![]() Then we just do: train.py path/to/setting.yaml In order to train a model we again need to set up a config.yaml, see above regarding for more details. Note, the debug and all other scripts are registered by PyPI, so it does not matter from which directory they are started and we dont need to use python for them, they should always work. It then also overfits a model on 4 batches for 1000 epochs, to check if gradients flow properly and the model does indeed learn. It starts with a fast dev run in PyTorch lightning, which is essentially just performing two train, validation and test loops. We can check if a model works as desired by running: debug.py path/to/setting.yaml If done in this way, the earthnet-models-pytorch logger automatically detects the correct naming for later. It is recommended to save the setting in a folder structure configs////base.yaml. In order to use it, we need to set up a config.yaml containing all configs for the different components. Since models often require both a lot of data and GPUs to test the complete training cycle, we use this debug option rather than classic unit testing. The design process of a new model or feature is supported in earthnet-models-pytorch by a debug option. Pip install pytorch-lightning earthnet segmentation-models-pytorch Mamba install -c conda-forge numpy matplotlib pillow xarray zarr netcdf4 Mamba install -c pytorch -c conda-forge pytorch torchvision torchaudio cudatoolkit=11.3 tensorboard cudatoolkit might have to be installed with a different cuda version. Please note the PyTorch installation requirements for your system, see ( ) - esp. The following bash commands create a suitable environment. We recommend using Anaconda for managing dependencies of this library. Task - Abstraction for the training, validation & test loops, tying together models and settings, normally both models and settings are task-specific. Setting - Dataset and Metrics for a particular problemģ. Model - plain PyTorch models just implementing simple forward passes.Ģ. In earthnet-models-pytorch there is three main components: 1. The library is build on PyTorch, a Python deep learning library, and PyTorch Lightning, a PyTorch wrapper reducing boilerplate code and adding functionality to scale experiments. It is currently under development, thus do expect bugs and please report them! This library contains models, dataloaders and scripts for Earth surface forecasting in the context of research surrounding the EarthNet challenge. A PyTorch lightning library for Earth surface forecasting.
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