Merge branch 'develop' of https://github.com/asfhyp3/hyp3-srg into ti…

…meseries-entrypoint

README.md

@@ -50,19 +50,40 @@ In order for Docker to be able to use the host's GPU, the host must have the [NV
 The process is different for different OS's and Linux distros. The setup process for the most common distros, including Ubuntu, 
 can be found [here](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html#configuration). Make sure to follow the [Docker configuration steps](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html#configuration) after installing the package.
 
+The AWS ECS-optimized GPU AMI has the configuration described already set up. You can find the latest version of this AMI by calling:
+```bash
+aws ssm get-parameters --names /aws/service/ecs/optimized-ami/amazon-linux-2/gpu/recommended --region us-west-2
+```
+
+### GPU Docker Container
+Once you have a compute environment set up as described above, you can build the GPU version of the container by running:
+```bash
+docker build --build-arg="GPU_ARCH={YOUR_ARCH}" -t ghcr.io/asfhyp3/hyp3-srg:{RELEASE}.gpu -f Dockerfile.gpu .
+```
+
+You can get the value of `COMPUTE_CAPABILITY_VERSION` by running `nvidia-smi` on the instance to obtain GPU type, then cross-reference this information with NVIDIA's [GPU type compute capability list](https://developer.nvidia.com/cuda-gpus). For a g6.2xlarge instance, this would be:
+```bash
+docker --build-arg="GPU_ARCH=89" -t ghcr.io/asfhyp3/hyp3-srg:{RELEASE}.gpu -f Dockerfile.gpu .
+```
+The compute capability version will always be the same for a given instance type, so you will only need to look this up once per instance type.
+The default value for this argument is `89` - the correct value for g6.2xlarge instances.
+**THE COMPUTE CAPABILITY VERSION MUST MATCH ON BOTH THE BUILDING AND RUNNING MACHINE!**
+
+The value of `RELEASE` can be obtained from the git tags.
+
+You can push a manual container to HyP3-SRG's container repository by following [this guide](https://docs.github.com/en/packages/working-with-a-github-packages-registry/working-with-the-container-registry#pushing-container-images).
+
 ### EC2 Setup
 > [!CAUTION]
-> Running the docker container on an Amazon Linux 2023 Deep Learning AMI runs, but will result in all zero outputs. Work is ongoing to determine what is causing this issue. For now, we recommend using option 2.i.
+> Running the docker container on an Amazon Linux 2023 Deep Learning AMI runs, but will result in all zero outputs. Work is ongoing to determine what is causing this issue. For now, we recommend using option 2.3.
 
 When running on an EC2 instance, the following setup is recommended:
 1. Create a [G6-family EC2 instance](https://aws.amazon.com/ec2/instance-types/g6/) that has **at least 32 GB of memory**.
 2. Launch your instance with one of the following setups (**option i is recommended**):
     1. Use the latest [Amazon Linux 2023 AMI](https://docs.aws.amazon.com/linux/al2023/ug/ec2.html) with `scripts/amazon_linux_setup.sh` as the [user script on launch](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/user-data.html).
     2. Use the latest [Ubuntu AMI](https://cloud-images.ubuntu.com/locator/ec2/) with the `scripts/ubuntu_setup.sh` as the [user script on launch](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/user-data.html).
-    3. Use the [Ubuntu Deep Learning Base OSS Nvidia Driver GPU AMI](https://aws.amazon.com/releasenotes/aws-deep-learning-base-gpu-ami-ubuntu-22-04/) (no install script required).
-3. Build the GPU docker container with the correct compute capability version. To determine this value, run `nvidia-smi` on the instance to obtain GPU type, then cross-reference this information with NVIDIA's [GPU type compute capability list](https://developer.nvidia.com/cuda-gpus). For a g6.2xlarge instance, this would be:
+    3. Use the latest AWS ECS-optimized GPU AMI (`aws ssm get-parameters --names /aws/service/ecs/optimized-ami/amazon-linux-2/gpu/recommended --region us-west-2`)
+3. Build the GPU docker container with the correct compute capability version (see section above). To determine this value, run `nvidia-smi` on the instance to obtain GPU type, then cross-referencke this information with NVIDIA's [GPU type compute capability list](https://developer.nvidia.com/cuda-gpus). For a g6.2xlarge instance, this would be:
 ```bash
-docker --build-arg="GPU_ARCH=89" -t hyp3-srg:gpu-89 -f Dockerfile.gpu .
+docker --build-arg="GPU_ARCH=89" -t ghcr.io/asfhyp3/hyp3-srg:{RELEASE}.gpu -f Dockerfile.gpu .
 ```
-The compute capability version will always be the same for a given instance type, so you will only need to look this up once per instance type.
-The default value for this argument is `89` - the correct value for g6.2xlarge instances.