Note: We've moved the active work on this repo to https://github.com/dmlc/mxnet/tree/master/docs. If you are looking for docs related to a new, dynamic, elegant and easy to use imperative interface for MXNet, check out http://gluon.mxnet.io/ or https://github.com/zackchase/mxnet-the-straight-dope
This repo contains various notebooks ranging from basic usages of MXNet to state-of-the-art deep learning applications.
The python notebooks are written in Jupyter.
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View We can view the notebooks on either github or nbviewer. But note that the former may be failed to render a page, while the latter has delays to view the recent changes.
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Run We can run and modify these notebooks if both mxnet and jupyter are installed. Here is an example script to install all these packages on Ubuntu.
If you have a AWS account, here is an easier way to run the notebooks:
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Launch a g2.2xlarge or p2.2xlarge instance by using AMI
ami-fe217de9on N. Virginia (us-east-1). This AMI is built by using this script. Remember to open the TCP port 8888 in the security group. -
Once launch is succeed, setup the following variable with proper value
export HOSTNAME=ec2-107-22-159-132.compute-1.amazonaws.com export PERM=~/Downloads/my.pem
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Now we should be able to ssh to the machine by
chmod 400 $PERM ssh -i $PERM -L 8888:localhost:8888 ubuntu@HOSTNAME
Here we forward the EC2 machine's 8888 port into localhost.
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Clone this repo on the EC2 machine and run jupyter
git clone https://github.com/dmlc/mxnet-notebooks jupyter notebook
We can optional run
~/update_mxnet.shto update MXNet to the newest version. -
Now we are able to view and edit the notebooks on the browser using the URL: http://localhost:8888/tree/mxnet-notebooks/python/outline.ipynb
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Some general guidelines
- A notebook covers a single concept or application
- Try to be as basic as possible. Put advanced usages at the end, and allow reader to skip it.
- Keep the cell outputs on the notebooks so that readers can see the results without running