DMLC link removal #15708
DMLC link removal #15708
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@mxnet-label-bot Add [Doc] |
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I'm a little concerned about that ! missing in the comment... might break the page.
If the nin example is now broken because of the missing model... can you put in a big bold thing at the top that says this example is broken, and link to an issue (that you create). If this were a tutorial, we'd have to remove it or exclude it from tests, but examples are not tested. Either way, we should have an issue and a notification present.
tools/coreml/README.md
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| @@ -1,4 +1,4 @@ | |||
| <!--- Licensed to the Apache Software Foundation (ASF) under one --> | |||
| <--- Licensed to the Apache Software Foundation (ASF) under one --> | |||
| @@ -17,7 +17,7 @@ | |||
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| # End to End Neural Art | |||
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| 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](#) for details of how it is implemented. | |||
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Maybe use this one? https://thomasdelteil.github.io/NeuralStyleTransfer_MXNet/
| @@ -50,7 +50,7 @@ This approach works with variable length sequences. For more complicated models | |||
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| ## Variable-length Sequence Training for Sherlock Holmes | |||
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| 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. | |||
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please only use relative links and not versioned ones
| @@ -151,8 +151,8 @@ def test_pred_vgg16(self): | |||
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| 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=["", | |||
| @@ -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" | |||
| ``` | |||
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| 2. [NiN](http://data.dmlc.ml/models/imagenet/nin/) | |||
| 2. [NiN](#) | |||
| @@ -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](#). | |||
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@marcoabreu Sorry about rushing that through without addressing your questions. We needed to be rid of the malware links ASAP. |
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@marcoabreu I removed those links because I couldn't find any replacement for the content that was hosted on dmlc.ml. It seems better to temporarily have replaced the links with a #, while waiting for that content to be recovered and rehosted, rather than having malware links still active on the site. The current experience is that the links redirect to the current page. |
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Issues related to missing content: |
Replaced v1.5.x Julia page with the content on master. Also, replaced dmlc.ml links with available content.