adaptive-scheduler 0.2.4

Run many adaptive.learners on many cores (>10k) using MPI.

An asynchronous scheduler using MPI for Adaptive

Run many adaptive.learners on many cores (>10k) using MPI.

What is this?

The Adaptive scheduler solves the following problem, you need to run a few 100 learners and can use >1k cores.   You can't use a centrally managed place that is responsible for all the workers (like with dask or ipyparallel) because >1k cores is too many for them to handle.   You also don't want to use dask or ipyparallel inside a job script because they write job scripts on their own. Having a job script that runs code that creates job scripts...

With adaptive_scheduler you only need to define the learners and then it takes care of the running (and restarting) of the jobs on the cluster.

How does it work?

You create a file where you define a bunch of learners and corresponding fnames such that they can be imported, like:

# learners_file.py
import adaptive
from functools import partial

def h(x, pow, a):
    return a * x**pow

combos = adaptive.utils.named_product(
    pow=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
    a=[0.1, 0.5],
)  # returns list of dicts, cartesian product of all values

learners = [adaptive.Learner1D(partial(h, **combo),
            bounds=(-1, 1)) for combo in combos]
fnames = [f"data/{combo}" for combo in combos]

Then you start a process that creates a job-script which runs the learners. Like:

import adaptive_scheduler

def goal(learner):
    return learner.npoints > 200

run_manager = adaptive_scheduler.server_support.RunManager(
    learners_file="learners_file.py",
    goal=goal,
    cores_per_job=12,  # every learner is one job
    log_interval=30,  #  write info such as npoints, cpu_usage, time, etc. to the job log file
    save_interval=300,  # save the data every 300 seconds
)
run_manager.start()

That's it! You can run run_manager.info() which will display an interactive ipywidget that shows the amount of running, pending, and finished jobs and buttons to cancel your job, and other information.

But how does really it work?

The RunManager basically does what is written below. So, you need to create a learners_file.py that defines learners and fnames. Them a "job manager" writes and submits as many jobs as there are learners but doesn't know which learner it is going to run! This is the responsibility of the "database manager", which keeps a database of job_id <--> learner.

In another Python file (the file that is run on the nodes) we do something like:

# run_learner.py
import adaptive
from adaptive_scheduler import client_support
from mpi4py.futures import MPIPoolExecutor

# the file that defines the learners we created above
from learners_file import learners, fnames


if __name__ == "__main__":  # ← use this, see warning @ https://bit.ly/2HAk0GG
    # the address of the "database manager"
    url = "tcp://10.75.0.5:37371"

    # ask the database for a learner that we can run
    learner, fname = client_support.get_learner(url, learners, fnames)

    # load the data
    learner.load(fname)

    # run until `some_goal` is reached with an `MPIPoolExecutor`
    runner = adaptive.Runner(
        learner, executor=MPIPoolExecutor(), shutdown_executor=True, goal=some_goal
    )

    # periodically save the data (in case the job dies)
    runner.start_periodic_saving(dict(fname=fname), interval=600)

    # log progress info in the job output script, optional
    client_support.log_info(runner, interval=600)

    # block until runner goal reached
    runner.ioloop.run_until_complete(runner.task)

    # tell the database that this learner has reached its goal
    client_support.tell_done(url, fname)

In a Jupyter notebook we can start the "job manager" and the "database manager" like:

from adaptive_scheduler import server_support
from learners_file import learners, fnames

# create a new database
db_fname = "running.json"
server_support.create_empty_db(db_fname, fnames)

# create unique names for the jobs
n_jobs = len(learners)
job_names = [f"test-job-{i}" for i in range(n_jobs)]

# start the "job manager" and the "database manager"
database_task = server_support.start_database_manager("tcp://10.75.0.5:37371", db_fname)

job_task = server_support.start_job_manager(
    job_names,
    db_fname,
    cores=200,  # number of cores per job
    run_script="run_learner.py",
)

So in summary, you have three files:

1. learners_file.py which defines the learners and its filenames
2. run_learner.py which picks a learner and runs it
3. a Jupyter notebook where you run the "database manager" and the "job manager"

You don't actually ever have to leave the Jupter notebook, take a look at the example notebook.

Jupyter notebook example

See example.ipynb.

Installation

WARNING: This is still the pre-alpha development stage.

Install the latest stable version from conda with (recommended)

conda install adaptive-scheduler

or from PyPI with

pip install adaptive_scheduler

or install master with

pip install -U https://github.com/basnijholt/adaptive-scheduler/archive/master.zip

or clone the repository and do a dev install (recommended for dev)

git clone [e-mail]:basnijholt/adaptive-scheduler.git
cd adaptive-scheduler
pip install -e .

Development

In order to not pollute the history with the output of the notebooks, please setup the git filter by executing

python ipynb_filter.py

in the repository.

We also use pre-commit, so pip install pre_commit and run

pre-commit install

in the repository.

Limitations

Right now adaptive_scheduler is only working for SLURM and PBS, however only the functions in adaptive_scheduler/slurm.py would have to be implemented for another type of scheduler. Also there are no tests at all!