Known issues¶

In addition to the issues noted below, you can refer to our bug tracker on GitHub.

Cannot find msvcr90d.dll¶

If you see this message coming up, find the file PythonDir\Lib\site-packages\numpy\distutils\mingw32ccompiler.py and modify the line msvcr_dbg_success = build_msvcr_library(debug=True) to read msvcr_dbg_success = False (you can comment out the existing line and add the new line immediately after).

“AttributeError: MSVCCompiler instance has no attribute ‘compiler_cxx’”¶

This is caused by a bug in some versions of numpy on Windows. The easiest solution is to update to the latest version of numpy.

If that isn’t possible, a hacky solution is to modify the numpy code directly to fix the problem. The following change may work. Modify line 388 of numpy/distutils/ccompiler.py from elif not self.compiler_cxx: to elif not hasattr(self, 'compiler_cxx') or not self.compiler_cxx:. If the line number is different, it should be nearby. Search for elif not self.compiler_cxx in that file.

“Missing compiler_cxx fix for MSVCCompiler”¶

If you keep seeing this message, do not worry. It’s not possible for us to hide it, but doesn’t indicate any problems.

Problems with numerical integration¶

In some cases, the automatic choice of numerical integration method will not be appropriate, because of a choice of parameters that couldn’t be determined in advance. In this case, typically you will get nan (not a number) values in the results, or large oscillations. In this case, Brian will generate a warning to let you know, but will not raise an error.

Jupyter notebooks and C++ standalone mode progress reporting¶

When you run simulations in C++ standalone mode and enable progress reporting (e.g. by using report='text' as a keyword argument), the progress will not be displayed in the jupyter notebook. If you started the notebook from a terminal, you will find the output there. Unfortunately, this is a tricky problem to solve at the moment, due to the details of how the jupyter notebook handles output.

Parallel Brian simulations with C++ standalone¶

Simulations using the C++ standalone device will create code and store results in a dedicated directory (output, by default). If you run multiple simulations in parallel, you have to take care that these simulations do not use the same directory – otherwise, everything from compilation errors to incorrect results can happen. Either chose a different directory name for each simulation and provide it as the directory argument to the set_device or build call, or use directory=None which will use a randomly chosen unique temporary directory (in /tmp on Unix-based systems) for each simulation. If you need to know the directory name, you can access it after the simulation run via device.project_dir.

Parallel Brian simulations with Cython on machines with NFS (e.g. a computing cluster)¶

Generated Cython code is stored in a cache directory on disk so that it can be reused when it is needed again, without recompiling it. Multiple simulations running in parallel could interfere during the compilation process by trying to generate the same file at the same time. To avoid this, Brian uses a file locking mechanism that ensures that only a process at a time can access these files. Unfortunately, this file locking mechanism is very slow on machines using the Network File System (NFS), which is often the case on computing clusters. On such machines, it is recommend to use an independent cache directory per process, and to disable the file locking mechanism. This can be done with the following code that has to be run at the beginning of each process:

from brian2 import *
import os
cache_dir = os.path.expanduser(f'~/.cython/brian-pid-{os.getpid()}')
prefs.codegen.runtime.cython.cache_dir = cache_dir
prefs.codegen.runtime.cython.multiprocess_safe = False


Slow C++ standalone simulations¶

Some versions of the GNU standard library (in particular those used by recent Ubuntu versions) have a bug that can dramatically slow down simulations in C++ standalone mode on modern hardware (see #803). As a workaround, Brian will set an environment variable LD_BIND_NOW during the execution of standalone simulations which changes the way the library is linked so that it does not suffer from this problem. If this environment variable leads to unwanted behaviour on your machine, change the prefs.devices.cpp_standalone.run_environment_variables preference.

Cython fails with compilation error on OS X: error: use of undeclared identifier 'isinf'¶

Try setting the environment variable MACOSX_DEPLOYMENT_TARGET=10.9.

CMD windows open when running Brian on Windows with the Spyder 3 IDE¶

This is due to the interaction with the integrated ipython terminal. Either change the run configuration to “Execute in an external system terminal” or patch the internal Python function used to spawn processes as described in github issue #1140.