Update README.md

README.md

@@ -6,3 +6,49 @@ This project explores using a deep generative adversarial network (GAN) to perfo
 See version 1 [here](https://github.com/ryanquinnnelson/CMU-02699-Image-Segmentation-via-GANs).
 
 See the final report [here](https://github.com/ryanquinnnelson/CMU-02699-Image-Segmentation-via-GANs-v2/blob/main/docs/Nelson_Parameterized_GAN_architecture.pdf).
+
+
+## How to run on an EC2 instance
+1. Copy `octopus` library to Deep Learning instance. 
+2. Copy data to instance. Data was is found at [here](https://warwick.ac.uk/fac/cross_fac/tia/data/glascontest/download/). I organized the files into the following format.
+```
+warwick
+    ├── a
+    ├── a_anno
+    ├── b
+    ├── b_anno
+    ├── training
+    └── training_anno
+```
+I also padded all the numbering in the filenames (i.e. testA_1_anno.bmp -> testA_01_anno.bmp)
+
+4. Copy this codebase to instance.
+5. Log into instance.
+6. (Optional) Mount drive on EC2.
+```
+bash octopus/bin/mount_drive
+```
+6. Activate `conda` environment.
+```
+conda activate pytorch_p38
+```
+7. Install `wandb`.
+```
+bash octopus/bin/setup_wandb
+```
+8. Run code for a single run or a sweep.
+
+Single run
+```
+python /home/ubuntu/CMU-02699-Image-Segmentation-via-GANs2/run_octopus.py --filename=/home/ubuntu/CMU-02699-Image-Segmentation-via-GANs2/configs/remote/config_remote_GAN_only_002.txt
+```
+Sweep
+```
+wandb sweep /home/ubuntu/CMU-02699-Image-Segmentation-via-GANs2/sweeps/remote/sweep_remote_grid_023.yaml
+````
+
+
+
+
+
+