[GitHub] piiswrong closed pull request #8879: fixed broken links on master

[GitBox <gi...@apache.org>]

piiswrong closed pull request #8879: fixed broken links on master
URL: https://github.com/apache/incubator-mxnet/pull/8879
 
 
   

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diff --git a/docs/faq/model_parallel_lstm.md b/docs/faq/model_parallel_lstm.md
index 3aab7d3e0c..4a02288d5e 100644
--- a/docs/faq/model_parallel_lstm.md
+++ b/docs/faq/model_parallel_lstm.md
@@ -25,7 +25,7 @@ for [natural language translation](https://arxiv.org/pdf/1409.0473.pdf), [speech
 and working with [time series data](https://arxiv.org/abs/1511.03677).
 For a general high-level introduction to LSTMs,
 see the excellent [tutorial](http://colah.github.io/posts/2015-08-Understanding-LSTMs/) by Christopher Olah. For a working example of LSTM training with model parallelism,
-see [example/model-parallelism-lstm/](https://github.com/dmlc/mxnet/blob/master/example/model-parallel-lstm/lstm.py).
+see [example/model-parallelism-lstm/](https://github.com/dmlc/mxnet/blob/master/example/model-parallel/lstm/lstm.py).
 
 
 ## Model Parallelism: Using Multiple GPUs As a Pipeline
@@ -44,7 +44,7 @@ This differs significantly from data parallelism.
 Here, there is no contention to update the shared model at the end of each iteration,
 and most of the communication happens when passing intermediate results between GPUs.
 
-In the current implementation, the layers are defined in [lstm_unroll()](https://github.com/dmlc/mxnet/blob/master/example/model-parallel-lstm/lstm.py).
+In the current implementation, the layers are defined in [lstm_unroll()](https://github.com/dmlc/mxnet/blob/master/example/model-parallel/lstm/lstm.py).
 
 ## Workload Partitioning
 
diff --git a/docs/install/index.md b/docs/install/index.md
index dc195fa37f..24d6aeeea1 100644
--- a/docs/install/index.md
+++ b/docs/install/index.md
@@ -1508,7 +1508,7 @@ These commands produce a library called ```mxnet.dll``` in the ```./build/Releas
 
  
 Next, we install ```graphviz``` library that we use for visualizing network graphs you build on MXNet. We will also install [Jupyter Notebook](http://jupyter.readthedocs.io/)  used for running MXNet tutorials and examples.
-- Install ```graphviz``` by downloading MSI installer from [Graphviz Download Page](http://www.graphviz.org/Download_windows.php).
+- Install ```graphviz``` by downloading MSI installer from [Graphviz Download Page](https://graphviz.gitlab.io/_pages/Download/Download_windows.html).
 **Note** Make sure to add graphviz executable path to PATH environment variable. Refer [here for more details](http://stackoverflow.com/questions/35064304/runtimeerror-make-sure-the-graphviz-executables-are-on-your-systems-path-aft)
 
 
diff --git a/docs/install/windows_setup.md b/docs/install/windows_setup.md
index b0d13c3bcc..bf1673ac87 100755
--- a/docs/install/windows_setup.md
+++ b/docs/install/windows_setup.md
@@ -41,7 +41,7 @@ These commands produce a library called ```mxnet.dll``` in the ```./build/Releas
 
  
 Next, we install ```graphviz``` library that we use for visualizing network graphs you build on MXNet. We will also install [Jupyter Notebook](http://jupyter.readthedocs.io/)  used for running MXNet tutorials and examples.
-- Install ```graphviz``` by downloading MSI installer from [Graphviz Download Page](http://www.graphviz.org/Download_windows.php).
+- Install ```graphviz``` by downloading MSI installer from [Graphviz Download Page](https://graphviz.gitlab.io/_pages/Download/Download_windows.html).
 **Note** Make sure to add graphviz executable path to PATH environment variable. Refer [here for more details](http://stackoverflow.com/questions/35064304/runtimeerror-make-sure-the-graphviz-executables-are-on-your-systems-path-aft)
 - Install ```Jupyter``` by installing [Anaconda for Python 2.7](https://www.continuum.io/downloads)
 **Note** Do not install Anaconda for Python 3.5. MXNet has few compatibility issue with Python 3.5.


 

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