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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2018/09/11 23:20:41 UTC
[GitHub] sandeep-krishnamurthy closed pull request #12508: Fix few broken
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sandeep-krishnamurthy closed pull request #12508: Fix few broken URLs
URL: https://github.com/apache/incubator-mxnet/pull/12508
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diff --git a/docs/architecture/rnn_interface.md b/docs/architecture/rnn_interface.md
index 42338763ce6..dc0b6a7958e 100644
--- a/docs/architecture/rnn_interface.md
+++ b/docs/architecture/rnn_interface.md
@@ -1,6 +1,6 @@
# Survey of Existing Interfaces and Implementations
-Commonly used deep learning libraries with good RNN/LSTM support include [Theano](http://deeplearning.net/software/theano/library/scan.html) and its wrappers [Lasagne](http://lasagne.readthedocs.org/en/latest/modules/layers/recurrent.html) and [Keras](http://keras.io/layers/recurrent/); [CNTK](https://cntk.codeplex.com/); [TensorFlow](https://www.tensorflow.org/versions/master/tutorials/recurrent/index.html); and various implementations in Torch, such as [this well-known character-level language model tutorial](https://github.com/karpathy/char-rnn), [this](https://github.com/Element-Research/rnn).
+Commonly used deep learning libraries with good RNN/LSTM support include [Theano](http://deeplearning.net/software/theano/library/scan.html) and its wrappers [Lasagne](http://lasagne.readthedocs.org/en/latest/modules/layers/recurrent.html) and [Keras](http://keras.io/layers/recurrent/); [CNTK](https://cntk.codeplex.com/); [TensorFlow](https://www.tensorflow.org/tutorials/sequences/recurrent); and various implementations in Torch, such as [this well-known character-level language model tutorial](https://github.com/karpathy/char-rnn), [this](https://github.com/Element-Research/rnn).
In this document, we present a comparative analysis of the approaches taken by these libraries.
@@ -93,7 +93,7 @@ The low-level API for recurrent connection seem to be a *delay node*. But I'm no
## TensorFlow
-The [current example of RNNLM](https://www.tensorflow.org/versions/master/tutorials/recurrent/index.html#recurrent-neural-networks) in TensorFlow uses explicit unrolling for a predefined number of time steps. The white-paper mentions that an advanced control flow API (Theano's scan-like) is planned.
+The [current example of RNNLM](https://www.tensorflow.org/tutorials/sequences/recurrent#recurrent-neural-networks) in TensorFlow uses explicit unrolling for a predefined number of time steps. The white-paper mentions that an advanced control flow API (Theano's scan-like) is planned.
## Next Steps
diff --git a/docs/install/index.md b/docs/install/index.md
index 4a6af31cee3..3a697ae20ee 100644
--- a/docs/install/index.md
+++ b/docs/install/index.md
@@ -272,7 +272,7 @@ Follow the four steps in this [docker documentation](https://docs.docker.com/eng
If you skip this step, you need to use *sudo* each time you invoke Docker.
-**Step 3** Install *nvidia-docker-plugin* following the [installation instructions](https://github.com/NVIDIA/nvidia-docker/wiki/Installation). *nvidia-docker-plugin* is required to enable the usage of GPUs from the docker containers.
+**Step 3** Install *nvidia-docker-plugin* following the [installation instructions](https://github.com/NVIDIA/nvidia-docker/wiki). *nvidia-docker-plugin* is required to enable the usage of GPUs from the docker containers.
**Step 4** Pull the MXNet docker image.
diff --git a/docs/install/windows_setup.md b/docs/install/windows_setup.md
index 99ce7f63e85..c974eeb858b 100755
--- a/docs/install/windows_setup.md
+++ b/docs/install/windows_setup.md
@@ -55,7 +55,7 @@ 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](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)
+- Install ```Jupyter``` by installing [Anaconda for Python 2.7](https://www.anaconda.com/download/)
**Note** Do not install Anaconda for Python 3.5. MXNet has few compatibility issue with Python 3.5.
@@ -69,7 +69,7 @@ We have installed MXNet core library. Next, we will install MXNet interface pack
## Install MXNet for Python
1. Install ```Python``` using windows installer available [here](https://www.python.org/downloads/release/python-2712/).
-2. Install ```Numpy``` using windows installer available [here](http://scipy.org/install.html).
+2. Install ```Numpy``` using windows installer available [here](https://scipy.org/index.html).
3. Next, we install Python package interface for MXNet. You can find the Python interface package for [MXNet on GitHub](https://github.com/dmlc/mxnet/tree/master/python/mxnet).
```bash
diff --git a/docs/tutorials/onnx/export_mxnet_to_onnx.md b/docs/tutorials/onnx/export_mxnet_to_onnx.md
index a9c03bed8b1..dc34bd520b4 100644
--- a/docs/tutorials/onnx/export_mxnet_to_onnx.md
+++ b/docs/tutorials/onnx/export_mxnet_to_onnx.md
@@ -55,7 +55,7 @@ Help on function export_model in module mxnet.contrib.onnx.mx2onnx.export_model:
export_model(sym, params, input_shape, input_type=<type 'numpy.float32'>, onnx_file_path=u'model.onnx', verbose=False)
Exports the MXNet model file, passed as a parameter, into ONNX model.
Accepts both symbol,parameter objects as well as json and params filepaths as input.
- Operator support and coverage - https://cwiki.apache.org/confluence/display/MXNET/ONNX
+ Operator support and coverage - https://cwiki.apache.org/confluence/display/MXNET/MXNet-ONNX+Integration
Parameters
----------
diff --git a/python/mxnet/contrib/onnx/mx2onnx/export_model.py b/python/mxnet/contrib/onnx/mx2onnx/export_model.py
index 33292bf664a..e5158051d6f 100644
--- a/python/mxnet/contrib/onnx/mx2onnx/export_model.py
+++ b/python/mxnet/contrib/onnx/mx2onnx/export_model.py
@@ -36,7 +36,8 @@ def export_model(sym, params, input_shape, input_type=np.float32,
onnx_file_path='model.onnx', verbose=False):
"""Exports the MXNet model file, passed as a parameter, into ONNX model.
Accepts both symbol,parameter objects as well as json and params filepaths as input.
- Operator support and coverage - https://cwiki.apache.org/confluence/display/MXNET/ONNX
+ Operator support and coverage -
+ https://cwiki.apache.org/confluence/display/MXNET/MXNet-ONNX+Integration
Parameters
----------
diff --git a/python/mxnet/contrib/onnx/onnx2mx/import_model.py b/python/mxnet/contrib/onnx/onnx2mx/import_model.py
index e190c3bdadc..b8d3bf28ee2 100644
--- a/python/mxnet/contrib/onnx/onnx2mx/import_model.py
+++ b/python/mxnet/contrib/onnx/onnx2mx/import_model.py
@@ -23,7 +23,8 @@
def import_model(model_file):
"""Imports the ONNX model file, passed as a parameter, into MXNet symbol and parameters.
- Operator support and coverage - https://cwiki.apache.org/confluence/display/MXNET/ONNX
+ Operator support and coverage -
+ https://cwiki.apache.org/confluence/display/MXNET/MXNet-ONNX+Integration
Parameters
----------
diff --git a/python/mxnet/contrib/text/embedding.py b/python/mxnet/contrib/text/embedding.py
index 38defb4b90b..277f7822292 100644
--- a/python/mxnet/contrib/text/embedding.py
+++ b/python/mxnet/contrib/text/embedding.py
@@ -490,7 +490,7 @@ class GloVe(_TokenEmbedding):
License for pre-trained embeddings:
- https://opendatacommons.org/licenses/pddl/
+ https://fedoraproject.org/wiki/Licensing/PDDL
Parameters
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