Using JupyterHub in HDC

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/]].

7

8

[[image:Jupyterhub-Logo-300x100px cropped.png||height="13%" width="20%"]]

= How it Works =

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.

= Prerequisites =

* Project Collaborator role or higher.

18

* JupyterHub has been configured for the Project by the Platform Administrator. See //Getting Access to JupyterHub//.

= Data Stewardship =

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.

23

24

= Getting Access to JupyterHub =

25

26

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.

27

28

= Launching JupyterHub =

29

30

[[image:HDC Project Workspace tool navigation Jupyterhub v1.0.0 2023-05-25.png||height="9%" width="25%"]]

31

32

1. Launch your Project and click the **JupyterHub icon** in the left menu bar.

33

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.

34

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.

35

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).

36

1. When finished using JupyterHub, click **Logout** to end your session.

37

38

= Creating a Notebook =

39

40

Users can create a new Jupyter Notebook with Python 3 inside JupyterHub, with dedicated and persistent storage under the users' Home Directory.

41

42

1. In the Launcher, click the **Python 3 Notebook **icon, or click **File > New > Notebook**.

43

1. Create your Notebook.

44

45

[[image:Project Workspace Jupyter Create Python Notebook v2.1.6 2023-02-07.png||height="22%" width="50%"]]

46

47

= Launching the Terminal =

48

49

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.

50

51

1. In the Launcher, click the **Terminal **icon, or click **File > New > Terminal**.

52

1. The terminal window opens.

53

54

[[image:Project Workspace Jupyter Launch Terminal v2.1.6 2023-02-07.png||height="9%" width="50%"]]

55

56

Ubuntu is used to host Jupyter Notebook. Use the command cat /etc/os-release to determine to current version of Ubuntu:

57

58

59

uname@jupyter-uname:/etc$ cat os-release

60

NAME="Ubuntu"

61

VERSION="20.04.4 LTS (Focal Fossa)"

62

ID=ubuntu

63

ID_LIKE=debian

64

PRETTY_NAME="Ubuntu 20.04.4 LTS"

65

VERSION_ID="20.04"

66

HOME_URL="https://www.ubuntu.com/"

67

SUPPORT_URL="https://help.ubuntu.com/"

68

BUG_REPORT_URL="https://bugs.launchpad.net/ubuntu/"

69

PRIVACY_POLICY_URL="https://www.ubuntu.com/legal/terms-and-policies/privacy-policy"

70

VERSION_CODENAME=focal

71

UBUNTU_CODENAME=focal

72

73

74

= Creating a Python Virtual Environment and Registering a Kernel =

75

76

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).

== Using conda ==

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.

81

82

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”:

83

84

85

username@jupyter-username:~$ source activate

86

(base) username@jupyter-username:~$

87

88

89

To create a new environment, run the following commands in the terminal after activating conda:

90

91

92

(base) username@jupyter-username:~$ conda create --name your_env_name

93

94

95

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:

96

97

98

(base) username@jupyter-username:~$ conda create --name sample_env

99

Collecting package metadata (current_repodata.json): done

100

Solving environment: done

## Package Plan ##

environment location: /home/username/.conda_envs/sample_env

Proceed ([y]/n)? y

Preparing transaction: done

109

Verifying transaction: done

110

Executing transaction: done

111

#

112

# To activate this environment, use

113

#

114

# $ conda activate sample_env

115

#

116

# To deactivate an active environment, use

#

# $ conda deactivate

(base) username@jupyter-username:~$

121

122

123

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):

124

125

126

(base) username@jupyter-username:~$ conda activate sample_env

127

(/home/username/.conda_envs/sample_env) username@jupyter-username:~$

128

129

130

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:

131

132

133

(/home/username/.conda_envs/sample_env) username@jupyter-username:~$ conda install python

134

135

136

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:

137

138

139

(base) username@jupyter-username:~$ conda list

140

141

142

To see a list of all existing conda environments, run:

143

144

145

(base) username@jupyter-username:~$ conda info --envs

146

147

148

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.

== Using venv ==

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:

153

154

155

username@jupyter-username:~$ python3 -m venv your_env_name

156

username@jupyter-username:~$ source your_env_name/bin/activate

157

158

159

== Registering the new Virtual Environment as Kernel ==

160

161

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:

162

163

164

username@jupyter-username:~$ python -m ipykernel install --user --name=your_env_name

165

166

167

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:

168

169

170

(your_env_name) username@jupyter-username:~$ conda install -c anaconda ipykernel

or:

username@jupyter-username:~$ pip install ipykernel

177

178

179

Once you have installed ipykernel, re-run the command above to register your novel environment via ipykernel.

**Example usage:**

(/home/username/.conda_envs/sample_env) username@jupyter-username:~$ python -m ipykernel install --user --name=sample_env

185

Installed kernelspec sample_env in /home/username/.local/share/jupyter/kernels/sample_env

186

(/home/username/.conda_envs/sample_env) username@jupyter-username:~$

187

188

189

Afterwards, the environment will be listed when you open the Launcher to open a new Jupyter Notebook:

190

191

[[image:Project Workspace Jupyter view new Kernel 2023-07-11.png||height="25%" width="50%"]]

192

193

194

and also from each opened Notebook, e.g., via **Kernel > Change Kernel…** :

195

196

[[image:Project Workspace Jupyter Kernel change Kernel dropdown 2023-07-11.png||height="32%" width="25%"]]

197

198

= Installing New Python Packages =

199

200

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.

201

202

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.

For example:

pip install my_package

208

209

210

If you are using conda to manage python packages:

211

212

213

conda install my_package

214

215

216

The above information is provided as examples only. Please refer to documentation provided by your IT department with respect to proxy configuration.

217

218

= Using the Pilot Command Line Interface in a JupyterHub Terminal =

219

220

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.

221

222

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.

223

224

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//.

225

226

== Launching Pilot Command Line Interface ==

227

228

1. Launch your Project and click the **JupyterHub** icon in in the workspace icon group.

229

1. Click the **Terminal **launcher icon to open the Terminal.

230

1. In the Jupyterhub Terminal, type {{code}}pilotcli{{/code}} to launch the latest version of the Pilot Command Line Interface.

231

1. Use the {{code}}pilotcli --help{{/code}} at any time to show the welcome message again.

232

233

234

collaborator4@jupyter-collaborator4:~$ pilotcli

235

Usage: pilotcli [OPTIONS] COMMAND [ARGS]...

236

237

What's new (Version 2.2.0):

238

239

1. CLI supports to perform multi-threading upload for file/folders

240

241

2. CLI supports to perform resumable upload for single file

Options:

--help Show this message and exit.

247

248

Commands:

249

container_registry Container Registry Actions.

250

dataset Dataset Actions.

251

file File Actions.

252

project Project Actions.

253

use_config Config Actions.

user User Actions.

== Logging into the Pilot Command Line Interface ==

258

259

Users are required to login with platform credentials before performing any tasks through Pilot Command Line Interface.

260

261

* Use the command {{code}}pilotcli user login{{/code}} to log into the Pilot Command Line Interface.

262

263

264

collaborator4@jupyter-collaborator4:~$ pilotcli user login

265

Please, access https://iam.staging.pilot.indocresearch.com/realms/pilot/device?user_code=XXXX-XXXX to proceed

266

▄▄▄▄▄▄▄ ▄ ▄▄ ▄ ▄▄▄▄ ▄ ▄▄▄▄▄▄▄

267

█ ▄▄▄ █ ▄ ▄███ ▀▀ █▀ ▀██▄ █ ▄▄▄ █

268

█ ▄ ▀ ▄ ▀▄ ▀▀ ▄█▀▄▀ ▀▀▄█▄▄▀ █████▄▄▀▄

269

▄▄▄▄▄▄▄ ▀ ▀█▄ ▀▄ ██▀█ ▄▀▄▄ █ ▄ █▀▄▄▄

270

█ ▄▄▄ █ █▀█▄▀ █▀ █▀▀█ ▀▄█▄█▄▄▄█▀▄█

271

█ ███ █ █▀██▀▄ █▀▄▄▀▀█▄▀▀█▄▀█ ▀ ▀▄▀██

272

█▄▄▄▄▄█ ▄▀▄▄██▄▄▀▄ ▀▀▄ ▄▄▀▀▀▄ █▄▄▄█

273

274

Waiting validation finish...

* (((

You’ll be asked to validate your HDC user account using one of the provided methods.

279

280

* Copy and paste the provided validation link into a new browser tab or

281

* Scan the QR code with your mobile device.

282

)))

283

* Open the login window and enter your HDC username and password (i.e. your EBRAINS account credentials).

284

* Grant access by clicking **Yes**.

285

286

[[image:Pilotcli Jupyter user login Grant Access window v2.4.0 2023-05-25.png||height="28%" width="30%"]]

287

288

[[image:Pilotcli Jupyter user login Device Login Successful v2.4.0 2023-05-25.png||height="10%" width="30%"]]

289

290

* After successful confirmation, return to the terminal in your JupyterHub browser tab.

291

292

293

Welcome to the Command Line Tool!

294

295

296

* You’re now ready to start using the Pilot Command Line Interface to work with your Project data in JupyterHub.

297

298

== Zone Restrictions when using Pilot Command Line Interface in JupyterHub ==

299

300

When using the Pilot Command Line Interface in JupyterHub and the following actions are possible on the derivative files generated in JupyterHub:

301

302

|=(% colspan="1" rowspan="1" %)(((

303

**File Operation**

304

)))|=(% colspan="1" rowspan="1" %)(((

305

**Permitted in the **

306

**Green Room**

307

)))|=(% colspan="1" rowspan="1" %)(((

308

**Permitted in the **

309

**Core**

310

)))

311

|(% colspan="1" rowspan="1" %)File upload

312

(upload derivative output files from JupyterHub to the Green Room or Core storage)|(% colspan="1" rowspan="1" %)(((

313

Yes

314

)))|(% colspan="1" rowspan="1" %)(((

315

Yes

316

)))

317

|(% colspan="1" rowspan="1" %)File download

318

(download files from Green Room or Core into JupyterHub)|(% colspan="1" rowspan="1" %)(((

319

**No**

320

)))|(% colspan="1" rowspan="1" %)(((

Yes

)))

== Downloading Project Data to JupyterHub using the Pilot Command Line Interface ==

325

326

After logging into the Pilot Command Line Interface, you can download data from the Project Core into the JupyterHub environment to start your data analyses.

327

328

File related commands are grouped in the {{code}}file{{/code}} category. To view the full list of commands in this category, type {{code}}pilotcli file --help{{/code}}. To download project data, use the file sync command. To view the full list of commands in this category, type {{code}}pilotcli file sync --help{{/code}}.

collaborator4@jupyter-collaborator4:~$ pilotcli file sync --help

333

Usage: pilotcli file sync [OPTIONS] [PATHS]... OUTPUT_PATH

334

335

Download files/folders from a given Project/folder/file in core zone.

336

337

Options:

338

-z, --zone TEXT Target Zone (i.e., core/greenroom)

339

--zip Download files as a zip.

340

-i, --geid Enable downloading by geid.

341

--help Show this message and exit.

=== Example ===

Downloading a file from the Core to your Home Directory:

347

348

Reminder: Please follow Linux conventions for file management. If your filename contains spaces, wrap it in single or double quotes.

349

350

* //Filename~:// “Chemical Tracking Data.csv”

351

* //Source~:// Project “Indoc Test Project”, “Core” storage zone, folder “collaborator4” {{code}}indoctestproject/collaborator4/Chemical Tracking Data.csv -z core{{/code}}

352

* //Destination: //user's Home directory in the Guacamole or JupyterHub VM {{code}}.{{/code}}

353

* //Command group/option: //{{code}}file sync{{/code}}

354

355

356

collaborator4@jupyter-collaborator4:~$ pilotcli file sync indoctestproject/collaborator4/'Chemical Tracking Data.csv' . -z core

357

start downloading...

358

Downloading Chemical Tracking Data.csv |██████████████████████████████ 100% 00:00

359

File has been downloaded successfully and saved to: ./Chemical Tracking Data.csv

360

361

362

To confirm successful download, type {{code}}ls{{/code}} and verify the file "Chemical Tracking Data.csv" is stored in the Home folder.

363

364

365

collaborator4@jupyter-collaborator4:~$ ls

366

'Chemical Tracking Data.csv' pilotcli

367

368

369

The file “Chemical Tracking Data.csv” can be viewed in the JupyterHub graphical user interface:

370

371

[[image:Jupyter downloaded file in Home folder v2.4.11 2023-05-25 1850.png||height="15%" width="50%"]]

372

373

374

== Uploading Project Data from JupyterHub using the Pilot Command Line Interface ==

375

376

After analyzing Project data inside the JupyterHub, you can upload the generated outputs back into the Project via the Pilot Command Line Interface.

=== Example ===

* //Filename//: Chemical Tracking Data rev.csv

381

* //Source~:// user's Home directory in JupyterHub {{code}}.{{/code}}

382

* //Destination//: Project “Indoc Test Project”, folder “collaborator4”, “Core” storage zone,

383

{{code}}indoctestproject/collaborator4{{/code}} {{code}}-z core{{/code}}

384

* //Command group/option~:// {{code}}file upload{{/code}}

385

* //User message// (for upload back to the Core): “my workbench output, no additional sensitive data"

386

* //Command~:// {{code}}pilotcli file upload ./'Chemical Tracking Data rev.csv' -p{{/code}} {{code}}indoctestproject/collaborator4 -z core -m "my workbench output, no additional sensitive data"{{/code}}

387

388

When uploading data to the Core, you are reminded that you are bypassing the usual Green Room upload workflow. To confirm, type {{code}}y{{/code}} at the prompt, or {{code}}N{{/code}} to cancel.

389

390

391

collaborator4@jupyter-collaborator4:~$ pilotcli file upload ./'Chemical Tracking Data rev.csv' -p indoctestproject/collaborator4 -z core -m "my workbench output, no additional sensitive data"

392

You are about to transfer data directly to the PILOT Core! In accordance with the PILOT Terms of Use, please confirm that you have made your best efforts to

393

pseudonymize or anonymize the data and that you have the legal authority to transfer and make this data available for dissemination and use within the PILOT .If you

394

need to process the data to remove sensitive identifiers, please cancel this transfer and upload the data to the Green Room to perform these actions.

395

To cancel this transfer, enter [n/No]

396

To confirm and proceed with the data transfer, enter [y/Yes]

397

[y/N]: y

398

Starting upload of: ./Chemical Tracking Data rev.csv

399

Pre-upload complete.

400

Uploading Chemical Tracking Data rev.csv: |██████████████████████████████ 100% 00:00

401

Upload Time: 2.92s for 1 files

402

All uploading jobs have finished.

403

404

405

After completing the upload, you can confirm the new file “Chemical Tracking Data rev.csv" exists in the correct directory using the pilotcli file list command and/or in the Portal File Explorer.

406

407

408

collaborator4@jupyter-collaborator4:~$ pilotcli file list indoctestproject/collaborator4 -z core

409

Chemical Tracking Data rev.csv Chemical Tracking Data.csv

410

411

412

[[image:Jupyterhub file upload back to core v2.4.11 2023-05-25 1926.png||height="13%" width="50%"]]

413

414