A proactive kernel for Jupyter
The ActiveEon Jupyter Kernel adds a kernel backend to Jupyter. This kernel interfaces directly with the ProActive scheduler and constructs tasks and workflows to execute them on the fly.
With this interface, users can run their code locally and test it using a native python kernel, and by a simple switch to ProActive kernel, run it on remote public or private infrastructures without having to modify the code. See the example below:
Python 2 or 3
1.2 Using PyPi
open a terminal
install the ProActive jupyter kernel with the following commands:
$ pip install proactive proactive-jupyter-kernel --upgrade $ python -m proactive-jupyter-kernel.install
1.3 Using source code
open a terminal
clone the repository on your local machine:
$ git clone firstname.lastname@example.org:ow2-proactive/proactive-jupyter-kernel.git
- install the ProActive jupyter kernel with the following commands:
$ pip install proactive-jupyter-kernel/ $ python -m proactive-jupyter-kernel.install
You can use any jupyter platform. We recommend to use 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.
As a quick start, we recommend the user to run the
#%help() pragma using the following script:
This script gives a brief description of all the different pragmas that the ProActive Kernel provides.
To get a more detailed description of a needed pragma, the user can run the following script:
4.1 Using connect()
If you are trying ProActive for the first time, sign up on the try platform.
Once you receive your login and password, connect to the trial platform using the
To connect to another ProActive server host, use the later pragma this way:
#%connect(host=YOUR_HOST, [port=YOUR_PORT], login=YOUR_LOGIN, password=YOUR_PASSWORD)
Notice that the
port parameter is optional. The default connexion port is
4.2 Using a configuration file:
For automatic sign in, create a file named proactive_config.ini in your notebook working directory.
Fill your configuration file according to the following format:
[proactive_server] host=YOUR_HOST port=YOUR_PORT [user] login=YOUR_LOGIN password=YOUR_PASSWORD
Save your 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 new task, use the pragma
#%task() followed by the task implementation script written into a notebook
To use this pragma, a task name has to be provided at least. 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'
Users can also provide more information about the task using the pragma's options. In the following, we give more details about the possible options:
language parameter is needed when the task script is not written in native Python. If not provided, Python will be
selected as the default language.
The supported programming languages are:
Here is an example that shows a task implementation 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, use the
dep parameter. Its value should be a list of all 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 some advanced ProActive variables called
generic_information, you should
generic_info parameter. Its value should be a list of tuples
(key,value) that corresponds to the names
and adequate values of the Generic Information. Example:
#%task(name=myTask, generic_info=[(var1,value1),(var2,value2)]) print('Hello world')
5.1.4 Export/import variables
import parameters ensure variables propagation between the different tasks of a workflow.
var2 are needed in
myTask2, both pragmas have to specify this information as
myTask1should include an
exportparameter with a list of these variable names,
myTask2should include an
importparameter with a list including the same names.
myTask1 implementation block would be:
#%task(name=myTask1, export=[var1,var2]) var1 = "Hello" var2 = "ActiveEon!"
myTask2 implementation block would be:
#%task(name=myTask2, dep=[myTask1], import[var1,var2]) print(var1 + " from " + var2)
5.1.5 Implementation file
It is also possible to use an external implementation file to define the task implementation. To do so, the option
should be used.
5.2 Importing libraries
The main difference between the ProActive and 'native language' kernels resides in the way the memory is accessed
during blocks execution. In a common native language kernel, the whole script code (all the notebook blocks) is
locally executed in the same shared memory space; whereas the ProActive kernel will execute each created task in an
independent process. In order to facilitate the transition from native language to ProActive kernels, we included the
#%import(). This pragma gives the user the ability to add libraries that are common to all created tasks, and
thus relative distributed processes, that are implemented in the same native script language.
The import pragma is used as follows:
#%import(language=Python) import os import pandas
NOTE: If the language is not specified, Python is considered as default language.
5.3 Adding a fork environment
To configure a fork environment for a task, use the
#%fork_env() pragma. To do so, you have to provide the name of the
corresponding task and the fork environment implementation.
#%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
Or, you can provide the task name 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, use the
#%selection_script() pragma. To do so, you have to provide the name of
the corresponding task and the selection code implementation.
#%selection_script(name=TASK_NAME) selected = True
Or, you can provide the task name 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, use:
#%job_fork_env([language=SCRIPT_LANGUAGE], [path=./FORK_ENV_FILE.py], [force=on/off])
force parameter defines whether the pragma has to overwrite the task selection scripts or the fork environment
5.6 Adding pre and/or post scripts
Sometimes, specific 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, use:
#%post_script(name=TASK_NAME, language=SCRIPT_LANGUAGE, [path=./POST_SCRIPT_FILE.py])
5.7 Create a job
To create a job, use the
If the job has already been created, the call of this pragma would just rename the already created job by the new provided name.
NOTE: It is not necessary to create and assign a name explicitly to the job. If not done by the user, this step is implicitly performed when the job is submitted (check section <<Submit your job to the scheduler>> for more information).
5.8 Visualize job
To visualize the created workflow, use the
#%draw_job() pragma to plot the workflow graph that represents the job
into a separate window:
Two optional parameters can be used to configure the way the kernel plots the workflow graph.
If this parameter is set to
off, plotting the workflow graph is done through a Matplotlib
external window. The default value is
save the workflow graph locally:
To be sure that the workflow is saved into a .png file, this option needs to be set to
on. The default value is
Note that the job's name can take one of the following possible values:
- The parameter
name's value, if provided
- The job's name, if created
- The notebook's name, if the kernel can retrieve it
#%draw_job([name=JOB_NAME], [inline=off], [save=on])
5.9 Export the workflow in dot format
To export the created workflow into a GraphViz .dot format, 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() creates a new job named as the old one.
To provide a new name, use the same pragma and provide a name as parameter:
If the job's name is not set, the ProActive kernel uses the current notebook name, if possible, or gives a random one.
5.11 List all submitted jobs
To get all submitted job IDs and names, use
list_submitted_jobs pragma this way:
5.12 Print 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.
6. Display and use ActiveEon Portals directly in Jupyter
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.
The example below shows how the user can directly monitor his submitted job's execution in the scheduling portal:
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
show_workflow_automation: opens the ActiveEon workflow automation 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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