Introduction

Motivation

The specific goal of this repository is to analyze CMS NanoAOD ROOT files with uproot eventually using analysis tools from coffea, but using a master/manager that sends computing tasks to workers running in an HTCondor batch system.

There are a handful of tools designed to do this more generally (dynamic task execution with brokered communication): htmap, parsl, dask, celery, ray, airflow, dramatiq .

Unfortunately, limitations on what ports are open/how processes communicate within and outside the batch system means it would be difficult to use these out of the box. So here I have a simple task queue system that allows me to have more low-level customization to suit ROOT file analysis (data locality, caching, compressed communication). The jobs are "embarrassingly parallel", so there's no real need for complex dynamic logic, direct inter-worker communication, DAGs, etc, and this relies on exactly one redis master server (one single public-facing ip/port).

Overview

I'm picking parts from the previous libraries as I go including what's most relevant/allowed for my use case. As the base, the task system, described in more detail here, takes functions and arguments from the user and serializes them with cloudpickle and compresses with lz4 before sending these out to workers which call the functions and return the output to the user.

TODO items are here.

Quick start

Clone the repository and work inside it:

git clone https://github.com/aminnj/redis-htcondor
cd redis-htcondor

Set up your own redis master server in a GNU screen on an SLC7 computer (e.g., uaf-1) with

singularity exec docker://redis redis-server --port $((RANDOM%1000+50000)) --loglevel verbose

Make note of the port that got used based on the output of that command. (More custom installation instructions, including installing from source on SLC6, can be found here).

While later scripts will accept a redis url to connect to, it's more convenient to specify it once, so make a config.py file in the current directory containing the following line (appropriately modified to point to the already-running server)

REDIS_URL = "redis://hostname:port"

Once the server is running, the rest can be done on an SLC6 or SLC7 computer.

Initial set up for workers:

# Make the tarballed environment for the condor worker nodes.
# This essentially tarballs a virtualenv with the minimal python
# dependencies for the worker.
scripts/make_worker_tarball.sh

# Submit a couple of workers initially
scripts/submit_workers.py --num_workers 2

Launch jupyter notebook for analysis:

scripts/start_analysis_server.sh

# visit the url printed out at the end and make sure to forward the port to your laptop first. e.g.,
ssh -N -f -L localhost:8895:localhost:8895 uaf-10.t2.ucsd.edu

# open `example.ipynb` and play around.

Unit tests

Unit tests can be run with

scripts/run_tests.sh

after verifying that config.py contains the correct/running redis server url. Arguments are passed to python call, which uses the unittest module, so you can run a single test with

scripts/run_tests.sh ManagerTest.test_local_map

Example usage

Start a local worker

>>> from worker import Worker
>>> w = Worker("redis://:mypass@hostname:port")
>>> w.run() # this blocks indefinitely

In a separate process,

>>> from manager import Manager
>>> m = Manager()
>>> results = m.remote_map(lambda x:x**2,range(10))
[0, 4, 1, 16, 25, 9, 49, 64, 81, 36]

aminnj/redis-htcondor

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