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| # MXNet documentation | ||
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| A built version of document is available at http://mxnet.io | ||
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| To build the documents locally, we need to first install [docker](https://docker.com). | ||
| Then use the following commands to clone and | ||
| build the documents. | ||
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| ```bash | ||
| git clone --recursive https://github.com/apache/incubator-mxnet.git mxnet | ||
| cd mxnet && make docs | ||
| ``` | ||
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| The results will be available at `docs/_build/html/`. | ||
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| Note: | ||
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| - If C++ codes have been changed, we suggest to remove the previous results to | ||
| trigger the rebuild for all pages, namely run `make clean_docs`. | ||
| - If C++ code fails to build, run `make clean` | ||
| - If CSS or javascript are changed, we often need to do a *force refresh* in the | ||
| browser to clear the cache. |
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| # MXNet - C++ API | ||
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| For namespaces, classes, and code files for the MXNet C++ package, see the following: | ||
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| * [Namespaces](http://mxnet.io/doxygen/namespaces.html) | ||
| * [Classes](http://mxnet.io/doxygen/annotated.html) | ||
| * [Code Files](http://mxnet.io/doxygen/files.html) | ||
| * [MXNet CPP Package](https://github.com/dmlc/mxnet/tree/master/cpp-package) |
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| MXNet - Julia API | ||
| ================= | ||
| 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. | ||
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| - It enables you to write seamless tensor/matrix computation with multiple GPUs in 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. | ||
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| Julia documents are available at [http://dmlc.ml/MXNet.jl/latest/](http://dmlc.ml/MXNet.jl/latest/). |
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| # MXNet - Perl API | ||
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| MXNet supports the Perl programming language. The MXNet Perl package brings flexible and efficient GPU | ||
| computing and state-of-art deep learning to Perl. It enables you to write seamless tensor/matrix computation with multiple GPUs in Perl. | ||
| It also lets you construct and customize the state-of-art deep learning models in Perl, | ||
| and apply them to tasks, such as image classification and data science challenges. | ||
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| One important thing to internalize is that Perl interface is written to be as close as possible to the Python's API, | ||
| so most if not all of Python's documentation and examples should just work in Perl after making few | ||
| changes in order to make the code a bit more Perlish. In nutshell just add $ sigils and replace . = \n with -> => ; and in 99% of cases | ||
| that's all that is needed there. | ||
| In addition please refer to [excellent metacpan doc interface](https://metacpan.org/release/AI-MXNet) and to very detailed | ||
| [MXNet Python API Documentation](http://mxnet.io/api/python/index.html). | ||
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| AI::MXNet is seamlessly glued with PDL, the C++ level state can be easily initialized from PDL and the results can be | ||
| transferred to PDL objects in order to allow you to use all the glory and power of the PDL! | ||
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| Here is how you can perform tensor or matrix computation in Perl with AI::MXNet and PDL: | ||
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| ```perl | ||
| pdl> use AI::MXNet qw(mx); # creates 'mx' module on the fly with the interface close to the Python's API | ||
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| pdl> print $arr = mx->nd->ones([2, 3]) | ||
| <AI::MXNet::NDArray 2x3 @cpu(0)> | ||
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| pdl> print Data::Dumper::Dumper($arr->shape) | ||
| $VAR1 = [ | ||
| 2, | ||
| 3 | ||
| ]; | ||
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| pdl> print (($arr*2)->aspdl) ## converts AI::MXNet::NDArray object to PDL object | ||
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| [ | ||
| [2 2 2] | ||
| [2 2 2] | ||
| ] | ||
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| pdl> print $arr = mx->nd->array([[1,2],[3,4]]) ## init the NDArray from Perl array ref given in PDL::pdl constructor format | ||
| <AI::MXNet::NDArray 2x2 @cpu(0)> | ||
| pdl> print $arr->aspdl | ||
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| [ | ||
| [1 2] | ||
| [3 4] | ||
| ] | ||
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| ## init the NDArray from PDL but be aware that PDL methods expect the dimensions order in column major format | ||
| ## AI::MXNet::NDArray is row major | ||
| pdl> print mx->nd->array(sequence(2,3))->aspdl ## 3 rows, 2 columns | ||
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| [ | ||
| [0 1] | ||
| [2 3] | ||
| [4 5] | ||
| ] | ||
| ``` | ||
| ## Perl API Reference | ||
| * [Module API](module.md) is a flexible high-level interface for training neural networks. | ||
| * [Symbolic API](symbol.md) performs operations on NDArrays to assemble neural networks from layers. | ||
| * [IO Data Loading API](io.md) performs parsing and data loading. | ||
| * [NDArray API](ndarray.md) performs vector/matrix/tensor operations. | ||
| * [KVStore API](kvstore.md) performs multi-GPU and multi-host distributed training. | ||
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