diff --git a/doc/source/deploying-on-slurm.rst b/doc/source/deploying-on-slurm.rst
index 3ff4dbd2b..e65ee4ee4 100644
--- a/doc/source/deploying-on-slurm.rst
+++ b/doc/source/deploying-on-slurm.rst
@@ -8,15 +8,15 @@ Clusters managed by Slurm may require that Ray is initialized as a part of the s
   #!/bin/bash
 
   #SBATCH --job-name=test
-  #SBATCH --cpus-per-task=20
+  #SBATCH --cpus-per-task=5
   #SBATCH --mem-per-cpu=1GB
-  #SBATCH --nodes=5
+  #SBATCH --nodes=3
   #SBATCH --tasks-per-node 1
 
-  worker_num=4 # Must be one less that the total number of nodes
+  worker_num=2 # Must be one less that the total number of nodes
 
-  module load Langs/Python/3.6.4 # This will vary depending on your environment
-  source venv/bin/activate
+  # module load Langs/Python/3.6.4 # This will vary depending on your environment
+  # source venv/bin/activate
 
   nodes=$(scontrol show hostnames $SLURM_JOB_NODELIST) # Getting the node names
   nodes_array=( $nodes )
@@ -26,38 +26,48 @@ Clusters managed by Slurm may require that Ray is initialized as a part of the s
   ip_prefix=$(srun --nodes=1 --ntasks=1 -w $node1 hostname --ip-address) # Making address
   suffix=':6379'
   ip_head=$ip_prefix$suffix
+  redis_password=$(uuidgen)
 
   export ip_head # Exporting for latter access by trainer.py
 
-  srun --nodes=1 --ntasks=1 -w $node1 ray start --block --head --redis-port=6379 & # Starting the head
+  srun --nodes=1 --ntasks=1 -w $node1 ray start --block --head --redis-port=6379 --redis-password=$redis_password & # Starting the head
   sleep 5
 
   for ((  i=1; i<=$worker_num; i++ ))
   do
     node2=${nodes_array[$i]}
-    srun --nodes=1 --ntasks=1 -w $node2 ray start --block --address=$ip_head & # Starting the workers
+    srun --nodes=1 --ntasks=1 -w $node2 ray start --block --address=$ip_head --redis-password=$redis_password & # Starting the workers
     sleep 5
   done
 
-  python trainer.py 100 # Pass the total number of allocated CPUs
+  python -u trainer.py $redis_password 15 # Pass the total number of allocated CPUs
 
 .. code-block:: python
 
   # trainer.py
+  from collections import Counter
   import os
   import sys
   import time
   import ray
-  
-  ray.init(address=os.environ["ip_head"])
- 
+
+  redis_password = sys.argv[1]
+  num_cpus = int(sys.argv[2])
+
+  ray.init(address=os.environ["ip_head"], redis_password=redis_password)
+
+  print("Nodes in the Ray cluster:")
+  print(ray.nodes())
+
   @ray.remote
   def f():
-    time.sleep(1)
+      time.sleep(1)
+      return ray.services.get_node_ip_address()
 
   # The following takes one second (assuming that ray was able to access all of the allocated nodes).
-  start = time.time()
-  num_cpus = int(sys.argv[1])
-  ray.get([f.remote() for _ in range(num_cpus)])  
-  end = time.time()
-  print(end - start)
+  for i in range(60):
+      start = time.time()
+      ip_addresses = ray.get([f.remote() for _ in range(num_cpus)])
+      print(Counter(ip_addresses))
+      end = time.time()
+      print(end - start)