merlin

To help you get started, we've collected the most common ways that merlin is being used within popular public projects.

pyre / pyre / tests / merlin.pkg / components / merlin_curator.py View on Github

def test():
    # access to the merlin executive
    from merlin import merlin

    # get the curator
    curator = merlin.curator
    # create a project description object
    project = merlin.newProject(name="test")
    # archive it
    curator.saveProject(project)
    # refresh the folder
    merlin.pfs['project'].discover()
    # and load it back in
    project = curator.loadProject()

    # check the name
    assert project.name == "test"

    # and return
    return project

View more ways to use merlin

A brief introduction to Merlin

Merlin is a tool for running machine learning based workflows. The goal of Merlin is to make it easy to build, run, and process the kinds of large scale HPC workflows needed for cognitive simulation.

At its heart, Merlin is a distributed task queuing system, designed to allow complex HPC workflows to scale to large numbers of simulations (we've done 100 Million on the Sierra Supercomputer).

Why would you want to run that many simulations? To become your own Big Data generator.

Data sets of this size can be large enough to train deep neural networks that can mimic your HPC application, to be used for such things as design optimization, uncertainty quantification and statistical experimental inference. Merlin's been used to study inertial confinement fusion, extreme ultraviolet light generation, structural mechanics and atomic physics, to name a few.

How does it work?

In essence, Merlin coordinates complex workflows through a persistent external queue server that lives outside of your HPC systems, but that can talk to nodes on your cluster(s). As jobs spin up across your ecosystem, workers on those allocations pull work from a central server, which coordinates the task dependencies for your workflow. Since this coordination is done via direct connections to the workers (i.e. not through a file system), your workflow can scale to very large numbers of workers, which means a very large number of simulations with very little overhead.

Furthermore, since the workers pull their instructions from the central server, you can do a lot of other neat things, like having multiple batch allocations contribute to the same work (think surge computing), or specialize workers to different machines (think CPU workers for your application and GPU workers that train your neural network). Another neat feature is that these workers can add more work back to central server, which enables a variety of dynamic workflows, such as may be necessary for the intelligent sampling of design spaces or reinforcement learning tasks.

Merlin does all of this by leveraging some key HPC and cloud computing technologies, building off open source components. It uses maestro to provide an interface for describing workflows, as well as for defining workflow task dependencies. It translates those dependencies into concrete tasks via celery, which can be configured for a variety of backend technologies (rabbitmq and redis are currently supported). Although not a hard dependency, we encourage the use of flux for interfacing with HPC batch systems, since it can scale to a very large number of jobs.

The integrated system looks a little something like this:

In this example, here's how it all works:

1. The scientist describes her HPC workflow as a maestro DAG (directed acyclic graph) "spec" file workflow.yaml
2. She then sends it to the persistent server with merlin run workflow.yaml . Merlin translates the file into tasks.
3. The scientist submits a job request to her HPC center. These jobs ask for workers via the command merlin run-workers workflow.yaml.
4. Coffee break.
5. As jobs stand up, they pull work from the queue, making calls to flux to get the necessary HPC resources.
6. Later, workers on a different allocation, with GPU resources connect to the server and contribute to processing the workload.

The central queue server deals with task dependencies and keeps the workers fed.

For more details, check out the rest of the documentation.

Need help?

Quick Start

Note: Merlin supports Python 3.6+.

To install Merlin and its dependencies, run:

$ pip3 install merlin

Create your application config file:

$ merlin config

That's it.

To run something a little more like what you're interested in, namely a demo workflow that has simulation and machine learning, first generate an example workflow:

$ merlin example feature_demo

Then install the workflow's dependencies:

$ pip install -r feature_demo/requirements.txt

Then process the workflow and create tasks on the server:

$ merlin run feature_demo/feature_demo.yaml

And finally, launch workers that can process those tasks:

$ merlin run-workers feature_demo/feature_demo.yaml

Documentation

Full documentation is available, or run:

$ merlin --help

(or add --help to the end of any sub-command you want to learn more about.)

Code of Conduct

Please note that Merlin has a Code of Conduct. By participating in the Merlin community, you agree to abide by its rules.

License

Merlin is distributed under the terms of the MIT LICENSE.

LLNL-CODE-797170