Frequently Asked Questions
If you have any additional questions, feel free to contact us through Support.
We recommend using the following options to help debug workflows:
from libensemble import logger logger.set_level("DEBUG") libE_specs["safe_mode"] = True
“Manager only - must be at least one worker (2 MPI tasks)” when running with multiprocessing and multiple workers specified.
If your code was recently switched from MPI to multiprocessing,
make sure that
libE_specs is populated
"comms": "local" and
“AssertionError: alloc_f did not return any work, although all workers are idle.”
This error occurs when the manager is waiting although all workers are idle. Note that a worker can be in a persistent state but is marked as idle when it has returned data to the manager and is ready to receive work.
Some possible causes of this error are:
An MPI libEnsemble run was initiated with only one process, resulting in one manager but no workers. Similarly, the error may arise when running with only two processes when using a persistent generator. The generator will occupy one worker, leaving none to run simulation functions.
An error in the allocation function. For example, perhaps the allocation waiting for all requested evaluations to be returned (e.g., before starting a new generator), but this condition is not returning True even though all scheduled evaluations have returned. This can be due to incorrect implementation (e.g., it has not considered points that are cancelled or paused or in some other state that prevents the allocation function from sending them out to workers).
A persistent worker (usually a generator) has sent a message back to the manager but is still performing work and may return further points. In this case, consider starting the generator in active_recv mode. This can be specified in the allocation function and will cause the worker to maintain its active status.
A persistent worker has requested resources that prevents any simulations from taking place. By default, persistent workers hold onto resources even when not active. This may require the worker to return from persistent mode.
When returning points to a persistent generator (often the top code block in allocation functions). For example,
support.avail_worker_ids(persistent=EVAL_GEN_TAG)Make sure that the
EVAL_GEN_TAGis specified and not just
libensemble.history (MANAGER_WARNING): Giving entries in H0 back to gen. Marking entries in H0 as ‘gen_informed’ if ‘sim_ended’.
This warning is harmless. It’s saying that as the provided History array is being “reloaded” into the generator, the copy is being slightly modified.
I keep getting: “Not enough processors per worker to honor arguments.” when using the Executor. Can I submit tasks to allocated processors anyway?
You may have set enforce_worker_core_bounds to True when setting up the Executor. Also, the resource manager can be completely disabled with:
libE_specs["disable_resource_manager"] = True
Note that the Executor
submit() method has a parameter
which will attempt to use all hyperthreads/SMT threads available if set to
FileExistsError: [Errno 17] File exists: “./ensemble”
This can happen when libEnsemble tries to create ensemble or simulation directories
that already exist from previous runs. To avoid this, ensure the ensemble directory
paths are unique by appending some unique value to
or automatically instruct runs to operate in unique directories via
libE_specs["use_workflow_dir"] = True.
PETSc and MPI errors with “[unset]: write_line error; fd=-1 buf=:cmd=abort exitcode=59”
python [test with PETSc].py --comms local --nworkers 4
This error occurs on some platforms when using PETSc with libEnsemble
local (multiprocessing) mode. We believe this is due to PETSc initializing MPI
before libEnsemble forks processes using multiprocessing. The recommended solution
is running libEnsemble in MPI mode. An alternative solution may be using a serial
build of PETSc.
This error may depend on how multiprocessing handles an existing MPI communicator in a particular platform.
HPC Errors and Questions
Why does libEnsemble hang on certain systems when running with MPI?
Another symptom may be the manager only communicating with Worker 1. This issue may occur if matching probes, which mpi4py uses by default, are not supported by the communications fabric, like Intel’s Truescale (TMI) fabric. This can be solved by switching fabrics or disabling matching probes before the MPI module is first imported.
Add these two lines BEFORE
from mpi4py import MPI:
import mpi4py mpi4py.rc.recv_mprobe = False
can’t open hfi unit: -1 (err=23) -  MPI startup(): tmi fabric is not available and fallback fabric is not enabled
This may occur on TMI when libEnsemble Python processes have been launched to a node and these, in turn, execute tasks on the node; creating too many processes for the available contexts. Note that while processes can share contexts, the system is confused by the fact that there are two phases: first libEnsemble processes and then subprocesses to run user tasks. The solution is to either reduce the number of processes running or to specify a fallback fabric through environment variables:
unset I_MPI_FABRICS export I_MPI_FABRICS_LIST=tmi,tcp export I_MPI_FALLBACK=1
Alternatively, libEnsemble can be run in central mode where all workers run on dedicated
nodes while launching all tasks onto other nodes. To do this add a node for libEnsemble,
libE_specs["dedicated_mode"] = True to your calling script.
What does “_pickle.UnpicklingError: invalid load key, “x00”.” indicate?
This has been observed with the OFA fabric when using mpi4py and usually indicates MPI messages aren’t being received correctly. The solution is to either switch fabric or turn off matching probes. See the answer to “Why does libEnsemble hang on certain systems when running with MPI?”
For more information see https://bitbucket.org/mpi4py/mpi4py/issues/102/unpicklingerror-on-commrecv-after-iprobe.
srun: Job ****** step creation temporarily disabled, retrying (Requested nodes are busy)
Note that this message has been observed on Perlmutter when none of the problems below are present, and is likely caused by interference with system processes that run between tasks. In this case, it may cause overhead but does not prevent correct functioning.
When running on a SLURM system, this implies that you are trying to run on a resource that is already dedicated to another task. The reason can vary, some reasons are:
All the contexts are in use. This has occurred when using TMI fabric on clusters. See question can’t open hfi unit: -1 (err=23) for more info.
In some cases using these environment variables will stop the issue:
export SLURM_EXACT=1 export SLURM_MEM_PER_NODE=0
Alternatively, this can be resolved by limiting the memory and other resources given to each task using the
--exactoption to srun along with other relevant options. For example:
srun --exact -n 4 -c 1 --mem-per-cpu=4G
would ensure that one CPU and 4 Gigabytes of memory are assigned to each MPI process. The amount of memory should be determined by the memory on the node divided by the number of CPUs. In the executor, this can be expressed via the
If libEnsemble is sharing nodes with submitted tasks (user applications launched by workers), then you may need to do this for your launch of libEnsemble also, ensuring there are enough resources for both the libEnsemble manager and workers and the launched tasks. If this is complicated, we recommended using a dedicated node for libEnsemble.
How can I debug specific libEnsemble processes?
This is most easily addressed when running libEnsemble locally. Try
mpiexec -np [num processes] xterm -e "python [calling script].py"
to launch an xterm terminal window specific to each process. Mac users will need to install xQuartz.
If running in
local mode, try using one of the
libensemble.tools to set breakpoints and debug similarly
pdb. How well this works varies by system.
from libensemble.tools import ForkablePdb ForkablePdb().set_trace()
Can I use the MPI Executor when running libEnsemble with multiprocessing?
Yes. The Executor type determines only how libEnsemble workers
execute and interact with user applications and is independent of
for manager/worker communications.
How can I disable libEnsemble’s output files?
If libEnsemble aborts on an exception, the History array and
dictionaries will be dumped. This can be suppressed by
See here for more information about these files.
How can I silence libEnsemble or prevent printed warnings?
Some logger messages at or above the
MANAGER_WARNING level are mirrored
to stderr automatically. To disable this, set the minimum stderr displaying level
CRITICAL via the following:
from libensemble import logger logger.set_stderr_level("CRITICAL")
This effectively puts libEnsemble in silent mode.
See the Logger Configuration docs for more information.
macOS and Windows Errors
Can I run libEnsemble on Windows?
Although we have run many libEnsemble workflows successfully on Windows using both MPI and local comms, Windows is not rigorously supported. We highly recommend Unix-like systems. Windows tends to produce more platform-specific issues that are difficult to reproduce and troubleshoot.
Windows - How can I run libEnsemble with MPI comms?
We have run Windows workflows with MPI comms. However, as most MPI
distributions have either dropped Windows support (MPICH and Open MPI) or are
no longer being maintained (
msmpi), we cannot guarantee success.
We recommend experimenting with the many Unix-like emulators, containers, virtual machines, and other such systems. The Installing PETSc On Microsoft Windows documentation contains valuable information.
mpi4py from conda and experiment, or use
Windows - “A required privilege is not held by the client”
Assuming you were trying to use the
gen_dir_symlink_files options, this indicates that to
allow libEnsemble to create symlinks, you need to run your current
cmd shell as administrator.
“RuntimeError: An attempt has been made to start a new process… this probably means that you are not using fork… “ if __name__ == “__main__”: freeze_support() …
You need to place your main entry point code underneath an
if __name__ == "__main__": block.
Explanation: Python chooses one of three methods to start new processes when using multiprocessing
--comms local with libEnsemble). These are
is the default on Unix, and in our experience is quicker and more reliable, but
"spawn" is the default
on Windows and macOS (See the Python multiprocessing docs).
Prior to libEnsemble v0.9.2, if libEnsemble detected macOS, it would automatically switch the multiprocessing
"fork". We decided to stop doing this to avoid overriding defaults and compatibility issues with
If you’d prefer to use
"fork" or not reformat your code, you can set the
multiprocessing start method by placing
the following near the top of your calling script:
import multiprocessing multiprocessing.set_start_method("fork", force=True)
“macOS - Fatal error in MPI_Init_thread: Other MPI error, error stack: … gethostbyname failed”
Resolve this by appending
127.0.0.1 [your hostname] to /etc/hosts.
127.0.0.1 localhost isn’t satisfactory for preventing this.
macOS - How do I stop the Firewall Security popups when running with the Executor?
There are several ways to address this nuisance, but all involve trial and error.
An easy (but insecure) solution is temporarily disabling the firewall through
System Preferences -> Security & Privacy -> Firewall -> Turn Off Firewall.
Alternatively, adding a firewall “Allow incoming connections” rule can be
attempted for the offending executable. We’ve had limited success running
sudo codesign --force --deep --sign - /path/to/application.app
on our executables, then confirming the next alerts for the executable