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a distributed process orchestration platform that supports both laptop and major cloud providers

Project description

Symphony: An orchestrating Library Supporting Multiple Backends

Symphony aims to ease the process of launching and monitoring multi-process / multi-node computation tasks. It provides a simple abstraction for launching multiple processes in a centralized place and supports multiple backends (e.g. tmux and kubernetes). It also provides a set of essential commandline interfaces to monitor the status of the processes involved.


replay_host = os.environ[‘SYMPH_REPLAY_SERVER_HOST’] replay_port = os.environ[‘SYMPH_REPLAY_SERVER_PORT’] server = ReplayServer(host=replay_host, port=replay_port) … ``` And similarly you can connect to this address in agent.

A process can declare networking in three ways: bind, connect, expose. * process.bind('service') tells symphony to assign a port to service-1 and expose both DNS address and port so that other processes can connect to the binding process. All pocesses will have access to environment variables SYMPH_SERVICE_1_HOST and SYMPH_SERVICE_1_PORT. One can also do process.bind({'tensorboard': 6006}) where a specific port is assigned. * connect to something (e.g. service-1) declares that the process expects some other process to bind to it. While the envorinment variables for the host/port will still be provided at run time (assuming that you did bind) even if you didn’t call connect, it is recommended as connecting to some non-existent name will be caught and cause the program to fail during declaration, before the experiment even starts. * expose is used when you are running experiments on a cloud. It tells symphony to expose this port to a global ip. If you have a process expose tensorboard you can later use symphony visit tensorboard to retrieve an ip and open a browser for it. There will also be environment variables SYMPH_TENSORBOARD_HOST and SYMPH_TENSORBOARD_PORT.

Monitoring Through the Commandline.

After you start running and experiment, symphony provides a convenient commandline interface to know how each of the processes are running. The script installed with symphony is mainly used for demonstration and prototyping. For your own project, you can merge the interface with your python script easily. See #this example.

  • symphony process or symphony p lists the status of processes in an experiment.

    $> symphony p
    Group     Name         Ready  Restarts  State
          agent-0      1      0         running: 23.2h
          eval-0       1      0         running: 23.2h
  • symphony logs <process_name> retrieves logs from a given process.

    $> symphony logs agent-0
    Agent starting ...
  • symphony list-experiments (symphony ls) lists all running experiments.

    $> symphony ls
  • symphony delete (symphony d), symphony delete-batch (symphony db) terminates experiments.

  • symphony visit [exposed_service] (symphony vi) opens a web browser to the exposed service (Use --url-only to only get the url).

  • Other convenient functionalities can be used for some clusters, (e.g. Kubernetes). exec, ssh, scp.

  • If you are using a process group and that process names are not unique, use process_group/process in place of process.


Symphony provides several optional functionalities to help organize experiments. They are controlled by SymphonyConfig singleton.

from symphony.engine import SymphonyConfig
  • set_username(name) makes all subsequently created experiments prepend username

    cluster ='tmux') # cluster is a TmuxCluster
    exp1 = cluster.new_experiment('rl') # exp is a TmuxExperimentSpec
    print( # 'sarah-rl'
  • set_experiment_folder(directory) saves all subsequently launched experiment specs to directory. You can retrieve your declaration of experiments later. It also allows the cluster to complain to you if you are going to overwrite an existing experiment. (You can still pass ‘force=True’ to force overwrite)

    cluster ='tmux') # cluster is a TmuxCluster
    exp1 = cluster.new_experiment('rl') # exp is a TmuxExperimentSpec
    # information about this experiment will be saved to ~/foo/rl

Using symphony as part of your project

To use symphony for your own project, the easiest way is to extend the provided parser. You only need to do three things in a class that extends SymphonyParser: 1. Overwrite create_cluster(self), define the backend that you want to use 2. Overwrite setup(self), add a new subcommand for launch (so you can launch things) and (optionally) set configs 3. Declare your experiment and launch it. (Here we show how to add it as another subcommand of the script.)

from symphony.commandline import SymphonyParser
from symphony.engine import Cluster
from symphony.kube import KubeCluster
import sys

class MyProjectParser(SymphonyParser):
    def create_cluster(self): # step 1

    def setup(self): # step 2
        parser = self.add_subparser('create')
        # add subcommand: `python create`
        # This subcommand is mapped to self.action_create(args)

    def action_create(self, args): # step 3
        exp = self.cluster.new_experiment('foo')
        p = exp.new_process(...)

if __name__ == '__main__':

Now not only can you do python create to launch an experiment, but you can also use python process to monitor the processes of your experiment.

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