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aaronmarkham pushed a commit to branch v1.5.x
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git
The following commit(s) were added to refs/heads/v1.5.x by this push:
new 0a3413f DMLC link removal (#15708)
0a3413f is described below
commit 0a3413fd09913faa273e9c7f36356f88a11fc2aa
Author: IvyBazan <[email protected]>
AuthorDate: Mon Aug 5 12:36:59 2019 -0700
DMLC link removal (#15708)
* replaced julia page and remaining dmlc.ml links
* Update README.md
* Update index.md
* Update README.md
* Update README.md
---
docs/api/julia/index.md | 11 ++++++-----
docs/faq/bucketing.md | 2 +-
docs/install/osx_setup.md | 2 +-
docs/install/ubuntu_setup.md | 2 +-
docs/install/windows_setup.md | 2 +-
docs/tutorials/scala/char_lstm.md | 2 +-
docs/tutorials/tensorrt/inference_with_trt.md | 2 +-
example/neural-style/end_to_end/README.md | 2 +-
tools/coreml/README.md | 2 +-
tools/coreml/test/test_mxnet_models.py | 4 ++--
10 files changed, 16 insertions(+), 15 deletions(-)
diff --git a/docs/api/julia/index.md b/docs/api/julia/index.md
index 8aa884e..20911b4 100644
--- a/docs/api/julia/index.md
+++ b/docs/api/julia/index.md
@@ -17,7 +17,7 @@
# MXNet - Julia API
-See the [MXNet Julia Reference
Manual](https://media.readthedocs.org/pdf/mxnet-test/latest/mxnet-test.pdf).
+See the [MXNet Julia Site](site/index.html) for examples and API reference
docs.
MXNet supports the Julia programming language. The MXNet Julia package brings
flexible and efficient GPU
computing and the state-of-art deep learning to Julia.
@@ -26,8 +26,9 @@ computing and the state-of-art deep learning to Julia.
- It also enables you to construct and customize the state-of-art deep
learning models in Julia,
and apply them to tasks such as image classification and data science
challenges.
+## Installation
+* [Ubuntu installation guide](../../install/ubuntu_setup.html)
+* Mac / Windows guides are not available (contributions welcome!)
-
-
-## Julia API Reference
-Julia documents are available at
[http://dmlc.ml/MXNet.jl/latest/](http://dmlc.ml/MXNet.jl/latest/).
+## Docs
+To build your own copy of the [MXNet Julia Site](site/index.html), run `make
-C julia/docs` from the MXNet source root directory. You can also generate it
with Docker by using `dev_menu.py` from the root directory and choosing to
build the entire website. The Julia site will be located in `api/julia/site/`.
diff --git a/docs/faq/bucketing.md b/docs/faq/bucketing.md
index b5fb987..e73a898 100644
--- a/docs/faq/bucketing.md
+++ b/docs/faq/bucketing.md
@@ -50,7 +50,7 @@ This approach works with variable length sequences. For more
complicated models
## Variable-length Sequence Training for Sherlock Holmes
-We use the [Sherlock Holmes language model
example](https://github.com/dmlc/mxnet/tree/master/example/rnn) for this
example. If you are not familiar with this example, see [this tutorial (in
Julia)](http://dmlc.ml/mxnet/2015/11/15/char-lstm-in-julia.html) first.
+We use the [Sherlock Holmes language model
example](https://github.com/dmlc/mxnet/tree/master/example/rnn) for this
example. If you are not familiar with this example, see [this tutorial (in
Julia)](https://mxnet.incubator.apache.org/versions/master/api/julia/site/tutorial/char-lstm/)
first.
In this example, we use a simple architecture
consisting of a word-embedding layer
diff --git a/docs/install/osx_setup.md b/docs/install/osx_setup.md
index 6d38c46..f32780f 100644
--- a/docs/install/osx_setup.md
+++ b/docs/install/osx_setup.md
@@ -217,7 +217,7 @@ You might want to add this command to your ```~/.bashrc```
file. If you do, you
Pkg.add("MXNet")
```
-For more details about installing and using MXNet with Julia, see the [MXNet
Julia documentation](http://dmlc.ml/MXNet.jl/latest/user-guide/install/).
+For more details about installing and using MXNet with Julia, see the [MXNet
Julia
documentation](https://mxnet.incubator.apache.org/versions/master/api/julia/site/user-guide/install/).
## Install the MXNet Package for Scala
diff --git a/docs/install/ubuntu_setup.md b/docs/install/ubuntu_setup.md
index ef700b4..9540e76 100644
--- a/docs/install/ubuntu_setup.md
+++ b/docs/install/ubuntu_setup.md
@@ -328,7 +328,7 @@ You might want to add this command to your ```~/.bashrc```
file. If you do, you
Pkg.add("MXNet")
```
-For more details about installing and using MXNet with Julia, see the [MXNet
Julia documentation](http://dmlc.ml/MXNet.jl/latest/user-guide/install/).
+For more details about installing and using MXNet with Julia, see the [MXNet
Julia
documentation](https://mxnet.incubator.apache.org/versions/master/api/julia/site/user-guide/install/).
<hr>
diff --git a/docs/install/windows_setup.md b/docs/install/windows_setup.md
index f256148..56128ac 100644
--- a/docs/install/windows_setup.md
+++ b/docs/install/windows_setup.md
@@ -494,7 +494,7 @@ You might want to add this command to your ```~/.bashrc```
file. If you do, you
Pkg.add("MXNet")
```
-For more details about installing and using MXNet with Julia, see the [MXNet
Julia documentation](http://dmlc.ml/MXNet.jl/latest/user-guide/install/).
+For more details about installing and using MXNet with Julia, see the [MXNet
Julia
documentation](https://mxnet.incubator.apache.org/versions/master/api/julia/site/user-guide/install/).
## Installing the MXNet Package for Scala
diff --git a/docs/tutorials/scala/char_lstm.md
b/docs/tutorials/scala/char_lstm.md
index aca08dc..4078196 100644
--- a/docs/tutorials/scala/char_lstm.md
+++ b/docs/tutorials/scala/char_lstm.md
@@ -21,7 +21,7 @@ This tutorial shows how to train a character-level language
model with a multila
There are many documents that explain LSTM concepts. If you aren't familiar
with LSTM, refer to the following before you proceed:
- Christopher Olah's [Understanding LSTM blog
post](http://colah.github.io/posts/2015-08-Understanding-LSTMs/)
-- [Training a LSTM char-rnn in Julia to Generate Random
Sentences](http://dmlc.ml/mxnet/2015/11/15/char-lstm-in-julia.html)
+- [Training a LSTM char-rnn in Julia to Generate Random
Sentences](https://mxnet.incubator.apache.org/versions/master/api/julia/site/tutorial/char-lstm/)
- [Bucketing in MXNet in
Python](https://github.com/dmlc/mxnet-notebooks/blob/master/python/tutorials/char_lstm.ipynb)
- [Bucketing in MXNet](http://mxnet.io/faq/bucketing.html)
diff --git a/docs/tutorials/tensorrt/inference_with_trt.md
b/docs/tutorials/tensorrt/inference_with_trt.md
index 409c96e..02bfd84 100644
--- a/docs/tutorials/tensorrt/inference_with_trt.md
+++ b/docs/tutorials/tensorrt/inference_with_trt.md
@@ -118,7 +118,7 @@ for i in range(0, 10000):
end = time.time()
print(time.process_time() - start)
```
-We run timing with a warmup once more, and on the same machine, run in
**18.99s**. A 1.8x speed improvement! Speed improvements when using libraries
like TensorRT can come from a variety of optimizations, but in this case our
speedups are coming from a technique known as [operator
fusion](http://dmlc.ml/2016/11/21/fusion-and-runtime-compilation-for-nnvm-and-tinyflow.html).
+We run timing with a warmup once more, and on the same machine, run in
**18.99s**. A 1.8x speed improvement! Speed improvements when using libraries
like TensorRT can come from a variety of optimizations, but in this case our
speedups are coming from a technique known as [operator fusion](#).
## Operators and Subgraph Fusion
diff --git a/example/neural-style/end_to_end/README.md
b/example/neural-style/end_to_end/README.md
index 209d98d..68d632d 100644
--- a/example/neural-style/end_to_end/README.md
+++ b/example/neural-style/end_to_end/README.md
@@ -17,7 +17,7 @@
# End to End Neural Art
-Please refer to this
[blog](http://dmlc.ml/mxnet/2016/06/20/end-to-end-neural-style.html) for
details of how it is implemented.
+Please refer to this
[blog](https://thomasdelteil.github.io/NeuralStyleTransfer_MXNet/) for details
of how it is implemented.
## How to use
diff --git a/tools/coreml/README.md b/tools/coreml/README.md
index 31982ba..00fc120 100644
--- a/tools/coreml/README.md
+++ b/tools/coreml/README.md
@@ -100,7 +100,7 @@ Any MXNet model that uses the above operators can be
converted easily. For insta
mxnet_coreml_converter.py --model-prefix='Inception-BN' --epoch=126
--input-shape='{"data":"3,224,224"}' --mode=classifier
--pre-processing-arguments='{"image_input_names":"data"}' --class-labels
synset.txt --output-file="InceptionBN.mlmodel"
```
-2. [NiN](http://data.dmlc.ml/models/imagenet/nin/)
+2. [NiN](#)
```bash
mxnet_coreml_converter.py --model-prefix='nin' --epoch=0
--input-shape='{"data":"3,224,224"}' --mode=classifier
--pre-processing-arguments='{"image_input_names":"data"}' --class-labels
synset.txt --output-file="nin.mlmodel"
diff --git a/tools/coreml/test/test_mxnet_models.py
b/tools/coreml/test/test_mxnet_models.py
index 8dd319a..d3080d6 100644
--- a/tools/coreml/test/test_mxnet_models.py
+++ b/tools/coreml/test/test_mxnet_models.py
@@ -151,8 +151,8 @@ class ModelsTest(unittest.TestCase):
def test_pred_nin(self):
self._test_model(model_name='nin', epoch_num=0,
-
files=["http://data.dmlc.ml/models/imagenet/nin/nin-symbol.json";,
-
"http://data.dmlc.ml/models/imagenet/nin/nin-0000.params";])
+ files=["",
+ ""])
@unittest.skip("You need to download and unzip file: "
"http://data.mxnet.io/models/imagenet/inception-v3.tar.gz
in order to run this test.")
