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Commit 4103bb44 authored by Neta Zmora's avatar Neta Zmora
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Fix a bunch of broken links 

Some links have changed with the latest version of mkdocs.
This closes issues #280 and #65 (reopened).
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README.md
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......@@ -17,11 +17,11 @@
Getting Started
</a>
<span> | </span>
<a href="https://nervanasystems.github.io/distiller/algo_pruning/index.html">
<a href="https://nervanasystems.github.io/distiller/algo_pruning.html">
Algorithms
</a>
<span> | </span>
<a href="https://nervanasystems.github.io/distiller/design/index.html">
<a href="https://nervanasystems.github.io/distiller/design.html">
Design
</a>
<span> | </span>
......@@ -158,16 +158,16 @@ Beware.
* **Quantization**
- Automatic mechanism to transform existing models to quantized versions, with customizable bit-width configuration for different layers. No need to re-write the model for different quantization methods.
- Post-training quantization of trained full-precision models, dynamic and static (statistics-based)
- Support for [quantization-aware training](https://nervanasystems.github.io/distiller/quantization/index.html#quantization-aware-training) in the loop
- Support for [quantization-aware training](https://nervanasystems.github.io/distiller/algo_quantization.html#quantization-aware-training) in the loop
* **Knowledge distillation**
- Training with [knowledge distillation](https://nervanasystems.github.io/distiller/knowledge_distillation/index.html), in conjunction with the other available pruning / regularization / quantization methods.
- Training with [knowledge distillation](https://nervanasystems.github.io/distiller/knowledge_distillation.html), in conjunction with the other available pruning / regularization / quantization methods.
* **Conditional computation**
- Sample implementation of Early Exit, with more to come
* Export statistics summaries using Pandas dataframes, which makes it easy to slice, query, display and graph the data.
* A set of [Jupyter notebooks](https://nervanasystems.github.io/distiller/jupyter/index.html) to plan experiments and analyze compression results. The graphs and visualizations you see on this page originate from the included Jupyter notebooks.
+ Take a look at [this notebook](https://github.com/NervanaSystems/distiller/blob/master/jupyter/alexnet_insights.ipynb), which compares visual aspects of dense and sparse Alexnet models.
+ [This notebook](https://github.com/NervanaSystems/distiller/blob/master/jupyter/model_summary.ipynb) creates performance indicator graphs from model data.
* Sample implementations of published research papers, using library-provided building blocks. See the research papers discussions in our [model-zoo](https://nervanasystems.github.io/distiller/model_zoo/index.html).
* Sample implementations of published research papers, using library-provided building blocks. See the research papers discussions in our [model-zoo](https://nervanasystems.github.io/distiller/model_zoo.html).
* Logging to the console, text file and TensorBoard-formatted file.
* Export to **ONNX** (export of quantized models pending ONNX standardization)
......@@ -242,10 +242,11 @@ We'll show you how to use it for some simple use-cases, and will point you to so
For more details, there are some other resources you can refer to:
+ [Frequently-asked questions (FAQ)](https://github.com/NervanaSystems/distiller/wiki/Frequently-Asked-Questions-(FAQ))
+ [Model zoo](https://nervanasystems.github.io/distiller/model_zoo/index.html)
+ [Compression scheduling](https://nervanasystems.github.io/distiller/schedule/index.html)
+ [Usage](https://nervanasystems.github.io/usage/index.html)
+ [Tutorial: Using Distiller to prune a PyTorch language model](https://github.com/NervanaSystems/distiller/wiki/Tutorial:-Using-Distiller-to-prune-a-PyTorch-language-model)
+ [Model zoo](https://nervanasystems.github.io/distiller/model_zoo.html)
+ [Compression scheduling](https://nervanasystems.github.io/distiller/schedule.html)
+ [Usage](https://nervanasystems.github.io/distiller/usage.html)
+ [Tutorial: Using Distiller to prune a PyTorch language model](https://nervanasystems.github.io/distiller/tutorial-lang_model.html)
+ [Tutorial: Pruning Filters & Channels](https://nervanasystems.github.io/distiller/tutorial-struct_pruning.html)
### Example invocations of the sample application
+ [Training-only](#training-only)
......@@ -298,7 +299,7 @@ This example performs 8-bit quantization of ResNet20 for CIFAR10. We've include
$ python3 compress_classifier.py -a resnet20_cifar ../../../data.cifar10 --resume ../ssl/checkpoints/checkpoint_trained_dense.pth.tar --quantize-eval --evaluate
```
The command-line above will save a checkpoint named `quantized_checkpoint.pth.tar` containing the quantized model parameters. See more examples [here](https://github.com/NervanaSystems/distiller/blob/master/examples/quantization/post_training_quant.md).
The command-line above will save a checkpoint named `quantized_checkpoint.pth.tar` containing the quantized model parameters. See more examples [here](https://github.com/NervanaSystems/distiller/blob/master/examples/quantization/post_train_quant/command_line.md).
### Explore the sample Jupyter notebooks
The set of notebooks that come with Distiller is described [here](https://nervanasystems.github.io/distiller/jupyter/index.html#using-the-distiller-notebooks), which also explains the steps to install the Jupyter notebook server.<br>
......@@ -414,7 +415,7 @@ If you used Distiller for your work, please use the following citation:
Any published work is built on top of the work of many other people, and the credit belongs to too many people to list here.
* The Python and PyTorch developer communities have shared many invaluable insights, examples and ideas on the Web.
* The authors of the research papers implemented in the [Distiller model-zoo](https://nervanasystems.github.io/distiller/model_zoo/index.html) have shared their research ideas, theoretical background and results.
* The authors of the research papers implemented in the [Distiller model-zoo](https://nervanasystems.github.io/distiller/model_zoo.html) have shared their research ideas, theoretical background and results.
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docs-src/docs/usage.md
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......@@ -4,8 +4,8 @@ The Distiller repository contains a sample application, ```distiller/examples/cl
You might also want to refer to the following resources:
* An [explanation](https://nervanasystems.github.io/distiller/schedule/index.html) of the scheduler file format.
* An in-depth [discussion](https://nervanasystems.github.io/distiller/model_zoo/index.html) of how we used these schedule files to implement several state-of-the-art DNN compression research papers.
* An [explanation](https://nervanasystems.github.io/distiller/schedule.html) of the scheduler file format.
* An in-depth [discussion](https://nervanasystems.github.io/distiller/model_zoo.html) of how we used these schedule files to implement several state-of-the-art DNN compression research papers.
The sample application supports various features for compression of image classification DNNs, and gives an example of how to integrate distiller in your own application. The code is documented and should be considered the best source of documentation, but we provide some elaboration here.
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docs/index.html
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<!--
MkDocs version : 1.0.4
Build Date UTC : 2019-05-19 08:14:18
Build Date UTC : 2019-06-10 19:22:03
-->
docs/sitemap.xml
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docs/sitemap.xml.gz
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docs/usage.html
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<p>The Distiller repository contains a sample application, <code>distiller/examples/classifier_compression/compress_classifier.py</code>, and a set of scheduling files which demonstrate Distiller's features. Following is a brief discussion of how to use this application and the accompanying schedules.</p>
<p>You might also want to refer to the following resources:</p>
<ul>
<li>An <a href="https://nervanasystems.github.io/distiller/schedule/index.html">explanation</a> of the scheduler file format.</li>
<li>An in-depth <a href="https://nervanasystems.github.io/distiller/model_zoo/index.html">discussion</a> of how we used these schedule files to implement several state-of-the-art DNN compression research papers.</li>
<li>An <a href="https://nervanasystems.github.io/distiller/schedule.html">explanation</a> of the scheduler file format.</li>
<li>An in-depth <a href="https://nervanasystems.github.io/distiller/model_zoo.html">discussion</a> of how we used these schedule files to implement several state-of-the-art DNN compression research papers.</li>
</ul>
<p>The sample application supports various features for compression of image classification DNNs, and gives an example of how to integrate distiller in your own application. The code is documented and should be considered the best source of documentation, but we provide some elaboration here.</p>
<p>This diagram shows how where <code>compress_classifier.py</code> fits in the compression workflow, and how we integrate the Jupyter notebooks as part of our research work.
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