I'm using AWS Batch for ML model-runs on Gnothi. I'm using hugginface/transformers, UKPLab/sentence-transformers, Gensim, spaCy, and more which download large model artifacts (ml-tools). I could add these to the Dockerfile run by Batch via their CLI download commands (eg python -m spacy download en), but the Dockerfile would be huge, incurring an unnecessary provision uptime cost to Batch. It's preferred to download these models once to an external mounted file-system, for re-mount & re-use across all the Batch runs. AWS's own tutorial on this was pretty lacking, so here are my steps.

1. Create two security groups (SG).
   1. One for the Batch compute environment. Mine is called "ml_jobs ". Outbound=* (0.0.0.0/0), inbound you'll likely want SSH (0.0.0.0/0)
   2. One for EFS. Outbound=*. Mine's called "EFS "
   3. Modify each SG to have Inbound=* from each other's SG. You only actually need NFS (and maybe SSH?), but hey, they're only talking to each other; you're safe.
      Discussion here.
2. Create an EFS file system (link)
   1. At the security-group step, x out each subnet it suggests, and replace it with the SG subnets you created in (1).
3. Create a launch template. Link here, this is the tutorial provided by AWS (the only info I found in this adventure).
   1. Per the rest of this post, I did mine in Console (not API), so just copy/paste the that big text-blob from the link onto (bottom of launch-template page) > Advanced details > User data (replacing the file_system_id_01)
   2. Add Storage (volumes) to reflect that tutorial. That is, Volume=EBS, Delete on termination=Yes, Device name=/dev/xvda, Volume type=gp2.
   3. Make sure everything else on this page, including sub-fields of Storage, is set to "Don't include in launch template ".
4. Setup Batch. Create a _compute environment.
   1. Add an EC2 keypair if you want to SSH in. You likely will, since you'll want to scp in files.
   2. Probably select managed/spot, 100% cost. That's my setup, up to you.
   3. p2 family or greater(I'm using p2.xlarge). Don't use g family, Nvidia drivers not compatible! Make sure you remove "Optimal "
   4. In one of the advanced bits, select your launch template from (3), version=$Latest
   5. VPC ID = the same VPC you used for your EFS, select all subnets
   6. Specify the SG from (1.1).
5. Job definition
   1. Volumes. Name=efs, Source path=/mnt/efs
   2. Mount points. Source volume=efs, Container path=/storage (or wherever your container expects the mount)
   3. Number of GPUs=1
   4. Privileged=true
      Details on the above. Volumes specifies the name/tag/label you're referring to this mount path on the system, and Mount points references that to place it into the container at "Container path ". Number of GPUs requests a GPU, and an Nvidia driver from host. Privileged is required for this setup, as the /mnt/efs will be chown root.