🤕Troubleshooting
Last updated
Last updated
Check if anaconda3 or miniconda3 exists
Check if conda environnement exists
Open a conda terminal
conda env list
Do you see a conda environnement named med_conda_env ?
yes ? Check environnement variable MED_ENV on your system
no ? Did you make this step ->
Your python environnement could have problems.
on WINDOWS:
open a cmd terminal( 🪟 + cmd) and write set MED_ENV
on LINUX and MACOS:
Open a terminal and write echo $MED_ENV
It should print a path to where your conda env is installed. If not:
Problem description:
The app seems to be stuck at startup. Or after startup, when opening multiple tabs (e.g. two different csv files), the app turns all white.
Probable cause:
If you have an insitutitonal antivirus like Trellix at Université de Sherbrooke, we believe the firewall of Trellix prevents MEDomicsLab to run properly. We hope the problem will be solved once we certify (signing Mac software) the MEDomicsLab app with Apple (work in progress).
Workaround:
If you encounter a similar issue, and you have an antivirus installed on your computer, we recommend that you disable it when using MEDomicsLab. For Mac users at Université de Sherbrooke with Trellix, one way to momentarily disable the utilities of Trellix is to:
Go to System Settings, Privacy & Security, Full Disk Access.
Disable the following six extensions: fmpd, VShieldScanManager, VShieldScanner, masvc, TrellixEndpointSecurity, TrellixNetworkExtension.
Restart yur computer.
Of course, once you are done working with MEDomicsLab, we recommend you enable again your antivirus! For Mac users at Unversité de Sherbrooke with Trellix, this would mean to enable again the six extensions above, and to restart your computer.
This error occurs when the wrong version of dtreeviz is installed. Please refer to the requirement files in our GitHub repository to install the right version (v1.4.1 is the one usually used).
The error message typically appears as ['column_1', 'column_2', 'column_3'] not in index. This issue occurs when column names in the file used for model training contain spaces (e.g., "column 1" instead of "column_1"). To prevent this, avoid using column names with spaces. While the code should handle this automatically, please open an issue on our GitHub if you encounter this error.
This error usually occurs due to a missing Python library (shown as a ModuleNotFoundError). To resolve it, first open the console (press CTRL + Shift + I) and identify the missing package name in the full error message. Then, install the specified package version from the requirements file on our GitHub repository. For a more comprehensive solution, compare your installed packages with those listed in the requirements file on GitHub and install any missing packages to prevent this error in the future.
If the missing package is not listed in our GitHub requirements, please contact us or open an issue so we can add it.
This is a common issue in Ubuntu that occurs when training a lightGBM model using n_jobs=-1. When you set n_jobs=-1, the process can sometimes hang or slow down due to how the library manages parallel threads on certain systems. Here are some common reasons for this behavior:
CPU Thread Saturation: Setting n_jobs=-1 tells LightGBM to use all available CPU cores. On Linux systems, particularly with high core counts, this can overwhelm the CPU scheduler, leading to inefficient thread management, especially if other processes are running concurrently.
OpenMP Configuration: LightGBM uses OpenMP for parallelism, and certain configurations of OpenMP on Linux can lead to deadlocks or excessive thread contention. This issue can be specific to how OpenMP handles threads in certain Linux distributions, including Ubuntu. More details here: https://lightgbm.readthedocs.io/en/latest/FAQ.html#lightgbm-hangs-when-multithreading-openmp-and-using-forking-in-linux-at-the-same-time
Memory Bandwidth and Latency: Using all CPU cores can lead to high memory bandwidth consumption. If LightGBM needs more memory per thread than available, this can slow down training significantly. Lowering the n_jobs setting reduces the number of simultaneous threads and can help manage memory load.
Compatibility with Specific Libraries: LightGBM's multithreading may not work smoothly with all versions of system libraries on Ubuntu, such as libgomp (GNU OpenMP). Sometimes, upgrading or downgrading certain libraries (e.g., libgomp1) can resolve this issue.
Limit n_jobs: Set n_jobs to a smaller number (e.g., 4 or 8), which often yields good performance without overloading the system.
Upgrade/Downgrade System Libraries: Update your OpenMP libraries.
You can test different n_jobs values to find the optimal setting, which balances speed and stability.
In a terminal, write the following command:
sudo apt remove medomicslab-applicationif you had installed the v0.0.1
sudo apt remove medappGo in your Applications Folder in Finder.
Then, click on the MEDomicsLab Icon while holding the Ctrl key.
Finally, click on "Move to Trash"
Go to Settings > Apps
Then, click on "Installed Apps"
Search for "MEDomicsLab"
Click on the ... and finally click on "Uninstall"
Now, you can follow the same instructions you followed for your first installation here.
Don't worry, the Python Environment installation won't be as long as the first time.
