Run many `adaptive.Learner`s on many cores (>10k) using `mpi4py.futures`, `ipyparallel`, or `dask-mpi`.
Project description
Run many adaptive.Learners on many cores (>10k) using mpi4py.futures, ipyparallel, or dask-mpi.
What is this?
The Adaptive scheduler solves the following problem, you need to run a few 100 learners and can use >1k cores.
ipyparallel and dask.distributed provide very powerful engines for interactive sessions. However, when you want to connect to >1k cores it starts to struggle. Besides that, on a shared cluster there is often the problem of starting an interactive session with ample space available.
Our approach is to schedule a different job for each adaptive.Learner. The creation and running of these jobs are managed by adaptive-scheduler. This means that your calculation will definitely run, even though the cluster might be fully occupied at the moment. Because of this approach, there is almost no limit to how many cores you want to use. You can either use 10 nodes for 1 job (learner) or 1 core for 1 job (learner) while scheduling hundreds of jobs.
Everything is written such that the computation is maximally local. This means that is one of the jobs crashes, there is no problem and it will automatically schedule a new one and continue the calculation where it left off (because of Adaptive’s periodic saving functionality). Even if the central “job manager” dies, the jobs will continue to run (although no new jobs will be scheduled.)
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 and submits as many job-scripts as there are 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 ~adaptive_scheduler.server_support.RunManager basically does what is written below. So, you need to create a learners_file.py that defines learners and fnames. Then 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`
# you can also use a ipyparallel.Client, or dask.distributed.Client
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:
learners_file.py which defines the learners and its filenames
run_learner.py which picks a learner and runs it
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-scheduleror from PyPI with
pip install adaptive_scheduleror install master with
pip install -U https://github.com/basnijholt/adaptive-scheduler/archive/master.zipor clone the repository and do a dev install (recommended for dev)
git clone git@github.com: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.pyin the repository.
We also use pre-commit, so pip install pre_commit and run
pre-commit installin 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!
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file adaptive_scheduler-0.2.6.tar.gz.
File metadata
- Download URL: adaptive_scheduler-0.2.6.tar.gz
- Upload date:
- Size: 28.3 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.21.0 setuptools/41.0.0 requests-toolbelt/0.9.1 tqdm/4.32.1 CPython/3.7.3
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 2c7eb2edf6330aa572447ce1d6c94fe2a406912e9272411dfc38868a3d7047b3 |
|
| MD5 | 0513af751890ff48ba495a8263bc7fc3 |
|
| BLAKE2b-256 | 98208195da6592805fa059c7da948151bc691427f84d258d24ce0e2d04e242b8 |
File details
Details for the file adaptive_scheduler-0.2.6-py3-none-any.whl.
File metadata
- Download URL: adaptive_scheduler-0.2.6-py3-none-any.whl
- Upload date:
- Size: 29.2 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.21.0 setuptools/41.0.0 requests-toolbelt/0.9.1 tqdm/4.32.1 CPython/3.7.3
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 31ff3e2f066eb4ea6bcaf8a23868fec0903ccd06b183065336a4051d3e6a695b |
|
| MD5 | 3a31f92e0b24481d7f1cd7b72350361d |
|
| BLAKE2b-256 | e2e48c6cf67e3259cb9b39602e849d53dd739ef7dbf1b966f84bfc584ed39f28 |
