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chore: Regenerate all playbooks
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@ -150,8 +150,8 @@ After this, you should be able to run docker commands without using `sudo`.
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```bash
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docker pull nvcr.io/nvidia/vllm:25.09-py3
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export VLLM_IMAGE=nvcr.io/nvidia/vllm:25.09-py3
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docker pull nvcr.io/nvidia/vllm:25.11-py3
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export VLLM_IMAGE=nvcr.io/nvidia/vllm:25.11-py3
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```
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@ -161,16 +161,23 @@ Launch the Ray cluster head node on Node 1. This node coordinates the distribute
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```bash
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## On Node 1, start head node
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export MN_IF_NAME=enP2p1s0f1np1
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bash run_cluster.sh $VLLM_IMAGE 192.168.100.10 --head ~/.cache/huggingface \
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-e VLLM_HOST_IP=192.168.100.10 \
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-e UCX_NET_DEVICES=$MN_IF_NAME \
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-e NCCL_SOCKET_IFNAME=$MN_IF_NAME \
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-e OMPI_MCA_btl_tcp_if_include=$MN_IF_NAME \
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-e GLOO_SOCKET_IFNAME=$MN_IF_NAME \
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-e TP_SOCKET_IFNAME=$MN_IF_NAME \
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-e RAY_memory_monitor_refresh_ms=0 \
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-e MASTER_ADDR=192.168.100.10
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## Get the IP address of the high-speed interface
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## Use the interface that shows "(Up)" from ibdev2netdev (enp1s0f0np0 or enp1s0f1np1)
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export MN_IF_NAME=enp1s0f1np1
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export VLLM_HOST_IP=$(ip -4 addr show $MN_IF_NAME | grep -oP '(?<=inet\s)\d+(\.\d+){3}')
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echo "Using interface $MN_IF_NAME with IP $VLLM_HOST_IP"
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bash run_cluster.sh $VLLM_IMAGE $VLLM_HOST_IP --head ~/.cache/huggingface \
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-e VLLM_HOST_IP=$VLLM_HOST_IP \
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-e UCX_NET_DEVICES=$MN_IF_NAME \
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-e NCCL_SOCKET_IFNAME=$MN_IF_NAME \
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-e OMPI_MCA_btl_tcp_if_include=$MN_IF_NAME \
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-e GLOO_SOCKET_IFNAME=$MN_IF_NAME \
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-e TP_SOCKET_IFNAME=$MN_IF_NAME \
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-e RAY_memory_monitor_refresh_ms=0 \
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-e MASTER_ADDR=$VLLM_HOST_IP
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```
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@ -180,17 +187,30 @@ Connect Node 2 to the Ray cluster as a worker node. This provides additional GPU
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```bash
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## On Node 2, join as worker
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export MN_IF_NAME=enP2p1s0f1np1
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bash run_cluster.sh $VLLM_IMAGE 192.168.100.10 --worker ~/.cache/huggingface \
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-e VLLM_HOST_IP=192.168.100.11 \
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-e UCX_NET_DEVICES=$MN_IF_NAME \
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-e NCCL_SOCKET_IFNAME=$MN_IF_NAME \
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-e OMPI_MCA_btl_tcp_if_include=$MN_IF_NAME \
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-e GLOO_SOCKET_IFNAME=$MN_IF_NAME \
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-e TP_SOCKET_IFNAME=$MN_IF_NAME \
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-e RAY_memory_monitor_refresh_ms=0 \
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-e MASTER_ADDR=192.168.100.10
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## Set the interface name (same as Node 1)
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export MN_IF_NAME=enp1s0f1np1
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## Get Node 2's own IP address
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export VLLM_HOST_IP=$(ip -4 addr show $MN_IF_NAME | grep -oP '(?<=inet\s)\d+(\.\d+){3}')
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## IMPORTANT: Set HEAD_NODE_IP to Node 1's IP address
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## You must get this value from Node 1 (run: echo $VLLM_HOST_IP on Node 1)
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export HEAD_NODE_IP=<NODE_1_IP_ADDRESS>
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echo "Worker IP: $VLLM_HOST_IP, connecting to head node at: $HEAD_NODE_IP"
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bash run_cluster.sh $VLLM_IMAGE $HEAD_NODE_IP --worker ~/.cache/huggingface \
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-e VLLM_HOST_IP=$VLLM_HOST_IP \
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-e UCX_NET_DEVICES=$MN_IF_NAME \
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-e NCCL_SOCKET_IFNAME=$MN_IF_NAME \
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-e OMPI_MCA_btl_tcp_if_include=$MN_IF_NAME \
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-e GLOO_SOCKET_IFNAME=$MN_IF_NAME \
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-e TP_SOCKET_IFNAME=$MN_IF_NAME \
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-e RAY_memory_monitor_refresh_ms=0 \
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-e MASTER_ADDR=$HEAD_NODE_IP
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```
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> **Note:** Replace `<NODE_1_IP_ADDRESS>` with the actual IP address from Node 1, specifically the QSFP interface nep1s0f1np1 configured in the [Connect two Sparks](https://build.nvidia.com/spark/connect-two-sparks) playbook.
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## Step 6. Verify cluster status
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@ -198,7 +218,11 @@ Confirm both nodes are recognized and available in the Ray cluster.
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```bash
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## On Node 1 (head node)
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docker exec node ray status
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## Find the vLLM container name (it will be node-<random_number>)
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export VLLM_CONTAINER=$(docker ps --format '{{.Names}}' | grep -E '^node-[0-9]+$')
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echo "Found container: $VLLM_CONTAINER"
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docker exec $VLLM_CONTAINER ray status
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```
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Expected output shows 2 nodes with available GPU resources.
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@ -219,7 +243,8 @@ Start the vLLM inference server with tensor parallelism across both nodes.
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```bash
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## On Node 1, enter container and start server
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docker exec -it node /bin/bash
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export VLLM_CONTAINER=$(docker ps --format '{{.Names}}' | grep -E '^node-[0-9]+$')
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docker exec -it $VLLM_CONTAINER /bin/bash
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vllm serve meta-llama/Llama-3.3-70B-Instruct \
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--tensor-parallel-size 2 --max_model_len 2048
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```
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@ -260,7 +285,8 @@ Start the server with memory-constrained parameters for the large model.
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```bash
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## On Node 1, launch with restricted parameters
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docker exec -it node /bin/bash
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export VLLM_CONTAINER=$(docker ps --format '{{.Names}}' | grep -E '^node-[0-9]+$')
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docker exec -it $VLLM_CONTAINER /bin/bash
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vllm serve hugging-quants/Meta-Llama-3.1-405B-Instruct-AWQ-INT4 \
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--tensor-parallel-size 2 --max-model-len 256 --gpu-memory-utilization 1.0 \
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--max-num-seqs 1 --max_num_batched_tokens 256
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@ -287,41 +313,25 @@ Perform comprehensive validation of the distributed inference system.
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```bash
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## Check Ray cluster health
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docker exec node ray status
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export VLLM_CONTAINER=$(docker ps --format '{{.Names}}' | grep -E '^node-[0-9]+$')
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docker exec $VLLM_CONTAINER ray status
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## Verify server health endpoint
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curl http://192.168.100.10:8000/health
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## Monitor GPU utilization on both nodes
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nvidia-smi
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docker exec node nvidia-smi --query-gpu=memory.used,memory.total --format=csv
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export VLLM_CONTAINER=$(docker ps --format '{{.Names}}' | grep -E '^node-[0-9]+$')
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docker exec $VLLM_CONTAINER nvidia-smi --query-gpu=memory.used,memory.total --format=csv
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```
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## Step 14. Cleanup and rollback
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Remove temporary configurations and containers when testing is complete.
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> [!WARNING]
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> This will stop all inference services and remove cluster configuration.
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```bash
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## Stop containers on both nodes
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docker stop node
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docker rm node
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## Remove network configuration on both nodes
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sudo ip addr del 192.168.100.10/24 dev enP2p1s0f1np1 # Node 1
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sudo ip addr del 192.168.100.11/24 dev enP2p1s0f1np1 # Node 2
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sudo ip link set enP2p1s0f1np1 down
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```
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## Step 15. Next steps
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## Step 14. Next steps
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Access the Ray dashboard for cluster monitoring and explore additional features:
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```bash
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## Ray dashboard available at:
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http://192.168.100.10:8265
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http://<head-node-ip>:8265
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## Consider implementing for production:
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## - Health checks and automatic restarts
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