/tilegym-monkey-patch-kernels-to-transformers

Setup GPU environment, Git branch, and Docker container

Work with user to prepare the experiment environment:

1. Get UUID of GPU(s) available on current node:

   nvidia-smi -L
   # Output: GPU 0: NVIDIA B200 (UUID: GPU-d8ea7ef9-442e-488f-bd23-d6912699e32d)

   If no GPU available, break and ask user instructions for accessing GPU nodes
2. Create a fresh git branch: Propose a branch name, e.g., auto-kernel-<transformer model name>-20260403 from target Transformer model ID and current date. The git branch <user name>/experiment/<branch name> must not exist. Checkout from current branch git checkout -b <user name>/experiment/<branch name>
3. Build Docker container for our experiment:

   # Build with source
   cd /path/to/project
   docker build --target source -f modeling/transformers/Dockerfile -t auto-kernel:latest .

4. Run all subsequent commands inside this Docker container. Do not substitute a host conda/venv. Only use a non-Docker environment if the user explicitly requests it.

   # Use the UUID from nvidia-smi -L output in step 1
   docker run --rm --gpus "device=GPU-d8ea7ef9-442e-488f-bd23-d6912699e32d" \
     -v /path/to/project:/workspace/tilegym \
     auto-kernel:latest \
     <command>

   Never use: --gpus all (potential multi-tenant conflicts) or --gpus 0 (device index, not UUID)
5. Check these tools exist inside Docker container:
   1. nvidia-smi -L prints same UUID as in step 1
   2. cuda.tile (cuTile in subsequent context) is installed
   3. nsys and ncu CLI available
   4. tileiras and ptxas available and versions match with each other