FAQ

This page is not meant to be an extensive documentation of how to address all questions that may arrise when using pySIPFENN as a result of things outside our control, such as interpreter configuration, permissions, operating system issues. These can often be resolved by searching the internet for the error message you are receiving.

First things to check

In case you are having issues, it is good to first check the following few things that helped other users.

System and Architecture

pySIPFENN should generally run on any operating system and computer architecture. We have tested it on 6 common combinations, which are listed below:

  • Windows 10/11 64-bit on x86_64 (i.e. Intel/AMD)

  • Windows 11 on ARM64 (e.g. Surface Pro X, Parallels VM on M1/M2 Macs)

  • Linux on x86_64 (i.e. Intel/AMD)

  • Linux on ARM64 (e.g. Raspberry Pi 4, Jetson Nano, etc.)

  • MacOS on x86_64 (most Macs from before 2021)

  • MacOS on ARM64 (Macs with M1/M2 chip)

Thus, it is unlikely that your operating system (PC vs Mac) or architecture (Intel vs M1/M2 Mac) is the issue. However, if you are using a different platform than above, please let us know, and we will try to help you.

Python Version

Make sure you are using the correct version of Python. pySIPFENN requires Python 3.9 or higher. We recommend 3.10 for longer support. The 3.11 increases computation speed but was not tested as thoroughly and may have some issues with dependencies.

If you are using a different version, you can either install a new version of Python or use a virtual environment to install pySIPFENN in. We recommend using Conda to create a virtual environment. See the installation instructions making sure to specify the version of Python.

Up-to-date Conda / pip

Some users had problems with (1) loading the models or (2) making predictions using them and we traced the issue to an outdated version of Conda installed on their work station by their IT department. Updating Conda, pip, and reinstalling pySIPFENN fixed the issue. You can try:

conda update -n base conda -c anaconda
conda update pip
pip install --upgrade --force-reinstall pysipfenn

If you downloaded the models before, they should be retained as long as you do not change the Conda environment. If you do, you will have to download them again.

Model Download

One of the most common concerns users have contacted us about is the inability to download the model files in reasonable time. This is usually correlated in time with a workshop, after which many users try to download them from Zenodo concurrently and exhaust the bandwidth. In such a case, we recommend trying again in a day or two.

If your download is slow or fails during normal time periods, please let us know, and we will try to help you by providing the files directly.

More Complex Issues

Out-Of-Memory Error / Models Cannot Load

RAM requirements to run:

While pySIPFENN shouldn’t have much trouble running on most modern desktop and protable computers, it may have issues on older machines or on machines with limited RAM such as VMs on cloud instances. In general, to run the models, you will need:

  • 512MB of RAM for Light Model (NN24)

  • 2.5GB of RAM for KS2022 Novel Materials Model (NN30)

  • 6GB of RAM to run all default models (NN9, NN20, NN24, NN30) and follow the workshop tutorial

RAM requirements to load:

Now, one needs to consider how much memory is needed to get the models loaded into the memory. This is a bit complicated, but in general, your system will need to use around twice the memory given above.

At the same time, if you are using any modern version of Windows or MacOS, you shouldn’t have any issues with this limits if your system has at least 8GB of RAM (assuming no memory-intensive applications running in the background) because they will utilize pagefile (Windows) or swap (MacOS) to automatically store the temporary data (as much as needed) that does not fit into RAM.

Memory on Linux

However, if you are using Linux, the swap is not dynamic and if it’s smaller than the amount of memory you need, you will get an error or the process will just be ‘killed’ by the system. You can check its size with

free -h

And if the sum of RAM and swap is less than 12GB, you likely won’t be able to use all of pySIPFENN models. Fortunately, it’s an easy fix for your administrator, or if you have sudo access, yourself. To temporarily increase swap you just need to allocate some space on your drive (e.g. 8GB)

sudo fallocate -l 8G /swapfile

limit access permissions for security reasons

sudo chmod 600 /swapfile

tell Linux that it is a swap file

sudo mkswap /swapfile

and enable it

sudo swapon /swapfile

Now, you should have no memory issues with running all pySIPFENN networks as of the time of writing (NN9, NN20, NN24, NN30). Please note that, as mentioned, this swap change is temporary and will disappear when ou reboot. To make it permanent, you can follow one of many guides available online.