Launch Stack on AWS (Costs Money!)
Picked 1 Manager Node (t2.micro), Picked 1 Worker Node (t2.medium or t2.large)
- Or 1 Manager Node(t2.medium or t2.large), 0 Worker Nodes(doesn’t matter)
 
Or CloudFormation->Stacks->Docker->Outputs->Managers
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Click on link under “Managers->Value” and see Public DNS of Manager Node
 - 
    
Either use “Connect” in EC2 or
 
ssh -i “mykey.pem” docker@
Make sure mykey.pem has correct permissions
- chmod 400 mykey.pem
 
**docker service create --name dsnb -p 8888:8888 jupyter/datascience-notebook**
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github link, dockerhub link
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took about 20 minutes to start up �
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docker service inspect dsnb
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**docker service ps dsnb** - 
    
CloudFormation->Stacks->Docker->Outputs->DefaultDNSTarget for web address
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**jupyter notebook list** 
Open 
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Use 8888 for
in this example  - 
    
!git clone https://github.com/TrackDR/Geospatial-Jupyter-Notebooks
 
!git clone https://github.com/rajathkumarmp/Python-Lectures
- Python-Lectures/01.ipynb to start, rest are good too
 
Docs
- Deploy your app
 
Other Jupyter/Ipython Github Repositories to clone
- 
    
!git clone https://github.com/TrackDR/ModdedIpythonNotebooks
 - 
    
!git clone https://github.com/JWarmenhoven/ISLR-python
 
!git clone https://github.com/jdwittenauer/ipython-notebooks
- 
    
notebooks/ipython-notebooks/notebooks/language/Intro.ipynb to start
 - 
    
skimage-tutorials/lectures/00_images_are_arrays.ipynb
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docs
 
Docker images to run
Jupyter
- jupyter/datascience-notebook
 
Docker images that will need to modify to run here, but looking to the future
Kaggle
- 
    
kaggle/python
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kaggle/rstats
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docker service create –name quantlib -p 8888:8888 lballabio/quantlib-notebook
 - 
    
Videos at vimeo, blog w/ex, book
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ipython docker image
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ontouchstart docker image
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Twisted logic docker image
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Torch/iTorch/Ubuntu 14.04 Docker image: docker pull kaixhin/torch
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Torch/iTorch/CUDA 7/Ubuntu 14.04 Docker image: docker pull kaixhin/cuda-torch
 
Alternatives to Docker for AWS for running Jupyter Notebooks
CoCalc (subscription based)
- 
    
Website, Pricing, Wikipedia, Chrome Extension, GitHub
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Or Run your own CoCalc using their Docker image
 
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