add megengine example docs (#134)

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@ -42,8 +42,10 @@ Check out some of ClearML's automatic reporting examples for supported packages:
TensorFlow flags
* [Tensorboard with PyTorch](../guides/frameworks/pytorch/pytorch_tensorboard.md) - logging TensorBoard scalars, debug samples, and text integrated into
code that uses PyTorch
* [TensorBoardX](../guides/frameworks/tensorboardx/tensorboardx.md) - logging TensorBoardX scalars, debug
* TensorBoardX
* [TensorBoardX with Pytorch](../guides/frameworks/tensorboardx/tensorboardx.md) - logging TensorBoardX scalars, debug
samples, and text in code using PyTorch
* [MegEngine MNIST](../guides/frameworks/megengine/megengine_mnist.md) - logging scalars using TensorBoardX's `SummaryWriter`
* Matplotlib
* [Matplotlib](../guides/frameworks/matplotlib/matplotlib_example.md) - logging scatter diagrams plotted with Matplotlib
* [Matplotlib with PyTorch](../guides/frameworks/pytorch/pytorch_matplotlib.md) - logging debug images shown

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@ -0,0 +1,54 @@
---
title: MegEngine MNIST
---
The [megengine_mnist.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/megengine/megengine_mnist.py)
example demonstrates the integration of ClearML into code that uses [MegEngine](https://github.com/MegEngine/MegEngine)
and [TensorBoardX](https://github.com/lanpa/tensorboardX). ClearML automatically captures models saved with `megengine`.
The example script does the following:
* Trains a simple deep neural network on MegEngine's built-in [MNIST](https://megengine.org.cn/doc/stable/en/reference/api/megengine.data.dataset.MNIST.html)
dataset.
* Creates a TensorBoardX `SummaryWriter` object to log scalars during training.
* Creates a ClearML experiment named `megengine mnist train`, which is associated with the `examples` project.
## Hyperparameters
ClearML automatically logs command line options defined with `argparse`. They appear in the experiment's **CONFIGURATIONS**
page under **HYPER PARAMETERS** **>** **Args**.
![Configuration tab](../../../img/examples_megengine_mnist_config.png)
## Scalars
The example script's `train` function calls TensorBoardX's `SummaryWriter.add_scalar` method to report `loss`.
ClearML automatically captures the data that is added to the `SummaryWriter` object.
These scalars can be visualized in plots, which appear in the ClearML [WebApp](../../../webapp/webapp_home.md), in the
experiment's **RESULTS** **>** **SCALARS** page.
![Scalars tab](../../../img/examples_megengine_mnist_scalars.png)
## Models
ClearML automatically captures the model logged using the `megengine.save` method, and saves it as an artifact.
View saved snapshots in the experiment's **ARTIFACTS** tab.
![Artifacts tab](../../../img/examples_megengine_models_1.png)
To view the model details, click the model name in the **ARTIFACTS** page, which will open the model's info tab. Alternatively, download the model.
The model info panel contains the model details, including:
* Model URL
* Framework
* Snapshot locations.
![Model info panel](../../../img/examples_megengine_models_2.png)
## Console
All console output during the scripts execution appears in the experiments **RESULTS > CONSOLE** page.
![Console tab](../../../img/examples_megengine_console.png)

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@ -67,11 +67,10 @@ module.exports = {
{'Frameworks': [
{'Autokeras': ['guides/frameworks/autokeras/integration_autokeras', 'guides/frameworks/autokeras/autokeras_imdb_example']},
{'FastAI': ['guides/frameworks/fastai/fastai_with_tensorboard']},
{
'Keras': ['guides/frameworks/keras/jupyter', 'guides/frameworks/keras/keras_tensorboard']
},
{'Keras': ['guides/frameworks/keras/jupyter', 'guides/frameworks/keras/keras_tensorboard']},
{'LightGBM': ['guides/frameworks/lightgbm/lightgbm_example']},
{'Matplotlib': ['guides/frameworks/matplotlib/matplotlib_example']},
{'MegEngine':['guides/frameworks/megengine/megengine_mnist']},
{'PyTorch':
['guides/frameworks/pytorch/pytorch_distributed_example', 'guides/frameworks/pytorch/pytorch_matplotlib',
'guides/frameworks/pytorch/pytorch_mnist', 'guides/frameworks/pytorch/pytorch_tensorboard', 'guides/frameworks/pytorch/pytorch_tensorboardx',