This page contains answers to frequently asked questions about GaNDLF.
The usage guide provides a good starting point for you to understand the application of GaNDLF. If you have any questions, please feel free to post a support request, and we will do our best to address it ASAP.
pkg_resources.DistributionNotFound: The 'GANDLF' distribution was not found?This means that GaNDLF was not installed correctly. Please ensure you have followed the installation guide properly.
Verify that the installation has been done correctly by running python ./gandlf_verifyInstall after activating the correct virtual environment. If you are still having issues, please feel free to post a support request, and we will do our best to address it ASAP.
Virtually all of it! For more details, please see the usage guide and our extensive samples. All available options are documented in the config_all_options.yaml file.
Yes, GaNDLF has successfully been run on an SGE cluster and another managed using Kubernetes. Please post a question with more details such as the type of scheduler, and so on, and we will do our best to address it.
Yes, look for logs_*.csv files in the output directory. It should be arranged in accordance with the cross-validation configuration. Furthermore, it should contain separate files for each data cohort, i.e., training/validation/testing, along with the values for all requested performance metrics, which are defined per problem type.
GaNDLF allows you to resume training from a previous checkpoint in 2 ways:
--resume CLI parameter in gandlf_run, only the model weights and state dictionary will be preserved, but parameters and data are taken from the new options in the CLI. This is helpful when you are updated the training data or some compatible options in the parameters.--resume and --reset are False in gandlf_run, the model weights, state dictionary, and all previously saved information (parameters, training/validation/testing data) is used to resume training.pip install --upgrade gandlf to get the latest version of GaNDLF, or if you are interested in the nightly builds, then you can run pip install --upgrade --pre gandlf.git pull from the base GaNDLF directory to get the latest master of GaNDLF. Follow this up with pip install -e . after activating the appropriate virtual environment to ensure the updates get passed through.Please post a support request.