Differences

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--- cluster:usage_tips:jupyter [2022/01/07 16:39] – benedikt
+++ cluster:usage_tips:jupyter [2023/03/16 08:48] (current) – [Jupyter] lukab
@@ Line 1: / Line 1: @@
 ====== Jupyter ======
 Jupyter is a notebook-based development platform for Python projects, widely used in machine learning projects.
-JuypyterHub is a service that provides Jypyter notebooks for users on a given machine. Currently, one jupyterHub server is accessible for Sleep Revolution researchers on the computing hub Salamander.
+JuypyterHub is a service that provides Jypyter notebooks for users on a given machine. Currently, one jupyterHub server is accessible for Sleep Revolution researchers on the computing hub Dunwich.
 ===== Access =====
-In order to access the JupyterHub server, access the VPN, and navigate to [[http://salamander.sleep.ru.is|salamander.sleep.ru.is]] (if that doesn't work you can also try [[http://130.208.209.2|130.208.209.2]]. You will be greeted with a login screen where you should use your Sleep revolution credentials (without @sleep.ru.is).
+In order to access the JupyterHub server, access the [[cluster:usage_tips:VPN|VPN]], and navigate to [[https://dunwich.sleep.ru.is/jupyter|dunwich.sleep.ru.is/jupyter]]. You will be greeted with a login screen where you should use your Sleep revolution credentials (without @sleep.ru.is).
-===== Usage =====
+==== Adding environment ====
-The default JupyterHub interface has 3 python environments installed one python3 environment, one TensorFlow environment, and one PyTorch environment. JupyterHub also allows each user to use their own python environments if the default ones have missing packages.
+JupyterHub allows each user to use their own python environments if the default ones have missing packages.
 To create a new python environment we use ''conda'', open a new terminal in jupyterhub.
-The command for creating a new conda environment named ''aardvark'' for example is
+The command for creating a new conda environment named ''myenv'' for example is
 <code>
-conda create -n aardvark
+conda create -n myenv
 </code>
 Note the location of the new environment.
@@ Line 20: / Line 21: @@
 <code>
-conda activate aardvark
+conda activate myenv
 conda install ipykernel
 </code>
-Finally, to register the environment with jupyterhub, type the command, replacing ''aardvark'' with the name of your environment, and ''displayname'' with a nice name for your environment that you can select from a dropdown list in jupyter.
+Finally, to register the environment with jupyterhub, type the command, replacing ''myenv'' with the name of your environment, and ''displayname'' with a nice name for your environment that you can select from a dropdown list in jupyter.
+You also need to replace the path to your environment with by replacing "myenv" with the name of your environment and adjusting the python version at the end of the path if necessary.
 <code>
-.conda/path/to/environment/bin/python -m ipykernel install --name 'aardvark' --display-name "displayname"
+.conda/envs/myenv/bin/python3 -m ipykernel install --name 'myenv' --display-name "displayname" --user
 </code>
-To remove the ''aardvark'' environment, the command is:
+To remove the ''myenv'' environment, the command is:
 <code>
-jupyter kernelspec uninstall aardvark
+jupyter kernelspec uninstall myenv
 </code>
+=====Working in Jupyter Notebooks=====
+Now you are ready to create your own Jupyter Notebook. Click the blue plus button in the main screen and select an environment, e.g. your newly created //myenv//.You can change the environment later by selecting a different kernel.
+==== Installing packages ====
+To install a package, ''examplepackage'' for example, to the example environment ''myenv'' that you created and added to the JupyterHub open a new **python console** on JupyterHub by selecting the ''myenv'' environment and executing
+''conda install examplepackage''
+After the installation, you should be able to restart the kernel, and use your new ''examplepackage''
+====Access Data=====
+The path to access data from the sleep database always starts with ///datasets//
+**EXAMPLE:**
+If the name of your data set is "examplesleepstudy" you can read an .csv file in this folder like this:
+<code>
+pd.read_csv("/datasets/examplesleepstudy/examplefile.csv")
+</code>
+In oder to explore the structure of the folder you are trying to access use the terminal to show the contents of the folder:
+<code>
+ls /datasets/examplesleepstudy
+</code>