Add AWS/Docker instructions.

README.md

@@ -102,3 +102,25 @@ Streamline generation total time: 54.24975609779358 sec
 Destroy GPUTracker...
 ```
 
+## Running on AWS with Docker
+First, set up an AWS instance with GPU and ssh into it (we recommend a P3 instance with at least 1 V100 16 GB GPU). Then do the following:
+1. Log in to GitHub docker registry:
+```
+$ docker login -u <github id> docker.pkg.github.com
+```
+2. Enter your GitHub access token. If you do not have one, create it on the GitHub general security settings page and enable package read access for that token.
+3. Pull the container:
+```
+$ docker pull docker.pkg.github.com/dipy/gpustreamlines/gpustreamlines:latest
+```
+4. Run the code, mounting the current directory into the container for easy result retrieval:
+```
+$ docker run --gpus=all -v ${PWD}:/opt/exec/output:rw -it docker.pkg.github.com/dipy/gpustreamlines/gpustreamlines:latest \
+ python run_dipy_gpu_hardi.py --chunk-size 100000 --ngpus 1 --output-prefix output/hardi_gpu_full --use-fast-write
+```
+5. The code produces a number of independent track files (one per processed "chunk"), but we have provided a merge script to combine them into a single trk file. To merge files, run:
+```
+$ docker run --gpus=all -v ${PWD}:/opt/exec/output:rw -it docker.pkg.github.com/dipy/gpustreamlines/gpustreamlines:latest \
+ ./merge_trk.sh -o output/hardi_tracks.trk output/hardi_gpu_full*
+```
+