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 p2.xlarge instance by using AMI
ami-6e5d6808
on Ireland (eu-west-1). The Deep Learning AMI v2.0 for Amazon Linux is designed to continue to provide a stable, secure, and high performance execution environment for deep learning applications running on Amazon EC2. 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 ec2-user@HOSTNAME
Here we forward the EC2 machine's 8888 port into localhost.
Clone this repo on the EC2 machine and run jupyter
sudo yum install -y graphviz sudo mkdir /efs sudo chown ec2-user:ec2-user /efs cd /efs git clone https://github.com/dmlc/mxnet-notebooks jupyter notebook
Leave this ssh session open and connected while using the python notebooks.
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
Finally you may want to connect another ssh session and run the following command to keep track of GPU memory and core usage
ssh -i $PERM ec2-user@HOSTNAME watch -n 1 nvidia-smi
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
mxnet-notebooks's People
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