From f6f464d4dc072f86ce13dc2fe8b60e4b6c676cef Mon Sep 17 00:00:00 2001 From: Henry Addison Date: Mon, 13 May 2024 11:43:38 +0100 Subject: [PATCH] Correct name of U-Net not unet, just in docs --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 6b2e4ecf9..4647f4baa 100644 --- a/README.md +++ b/README.md @@ -10,7 +10,7 @@ Diffusion model implementation forked from PyTorch implementation for the paper 2. Create conda environment: `conda env create -f environment.lock.yml` (or add dependencies to your own: `conda env install -f environment.txt`) 3. Activate the conda environment (if not already done so) 4. Install ml_downscaling_emulator locally: `pip install -e .` -5. Install unet code: `git clone --depth 1 https://github.com/henryaddison/Pytorch-UNet.git src/ml_downscaling_emulator/unet` +5. Install U-Net code: `git clone --depth 1 https://github.com/henryaddison/Pytorch-UNet.git src/ml_downscaling_emulator/unet` 6. Configure application behaviour with environment variables. See `.env.example` for variables that can be set. Any datasets are assumed to be found in `${DERIVED_DATA}/moose/nc-datasets/{dataset_name}/`. In particular, the config key config.data.dataset_name is the name of the dataset to use to train the model.