MEDomicsLab-docs
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  • 👋Welcome!
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      • Extraction Module
        • Image Extraction Page
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    • 🟠Development
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        • Overview
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    • 🟢Deployment
      • Application Module
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  • 📄Testing Phase with MIMIC
    • MIMIC data access
    • Step 1: Install and Explore
    • Step 2: Extract Data
    • Step 3: Prepare ML tables
    • Step 4: Explore Data
    • Step 5: Vacations
    • Step 6: Create Model
    • Step 7: Evaluate & Apply Model
    • Step 8: Challenge
    • Wrap-Up
  • 👩‍💻Contributing
    • Our coding standards
    • How to push my modification ?
  • 🤕Troubleshooting
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Troubleshooting

PreviousHow to push my modification ?NextFAQ

Last updated 6 months ago

Go server not working ?
  • 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 ->

Go server is working properly but I got errors when clicking on process buttons

Your python environnement could have problems.

Test the environnement variable MED_ENV

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 env is installed. If not:

MacOS: the app does not want to launch and/or the app seems buggy when opening multiple tabs. KNOWN ISSUE FOR MAC USERS AT UNIVERSITÉ DE SHERBROOKE WITH THE TRELLIX ANTIVIRUS.

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:

  1. Go to System Settings, Privacy & Security, Full Disk Access.

  2. Disable the following six extensions: fmpd, VShieldScanManager, VShieldScanner, masvc, TrellixEndpointSecurity, TrellixNetworkExtension.

  3. 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.

ImportError: cannot import name 'dtreeviz' from 'dtreeviz.trees'

This error occurs when the wrong version of is installed. Please refer to the requirement files in our to install the right version (v1.4.1 is the one usually used).

Columns missing in evaluation and application modules

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 on our GitHub if you encounter this error.

Process stopped with toast message: "Exist status 1"

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 . For a more comprehensive solution, compare your installed packages with those listed in the requirements file on and install any missing packages to prevent this error in the future.

If the missing package is not listed in our GitHub requirements, please or so we can add it.

LightGBM Model is taking forever to train

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:

  1. 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.

  2. 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.

  3. 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.

Solutions:

  • 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.

How to update to the latest release ?

1. Uninstall the application first

Ubuntu

In a terminal, write the following command:

sudo apt remove medomicslab-application

if you had installed the v0.0.1

sudo apt remove medapp

MacOS

Go in your Applications Folder in Finder.

Then, click on the MEDomicsLab Icon while holding the Ctrl key.

Finally, click on "Move to Trash"


Windows

Go to Settings > Apps

Then, click on "Installed Apps"

Search for "MEDomicsLab"

Click on the ... and finally click on "Uninstall"

2. Reinstall the application

Don't worry, the Python Environment installation won't be as long as the first time.

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:

Now, you can follow the same instructions you followed for your first installation .

🤕
https://lightgbm.readthedocs.io/en/latest/FAQ.html#lightgbm-hangs-when-multithreading-openmp-and-using-forking-in-linux-at-the-same-time
here
conda
dtreeviz
GitHub repository
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GitHub repository
GitHub
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#2.-unzip-the-python-environment-installer-1