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:
This repo presents some Notebook examples that use the ProActive Jupyter Kernel or the ProActive Python Client.
To facilitate the understanding of these notebooks, please find below the ProActive Jupyter Kernel documentation.
As a quick start, we recommend the user to run the #%help()
pragma using the following script:
#%help()
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:
#%help(pragma=PRAGMA_NAME)
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 #%connect()
pragma:
#%connect(login=YOUR_LOGIN)
To connect to another ProActive server host, use the later pragma this way:
#%connect(host=YOUR_HOST, [port=YOUR_PORT], login=YOUR_LOGIN)
Notice that the port
parameter is optional. The default connexion port is 8080
.
You can also connect to a distant server by providing its url
in the following way:
#%connect(url=YOUR_SERVER_URL, login=YOUR_LOGIN)
By providing the complete url
of the server, users can eventually connect through the secure HTTPS protocol.
For automatic sign in, create a file named proactive_config.ini in your notebook working directory.
Fill your configuration file according to one of the following two formats:
- By providing the server
host
andport
:
[proactive_server]
host=YOUR_HOST
port=YOUR_PORT
[user]
login=YOUR_LOGIN
password=YOUR_PASSWORD
- By providing the server
url
:
[proactive_server]
url=YOUR_SERVER_URL
[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 #%connect()
pragma:
#%connect(path=PATH_TO/YOUR_CONFIG_FILE.ini)
(For more information about this format please check configParser)
To create a new task, use the pragma #%task()
followed by the task implementation script written into a notebook
block code.
To use this pragma, a task name has to be provided at least. Example:
#%task(name=myTask)
print('Hello world')
General usage:
#%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:
The 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:
- Linux_Bash
- Windows_Cmd
- DockerCompose
- Scalaw
- Groovy
- Javascript
- Jython
- Python
- Ruby
- Perl
- PowerShell
- R
Here is an example that shows a task implementation written in Linux_Bash
:
#%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')
To specify the values of some advanced ProActive variables called
generic_information, you should
provide the 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')
The export
and import
parameters ensure variables propagation between the different tasks of a workflow.
If myTask1
variables var1
and var2
are needed in myTask2
, both pragmas have to specify this information as
follows:
myTask1
should include anexport
parameter with a list of these variable names,myTask2
should include animport
parameter with a list including the same names.
Example:
myTask1
implementation block would be:
#%task(name=myTask1, export=[var1,var2])
var1 = "Hello"
var2 = "ActiveEon!"
and myTask2
implementation block would be:
#%task(name=myTask2, dep=[myTask1], import[var1,var2])
print(var1 + " from " + var2)
It is also possible to use an external implementation file to define the task implementation. To do so, the option path
should be used.
Example:
#%task(name=myTask,path=PATH_TO/IMPLEMENTATION_FILE.py)
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
pragma #%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=SCRIPT_LANGUAGE])
.
Example:
#%import(language=Python)
import os
import pandas
NOTE: If the language is not specified, Python is considered as default language.
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.
Example:
#%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:
#%fork_env(name=TASK_NAME, path=PATH_TO/FORK_ENV_FILE.py)
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.
Example:
#%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:
#%selection_script(name=TASK_NAME, path=PATH_TO/SELECTION_CODE_FILE.py)
If the selection scripts and/or the fork environments are the same for all job tasks, we can add them just once using
the job_selection_script
and/or the job_fork_env
pragmas.
Usage:
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])
The force
parameter defines whether the pragma has to overwrite the task selection scripts or the fork environment
already set.
Sometimes, specific scripts has to be executed before and/or after a particular task. To do that, the solution provides
pre_script
and post_script
pragmas.
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])
The branch control provides the ability to choose between two alternative task flows, with the possibility to merge back to a common one.
To add a branch control to the current workflow, four specific tasks and one control condition should be added in accordance with the following order:
- a
branch
task, - the related branching
condition
script, - an
if
task that should be executed if the result of thecondition
task iftrue
, - an
else
task that should be executed if the result of thecondition
task iffalse
, - a
continuation
task that should be executed after theif
or theelse
tasks.
To add a branch
task, you can rely on the following macro:
#%branch([name=TASK_NAME], [dep=[TASK_NAME1,TASK_NAME2,...]], [generic_info=[(KEY1,VAL1), (KEY2,VALUE2),...]], [language=SCRIPT_LANGUAGE], [path=./FORK_ENV_FILE.py])
For the branching condition
script, use:
#%condition()
For an if
task, please use:
#%if([name=TASK_NAME], [generic_info=[(KEY1,VAL1),(KEY2,VALUE2),...]], [language=SCRIPT_LANGUAGE], [path=./FORK_ENV_FILE.py])
For an else
task, use:
#%else([name=TASK_NAME], [generic_info=[(KEY1,VAL1),(KEY2,VALUE2),...]], [language=SCRIPT_LANGUAGE], [path=./FORK_ENV_FILE.py])
And finally, for the continuation
task:
#%continuation([name=TASK_NAME], [generic_info=[(KEY1,VAL1),(KEY2,VALUE2),...]], [language=SCRIPT_LANGUAGE], [path=./FORK_ENV_FILE.py])
The loop control provides the ability to repeat a set of tasks.
To add a loop control to the current workflow, two specific tasks and one control condition should be added in the following order:
- a
start
task, - the related looping
condition
script, - a
loop
task.
For a start
task, use:
#%start([name=TASK_NAME], [dep=[TASK_NAME1,TASK_NAME2,...]], [generic_info=[(KEY1,VAL1), (KEY2,VALUE2),...]], [language=SCRIPT_LANGUAGE], [path=./FORK_ENV_FILE.py])
For the looping condition
script, use:
#%condition()
For a loop
task, please use:
#%loop([name=TASK_NAME], [generic_info=[(KEY1,VAL1),(KEY2,VALUE2),...]], [language=SCRIPT_LANGUAGE], [path=./FORK_ENV_FILE.py])
The replication allows the execution of multiple tasks in parallel when only one task is defined, and the number of tasks to run could change.
Through the ProActive Jupyter Kernel, users can add replicate controls in two main ways, a generic and a straight forward way.
To add a replicate control to the current workflow in the generic method, three specific tasks and one control runs script should be added according to the following order:
- a
split
task, - the related replication
runs
script, - a
process
task, - a
merge
task.
For a split
task, use:
#%split([name=TASK_NAME], [dep=[TASK_NAME1,TASK_NAME2,...]], [generic_info=[(KEY1,VAL1), (KEY2,VALUE2),...]], [language=SCRIPT_LANGUAGE], [path=./FORK_ENV_FILE.py])
For the replication runs
script, use:
#%runs()
For a process
task, please use:
#%process([name=TASK_NAME], [generic_info=[(KEY1,VAL1),(KEY2,VALUE2),...]], [language=SCRIPT_LANGUAGE], [path=./FORK_ENV_FILE.py])
And finally, for a merge
task, use:
#%merge([name=TASK_NAME], [generic_info=[(KEY1,VAL1),(KEY2,VALUE2),...]], [language=SCRIPT_LANGUAGE], [path=./FORK_ENV_FILE.py])
The straight forward method to add a replication is most of all useful when the parallelism that should be implemented is a task parallelism (the generic usage is more adapted to data parallelism).
To add a replication to a task, just add the runs control script by providing the runs
option of the task
pragma.
Example:
#%task(name=T2,dep=[T1],runs=3)
print("This output should be displayed 3 times ...")
NOTE: To construct a valid workflow, straight forward replicated tasks must have one and only one parent task and one child task at most. More information about replicate validation criteria are available here.
To delete a task from the workflow, the user should run the pragma #%delete_task()
in the following way:
#%delete_task(name=TASK_NAME)
To create a job, use the #%job()
pragma:
#%job(name=JOB_NAME)
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).
To visualize the created workflow, use the #%draw_job()
pragma to plot the workflow graph that represents the job
into a separate window:
#%draw_job()
Two optional parameters can be used to configure the way the kernel plots the workflow graph.
inline plotting:
If this parameter is set to off
, plotting the workflow graph is done through a Matplotlib
external window. The default value is on
.
#%draw_job(inline=off)
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
off
.
#%draw_job(save=on)
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
Unnamed_job
, otherwise
General usage:
#%draw_job([name=JOB_NAME], [inline=off], [save=on])
To export the created workflow into a GraphViz .dot format, use the #%write_dot()
pragma:
#%write_dot(name=FILE_NAME)
To create a workflow according to a GraphViz .dot file, use the pragma #%import_dot()
:
#%import_dot(path=PATH_TO/FILE_NAME.dot)
By default, the workflow will contain Python tasks with empty implementation scripts. If you want to modify or add
any information to a specific task, please use, as explained in Creating a Task, the #%task()
pragma.
To submit the job to the ProActive Scheduler, the user has to use the #%submit_job()
pragma:
#%submit_job()
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:
#%submit_job([name=JOB_NAME])
If the job's name is not set, the ProActive kernel uses the current notebook name, if possible, or gives a random one.
To get all submitted job IDs and names, use list_submitted_jobs
pragma this way:
#%list_submitted_jobs()
To export the created workflow in .xml format, use the #%export_xml()
pragma:
#%export_xml([name=FILENAME])
Notice that the .xml file will be saved under one of the following names:
- The parameter
name
's value, if provided - The job's name, if created
- The notebook's name, if the kernel can retrieve it
Unnamed_job
, otherwise
After the execution of a ProActive workflow, two outputs can be obtained,
- results: values that have been saved in the task result variable,
- console outputs: classic outputs that have been displayed/printed
To get task results, please use the #%get_task_result()
pragma by providing the task name, and either the job ID or
the job name:
#%get_task_result([job_id=JOB_ID], [job_name=JOB_NAME], task_name=TASK_NAME)
The result(s) of all the tasks of a job can be obtained with the #%get_job_result()
pragma, by providing the job name
or the job ID:
#%get_job_result([job_id=JOB_ID], [job_name=JOB_NAME])
To get and display console outputs of a task, you can use the #%print_task_output()
pragma in the following
way:
#%print_task_output([job_id=JOB_ID], [job_name=JOB_NAME], task_name=TASK_NAME)
Finally, the #%print_job_output()
pragma allows to print all job outputs, by providing the job name or the job ID:
#%print_job_output([job_id=JOB_ID], [job_name=JOB_NAME])
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:
#%show_resource_manager([height=HEIGHT_VALUE, width=WIDTH_VALUE])
For the related scheduling portal:
#%show_scheduling_portal([height=HEIGHT_VALUE, width=WIDTH_VALUE])
And, for the related workflow automation:
#%show_workflow_automation([height=HEIGHT_VALUE, width=WIDTH_VALUE])
NOTE: The parameters height
and width
allow the user to adjust the size of the window inside the notebook.
Features:
-
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
-
split: creates/modifies a splitting task of a replicate control
-
runs: creates/modifies the configuration script of a replicate control
-
process: creates/modifies the script of a replicated processing task
-
merge: creates/modifies a merging task of a replicate control
-
start: creates/modifies a start task of a loop control
-
loop: creates/modifies a loop task of a loop control
-
condition: creates/modifies the condition script of a branch/loop control
-
branch: creates/modifies a branch task of a branching control
-
if: creates/modifies an if task of a branching control
-
else: creates/modifies an else task of a branching control
-
continuation: creates/modifies a continuation task of a branching control
-
delete_task: deletes a task from the workflow
-
job: creates/renames the job
-
draw_job: plot the workflow
-
write_dot: writes the workflow in .dot format
-
import_dot: imports the workflow from a .dot file
-
submit_job: submits the job to the scheduler
-
get_result: gets and prints the job results
-
get_job_result: gets and prints the job results
-
get_task_result: gets and prints the results of a given task
-
print_job_output: gets and prints the job outputs
-
print_task_output: gets and prints the outputs of a given task
-
list_submitted_jobs: gets and prints the ids and names of the submitted jobs
-
export_xml: exports the workflow in .xml format
-
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