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.
How to use
The python notebooks are written in Jupyter.
If you have a AWS account, here is an easier way to run the notebooks:
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
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.
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
How to develop
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