This is an automated email from the ASF dual-hosted git repository. 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 <45951687+ivyba...@users.noreply.github.com> 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.")