ml-notebook

This project is aimed at providing an accessible and reproducible environment for a variety of machine learning toolkits, with a focus on deep learning toolkits. Instead of asking you to follow a set of complex setup instructions, ml-notebook asks you to wait while a tested, pre-built image is installed.
The following tools are available inside the Ubuntu 14.04 image, with Jupyter as an interface:
General:

• matplotlib*
• scipy*
• numpy*
• pyOSC
• Jupyter

Machine Learning:

• gensim
• scikit-learn
• scipy*
• pandas*

Deep Learning:

• Caffe (with pycaffe)
• Chainer
• TensorFlow
• Theano (with Keras and Lasagne)
• Torch

*These are requirements of other libraries, but also interesting in their own right.

Usage

This is only tested this on OSX. Something similar should work on Linux, and possibly Windows with some changes.

1. Install Docker. On Mac, use Docker for Mac.
2. Clone this repository. git clone https://github.com/kylemcdonald/ml-notebook.git && cd ml-notebook
3. (Optional) Run ./update.sh Note: this downloads 2+GB of data and examples. If you just want to look around, browse ml-examples.
4. Run ./run.sh Note: this downloads another 2+GB of data, a pre-built image from Docker.

Type Ctrl-D to exit the ml-notebook Docker. If you accidentally close the Terminal, the Jupyter notebook will keep running in the background. Whenever you want to run the environment again, just call ./run.sh. Calling ./run.sh when ml-notebook is running in the background will restart ml-notebook.

Acknowledgements

Some of the deep learning toolkits are built based on Dockerfiles from Kaixhin.