Wiki source code of Using JupyterHub in HDC

Version 1.5 by Susan Evans on 2023/07/11 13:55
Hide last authors
Susan Evans 1.1 1 JupyterHub is an open-source, multi-user version of Jupyter Notebook for performing analysis of Project files in the Core. More information can be found in the application documentation [[https:~~/~~/jupyter.org/>>https://jupyter.org/]].
2
3 = How it Works =
4
5 JupyterHub allows Project members to create or import Jupyter Notebooks into the Project Workspace environment, retrieve Project files from the Core, perform computational workflows on the data, and write the outputs back to the Core where they can be accessed by other Project members. JupyterHub spins up a new JupyterLab instance for each Project member.
6
7 = Prerequisites =
8
9 * Project Collaborator role or higher.
10 * JupyterHub has been configured for the Project by the Platform Administrator. See //Getting Access to JupyterHub//.
11
12 = Data Stewardship =
13
14 Users are reminded to abide by the Platform Terms of Use and any Project-specific restrictions when using Workspace tools to access data and code.
15
16 = Getting Access to JupyterHub =
17
18 JupyterHub is configured at the time of Project Setup. If you launch JupyterHub and receive a notice that it hasn’t been deployed for your project, please contact your Platform Administrator.
19
20 = Launching JupyterHub =
21
22 [[image:HDC Project Workspace tool navigation Jupyterhub v1.0.0 2023-05-25.png||height="10%" width="30%"]]
23
24 1. Launch your Project and click the **JupyterHub icon** in the left menu bar.
25 1. Click **Sign in with Keycloak** to initiate your session. JupyterHub automatically authenticates with your existing username and password and launches your session - no additional sign-in is required.
26 1. You can chose to either start a **Minimal environment**, which comes with Python, or a **Datascience environment**, which also includes R and Julia in addition to Python.
27 1. From the JupyterHub home page (a JupyterLab interface) you can now perform various actions such as creating and working on Jupyter Notebooks, importing existing ones, and using the Pilot Command Line Interface in the terminal to retrieve, analyze, and re-upload Project Core data, and create. Moreover, you can also use the pre-deployed and configured package management software conda to download, install, and manage for instance Python packages as per individual demand (see the sections //Installing New Python Packages// and //Creating a Virtual Python Environment and Registering a Kernel// below for more details).
28 1. When finished using JupyterHub, click **Logout** to end your session.
29
30 = Creating a Notebook =
31
32 Users can create a new Jupyter Notebook with Python 3 inside JupyterHub, with dedicated and persistent storage under the users' Home Directory.
33
34 1. In the Launcher, click the **Python 3 Notebook **icon, or click **File > New > Notebook**.
35 1. Create your Notebook.
36
37 [[image:Project Workspace Jupyter Create Python Notebook v2.1.6 2023-02-07.png||height="22%" width="50%"]]
38
39 = Launching the Terminal =
40
41 JupyterHub provides browser-based terminal access for advanced users to run commands directly in the system shell. Importantly, this allows users to sync data between for instance the Projects Core and their JupyterHub home directory using pilotcli, or to download and manage Python packages.
42
43 1. In the Launcher, click the **Terminal **icon, or click **File > New > Terminal**.
44 1. The terminal window opens.
45
46 [[image:Project Workspace Jupyter Launch Terminal v2.1.6 2023-02-07.png||height="9%" width="50%"]]
47
48 Ubuntu is used to host Jupyter Notebook. Use the command cat /etc/os-release to determine to current version of Ubuntu:
49
50 {{code language="none"}}
51 uname@jupyter-uname:/etc$ cat os-release
52 NAME="Ubuntu"
53 VERSION="20.04.4 LTS (Focal Fossa)"
54 ID=ubuntu
55 ID_LIKE=debian
56 PRETTY_NAME="Ubuntu 20.04.4 LTS"
57 VERSION_ID="20.04"
58 HOME_URL="https://www.ubuntu.com/"
59 SUPPORT_URL="https://help.ubuntu.com/"
60 BUG_REPORT_URL="https://bugs.launchpad.net/ubuntu/"
61 PRIVACY_POLICY_URL="https://www.ubuntu.com/legal/terms-and-policies/privacy-policy"
62 VERSION_CODENAME=focal
63 UBUNTU_CODENAME=focal
64 {{/code}}
65
66 = Creating a Python Virtual Environment and Registering a Kernel =
67
68 The user has full flexibility to use different virtual environment and/or package management systems. Please find the examples of using conda or Pythons in-built venv options described below. Importantly, in either case, the user has to register the new environment as a kernel using ipykernel, to make is accessible via the Jupyter Notebooks (see //Registering the new Virtual Environment as Kernel// for more details).
69
70 == Using conda ==
71
72 The package management software conda by Anaconda has become one of the most popular package management systems, especially for Data and Life Sciences. Therefore, conda is already pre-deployed and configured in each user’s JupyterHub. Please find the full documentation of conda [[here>>url:https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html]], and the corresponding documentation of how to manage virtual environments using conda [[here>>url:https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html]]. The following steps provide a short example of how you can use conda to create a new virtual environment using the JupyterHub terminal within the Platform.
73
74 At first, you need to activate conda. Since it is already pre-deployed and configured for you, all you need to do is launch a terminal within JupyterHub (see //Launching the Terminal// above) and execute the command {{code}}source activate{{/code}}. This will activate conda and you can see the success of this by the indication of the currently activated conda environment at the beginning of the line, displayed in parentheses - usually “base”:
75
76 {{code language="none"}}
77 username@jupyter-username:~$ source activate
78 (base) username@jupyter-username:~$
79 {{/code}}
80
81 To create a new environment, run the following commands in the terminal after activating conda:
82
83 {{code language="none"}}
84 (base) username@jupyter-username:~$ conda create --name your_env_name
85 {{/code}}
86
87 Replace {{code}}your_env_name{{/code}} with your preferred name for the environment. When being prompted by conda to confirm the creation of the environment at the specified location (per default in the users home directory - please do not change this location, to ensure persistency of your created environment), proceed with the creation by typing “y”, or abort the process by typing “N”. Once confirmed, conda will complete the environment creation process and remind you to activate the environment:
88
89 {{code language="none"}}
90 (base) username@jupyter-username:~$ conda create --name sample_env
91 Collecting package metadata (current_repodata.json): done
92 Solving environment: done
93
94 ## Package Plan ##
95
96 environment location: /home/username/.conda_envs/sample_env
97
98 Proceed ([y]/n)? y
99
100 Preparing transaction: done
101 Verifying transaction: done
102 Executing transaction: done
103 #
104 # To activate this environment, use
105 #
106 # $ conda activate sample_env
107 #
108 # To deactivate an active environment, use
109 #
110 # $ conda deactivate
111
112 (base) username@jupyter-username:~$
113 {{/code}}
114
115 Please note, at the end of the environment creation process, you will still remain in the previously activate environment (“base”, in this example). Therefore, please remember to activate the novel environment before installing any packages by running the command {{code}}conda activate your_env_name{{/code}} and replace “your_env_name” with the corresponding name you chose (“sample_env” in this example):
116
117 {{code language="none"}}
118 (base) username@jupyter-username:~$ conda activate sample_env
119 (/home/username/.conda_envs/sample_env) username@jupyter-username:~$
120 {{/code}}
121
122 You can now install the desired packages in this new conda environment, for instance using the {{code}}conda install{{/code}} command. For example, in order to install the latest version of Python, run:
123
124 {{code language="none"}}
125 (/home/username/.conda_envs/sample_env) username@jupyter-username:~$ conda install python
126 {{/code}}
127
128 To see a list of all installed packages in the currently activated environment (indicated in parentheses at the beginning of the line, “base” in this case), run:
129
130 {{code language="none"}}
131 (base) username@jupyter-username:~$ conda list
132 {{/code}}
133
134 To see a list of all existing conda environments, run:
135
136 {{code language="none"}}
137 (base) username@jupyter-username:~$ conda info --envs
138 {{/code}}
139
140 Please find many more examples and the full documentation of how to manage conda environments [[here>>url:https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#]]. Importantly, please remember to follow the instructions in the //Registering the new Virtual Environment as Kernel// section below, to make the virtual environment accessible via the Jupyter Notebooks.
141
142 == Using venv ==
143
144 As an alternative to using conda, you can also use the Python native package venv. Please find the full documentation of venv [[here>>url:https://docs.python.org/3/library/venv.html#]], and a short example of how to create a new virtual environment using venv below:
145
146 {{code language="none"}}
147 username@jupyter-username:~$ python3 -m venv your_env_name
148 username@jupyter-username:~$ source your_env_name/bin/activate
149 {{/code}}
150
151 == Registering the new Virtual Environment as Kernel ==
152
153 In order to make the newly created virtual environment accessible for the Jupyter Notebooks, you have to register it using ipykernel. Importantly, please make sure that the corresponding environment is currently active before running the following commands:
154
155 {{code language="none"}}
156 username@jupyter-username:~$ python -m ipykernel install --user --name=your_env_name
157 {{/code}}
158
159 Please replace {{code}}your_env_name{{/code}} with the name of your newly created environment. Depending on which package and/or virtual environment management system you chose to use, you may have to install ipykernel in the newly created environment first. Remember to activate the newly created environment and then run one of the following commands to install ipykernel, depending on your package management system of choice:
160
161 {{code language="none"}}
162 (your_env_name) username@jupyter-username:~$ conda install -c anaconda ipykernel
163 {{/code}}
164
165 or:
166
167 {{code language="none"}}
168 username@jupyter-username:~$ pip install ipykernel
169 {{/code}}
170
171 Once you have installed ipykernel, re-run the command above to register your novel environment via ipykernel.
172
173 **Example usage:**
174
175 {{code language="none"}}
176 (/home/username/.conda_envs/sample_env) username@jupyter-username:~$ python -m ipykernel install --user --name=sample_env
177 Installed kernelspec sample_env in /home/username/.local/share/jupyter/kernels/sample_env
178 (/home/username/.conda_envs/sample_env) username@jupyter-username:~$
179 {{/code}}
180
181 Afterwards, the environment will be listed when you open the Launcher to open a new Jupyter Notebook:
182
Susan Evans 1.2 183 [[image:Project Workspace Jupyter view new Kernel 2023-07-11.png||height="25%" width="50%"]]
Susan Evans 1.1 184
185
Susan Evans 1.2 186 and also from each opened Notebook, e.g., via **Kernel > Change Kernel…** :
Susan Evans 1.1 187
Susan Evans 1.2 188 [[image:Project Workspace Jupyter Kernel change Kernel dropdown 2023-07-11.png||height="64%" width="50%"]]
Susan Evans 1.1 189
Susan Evans 1.2 190 = Installing New Python Packages =
191
192 We highly recommend the use of virtual environments when installing new packages (see //Creating a Python Virtual Environment and Registering a Kernel// above for more details). Consequently, we recommend installing new packages via commands in the JupyterHub terminal in the corresponding virtual environments, instead of installing packages from within Jupyter Notebooks.
193
194 Depending on the IT policies, outbound traffic may need to go through a proxy. If so, users will be required to provide the proxy command line argument such as pip, curl, wget, etc.
195
196 For example:
197
198 {{code language="none"}}
199 pip install my_package
200 {{/code}}
201
202 If you are using conda to manage python packages:
203
204 {{code language="none"}}
205 conda install my_package
206 {{/code}}
207
208 The above information is provided as examples only. Please refer to documentation provided by your IT department with respect to proxy configuration.
209
210 = Using the Pilot Command Line Interface in a JupyterHub Terminal =
211
212 The Pilot Command Line Interface (CLI) is deployed within JupyterHub as extension resource. Project members can use the Pilot Command Line Interface in a JupyterHub terminal to download Project data from the Core for further analysis, and upload the derivative outputs back to the Green Room or Core.
213
214 The Home Directory is your default directory. When you download a copy of your Core files to JupyterHub, the files persists in the JupyterHub environment until deleted by you, so you can return to the session and continue your work at a later time without the need to retrieve the data from the Core again.
215
216 The following sections focus on getting started with basic pilotcli commands in JupyterHub. For additional pilotcli commands and usage, see the article //Working with HDC Project Files in the Command Line Interface//.
217
218 == Launching Pilot Command Line Interface ==
219
220 1. Launch your Project and click the **JupyterHub** icon in in the workspace icon group.
221 1. Click the **Terminal **launcher icon to open the Terminal.
222 1. In the Jupyterhub Terminal, type {{code}}pilotcli{{/code}} to launch the latest version of the Pilot Command Line Interface.
223 1. Use the {{code}}pilotcli --help{{/code}} at any time to show the welcome message again.
224
Susan Evans 1.3 225 {{code language="none"}}
226 collaborator4@jupyter-collaborator4:~$ pilotcli
227 Usage: pilotcli [OPTIONS] COMMAND [ARGS]...
Susan Evans 1.2 228
Susan Evans 1.3 229 What's new (Version 2.2.0):
230
231 1. CLI supports to perform multi-threading upload for file/folders
232
233 2. CLI supports to perform resumable upload for single file
234
235
236
237 Options:
238 --help Show this message and exit.
239
240 Commands:
241 container_registry Container Registry Actions.
242 dataset Dataset Actions.
243 file File Actions.
244 project Project Actions.
245 use_config Config Actions.
246 user User Actions.
247 {{/code}}
248
249 == Logging into the Pilot Command Line Interface ==
250
251 Users are required to login with platform credentials before performing any tasks through Pilot Command Line Interface.
252
253 * Use the command {{code}}pilotcli user login{{/code}} to log into the Pilot Command Line Interface.
254
255 {{code language="none"}}
256 collaborator4@jupyter-collaborator4:~$ pilotcli user login
257 Please, access https://iam.staging.pilot.indocresearch.com/realms/pilot/device?user_code=XXXX-XXXX to proceed
258 ▄▄▄▄▄▄▄ ▄ ▄▄ ▄ ▄▄▄▄ ▄ ▄▄▄▄▄▄▄
259 █ ▄▄▄ █ ▄ ▄███ ▀▀ █▀ ▀██▄ █ ▄▄▄ █
260 █ ▄ ▀ ▄ ▀▄ ▀▀ ▄█▀▄▀ ▀▀▄█▄▄▀ █████▄▄▀▄
261 ▄▄▄▄▄▄▄ ▀ ▀█▄ ▀▄ ██▀█ ▄▀▄▄ █ ▄ █▀▄▄▄
262 █ ▄▄▄ █ █▀█▄▀ █▀ █▀▀█ ▀▄█▄█▄▄▄█▀▄█
263 █ ███ █ █▀██▀▄ █▀▄▄▀▀█▄▀▀█▄▀█ ▀ ▀▄▀██
264 █▄▄▄▄▄█ ▄▀▄▄██▄▄▀▄ ▀▀▄ ▄▄▀▀▀▄ █▄▄▄█
265
266 Waiting validation finish...
267 {{/code}}
268
269 * (((
270 You’ll be asked to validate your HDC user account using one of the provided methods.
271
272 * Copy and paste the provided validation link into a new browser tab or
273 * Scan the QR code with your mobile device.
274 )))
275 * Open the login window and enter your HDC username and password (i.e. your EBRAINS account credentials).
276 * Grant access by clicking **Yes**.
277
278 [[image:Pilotcli Jupyter user login Grant Access window v2.4.0 2023-05-25.png||height="46%" width="50%"]]
279
280 [[image:Pilotcli Jupyter user login Device Login Successful v2.4.0 2023-05-25.png||height="16%" width="50%"]]
281
282 * After successful confirmation, return to the terminal in your JupyterHub browser tab.
283
284 {{code language="none"}}
285 Welcome to the Command Line Tool!
286 {{/code}}
287
288 * You’re now ready to start using the Pilot Command Line Interface to work with your Project data in JupyterHub.
289
290 == Zone Restrictions when using Pilot Command Line Interface in JupyterHub ==
291
292 When using the Pilot Command Line Interface in JupyterHub and the following actions are possible on the derivative files generated in JupyterHub:
293
294 |=(% colspan="1" rowspan="1" %)(((
295 **File Operation**
296 )))|=(% colspan="1" rowspan="1" %)(((
297 **Permitted in the **
Susan Evans 1.4 298 **Green Room**
Susan Evans 1.3 299 )))|=(% colspan="1" rowspan="1" %)(((
300 **Permitted in the **
Susan Evans 1.4 301 **Core**
Susan Evans 1.3 302 )))
303 |(% colspan="1" rowspan="1" %)File upload 
304 (upload derivative output files from JupyterHub to the Green Room or Core storage)|(% colspan="1" rowspan="1" %)(((
305 Yes
306 )))|(% colspan="1" rowspan="1" %)(((
307 Yes
308 )))
309 |(% colspan="1" rowspan="1" %)File download
310 (download files from Green Room or Core into JupyterHub)|(% colspan="1" rowspan="1" %)(((
311 **No**
312 )))|(% colspan="1" rowspan="1" %)(((
313 Yes
314 )))
315
316
Susan Evans 1.5 317
318
319