blog style readme for torch sparse-quant TL notebook

examples/pytorch_sparse_quantized_transfer_learning/README.md

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+
+# PyTorch Sparse-Quantized Transfer Learning with SparseML
+
+<figure class="image">
+  <img src="https://docs.neuralmagic.com/docs/source/sparsification/flow-sparsification-model_recipe_sparsezoo-sparseml_transfer_learning.svg" width="960px">
+</figure>
+
+## Overview
+[Pruning](https://neuralmagic.com/blog/pruning-overview/) and
+[quantization](https://arxiv.org/abs/1609.07061) are well-established methods for accelerating
+neural networks.  Individually, both methods yield significant speedups for CPU inference
+(a theoretical maximum of 4x for INT8 quantization) and can make CPU deployments an attractive
+option for real-time model inference.
+
+Sparse-quantized models leverage both techniques and
+[can achieve speedups upwards of 7x](https://neuralmagic.com/blog/benchmark-resnet50-with-deepsparse)
+when using the [DeepSparse Engine](https://github.com/neuralmagic/deepsparse) with
+[compatible hardware](https://docs.neuralmagic.com/deepsparse/hardware.html).
+
+Using powerful [SparseML](https://github.com/neuralmagic/sparseml) recipes, it is easy to create sparse-quantized models.
+Additionally, the SparseML team is actively creating pre-trained sparse-quantized models that maintain accuracy
+targets and achieve high CPU speedups - and it is easy to leverage these models for speedups with your own datasets
+using sparse-quantized transfer learning.
+
+## Sparse-Quantized Transfer Learning
+
+[Transfer learning](https://en.wikipedia.org/wiki/Transfer_learning) is a technique that
+involves retraining a pre-trained model to learn a new task, with the benefit of starting
+from the pre-trained model's already learned behavior. Sparse-quantized transfer takes the
+additional step of reusing both the pre-trained weights, and pre-trained sparse model
+structure of an existing sparse-quantized model to train it on a new ML task.
+
+This technique allows engineers and researchers to create sparse-quantized optimizations
+for one model and then easily re-apply them to accelerate many tasks.
+
+Sparse-quantized transfer learning takes place in two phases:
+1. Sparse transfer learning \- fine tuning the pre-trained model with the new dataset
+while maintaining the existing pre-optimized sparsity structure.  This creates a model 
+that learns to predict a new task, while preserving the predetermined optimized structure
+from pruning.
+2. [Quantization-aware training](https://pytorch.org/blog/introduction-to-quantization-on-pytorch/#quantization-aware-training)
+\- emulating the effects of INT8 quantization while training the model to overcome the loss of precision
+
+
+## ResNet-50 Imagenette Example
+
+The [SparseZoo](https://github.com/neuralmagic/sparseml) hosts a sparse-quantized ResNet-50 model trained
+on the ImageNet dataset.  It maintains 99% of the baseline accuracy and can achieve over 6.5x
+speedup using the DeepSparse Engine.  There are multiple paths to explore sparse-quantized
+transfer learning with this model.
+
+### Notebook
+`sparseml/examples/pytorch_sparse_quantized_transfer_learning/pytorch_sparse_quantized_transfer_learning.ipynb`
+is a Jupyter Notebook that provides a step-by-step walk-through for
+ - setting up sparse-quantized transfer learning
+ - integrating SparseML with any PyTorch training flow
+ - ONNX export
+ - benchmarking with the DeepSparse Engine 
+
+Run `jupyter notebook` and navigate to this notebook file to run the example.
+
+### Script
+`sparseml/scripts/pytorch_vision.py` is a script for running tasks related to pruning and
+quantization with SparseML for image classification and object detection use cases.
+Using the following example command, you can run sparse-quantized transfer learning on a custom
+[ImageFolder](https://pytorch.org/vision/0.8/datasets.html#imagefolder) based
+classification dataset.
+
+Note that for datasets other than Imagenette, you may need to edit
+the recipe to better train for the dataset following instructions in the downloaded recipe card.
+
+```
+python scripts/pytorch_vision.py train \
+    --recipe-path zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned_quant-moderate?recipe_type=transfer_learn \
+    --checkpoint-path zoo \
+    --arch-key resnet50 \
+    --model-kwargs '{"ignore_error_tensors": ["classifier.fc.weight", "classifier.fc.bias"]}' \
+    --dataset imagefolder \
+    --dataset-path /PATH/TO/IMAGEFOLDER/DATASET  \
+    --train-batch-size 32 --test-batch-size 64 \
+    --loader-num-workers 8 \
+    --optim Adam \
+    --optim-args '{}' \
+    --model-tag resnet50-imagenette-pruned_quant-transfer_learned
+```
+
+
+### Further Reading
+To learn more about this sparse-quantized ResNet-50 model's benchmarks check out
+[this blog post](https://neuralmagic.com/blog/benchmark-resnet50-with-deepsparse).
+
+For more information on creating sparse and quantized models, check out our
+[documentation](https://neuralmagic.com/contact/).
+
+SparseML and its companion packages are open-source and constantly improving.
+Keep an eye out for new models, algorithms, and faster speeds.
+If you have any questions, extensions, or use cases feel free to,
+[contribute](https://github.com/neuralmagic/sparseml/blob/main/CONTRIBUTING.md),
+[open an issue](https://github.com/neuralmagic/sparseml/issues),
+or [contact us](https://neuralmagic.com/contact/).

notebooks/README.md

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 You may need to enable the Jupyter extension to properly see the UIs with the following command:
 
 ```bash
-jupyter nbextension enable --py widgetsnbextension.
+jupyter nbextension enable --py widgetsnbextension
 ```
 </li>