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https://github.com/toddsundsted/mxnet.cr

MXNet (AI/ML) bindings for the Crystal language.
https://github.com/toddsundsted/mxnet.cr

crystal deep-learning gluon machine-learning mxnet neural-networks

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MXNet (AI/ML) bindings for the Crystal language.

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README 

        
# Deep Learning for Crystal



[![GitHub Release](https://img.shields.io/github/release/toddsundsted/mxnet.cr.svg)](https://github.com/toddsundsted/mxnet.cr/releases)

[![Build Status](https://github.com/toddsundsted/mxnet.cr/actions/workflows/ci.yml/badge.svg)](https://github.com/toddsundsted/mxnet.cr/actions)

[![Documentation](https://img.shields.io/badge/docs-available-brightgreen.svg)](https://toddsundsted.github.io/mxnet.cr/)



[MXNet.cr](https://github.com/toddsundsted/mxnet.cr)

provides [MXNet](https://mxnet.incubator.apache.org/)

bindings for the [Crystal](https://crystal-lang.org/) programming

language. MXNet is a framework for machine learning and deep learning

written in C++, supporting distributed training across multiple

machines and multiple GPUs (if available).



MXNet.cr follows the design of the Python bindings, albeit with

Crystal syntax. The following code:



```crystal

require "mxnet"

a = MXNet::NDArray.array([[1, 2], [3, 4]])

b = MXNet::NDArray.array([1, 0])

puts a * b

```



outputs:



```

[[1, 0], [3, 0]]



```



# Examples



If you want to see what MXNet.cr can do, check out

[toddsundsted/deep-learning](https://github.com/toddsundsted/deep-learning).

It is a collection of problems and solutions from [Deep Learning - The

Straight Dope](https://gluon.mxnet.io/), a set of notebooks teaching

deep learning using MXNet.



# Installation



MXNet.cr requires MXNet.



Build MXNet from source (including Python language bindings) or

install the library from prebuilt packages using the Python package

manager *pip*, per the MXNet installation instructions:



https://mxnet.incubator.apache.org/install/index.html



And add the following to your application's *shard.yml*:



```yaml

dependencies:

  mxnet:

    github: toddsundsted/mxnet.cr

```



## Troubleshooting



MXNet.cr relies on the Python library to find the installed MXNet

shared library ("libmxnet.so"). You can verify MXNet is installed with

the following Python code:



```python

import mxnet as mx

a = mx.ndarray.array([[1, 2], [3, 4]])

b = mx.ndarray.array([1, 0])

print(a * b)

```



which outputs:



```

[[1. 0.]

 [3. 0.]]



```



## OSX



On OSX, you may need to give your program a hint about the location of

the MXNet shared library (*libmxnet.so*). If you build and run your

program and see an error message like the following:



```

dyld: Library not loaded: lib/libmxnet.so

  Referenced from: /Users/homedirectory/.cache/crystal/crystal-run-eval.tmp

  Reason: image not found

```



you need to either: 1) explicitly set the `DYLD_FALLBACK_LIBRARY_PATH`

environment variable to point to the directory containing *libmxnet.so*,

or 2) move or copy *libmxnet.so* into a well-known location (such as

the project's own *lib* directory).



Alternatively, and more permanently, you can modify the *libmxnet.so*

shared library so that it knows where it's located at runtime (you

will modify the library's LC\_ID\_DYLIB information):



```

LIBMXNET=/Users/homedirectory/mxnet-1.5.1/lib/python3.6/site-packages/mxnet/libmxnet.so # the full path

install_name_tool -id $LIBMXNET $LIBMXNET

```



# Status



MXNet.cr currently implements a subset of

[Gluon](https://gluon.mxnet.io/), and supports a rich set of

operations on arrays and symbols (arithmetic, trigonometric,

hyperbolic, exponents and logarithms, powers, comparison, logical,

rounding, sorting, searching, reduction and indexing) with automatic

differentiation built in.



Implemented classes:

* MXNet

  * Autograd

  * Context

  * Executor

  * Optimizer

  * NDArray

  * Symbol

  * Gluon

    * Block

    * HybridBlock

    * Sequential

    * HybridSequential

    * SymbolBlock

    * Dense

    * Pooling

    * Conv1D

    * Conv2D

    * Conv3D

    * MaxPool1D

    * MaxPool2D

    * MaxPool3D

    * Flatten

    * L1Loss

    * L2Loss

    * SoftmaxCrossEntropyLoss

    * Activation

    * Trainer

    * Parameter

    * Constant