Parallel sampling

pyABC offers a variety of multi-core parallel and distributed samplers, which handle the usually most time-expensive part of an ABC analysis: the simulation of data from the model for sampled parameters, the generation of summary statistics, and the calculation of the distance of simulated and observed data.

The most-used and best-supported samplers are the pyabc.sampler.MulticoreEvalParallelSampler for multi-processed sampling, the pyabc.sampler.RedisEvalParallelSampler for distributed sampling, and for deterministic sampling purposes the non-parallelized pyabc.sampler.SingleCoreSampler. These should be preferably used, however also the other parallelization engines mentioned below should work.


The various samplers implement two different sampling strategies: “Static Scheduling (STAT)” and “Dynamic Scheduling (DYN)”. STAT minimizes the total execution time, whereas DYN minimizes the wall-time and is generally preferable as it finishes faster. For details see [Klinger2018].

The ParticleParallel samplers, the MappingSampler and the RedisStaticSampler implement the “Static Scheduling (STAT)” strategy.

The EvalParallel samplers, the DaskDistributedSampler and the ConcurrentFutureSampler implement the “Dynamic Scheduling (DYN)” strategy.

The batchsize arguments of the DaskDistributedSampler, the ConcurrentFutureSampler and the RedisEvalParallelSampler allow to interpolate between dynamic and static scheduling and to reduce communication overhead.


Emmanuel Klinger, Dennis Rickert, Jan Hasenauer. pyABC: distributed, likelihood-free inference. Bioinformatics 2018;

Single-core execution

For single-core execution, pyABC offers the pyabc.sampler.SingleCoreSampler. This one just generates sample by sample sequentially. This sampler is intended for debugging purposes as debugging parallel code can be hard sometimes.

Multi-core only samplers

For multi-core execution, pyABC implements two possible parallelization strategies.

Both samplers are highly specialized to the multi-core setting and have very little communication overhead. Even for very small model evaluation times these samplers are about as fast as the single core sampler. This is achieved circumventing object serialization by forking. As Microsoft Windows does not support forking, these samplers might not work as expected on Windows.

Distributed samplers

The distributed samplers can be used in a distributed setting, and of course also locally by setting up a local cluster. However, for local execution, the multi-core samplers are recommended as they are easier to set up.

The pyabc.sampler.RedisEvalParallelSampler has very low communication overhead, and when running workers and redis-server locally is actually competitive with the multi-core only samplers. The pyabc.sampler.RedisEvalParallelSampler performs parameter sampling on a per worker basis, and can handle fast function evaluations efficiently. Further, it is the only sampler that allows proactive sampling to minimize the overall wall-time (“look-ahead mode”). The pyabc.sampler.RedisStaticSampler implements static scheduling and may be of interest if the simulation time needs to be minimized.

The pyabc.sampler.DaskDistributedSampler has slightly higher communication overhead, however this can be compensated with the batch submission mode. As it performs parameter sampling locally on the master, it is unsuitable for simulation functions with a runtime below 100ms, as network communication becomes prohibitive at this point.

The Redis based sampler can require slightly more effort in setting up than the Dask based sampler, but has fewer constraints regarding simulation function runtime. The Dask sampler is in turn better suited to handle worker failures and unexpected execution host terminations.

General extensible samplers

Moreover, there are two more generic samplers which can be used in a multicore and distributed fashion. These samplers facilitate adaptation of pyABC to new parallel environments.

The pyabc.sampler.MappingSampler can be used in a multi-core context if the provided map implementation is a multi-core one, such as, e.g., or distributed if the map is a distributed one, such as

Similarly, the pyabc.sampler.ConcurrentFutureSampler can use any implementation of the python concurrent.futures.Executor interface. Again, implementations are available for both multi-core (e.g. concurrent.futures.ProcessPoolExecutor) and distributed (e.g. Dask) environments

Check the API documentation for more details.

How to set up a Redis based distributed cluster

Step 0: Prepare the redis server

To run the redis server, use a machine which is reachable both by the main application and by the workers. If you’re on Linux, you can install redis either via your package manager, or, if you’re using anaconda, via

conda install redis

Windows is currently not officially supported by the redis developers.

It is recommended to run a redis server only with password protection, since it otherwise accepts any incoming connection. To set up password protection on the server, you need to modify the redis.conf file. Usually, such a file exists under REDIS_INSTALL_DIR/etc/redis.conf. You can however also set up your own file. It suffices to add or uncomment the line

requirepass PASSWORD

where PASSWORD should be replaced by a more secure password.

Step 1: Start a redis server

In this example, we assume that the IP address of the machine running the redis server is (the default is localhost), and that the server should listen on port 6379 (the redis default).

If password protection is used, start the server via

redis-server /path/to/redis.conf --port 6379

If no password protection is required, instead use

redis-server --port 6379 --protected-mode no

You should get an output looking similar to the one below:

30656:M 23 May 13:19:20.718 # You requested maxclients of 10000 requiring at least 10032 max file descriptors.
30656:M 23 May 13:19:20.718 # Server can't set maximum open files to 10032 because of OS error: Operation not permitted.
30656:M 23 May 13:19:20.718 # Current maximum open files is 4096. maxclients has been reduced to 4064 to compensate for low ulimit. If you need higher maxclients increase 'ulimit -n'.
           _.-``__ ''-._
      _.-``    `.  `_.  ''-._           Redis 3.2.9 (00000000/0) 64 bit
  .-`` .-```.  ```\/    _.,_ ''-._
 (    '      ,       .-`  | `,    )     Running in standalone mode
 |`-._`-...-` __...-.``-._|'` _.-'|     Port: 6379
 |    `-._   `._    /     _.-'    |     PID: 30656
  `-._    `-._  `-./  _.-'    _.-'
 |`-._`-._    `-.__.-'    _.-'_.-'|
 |    `-._`-._        _.-'_.-'    | 
  `-._    `-._`-.__.-'_.-'    _.-'
 |`-._`-._    `-.__.-'    _.-'_.-'|
 |    `-._`-._        _.-'_.-'    |
  `-._    `-._`-.__.-'_.-'    _.-'
      `-._    `-.__.-'    _.-'
          `-._        _.-'

30656:M 23 May 13:19:20.719 # WARNING: The TCP backlog setting of 511 cannot be enforced because /proc/sys/net/core/somaxconn is set to the lower value of 128.
30656:M 23 May 13:19:20.719 # Server started, Redis version 3.2.9
30656:M 23 May 13:19:20.719 # WARNING overcommit_memory is set to 0! Background save may fail under low memory condition. To fix this issue add 'vm.overcommit_memory = 1' to /etc/sysctl.conf and then reboot or run the command 'sysctl vm.overcommit_memory=1' for this to take effect.
30656:M 23 May 13:19:20.719 # WARNING you have Transparent Huge Pages (THP) support enabled in your kernel. This will create latency and memory usage issues with Redis. To fix this issue run the command 'echo never > /sys/kernel/mm/transparent_hugepage/enabled' as root, and add it to your /etc/rc.local in order to retain the setting after a reboot. Redis must be restarted after THP is disabled.
30656:M 23 May 13:19:20.719 * The server is now ready to accept connections on port 6379

Step 2 or 3: Start pyABC

It does not matter what you do first: starting pyABC or starting the workers. In your main program, assuming the models, priors and the distance function are defined, configure pyABC to use the redis sampler. For the pyabc.sampler.RedisEvalParallelSampler, use

from pyabc.sampler import RedisEvalParallelSampler

redis_sampler = RedisEvalParallelSampler(host="", port=6379)

abc = pyabc.ABCSMC(models, priors, distance, sampler=redis_sampler)

If password protection is used, in addition pass the argument password=PASSWORD to the RedisEvalParallelSampler.

For the pyabc.sampler.RedisStaticSampler, the same applies, no modifications of the workers are necessary.

Then start the ABC-SMC run as usual with

passing the stopping conditions.

Step 2 or 3: Start the workers

It does not matter what you do first: starting pyABC or starting the workers. You can even dynamically add workers after the sampling has started. Start as many workers as you wish on the machines you wish. Up to 10,000 workers should not pose any problem if the model evaluation times are on the scale or seconds or longer. You start workers on your cluster via

abc-redis-worker --host= --port=6379

If password protection is used, you need to append --password=PASSWORD. You should get an output similar to

INFO:REDIS-WORKER:Start redis worker. Max run time 7200.0s, PID=2731

The abc-redis-worker command has further options (see them via abc-redis-worker --help), in particular to set the maximal runtime of a worker, e.g. --runtime=2h, --runtime=3600s, --runtime=2d, to start a worker running for 2 hours, 3600 seconds or 2 days (the default is 2 hours). It is OK if a worker stops during the sampling of a generation. You can add new workers during the sampling process at any time. The abc-redis-worker command also has an option --processes which allows you to start several worker procecces in parallel, e.g. --processes=12. This might be handy in situations where you have to use a whole cluster node with several cores.

Optional: Monitoring

pyABC ships with a small utility to manage the Redis based sampler setup. To monitor the ongoing sampling, execute

abc-redis-manager info --host=

If password protection is used, you need to specify the password via --password=PASSWORD. If no sampling has happened yet, the output should look like

Workers=None Evaluations=None Acceptances=None/None

The keys are to be read as follows:

  • Workers: Currently sampling workers. This will show None or zero, even if workers are connected but they are not running. This number drops to zero at the end of a generation.

  • Evaluations: Number of accumulated model evaluations for the current generations. This is a sum across all workers.

  • Acceptances: In i/n, i particles out of a requested population size of n have been accepted already. It can be i>n at the end of a population due to excess sampling.

Optional: Stopping workers

Use abc-redis-manager stop to send a signal to the workers that they should shutdown at the end of the current generation.

You can also stop workers with Ctrl-C, or even send a kill signal when pyABC has finished.

Optional: Something with the workers went wrong in the middle of a run

It can happen that workers get unexpectedly killed. If they are not able to communicate to the redis-server that they’ve finished working on the current population before they’re killed, the pyABC master process will wait forever. In such cases, the following can be done

  1. Terminate all running workers (but not the pyABC master process and also not the redis-server)

  2. Execute abc-redis-manager reset-workers to manually reset the number of registered workers to zero.

  3. Start worker processes again.

High-performance infrastructure

pyABC has been successfully employed on various high-performance computing (HPC) infrastructures. There are a few things to keep in mind.

Long-running master process

While most of the work happens on parallel workers, pyABC requires one long-running master process in the background for all of the analysis (or rather two processes, namely the master process running the execution script, and in addition possibly a task scheduler like the redis server). If the HPC infrastructure does not allow for such long-running processes with low CPU and memory requirements, one has to find a way around. Eventually, it is planned for pyABC to support loss-free automatic checkpointing and restarting, but presently this is not yet implemented. If possible, the master process can be run on external servers, login nodes, or on execution nodes while taking maximum runtimes and reliability of server and connections into consideration.

Job scheduling

HPC environments usually employ a job scheduler for distributing work to the execution nodes. Here, we shortly outline how pyABC can be integrated in such a setup. Exemplarily, we use a redis sampler, usage of in particular the dask sampler being similar.

Let us consider the widely used job scheduler slurm. First, we need a script that starts the redis worker:


# slurm settings
#SBATCH -p {partition_id}
#SBATCH -c {number_of_cpus}
#SBATCH -t {time_in_minutes}
#SBATCH -o {output_file_name}
#SBATCH -e {error_file_name}

# prepare environment, e.g. set path

# run
abs-redis-worker --host={host_ip} --port={port} --runtime={runtime} \

Here, n_processes defines the number of processes started for that batch job via multiprocessing. Some HPC setups prefer larger batch jobs, e.g. on a node level, so here each job can already be given some parallelity. The SBATCH macros define the slurm setting to be used.

The above script would be submitted to the slurm job manager via sbatch. It makes sense to define a script for this as well:


for i in {1..{n_jobs}}

Here, n_jobs would be the number of jobs submitted. When the job scheduler is based on qsub, e.g. SGE/UGE, instead use a script like


for i in {1..{n_jobs}}
  qsub -o {output_file_name} -e {error_file_name} \

and adapt the worker script. For both, there exist many more configuration options. For further details see the respective documentation.

Note that when planning for the number of overall redis workers, batches, and cpus per batch, also the parallelization on the level of the simulations has to be taken into account. Also, memory requirements should be checked in advance.


As an intermediate step between local desktop systems and a full HPC cluster system, Jupyterhub ( can speed up computations with pyABC without requiring detailed knowledge about the HPC system. From the user’s perspective, JupyterHub provides a web based interface to computational resources through standard Jupyter notebooks. With minimal adaptions to the local workflow, the resources of one full computing node of a cluster can be utilized for pyABC by using the multicore samplers. Depending on the hardware, this could be as much as 128 CPU cores. No interaction with the command line and the batch system is required. Jupyterhub is installed on many HPC centers, e.g. at the Centre for Information Services and High Performance Computing at TU Dresden ( or at the Juelich Supercomputing Centre (



This section is of interest to developers, or if you encounter memory problems.

For most of the samplers, pyABC uses cloudpickle to serialize objects over the network and run simulations on remote nodes. In particular, this enables us to use lambda functions.

However, care must be taken w.r.t. the size of the serialized object, i.e. to only include what is really required. This is why in the pyabc.ABCSMC class we had to write some functions that prevent the whole ABCSMC object from being serialized. For developers, the following example illustrates the problem:

import cloudpickle as pickle
import numpy as np

class A:

   def __init__(self):
      self.big_arr = np.eye(10000)
      self.small_arr = np.zeros(2)

   def costly_function(self):
      def fun(x):
         print(self.small_arr, x)

      return fun

   def efficient_function(self):
      small_arr = self.small_arr

      def fun(x):
         print(small_arr, x)

      return fun

a = A()

print("The whole class:")
# 800001025

print("Costly function:")
# 800001087

print("Efficient function:")
# 522

SGE cluster scheduling

Quick start

The pyabc.sge package provides as most important class the SGE. Its map method automatically parallelizes across an SGE/UGE cluster. The SGE class can be used in standalone mode or in combination with the ABCSMC class (see below Usage notes).

Usage of the parallel package is fairly easy. For example:

from pyabc.sge import SGE
sge = SGE(priority=-200, memory="3G")

def f(x):
    return x * 2

tasks = [1, 2, 3, 4]

result =, tasks)

[2, 4, 6, 8]

The job scheduling is either done via an SQLite database or a REDIS instance. REDIS is recommended as it works more robustly, in particular in cases where distributed file systems are rather slow.


A configuration file in ~/.parallel is required. See SGE.

The pyabc.sge.sge_available can be used to check if an SGE cluster can be used on the machine.

Check the API documentation for more details.

Information about running jobs

Use the python -m pyabc.sge.job_info_redis to get a nicely formatted output of the current execution state, in case the REDIS mode is used. Check python -m pyabc.sge.job_info_redis --help for more details.

Usage notes

The SGE class can be used in standalone mode for convenient parallelization of jobs across a cluster, completely independent of the rest of the pyABC package. The SGE class can also be combined, for instance, with the pyabc.sampler.MappingSampler class for simple parallelization of ABC-SCM runs across an SGE cluster.