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1. Request an account for biohpc Biohpc at  https://biohpc.cornell.edu/lab/lab.aspx

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a. at your computer terminal, type: ssh yournetid@cbsujohnson.biohpc.cornell.edu

b. enter your password 

(c. type: screen -r(enter screen session)

d. run the following command: 

docker1 run \

  --volume "/workdir/yournetid/get_colors:/home/jovyan/work" \

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Copy this link to your browser, you can then see the jupyter notebook page

e. type control-a, releasing, the type d (detach from the screen session) 

Theoretically, by using the screen command, you should be able to start the notebook this way and leave it running indefinitely (assuming it doesn't crash anymore). It should only need to be restarted when the server is rebooted. Using the screen command is good practice for any command that you don't want to get killed if you get disconnected from the server.

f. next time when you log in, type: screen - r (resume session)

4. More on The Screen Command & Provide Access for other team members

a. More details on the screen command are in this pdf: 

https://biohpc.cornell.edu/lab/doc/Linux_exercise_part2.pdf

b. As far as sharing with other team members: 

• they could just visit the same URL as you, but they will be working on the exact same files as you. If multiple people are working on the same notebook at the same time, you can run into problems with overwriting each other's work or interfering with each other's sessions. 
• If you prefer for each lab member to have their own Jupyter Notebook environment, then they can run the same 'docker1 start' command as you, but would have to use a different port number (if you are using 8031, anything between 8009-8039 should work), and change your username to theirs in the command. Then, contact Biohpc to fix the ownership of the files as it requires admin privileges.

5. Steps to edit and run the data pipeline in the Jupyter Notebook

• Edit in the extract_colors_pipeline.py file
• Execute %run extract_colors_pipeline.py in extract_colors_pipeline.ipynb

6. Steps to edit and run the data pipeline in your computer terminal:

(Currently don't have permission to edit or run Jupyter Notebook in Browser, the connection was interruptedin case the Jupyter Notebook does not work) 

a. cd /workdir/yournetid/get_colors

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f.  to run: type python /local/workdir/yournetid/get_colors/extract_colors_pipeline.py

PS: All the steps above require access to the Cornell Network, if you are outside of Cornell University, you can connect to the VPN: https://it.cornell.edu/cuvpn 

google doc version of this documentation:  https://docs.google.com/document/d/10mJzJUvUKSlWoK7e-n8yJ-766NdSm0LfIWmDjoRTBQ8/edit

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