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+It is not uncommon for me to get exuberantly excited over a open source
+project that I stumble upon, however, Jupyter Lab has taken the
+cake this month. The Jypyter project is an open-source community
+that extended IPython notebook project to the web browser and added
+support for multiple languages.
+
+# Why Notebooks?
+
+As a researcher and educator I love notebooks because they enable 
+you to easily share your code with others. Notebooks are much more
+interactive than simply sharing source code because you can
+mix text(markdown), code, and outputs from code execution. For classes and
+when working, this makes it very easy to generate quick reports.
+You can simply write a document that auto generates the graphs and figures
+you want to talk about in your document.
+
+Last week I worked on a computer vision assignment that required me to
+use Open CV to manipulate images using filters, convolutions, etc.
+The entirety of the assignment required me to produce roughly 30 images.
+A majority of the class wrote python scripts and threw each image they 
+generated into a massive word document and  typed up their
+analysis and submitted their assignment as a PDF along side a bunch of
+python scripts. There is nothing wrong with doing that; however, what
+happens if at the end of the assignment you realized that you were
+generating Gaussian filters incorrectly? If you wrote everything in
+a Jupyter notebook you would just have to fix the dubious code and
+re-run the notebook and it would produce your report in its entirety.
+But, if you had your scripts as separate files you would have to fix your
+code and then go through and generate a dozen new images that required
+Gaussian filters and place them in your document.
+
+
+The ability to accurately reproduce your report is pinnacle to making
+research more verifiable and reproducible. This is something that the
+R and open-science communities heavily focus on. Directly mixing your
+code and analysis with your report is very useful. Also, consider if the
+data that you are working with changes half way through writing your
+research report. With a notebook, you would just have to re-run the 
+notebook where if you had the report as a separate word or Latex file,
+you now run the risk of misreporting your results.
+
+
+# Jupyter Notebook
+
+
+
+
+# Jupyter Lab
+
+
+# Running and Installing
+
+
+# Running for remote use
+
+Imagine that you are running an old computer and you simply want your
+code to run on a remote computer that has a beefie GPU for ML.
+With Jupyter Lab or Notebook you can do that, but, it takes a little
+trickery. The easiest solution that I found involves using a reverse
+SSH proxy.
+
+![network diagram](media/jupyter/network.jpeg)
+
+
+The first thing that you want to do is set up a password so that you
+can connect to the jupyter lab instance using a password rather than using
+a authentication key which gets hidden in the terminal.
+
+```bash
+jupyter notebook password
+```
+
+** note ** the password that you set is configured in the same config used by both jupyter lab and jupyter notebook.
+
+The next thing you should do is run the jupyter lab instance on the port that you want it to listen to.
+
+```bash
+jupyter lab --no-browser --port=6000
+```
+
+The "--no-browser" will prevent jupyter from opening in your default web browser.
+
+
+The next step is to do a local SSH port forward on your machine
+so you can access the jupyter instance on the remote server.
+The benefit of doing this is that you can get behind firewalls and that
+all your traffic is encrypted.
+
+![local port forwarding](media/jupyter/localForward.png)
+
+The image above comes from my presentation on "[Everything SSH](https://jrtechs.net/open-source/teaching-ssh-through-a-ctf)".
+The essence of the command bellow is that you will forward all
+connections on your machines to port 6000 to a remote's servers connection to localhost:6000.
+
+```bash
+ssh -L 6000:localhost:6000 user@some-remote-host.rit.edu
+```
+
+After you run that command you can access the jupyter lab instance
+by opening your favorite web client and going to localhost:6000.
+Typing that command every time is tedious so I recommend that you
+allias it in your shells config file.
+
+
+```bash
+alias jj="ssh -L 6000:localhost:6000 user@some-remote-host.rit.edu"
+```
+
+Now all you have to type in your command prompt is jj to connect to 
+your remote jupyter server. Neat.
+
+But, what if your roommate trips and your server gets restarted? Well,
+you can write a systemd script to automatically start your jupyter
+server when the computer boots. This is what my system d script looks like.
+
+```bash
+# location /lib/systemd/system
+#
+# After file creation run: systemctl daemon-reload
+# enable service on start up: systemctl enable jupyter-lab
+# start the service: systemctl start jupyter-lab
+
+
+[Unit]
+Description=Script to start jupyter lab
+Documentation=https://jrtechs.net
+After=network.target
+
+[Service]
+Type=simple
+User=jeff
+WorkingDirectory=/home/jeff/Documents/school/csci-431/
+ExecStart=/usr/local/bin/jupyter lab --no-browser --port=6969
+Restart=on-failure
+
+[Install]
+WantedBy=multi-user.target
+```
+
+
+You want to set the working directory to be the location where your jupyter notebooks are stored.
+You also want to make sure that you specify the absolute path to the jupyter binary in the execstart parameter. You can find that using the which command:
+
+```bash
+which jupyter
+```
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