checking broken link fixes work

docs/python_docs/python/tutorials/deploy/export/onnx.md

@@ -44,7 +44,7 @@ logging.basicConfig(level=logging.INFO)
 
 ## Downloading a model from the MXNet model zoo
 
-We download the pre-trained ResNet-18 [ImageNet](http://www.image-net.org/) model from the [MXNet Model Zoo](http://data.mxnet.io/models/imagenet/).
+We download the pre-trained ResNet-18 [ImageNet](http://www.image-net.org/) model from the [MXNet Model Zoo](/api/python/docs/api/gluon/model_zoo/index.html).
 We will also download synset file to match labels.
 
 ```python

docs/python_docs/python/tutorials/performance/backend/mkldnn/mkldnn_readme.md

@@ -20,7 +20,7 @@
 A better training and inference performance is expected to be achieved on Intel-Architecture CPUs with MXNet built with [Intel MKL-DNN](https://github.com/intel/mkl-dnn) on multiple operating system, including Linux, Windows and MacOS.
 In the following sections, you will find build instructions for MXNet with Intel MKL-DNN on Linux, MacOS and Windows.
 
-Please find MKL-DNN optimized operators and other features in the [MKL-DNN operator list](../mkldnn/operator_list.md).
+Please find MKL-DNN optimized operators and other features in the [MKL-DNN operator list](https://github.com/apache/incubator-mxnet/blob/v1.5.x/docs/tutorials/mkldnn/operator_list.md).
 
 The detailed performance data collected on Intel Xeon CPU with MXNet built with Intel MKL-DNN can be found [here](https://mxnet.apache.org/api/faq/perf#intel-cpu).
 

docs/python_docs/themes/mx-theme/mxtheme/footer.html

@@ -10,7 +10,7 @@ <h4 class="footer-category-title">Resources</h4>
                     <li><a href="https://issues.apache.org/jira/projects/MXNET/issues">Jira Tracker</a></li>
                     <li><a href="https://github.com/apache/incubator-mxnet/labels/Roadmap">Github Roadmap</a></li>
                     <li><a href="https://discuss.mxnet.io">MXNet Discuss forum</a></li>
-                    <li><a href="/mxnet.io-v2/community/contribute">Contribute To MXNet</a></li>
+                    <li><a href="/community/contribute">Contribute To MXNet</a></li>
 
                 </ul>
             </div>
@@ -43,4 +43,4 @@ <h4 class="footer-category-title">Resources</h4>
             </div>
         </div>
     </div>
-</footer>
+</footer>

docs/static_site/src/_includes/get_started/linux/cpp/cpp.md

@@ -1,4 +1,4 @@
 To enable the C++ package, build from source using `make USE_CPP_PACKAGE=1`.
-Refer to the [MXNet C++ setup guide](get_started/c_plus_plus)
+Refer to the [MXNet C++ setup guide](/get_started/cpp_setup.html)
 for full instructions.
 

docs/static_site/src/_includes/get_started/macos/cpp/cpp.md

@@ -1,3 +1,3 @@
 To enable the C++ package, build from source using `make USE_CPP_PACKAGE=1`.
-Refer to the [MXNet C++ setup guide](get_started/c_plus_plus) for full instructions.
+Refer to the [MXNet C++ setup guide](/get_started/cpp_setup.html) for full instructions.
 

docs/static_site/src/_includes/get_started/macos/scala/cpu.md

@@ -1,5 +1,5 @@
 You can use the Maven packages defined in the following dependency to include MXNet in your Scala
-project. Please refer to the [MXNet-Scala setup guide](scala_setup.html) for a detailed set
+project. Please refer to the [MXNet-Scala setup guide](/get_started/scala_setup.html) for a detailed set
 of instructions to help you with the setup process.
 
 <a href="https://mvnrepository.com/artifact/org.apache.mxnet/mxnet-full_2.11-osx-x86_64-cpu"><img

docs/static_site/src/_includes/get_started/windows/cpp/cpp.md

@@ -1,3 +1,3 @@
 To enable the C++ package, build from source using `make USE_CPP_PACKAGE=1`.
-Refer to the [MXNet C++ setup guide](get_started/c_plus_plus) for full instructions.
-
+Refer to the [MXNet C++ setup guide](/get_started/cpp_setup.html) for full instructions.
+

docs/static_site/src/pages/api/faq/env_var.md

@@ -317,7 +317,7 @@ If ctypes is used, it must be `mxnet._ctypes.ndarray.NDArrayBase`.
 * MXNET_SUBGRAPH_BACKEND
   - Values: String ```(default="MKLDNN")``` if MKLDNN is avaliable, otherwise ```(default="")```
   - This variable controls the subgraph partitioning in MXNet.
-  - This variable is used to perform MKL-DNN FP32 operator fusion and quantization. Please refer to the [MKL-DNN operator list](../tutorials/mkldnn/operator_list.md) for how this variable is used and the list of fusion passes.
+  - This variable is used to perform MKL-DNN FP32 operator fusion and quantization. Please refer to the [MKL-DNN operator list](https://github.com/apache/incubator-mxnet/blob/v1.5.x/docs/tutorials/mkldnn/operator_list.md) for how this variable is used and the list of fusion passes.
   - Set ```MXNET_SUBGRAPH_BACKEND=NONE``` to disable subgraph backend.
 
 * MXNET_SAFE_ACCUMULATION

docs/static_site/src/pages/features.html

@@ -16,7 +16,7 @@ <h3 class="feature-title">Hybrid Front-End</h3>
         <p class="feature-paragraph">The Gluon Python API lets you use MXNet in a fully imperative manner. It also
             allows you to simply switch to
             symbolic mode by calling the <a
-                    href="{{'/api/python/docs/tutorials/packages/gluon/hybridize' | relative_url}}">hybridize</a>
+                    href="/api/python/docs/tutorials/packages/gluon/blocks/hybridize.html">hybridize</a>
             functionality. The symbolic execution provides faster and more optimized
             execution as well as the ability to export the network for inference in different language bindings like
             java or C++.

docs/static_site/src/pages/get_started/windows_setup.md

@@ -74,7 +74,7 @@ Install MXNet with CPU support with Python:
 pip install mxnet
 ```
 
-Now [validate your MXNet installation with Python](get_started/validate_mxnet).
+Now [validate your MXNet installation with Python](/get_started/validate_mxnet).
 
 ### Install with Intel CPUs
 
@@ -88,7 +88,7 @@ The following steps will setup MXNet with MKL. MKL-DNN can be enabled only when
 pip install mxnet-mkl
 ```
 
-Now [validate your MXNet installation with Python](validate_mxnet).
+Now [validate your MXNet installation with Python](/get_started/validate_mxnet).
 
 ### Install with NVIDIA GPUs
 

example/gluon/word_language_model/README.md

@@ -29,7 +29,7 @@ The following techniques have been adopted for SOTA results:
 
 ### Wiki Text
 
-The wikitext-2 data is from [(The wikitext long term dependency language modeling dataset)](https://www.salesforce.com/products/einstein/ai-research/the-wikitext-dependency-language-modeling-dataset/). The training script automatically loads the dataset into `$PWD/data`.
+The wikitext-2 data is from [(The wikitext long term dependency language modeling dataset)](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/). The training script automatically loads the dataset into `$PWD/data`.
 
 
 ## Usage

python/mxnet/gluon/block.py

@@ -378,7 +378,7 @@ def save_parameters(self, filename):
         References
         ----------
         `Saving and Loading Gluon Models \
-        <https://mxnet.incubator.apache.org/tutorials/gluon/save_load_params.html>`_
+        <https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html>`_
         """
         params = self._collect_params_with_prefix()
         arg_dict = {key : val._reduce() for key, val in params.items()}
@@ -430,7 +430,7 @@ def load_parameters(self, filename, ctx=None, allow_missing=False,
         References
         ----------
         `Saving and Loading Gluon Models \
-        <https://mxnet.incubator.apache.org/tutorials/gluon/save_load_params.html>`_
+        <https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html>`_
         """
         if is_np_array():
             # failure may happen when loading parameters saved as NDArrays within

python/mxnet/gluon/contrib/data/text.py

@@ -106,7 +106,7 @@ class WikiText2(_WikiText):
     """WikiText-2 word-level dataset for language modeling, from Salesforce research.
 
     From
-    https://einstein.ai/research/the-wikitext-long-term-dependency-language-modeling-dataset
+    https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/
 
     License: Creative Commons Attribution-ShareAlike
 
@@ -144,7 +144,7 @@ class WikiText103(_WikiText):
     """WikiText-103 word-level dataset for language modeling, from Salesforce research.
 
     From
-    https://einstein.ai/research/the-wikitext-long-term-dependency-language-modeling-dataset
+    https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/
 
     License: Creative Commons Attribution-ShareAlike
 

python/mxnet/gluon/trainer.py

@@ -43,7 +43,7 @@ class Trainer(object):
         The set of parameters to optimize.
     optimizer : str or Optimizer
         The optimizer to use. See
-        `help <https://mxnet.incubator.apache.org/api/python/tutorials/packages/optimizer/optimizer.html>`_
+        `help <https://mxnet.apache.org/api/python/docs/api/optimizer/index.html#mxnet.optimizer.Optimizer>`_
         on Optimizer for a list of available optimizers.
     optimizer_params : dict
         Key-word arguments to be passed to optimizer constructor. For example,