pycompss-player 2.10.1

Description:

pycompssplayer 2.10.1

Introduction
The PyCOMPSs player (pycompss) provides a tool to use PyCOMPSs within
local machines interactively through docker images. This tool has been
implemented on top of PyCOMPSs programming
model, and it is being developed by the
Workflows and Distributed Computing
group of the Barcelona Supercomputing
Center.


Contents

Quickstart
License



Quickstart
There are two ways in which you can get started with PyCOMPSs. You can
perform a local installation by installing the pycompss
package, or you can use it
through our ready-to-use docker image thorugh this pycompss-player
tool.

Installation

Dependencies
pycompss-player currently requires:

docker >= 17.12.0-ce



Installation steps

Install docker (continue with step 2 if already installed)

pycompss-player requires docker 17.12.0-ce or greater.


Follow these instructions

Docker for
Mac.
Or, if you prefer to use Homebrew.
Docker for
Ubuntu.
Docker for Arch
Linux.

Be aware that for some distros the docker package has been renamed
from docker to docker-ce. Make sure you install the new
package.

Add user to docker group to run pycompss as a non-root user.

Instructions


Check that docker is correctly installed

docker --version
docker ps # this should be empty as no docker processes are yet running.

Install
docker-py

python3 -m pip install docker

Install pycompss-player:

python3 -m pip install pycompss-player
This should add the pycompss-player executables (pycompss,
compss and dislib) to your path. They can be used indiferently.
Warning: The user executable path may not be automatically exported
into the PATH environment variable. So, take this into account if
installed with the --user flag, since the
pycompss|compss command will be unreachable until the path
is exported into PATH.



Usage

Start pycompss in your development directory
Initialize the COMPSs infrastructure where your source code will be (you
can re-init anytime). This will allow docker to access your local code
and run it inside the container.
Note that the first time needs to download the docker image from the
registry, and it may take a while.
# Without a path it operates on the current working directory.
pycompss init

# You can also provide a path
pycompss init -w /home/user/replace/path/

# Or the COMPSs docker image to use
pycompss init -i compss/compss:2.10

# Or both
pycompss init -w /home/user/replace/path/ -i compss/compss:2.10


Running applications
First clone the PyCOMPSs’ tutorial apps repository:
git clone https://github.com/bsc-wdc/tutorial_apps.git
Init the COMPSs environment in the root of the repository. The source
files path are resolved from the init directory which sometimes can be
confusing. As a rule of thumb, initialize the library in a current
directory and check the paths are correct running the file with
python3 path_to/file.py (in this case
python3 python/simple/src/simple.py).
cd tutorial_apps
pycompss init
pycompss run python/simple/src/simple.py 1
The log files of the execution can be found at $HOME/.COMPSs.
You can also init the COMPSs environment inside the examples folder.
This will mount the examples directory inside the container so you can
execute it without adding the path:
cd python/simple/src
pycompss init
pycompss run simple.py 1


Running the COMPSs monitor
The COMPSs monitor can be started using the pycompss monitor start
command. This will start the COMPSs monitoring facility which enables to
check the application status while running. Once started, it will show
the url to open the monitor in your web browser
(http://127.0.0.1:8080/compss-monitor)
Reminder: Include the monitor flag in the execution before the
binary to be executed.
cd python/simple/src
pycompss init
pycompss run --monitor=1000 -g simple.py 1
If running a notebook, just add the monitoring parameter into the COMPSs
runtime start call.
Once finished, it is possible to stop the monitoring facility by using
the pycompss monitor stop command.


Running Jupyter notebooks
Notebooks can be run using the pycompss jupyter command. Run the
following snippet from the root of the project:
cd tutorial_apps/python
pycompss init
pycompss jupyter ./notebooks
An alternative and more flexible way of starting jupyter is using the
pycompss run command in the following way:
pycompss run jupyter-notebook ./notebooks --ip=0.0.0.0 --allow-root
Access your notebook by ctrl-clicking or copy pasting into the browser
the link shown on the CLI (e.g.
http://127.0.0.1:8888/?token=TOKEN_VALUE).
If the notebook process is not properly closed, you might get the
following warning when trying to start jupyter notebooks again:
The port 8888 is already in use, trying another port.
To fix it, just restart the pycompss container with pycompss init.


Generating the task graph
COMPSs is able to produce the task graph showing the dependencies that
have been respected. In order to producee it, include the graph flag in
the execution command:
cd python/simple/src
pycompss init
pycompss run --graph simple.py 1
Once the application finishes, the graph will be stored into the
~\.COMPSs\app_name_XX\monitor\complete_graph.dot file. This dot file
can be converted to pdf for easier visualilzation through the use of the
gengraph parameter:
pycompss gengraph .COMPSs/simple.py_01/monitor/complete_graph.dot
The resulting pdf file will be stored into the
~\.COMPSs\app_name_XX\monitor\complete_graph.pdf file, that is, the
same folder where the dot file is.


Tracing applications or notebooks
COMPSs is able to produce tracing profiles of the application execution
through the use of EXTRAE. In order to enable it, include the tracing
flag in the execution command:
cd python/simple/src
pycompss init
pycompss run --tracing simple.py 1
If running a notebook, just add the tracing parameter into the COMPSs
runtime start call.
Once the application finishes, the trace will be stored into the
~\.COMPSs\app_name_XX\trace folder. It can then be analysed with
Paraver.


Adding more nodes
Note: adding more nodes is still in beta phase. Please report
issues, suggestions, or feature requests on
Github.
To add more computing nodes, you can either let docker create more
workers for you or manually create and config a custom node.
For docker just issue the desired number of workers to be added. For
example, to add 2 docker workers:
pycompss components add worker 2
You can check that both new computing nodes are up with:
pycompss components list
If you want to add a custom node it needs to be reachable through ssh
without user. Moreover, pycompss will try to copy the working_dir
there, so it needs write permissions for the scp.
For example, to add the local machine as a worker node:
pycompss components add worker '127.0.0.1:6'

‘127.0.0.1’: is the IP used for ssh (can also be a hostname like
‘localhost’ as long as it can be resolved).
‘6’: desired number of available computing units for the new node.

Please be aware that pycompss components will not list your
custom nodes because they are not docker processes and thus it can’t be
verified if they are up and running.


Removing existing nodes
Note: removing nodes is still in beta phase. Please report issues,
suggestions, or feature requests on
Github.
For docker just issue the desired number of workers to be removed. For
example, to remove 2 docker workers:
pycompss components remove worker 2
You can check that the workers have been removed with:
pycompss components list
If you want to remove a custom node, you just need to specify its IP and
number of computing units used when defined.
pycompss components remove worker '127.0.0.1:6'


Stop pycompss
The infrastructure deployed can be easily stopped and the docker instances
closed with the following command:
pycompss kill




License
Apache License Version 2.0

Workflows and Distributed Computing
Department of Computer Science
Barcelona Supercomputing Center (http://www.bsc.es)