spark-vllm-docker/README.md

vLLM Ray Cluster Node Docker for DGX Spark

This repository contains the Docker configuration and startup scripts to run a multi-node vLLM inference cluster using Ray. It supports InfiniBand/RDMA (NCCL) and custom environment configuration for high-performance setups.

1. Building the Docker Image

The Dockerfile includes specific Build Arguments to allow you to selectively rebuild layers (e.g., update the vLLM source code without re-downloading PyTorch).

Option A: Standard Build (First Time)

docker build -t vllm-node .

Option B: Fast Rebuild (Update vLLM Source Only)

Use this if you want to pull the latest code from GitHub but keep the heavy dependencies (Torch, FlashInfer, system deps) cached.

docker build \
  --build-arg CACHEBUST_VLLM=$(date +%s) \
  -t vllm-node .

Option C: Full Rebuild (Update All Dependencies)

Use this to force a re-download of PyTorch, FlashInfer, and system packages.

docker build \
  --build-arg CACHEBUST_DEPS=$(date +%s) \
  -t vllm-node .

Copying the container to another Spark node

To avoid extra network overhead, you can copy the image directly to your second Spark node via ConnectX 7 interface by using the following command:

docker save vllm-node | ssh your_username@another_spark_hostname_or_ip "docker load"

---

2. Running the Container

Ray and NCCL require specific Docker flags to function correctly across multiple nodes (Shared memory, Network namespace, and Hardware access).

docker run -it --rm \
  --gpus all \
  --net=host \
  --ipc=host \
  --privileged \
  --name vllm_node \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  vllm-node bash

Or if you want to start the cluster node (head or regular), you can launch with the run-cluster.sh script (see details below):

On head node:

docker run --privileged --gpus all -it --rm \
  --ipc=host \
  --network host \
  --name vllm_node \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  vllm-node ./run-cluster-node.sh \
    --role head \
    --host-ip 192.168.177.11 \
    --eth-if enp1s0f1np1 \
    --ib-if rocep1s0f1 

On worker node

docker run --privileged --gpus all -it --rm \
  --ipc=host \
  --network host \
  --name vllm_node \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  vllm-node ./run-cluster-node.sh \
    --role node \
    --host-ip 192.168.177.12 \
    --eth-if enp1s0f1np1 \
    --ib-if rocep1s0f1 \
    --head-ip 192.168.177.11

Flags Explained:

• --net=host: Required. Ray needs full control over network ports; port mapping is insufficient for multi-node clusters.
• --ipc=host: Required. Allows shared memory access for PyTorch/NCCL.
• --privileged: Required for InfiniBand. Grants the container access to RDMA devices (/dev/infiniband).

---

3. Using run-cluster-node.sh

The script is used to configure the environment and launch Ray either in head or node mode.

Normally you would start it with the container like in the example above, but you can launch it inside the Docker session manually if needed (but make sure it's not already running).

Syntax

./run-cluster-node.sh [OPTIONS]

Flag	Long Flag	Description	Required?
-r	--role	Role of the machine: head or node.	Yes
-h	--host-ip	The IP address of this specific machine (IB or Eth IP).	Yes
-e	--eth-if	Ethernet interface name (e.g., eth0, enp3s0).	Yes
-i	--ib-if	InfiniBand interface name (e.g., ib0, rocep1s0f1).	Yes
-m	--head-ip	The IP address of the Head Node.	Only if role is node

Example: Starting inside the Head Node

./run-cluster-node.sh \
  --role head \
  --host-ip 192.168.177.11 \
  --eth-if enp1s0f1np1 \
  --ib-if rocep1s0f1

Example: Starting inside a Worker Node

./run-cluster-node.sh \
  --role node \
  --host-ip 192.168.177.12 \
  --eth-if enp1s0f1np1 \
  --ib-if rocep1s0f1 \
  --head-ip 192.168.177.11

---

4. Configuration Details

Environment Persistence

The script automatically appends exported variables to ~/.bashrc. If you need to open a second terminal into the running container for debugging, simply run:

docker exec -it vllm_node bash

All environment variables (NCCL, Ray, vLLM config) set by the startup script will be loaded automatically in this new session.

5.. Using cluster mode for inference

First, start follow the instructions above to start the head container on your first Spark, and node container on the second Spark. Then, on the first Spark, run vllm like this:

docker exec -it vllm_node bash -i -c "vllm serve RedHatAI/Qwen3-VL-235B-A22B-Instruct-NVFP4 --port 8888 --host 0.0.0.0 --gpu-memory-utilization 0.7 -tp 2 --distributed-executor-backend ray --max-model-len 32768"

Alternatively, run an interactive shell first:

docker exec -it vllm_node

And execute vllm command inside.

Hardware Architecture

Note: The Dockerfile defaults to TORCH_CUDA_ARCH_LIST=12.1a (NVIDIA GB10). If you are using different hardware, update the ENV variable in the Dockerfile before building:

• H100: 9.0
• A100: 8.0
• L40S: 8.9