pycompss-player 2.6.4

PyCOMPSs player

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

1. Install docker (continue with step 2 if already installed)

   • pycompss-player requires docker 17.12.0-ce or greater.

   1. 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.

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

      • Instructions

   3. Check that docker is correctly installed

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

   4. Install docker-py

   python3 -m pip install docker

2. Install pycompss-player:

python3 -m pip install pycompss-player

This should add the pycompss-player executables (pycompss and compss) 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-tutorial:2.6

# Or both
pycompss init -w /home/user/replace/path/ -i compss/compss-tutorial:2.6

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'

License

Apache License Version 2.0

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Workflows and Distributed Computing

Department of Computer Science

Barcelona Supercomputing Center (http://www.bsc.es)