I started getting the following warning repeatedly when running my PyTorch Lightning deep learning scripts, at execution start and then all through the training:
"OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead."
I get them when executing the main.py script; my scripts are publicly available here.
Symptoms:
I don't think it has anything to do with PyTorch Lightning, maybe even PyTorch.
It appeared overnight, so I don't know what could cause it.
It runs fine without those warnings on my PC.
I get the warnings when I run from my M1 Mac.
I use VSCode for both, each up to date.
I use separate miniconda environments.
Thanks for taking the time to reply!
I managed to sort myself out in the end.
I spotted the numba package in my miniconda env, which is a Python compiler and that seemed to be the root of the problem.
It was version 0.55.2 but the last version to date is 0.56.0. Trying to upgrade it via conda or pip didn't work for some reason (the 0.55.2 version couldn't be replaced).
I recreated my env step by step, and noticed this package comes with torch-audiomentations, a package for audio data augmentation for deep learning, under torch, that I use.
Re-installing it had numba version 0.56.0 installed properly, and the warnings disappeared.
That routine is deprecated, meaning that newer standards want you to use another routine. If you can not edit the code, just ignore. For now it's only an info message. You got that message when you switched to a newer OS / compiler version / OpenMP version.
Related
I have installed WinPython and want to use Spyder. I use pip and virtual environments. I have followed the instructions here modular approach. Everything works just dandy until the very last instruction "Start a new IPython console (in Spyder). All packages installed in your venv environment should be available there.".
It get error Your Python environment or installation doesn't have the spyder‑kernels module or the right version of it installed (>= 1.9.0 and < 1.10.0). Without this module is not possible for Spyder to create a console for you.
But I installed spyder-kernals in my venv, I can literally see them there, I set the path the the python installed in the venv, everything should work, but it doesn't!
Any thoughts?
I asked CAM Gerlach as suggested, and he spotted my error very quickly. The instructions at modular approach are correct except they say pip install spyder-kernels==0.* which I took literally. In fact as per the error message you need to use later versions, so I used pip install spyder-kernels==1.10 and it fixed it.
You may have to ask to "C.A.M. Gerlach" if he has an update on the procedure: Spyder has evolved a bit with Spyder-4.
I wrote some code months ago testing different optimizations of a function. I've since updated my anaconda installations of scipy, numpy, etc. that the code uses. Now the functions have different speeds (some faster, some slower), despite using the same code.
Is there anyway to check what the previous version of scipy that was installed was so I can attempt to see if something in scipy changed affecting my code? I use anaconda for package management, so I'm not sure if there's some record of previously installed versions somewhere. In particular I'd like to check cKDTree and cdist to see if either has changed in any meaningful way since my last installed version.
I need to install SUMO 0.30.0 to be used with the VEINS_INET subproject in veins 4.6. I have tried following the instructions here and suggestions from forums but haven't had any luck being able to install sumo. I run ./configure (trying various tool/library options) then run sudo make but all I get is target marouter failed or nothing to be done for 'install-exec-am' 'install-data-am'.
Does anyone know how to install sumo-0.30.0 from source and/or make the veins_inet subproject work with the latest version of sumo-0.32.0?
Don't run sudo make.
Don't run sudo make.
Your problem is probably related to a dependency/packaging change in 16.04, which is explicitly pointed out in the veins tutorial:
Note that Ubuntu 16.04 no longer includes libproj0; this can be worked around by temporarily adding the packet repository of, e.g., Ubuntu Vivid when installing this package.
Short answer: Unfortunately this means that long-term, you're going to either have to package SUMO yourself, use the versions someone else compiled (see this launchpad for example) or rely on an old version.
Long answer:
In general, I would recommend building SUMO from source by building its' dependencies from source, since I've encountered this problem on various distributions. In particular, the fox, proj and gdal libraries tend to be packaged in different versions, and along with changes in the SUMO source code. I currently use this script (with the package versions downloaded) to compile SUMO -- but this is for 0.30.0, and it breaks if any of the referenced source packages are moved (which happens quite often). My general recommendation would be to either use a completely isolated version of SUMO (i.e., compiling by hand as much as possible) or relying on a pre-packaged version (see above), as long as that version is recent enough to work with VEINS.
I´m trying to build paraview from source, therefore using:
Windows 7
Visual Studio 2010
Qt 4.8.7
Python 2.7.8
msmpi 7
paraview source, version 5.1.0
In CMake I can choose different options to specify what functionality to include into the build process. I tried different combinations, like setting BUILD_EXAMPLES or PARAVIEW_USE_MPI, respectively. Now I have got following questions:
When I set BUILD_SHARED_LIBS and PARAVIEW_ENABLE_PYTHON as well (besides others), configuring and generating the project with CMake is successful, but compiling in VS fails; it keeps freezing right after starting the compilation. Did anybody experience the same problem and how did you solve it? (By the way, if I unset BUILD_SHARED_LIBS it works, but I don´t want a static build of Paraview).
By using the combination BUILD_EXAMPLES, BUILD_TESTING, PARAVIEW_BUILD_QT_GUI, PARAVIEW_ENABLE_CATALYST, PARAVIEW_ENABLE_PYTHON and PARAVIEW_USE_MPI the same problem as described in 1.) occurs, but that is more or less what I need to use Catalyst to perform in-situ analysis of my FEM simulation. (Incidentally, if I unset BUILD_TESTING in the above combination it works, but I need CTest to test the Catalyst examples as described here. Does anybody now how to fix that problem?
As shown at GitHub, some examples have been updated to work properly in Paraview 4.4. Is my version of Paraview (5.1.0) unsuitable for the Catalyst examples? Is that the reason why VS is always hanging up for particular variable settings in CMake and which version of Paraview is most suitable to get the Catalyst examples going?
I'd appreciate any help!
That's odd! There's no known reason for this. Although I haven't used VS2010 explicitly, we do have dashboards testing with 2013 and I build with VS2015 with no issue.
I'd recommend using the Ninja as the builder rather than the IDE, however. Just run cmake-gui.exe from appropriate VS studio command prompt and pick Ninja as the build generator. Then, to build, just run ninja in the build directory.
I'm using this amazing IPython notebook. I'm very interested into parallel computing right now and would like to use MPI with IPython (and MPI4py). But I can't start a cluster with
ipcluster start -n 4
on Windows7. I just get back "failed to create process". If I use the notebook and start a cluster in the "Clusters" register it's all working fine. But with cmd (even with admin rights) I just get this message. Same with all attempts of using MPI (MPICH2). All path vars are set. Maybe this problem has no connection to Python at all...
I can't say anything about IPython's parallel features, but if you're having problems with MPI in Windows in general, I would offer these suggestions. I've had quite a few issues in the past in trying to get MPI working in Windows. The most convenient method for me in the past has been to use an OpenMPI Windows binary http://www.open-mpi.org/software/ompi/v1.6/. These are now only available in previous releases. And even then, you might have to try more than one before you find one that works. I don't know why, but the latest didn't work on my machine. The release before that one did, however. After this, you have to call mpicc and mpiexec from the Microsoft Visual Studio Command Prompt or it won't work (without a lot of other stuff).
After you have verified that MPI is working, you can try installing mpi4py separately and see if that works. In my experience, sometimes this has worked fine and sometimes I've had to wrestle with configurations. You might just try your luck with an unofficial, prepackaged binary (for example, http://www.lfd.uci.edu/~gohlke/pythonlibs/).
Hope this helps!