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d. run the following command: 

chmod -R a+rwX  /workdir/yournetid/get_colors

Then run: 

docker1 run \

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

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  "jupyter/tensorflow-notebook:20221209" \

  "start-notebook.sh"

If you don't have permission to edit or run after opening the Jupyter Notebook 

Run this command before running the docker1 command: 

chmod -R a+rwX  /workdir/yournetid/get_colors

You will see a token like this:  http://127.0.0.1:8888/lab?token=c7007fb9f1a6aea3c4685328c35741c968d2e24e8821ce2d

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

4. Steps to run the data pipeline in Jupyter Notebook

a. Run the pipeline: 

Run extract_colors_pipeline.py in run_extract_colors_pipeline.ipynb

b. Store the results in the output_colors folder to Backend Database:

Run store_backend.py in run_store_backend.ipynb

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

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• 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.

56. 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

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

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