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Fixing broken links #16500
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Fixing broken links #16500
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fix broken links fix more broken links
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aaronmarkham
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Oct 16, 2019
aaronmarkham
commented
Oct 16, 2019
| @@ -91,7 +91,7 @@ In the case of distributed training though, we would need to divide the dataset | |||
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| Typically, this split of data for each worker happens through the data iterator, | |||
| on passing the number of parts and the index of parts to iterate over. | |||
| Some iterators in MXNet that support this feature are [mxnet.io.MNISTIterator]({{'/api/python/docs/api/gluon-related/_autogen/mxnet.io.MNISTIter.html#mxnet.io.MNISTIter'|relative_url}}) and [mxnet.io.ImageRecordIter]({{'/api/python/docs/api/gluon-related/_autogen/mxnet.io.ImageRecordIter.html#mxnet.io.ImageRecordIter'|relative_url}}). | |||
| Some iterators in MXNet that support this feature are [mxnet.io.MNISTIterator]({{'//api/mxnet/io/index.html#mxnet.io.MNISTIter'|relative_url}}) and [mxnet.io.ImageRecordIter]({{'/api/mxnet/io/index.html#mxnet.io.ImageRecordIter'|relative_url}}). | |||
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@ThomasDelteil what are these fancy urls all about? In what context should they be used?
aaronmarkham
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Oct 16, 2019
docs/python_docs/python/tutorials/packages/onnx/super_resolution.md
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Oct 16, 2019
docs/python_docs/python/tutorials/packages/onnx/fine_tuning_gluon.md
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Oct 16, 2019
docs/python_docs/python/tutorials/packages/onnx/inference_on_onnx_model.md
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removed request for
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@IvyBazan @TEChopra1000 - can you review? |
TEChopra1000
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Oct 16, 2019
docs/python_docs/python/tutorials/packages/gluon/blocks/hybridize.md
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docs/python_docs/python/tutorials/packages/gluon/blocks/hybridize.md
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docs/python_docs/python/tutorials/packages/ndarray/gotchas_numpy_in_mxnet.md
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Co-Authored-By: Talia <[email protected]>
| @@ -28,7 +28,7 @@ In this tutorial, we will learn how to use MXNet to ONNX exporter on pre-trained | |||
| ## Prerequisites | |||
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| To run the tutorial you will need to have installed the following python modules: | |||
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| To run the tutorial you will need to have installed the following python modules: | |
| To run the tutorial, install the following Python modules: |
| @@ -28,7 +28,7 @@ In this tutorial, we will learn how to use MXNet to ONNX exporter on pre-trained | |||
| ## Prerequisites | |||
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| To run the tutorial you will need to have installed the following python modules: | |||
| - [MXNet >= 1.3.0](http://mxnet.apache.org/install/index.html) | |||
| - [MXNet >= 1.3.0](/get_started) | |||
| - [onnx]( https://github.com/onnx/onnx#installation) v1.2.1 (follow the install guide) | |||
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| *Note:* MXNet-ONNX importer and exporter follows version 7 of ONNX operator set which comes with ONNX v1.2.1. | |||
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| *Note:* MXNet-ONNX importer and exporter follows version 7 of ONNX operator set which comes with ONNX v1.2.1. | |
| *Note:* MXNet-ONNX importer and exporter follows version 7 of ONNX operator set, which comes with ONNX v1.2.1. |
| 1. Use [Amazon SageMaker](https://aws.amazon.com/sagemaker/developer-resources/) | ||
| 1. Use the [AWS Deep Learning AMI with Conda](https://docs.aws.amazon.com/dlami/latest/devguide/overview-conda.html) (comes preinstalled!) | ||
| 1. Use an [AWS Deep Learning Container](https://docs.aws.amazon.com/dlami/latest/devguide/deep-learning-containers.html) | ||
| 1. Install MXNet on a [AWS Deep Learning Base AMI](https://docs.aws.amazon.com/dlami/latest/devguide/overview-base.html) |
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| 1. Install MXNet on a [AWS Deep Learning Base AMI](https://docs.aws.amazon.com/dlami/latest/devguide/overview-base.html) | |
| 1. Install MXNet on an [AWS Deep Learning Base AMI](https://docs.aws.amazon.com/dlami/latest/devguide/overview-base.html) |
TEChopra1000
approved these changes
Oct 17, 2019
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Lots more to do, but let's get some fixes in there!