A proactive kernel for Jupyter
The ActiveEon Jupyter Kernel adds a kernel backend to Jupyter to interface directly with the ProActive scheduler and construct tasks and workflows to execute them on the fly.
Python 2 or 3
2.1 Using PyPi
open a terminal
install the proactive jupyter kernel
$ pip install proactive proactive-jupyter-kernel --upgrade $ python -m proactive-jupyter-kernel.install
2.2 Using source code
open a terminal
clone the repository on your local machine:
$ git clone email@example.com:ow2-proactive/proactive-jupyter-kernel.git
- install the proactive jupyter kernel:
$ pip install proactive-jupyter-kernel/ $ python -m proactive-jupyter-kernel.install
You can use any jupyter platform. We recommend the use of jupyter lab. To launch it from your terminal after having installed it:
$ jupyter lab
or in daemon mode:
$ nohup jupyter lab &>/dev/null &
When opened, click on the Proactive icon to open a notebook based on the proactive kernel.
4.1 Using connect()
If you are trying proactive for the first time, please sign up on try platform.
Once you receive your login and password, connect using the
To connect to another host, please use the later pragma this way:
#%connect(host=YOUR_HOST, port=YOUR_PORT, login=YOUR_LOGIN, password=YOUR_PASSWORD)
4.2 Using config file:
For automatic sign in, create a file named proactive_config.ini in your notebook location.
Fill your configuration file according to the format:
[proactive_server] host=YOUR_HOST port=YOUR_PORT [user] login=YOUR_LOGIN password=YOUR_PASSWORD
Save your file changes and restart the proactive kernel.
You can also force the current Kernel to connect using any .ini config file through the
(for more information about this format please check configParser)
5.1 Creating a Python task
To create a task, please use the pragma
#%task() followed by the task implementation script wrote into a notebook block code.
To use this pragma, at least, a task name has to be provided. Example:
#%task(name=myTask) print('Hello world')
#%task(name=TASK_NAME, [language=SCRIPT_LANGUAGE], [dep=[TASK_NAME1,TASK_NAME2,...]], [generic_info=[(KEY1,VAL1), (KEY2,VALUE2),...]], [export=[VAR_NAME1,VAR_NAME2,...]], [import=[VAR_NAME1,VAR_NAME2,...]], [path=IMPLEMENTATION_FILE_PATH])\n'
As seen in the general usage, users can also provide more information about the task by using the pragma's options:
language parameter is needed when the task script is not written in native Python, the default language.
The handled programming languages are:
Example of usage when the task is written in
#%task(name=myTask, language=Linux_Bash) echo 'Hello, World!'
One of the most important notions in workflows is the dependencies between tasks. To specify this information, please use the
dep parameter. The value should be a list of all the tasks on which the new task depends. Example:
#%task(name=myTask,dep=[parentTask1,parentTask2]) print('Hello world')
5.1.3 Generic information
To specify the values of the advanced ProActive variables
generic_info is provided. The value should be a tuples
(key,value) list of all the names and values of the
ProActive parameters. Example:
#%task(name=myTask, generic_info=[(var1,value1),(var2,value2)]) print('Hello world')
5.1.4 Export/import variables
import parameters make possible variables propagation between the different tasks of a workflow.
var2 are needed in
myTask1 pragma should include and
a list of these variable names and the
myTask2 pragma an
import with a list including the same names. Example:
myTask1 implementation bloc would be:
#%task(name=myTask1, export=[var1,var2]) var1 = "Hello" var2 = "ActiveEon!"
myTask2 implementation bloc would be:
#%task(name=myTask2, dep=[myTask1], import[var1,var2]) print(var1 + " from " + var2)
5.1.5 Implementation file
It is possible to use an external implementation file as task implementation. To do so, the option
path should be used.
5.2 Imports libraries
The main difference between Proactive kernel and a native language one resides in the memory access during the
execution of the different blocs. While in a common native language kernel the whole script code (all the notebook
blocs) is executed locally in the same shared memory zone, the ProActive kernel each created task will be executed as
an independent process.
To facilitate the transition from native language kernels to the ProActive one we included the pragma
This pragma gives the ability to the user to add, just once in the notebook, libraries that are common to all created
tasks implemented in a same native script language.
The pragma is used in this general manner:
#%import(language=Python) import os import pandas
Notice that if the language is not specified, Python is considered by default.
5.3 Adding a fork environment
To configure a fork environment for a task, please use the
#%fork_env() pragma. A first way to do this
is by providing the name of the corresponding task, and the fork environment implementation after that:
#%fork_env(name=TASK_NAME) containerName = 'activeeon/dlm3' dockerRunCommand = 'docker run ' dockerParameters = '--rm ' paHomeHost = variables.get("PA_SCHEDULER_HOME") paHomeContainer = variables.get("PA_SCHEDULER_HOME") proActiveHomeVolume = '-v '+paHomeHost +':'+paHomeContainer+' ' workspaceHost = localspace workspaceContainer = localspace workspaceVolume = '-v '+localspace +':'+localspace+' ' containerWorkingDirectory = '-w '+workspaceContainer+' ' preJavaHomeCmd = dockerRunCommand + dockerParameters + proActiveHomeVolume + workspaceVolume + containerWorkingDirectory + containerName
A second way is by providing the name of the task, and the path of a _.py_ file containing the fork environment code:
5.4 Adding a selection script
To add a selection script to a task, please use the
#%selection_script() pragma. A first way to do it,
provide the name of the corresponding task, and the selection code implementation after that:
#%selection_script(name=TASK_NAME) selected = True
A second way is by providing the name of the task, and the path of a .py file containing the selection code:
5.5 Adding job fork environment and/or selection script
If the selection scripts and/or the fork environments are the same for all job tasks, we can add them just once using
job_selection_script and/or the
For a job selection script please use:
#%job_selection_script([language=SCRIPT_LANGUAGE], [path=./SELECTION_CODE_FILE.py], [force=on/off])
For a job fork environment please use:
#%job_fork_env([language=SCRIPT_LANGUAGE], [path=./FORK_ENV_FILE.py], [force=on/off])
force parameter says if the pragma has to overwrite the task selection scripts or fork environment already set or not.
5.6 Adding pre and/or post scripts
Sometimes, specified scripts has to be executed before and/or after a particular task. To do that, the solution provides
To add a pre-script to a task, please use:
#%pre_script(name=TASK_NAME, language=SCRIPT_LANGUAGE, [path=./PRE_SCRIPT_FILE.py])
To add a post-script to a task, please use:
#%post_script(name=TASK_NAME, language=SCRIPT_LANGUAGE, [path=./POST_SCRIPT_FILE.py])
5.7 Create a job
To create a job, please use the
If the job was already been created, the call of this pragma would just rename the job already created by the new provided name.
Notice that it is not necessary to create and name explicitly the job. If not done by the user, this step is implicitly performed when the job is submitted (check section 5.7 for more information).
5.8 Plot job
To verify the created workflow, please use the
#%draw_job() pragma to plot it into a separate window:
Two optional parameters can be used to configure the way the kernel plot the workflow.
If this parameter is set to 'off', the workflow plotting is made through the Matplotlib external window. The default value is 'on'.
saving into hard disk:
To be sure the workflow is saved into a _.png_ file, this option needs to be set to on. The default value is off.
Note that the job will be named (in the order of existence) by the name provided using the 'name' parameter, by the name of the job if it is created, by the name of the notebook if reachable or at worst by "Unnamed_job".
#%draw_job([name=JOB_NAME], [inline=off], [save=on])
5.9 Save workflow in dot format
To save the created workflow into a GraphViz _.dot_ format, please use the
5.10 Submit your job to the scheduler
To submit the job to the proactive scheduler, the user has to use the
If the job is not created, or is not up-to-date, the
#%submit_job() starts by creating a new job named as the old one.
To provide a new name, please use the same pragma and provide a name as parameter:
If the kernel, during its execution, never received a job name, he uses the current notebook name, if possible, or gives a random one.
5.11 List all submitted jobs
To get all the submitted job ids and names, please use
list_submitted_jobs pragma this way:
5.12 Printing results
To get the job result(s), the user has to use the
#%get_result() pragma by providing the job name:
or by the job id:
The returned values of your final tasks will be automatically printed.
5.13 Showing ActiveEon portals
Finally, to have the hand on more parameters and features, the user should use ActiveEon Studio portals. The main ones are the _Resource Manager_, the _Scheduling Portal_ and the _Workflow Automation_.
To show the resource manager portal related to the host you are connected to, just run:
for the related scheduling portal:
and for the related workflow automation:
NOTE: The parameters
width allow the user to adjust the size of the window inside the notebook.
help: prints all different pragmas/features of the kernel
connect: connects to an ActiveEon server (OPTION: connection using a configuration file)
import: import specified libraries to all tasks of a same script language
task: creates a task
pre_script: sets the pre-script of a task
post_script: sets the post-script of a task
selection_script: sets the selection script of a task
job_selection_script: sets the default selection script of a job
fork_env: sets the fork environment script
job_fork_env: sets the default fork environment of a job
job: creates/renames the job
draw_job: plot the workflow
write_dot: writes the workflow in .dot format
submit_job: submits the job to the scheduler
get_result: gets and prints the job results
list_submitted_jobs: gets and prints the ids and names of the submitted jobs
show_resource_manager: opens the ActiveEon resource manager portal
show_scheduling_portal: opens the ActiveEon scheduling portal
- execute in local a pragma free block
- add options import_as_json/export_as_json
- add draw(on/off), print_result(on/off) options in submit job pragma
- multiple pragmas in a block handling
- apply selection_script and fork_env to a list of names (tasks)
- add auto-complete
- add some examples pictures
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