Skip to content

Latest commit

 

History

History
118 lines (76 loc) · 5 KB

README.md

File metadata and controls

118 lines (76 loc) · 5 KB
Raw

MXNet Notebooks

This repo contains various notebooks ranging from basic usages of MXNet to state-of-the-art deep learning applications.

How to use

Python

The python notebooks are written in Jupyter.

  • View We can view the notebooks on either github or nbviewer. But note that the former may fail to render a page, while the latter has delays to view the recent changes.

  • 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:

    1. Launch a g2.2xlarge or p2.2xlarge instance by using AMI ami-fe217de9 on N. Virginia (us-east-1). This AMI is built by using this script. Remember to open the TCP port 8888 in the security group.

    2. Once the instance successfully launches, setup the following variables with proper values:

      export HOSTNAME=
  export PERM=

    1. 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 to local port 8888.

    2. 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.sh to update MXNet to the newest version.

    3. Now, we are able to view and edit the notebooks from the browser using the URL: http://localhost:8888/tree/mxnet-notebooks/python/outline.ipynb

Scala

The scala notebooks are written in Jupyter using Jupyter-Scala Kernel V0.3.x.

  • Run We can run and modify these notebooks if both mxnet scala package, jupyter and Jupyter-Scala Kernel are installed. There are various options for jupyter scala kernel. You can choose whichever you like.

    If you have a AWS account, here is an easier way to run the notebooks:

    1. Launch a g2.2xlarge or p2.2xlarge instance by using AMI ami-fe217de9 on N. Virginia (us-east-1). This AMI is built by using this script. Remember to open the TCP port 8888 in the security group.

    2. Once the instance successfully launches, setup the following variables with proper values:

      export HOSTNAME=
  export PERM=

    1. 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 to local port 8888.

    2. Install Maven. Install Scala 2.11.8. Go to MXNet source code, compile scala-package by running command make scalapkg. Compiled jar file will be created in mxnet/scala-package/assembly/{your-architecture}/target directory.

    3. Install coursier, a Scala library to fetch dependencies from Maven / Ivy repositories as follows.

      On OS X, brew install --HEAD paulp/extras/coursier On Linux,

        curl -L -o coursier https://git.io/vgvpD && chmod +x coursier && ./coursier --help

      Make sure coursier launcher is available in the PATH.

    4. Install Jupyter-Scala Kernel V0.3.x by following the instructions given below:

    ```bash
	git clone https://github.com/alexarchambault/jupyter-scala.git
	git checkout 0.3.x
	./jupyter-scala
```

To check if scala-kernel is installed, type command `jupyter kernelspec list`.
    1. Clone this repo on the EC2 machine and run jupyter
    ```bash
  git clone https://github.com/dmlc/mxnet-notebooks
  jupyter notebook
```
    1. Now we are able to view and edit the notebooks from the browser using the URL: http://localhost:8888/tree/mxnet-notebooks/scala/. Choose scala211 kernel if asked. Include mxnet-scala jar created in step-4 in classpath by command classpath.addPath("jar-path") in the notebook you want to run.

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