch_quantization_openvino]: <https://github.com/intel-analytics/BigDL/blob/main/python/nano/notebooks/pytorch/tutorial/pytorch_quantization_openvino.ipynb>
[Nano_tensorflow_quantization_inc]: <https://github.com/intel-analytics/BigDL/blob/main/python/nano/notebooks/tensorflow/tutorial/tensorflow_quantization.ipynb>
[Nano_tensorflow_embedding]: <https://github.com/intel-analytics/BigDL/blob/main/python/nano/notebooks/tensorflow/tutorial/tensorflow_embedding.ipynb>
[Nano_hpo_tf_seq_func]: <https://github.com/intel-analytics/BigDL/blob/main/python/nano/notebooks/hpo/seq_and_func.ipynb>
[Nano_hpo_tf_seq_func_colab]: <https://colab.research.google.com/github/intel-analytics/BigDL/blob/main/python/nano/notebooks/hpo/seq_and_func.ipynb>
[Nano_hpo_tf_subclassing]: <https://github.com/intel-analytics/BigDL/blob/main/python/nano/notebooks/hpo/custom.ipynb>
[Nano_hpo_tf_subclassing_colab]: <https://colab.research.google.com/github/intel-analytics/BigDL/blob/main/python/nano/notebooks/hpo/custom.ipynb>