Merge branch 'dataloader' of https://github.com/geoelements/gns into …

…dataloader

.github/workflows/container-gpu.yml

@@ -0,0 +1,44 @@
+name: Build and Push GPU Image to GHCR
+
+on:
+  push:
+    paths:
+      - Dockerfile-GPU
+      - requirements.txt
+
+env:
+  REGISTRY: ghcr.io
+  IMAGE_NAME: ${{ github.repository }}
+
+jobs:
+  build-and-push:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+      packages: write
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - name: Log in to the Container registry
+        uses: docker/login-action@v3
+        with:
+          registry: ${{ env.REGISTRY }}
+          username: ${{ github.actor }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Extract metadata (tags, labels) for Docker
+        id: meta
+        uses: docker/metadata-action@v5
+        with:
+          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
+
+      - name: Build and push Docker image
+        uses: docker/build-push-action@v5
+        with:
+          context: .
+          file: ./Dockerfile-GPU
+          push: true
+          tags: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:gpu
+          labels: ${{ steps.meta.outputs.labels }}

Dockerfile-GPU

@@ -16,6 +16,4 @@ RUN pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_co
 RUN pip install absl-py autopep8 numpy==1.23.1 dm-tree matplotlib pyevtk pytest tqdm toml
 RUN pip install -r requirements.txt
 
-
-
 CMD ["/bin/bash"]

README.md

@@ -1,8 +1,8 @@
 # Graph Network Simulator (GNS) and MeshNet
 
 [![DOI](https://zenodo.org/badge/427487727.svg)](https://zenodo.org/badge/latestdoi/427487727)
-[![CircleCI](https://dl.circleci.com/status-badge/img/gh/geoelements/gns/tree/main.svg?style=svg)](https://dl.circleci.com/status-badge/redirect/gh/geoelements/gns/tree/main)
-[![Docker](https://quay.io/repository/geoelements/gns/status "Docker Repository on Quay")](https://quay.io/repository/geoelements/gns)
+[![GitHub Actions](https://github.com/geoelements/gns/actions/workflows/train.yml/badge.svg)](https://github.com/geoelements/gns/actions/workflows/train.yml)
+[![Docker](https://img.shields.io/badge/container-gpu-limegreen.svg)](https://ghcr.io/geoelements/gns:gpu)
 [![License](https://img.shields.io/badge/license-MIT-blue.svg)](https://raw.githubusercontent.com/geoelements/gns/main/license.md)
 
 > Krishna Kumar, The University of Texas at Austin.
@@ -227,53 +227,29 @@ The dataset is shared on [DesignSafe DataDepot](https://doi.org/10.17603/ds2-fzg
 
 GNS uses [pytorch geometric](https://www.pyg.org/) and [CUDA](https://developer.nvidia.com/cuda-downloads). These packages have specific requirements, please see [PyG installation]((https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html) for details. 
 
-> CPU-only installation on Linux (Conda)
+> CPU-only installation on Linux/MacOS
 
 ```shell
-conda install -y pytorch torchvision torchaudio cpuonly -c pytorch
-conda install -y pyg -c pyg
-conda install -y pytorch-cluster -c pyg
-conda install -y absl-py -c anaconda 
-conda install -y numpy dm-tree matplotlib-base pyevtk -c conda-forge 
+pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
+pip3 install torch_geometric
+pip3 install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.3.0+cpu.html
+pip3 install -r requirements.txt
 ```
-You can use the [WaterDropletSample](https://github.com/geoelements/gns-sample) dataset to check if your `gns` code is working correctly.
 
 To test the code you can run:
 
 ```
 pytest test/
 ```
 
-To test on the small waterdroplet sample:
-
-```
-git clone https://github.com/geoelements/gns-sample
-
-TMP_DIR="./gns-sample"
-DATASET_NAME="WaterDropSample"
-
-mkdir -p ${TMP_DIR}/${DATASET_NAME}/models/
-mkdir -p ${TMP_DIR}/${DATASET_NAME}/rollout/
-
-DATA_PATH="${TMP_DIR}/${DATASET_NAME}/dataset/"
-MODEL_PATH="${TMP_DIR}/${DATASET_NAME}/models/"
-ROLLOUT_PATH="${TMP_DIR}/${DATASET_NAME}/rollout/"
-
-python -m gns.train --data_path=${DATA_PATH} --model_path=${MODEL_PATH} --ntraining_steps=10
-```
-
 ### Build Docker Image
 
 Dockerfile-GPU is supplied to build image with GPU support.
 
-
-### Building GNS on MacOS
-```shell
-pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
-pip3 install torch_geometric
-pip3 install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.3.0+cpu.html
-pip3 install -r requirements.txt
 ```
+docker pull ghcr.io/geoelements/gns:gpu
+```
+
 
 ## GNS training in parallel
 GNS can be trained in parallel on multiple nodes with multiple GPUs.