Add official PyTorch-based Dockerfile for DeepEP

[YidaWeng]

Summary

Addresses #615: add a minimal official Dockerfile based on the PyTorch CUDA devel image, plus short usage docs.

What

• docker/Dockerfile — pytorch/pytorch:2.14.1-cuda13.2-cudnn9-devel, install nvidia-nccl-cu13>=2.32.3 --no-deps, build DeepEP with install.sh
• docker/README.md — build/run instructions and limits
• .dockerignore — keep the context small; exclude .git so setup.py's dirty-tree assert cannot fail in the image build
• README — short Docker pointer under Installation

Why devel (not runtime)

DeepEP compiles kernels at runtime via DeepJIT, which needs nvcc from a CUDA 13.1+ toolkit. A runtime-only base would build the host extension and then fail JIT at import/first use.

Testing

On this contributor machine (macOS, no NVIDIA GPU / Docker daemon unavailable for a full CUDA build):

• content checks for the Dockerfile and .dockerignore
• could not run docker build against an NVIDIA host

Please run on a Linux NVIDIA box:

git submodule update --init --recursive
docker build --platform=linux/amd64 -f docker/Dockerfile -t deepep:local .
docker run --rm --gpus all --ipc=host deepep:local \
  python -c "import deep_ep, torch; print(deep_ep.__version__, torch.cuda.is_available())"

Notes

• Build context must already contain third-party/deep_jit (submodule init).
• RDMA / multi-node device mounts are intentionally out of scope; documented in docker/README.md.
• Installed version suffix is typically +local because .git is dockerignored.