chore: import upstream snapshot with attribution

doc/source/cluster/doc_code/slurm-basic.sh

@@ -0,0 +1,45 @@
+#!/bin/bash
+# shellcheck disable=SC2206
+#SBATCH --job-name=test
+#SBATCH --cpus-per-task=5
+#SBATCH --mem-per-cpu=1GB
+#SBATCH --nodes=4
+#SBATCH --tasks-per-node=1
+#SBATCH --time=00:30:00
+
+set -x
+
+# __doc_head_address_start__
+
+# Getting the node names
+nodes=$(scontrol show hostnames "$SLURM_JOB_NODELIST")
+nodes_array=($nodes)
+
+head_node=${nodes_array[0]}
+
+port=6379
+ip_head=$head_node:$port
+export ip_head
+echo "IP Head: $ip_head"
+# __doc_head_address_end__
+
+# __doc_symmetric_run_start__
+# Start Ray cluster using symmetric_run.py on all nodes.
+# Symmetric run will automatically start Ray on all nodes and run the script ONLY the head node.
+# Use the '--' separator to separate Ray arguments and the entrypoint command.
+# The --min-nodes argument ensures all nodes join before running the script.
+
+# All nodes (including head and workers) will execute this block.
+# The entrypoint (simple-trainer.py) will only run on the head node.
+srun --nodes="$SLURM_JOB_NUM_NODES" --ntasks="$SLURM_JOB_NUM_NODES" \
+    ray symmetric-run \
+    --address "$ip_head" \
+    --min-nodes "$SLURM_JOB_NUM_NODES" \
+    --num-cpus="${SLURM_CPUS_PER_TASK}" \
+    --num-gpus="${SLURM_GPUS_PER_TASK}" \
+    -- \
+    python -u simple-trainer.py "$SLURM_CPUS_PER_TASK"
+# __doc_symmetric_run_end__
+
+# __doc_script_start__
+# The entrypoint script (simple-trainer.py) will be run on the head node by symmetric_run.