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new 3d83c89 Fix broken URLs (#12508)
3d83c89 is described below
commit 3d83c896fd8b237c53003888e35a4d792c1e5389
Author: Sandeep Krishnamurthy <[email protected]>
AuthorDate: Tue Sep 11 16:20:40 2018 -0700
Fix broken URLs (#12508)
---
docs/architecture/rnn_interface.md | 4 ++--
docs/install/index.md | 2 +-
docs/install/windows_setup.md | 4 ++--
docs/tutorials/onnx/export_mxnet_to_onnx.md | 2 +-
python/mxnet/contrib/onnx/mx2onnx/export_model.py | 3 ++-
python/mxnet/contrib/onnx/onnx2mx/import_model.py | 3 ++-
python/mxnet/contrib/text/embedding.py | 2 +-
7 files changed, 11 insertions(+), 9 deletions(-)
diff --git a/docs/architecture/rnn_interface.md
b/docs/architecture/rnn_interface.md
index 4233876..dc0b6a7 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
[...]
+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 tu [...]
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 4a6af31..3a697ae 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 99ce7f6..c974eeb 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 a9c03be..dc34bd5 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 33292bf..e515805 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 e190c3b..b8d3bf2 100644
--- a/python/mxnet/contrib/onnx/onnx2mx/import_model.py
+++ b/python/mxnet/contrib/onnx/onnx2mx/import_model.py
@@ -23,7 +23,8 @@ from .import_onnx import GraphProto
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 38defb4..277f782 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
