mirror of
https://github.com/vale981/ray
synced 2025-03-08 19:41:38 -05:00
63 lines
1.6 KiB
ReStructuredText
63 lines
1.6 KiB
ReStructuredText
Deploying on Slurm
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==================
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Clusters managed by Slurm may require that Ray is initialized as a part of the submitted job. This can be done by using ``srun`` within the submitted script. For example:
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.. code-block:: bash
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#!/bin/bash
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#SBATCH --job-name=test
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#SBATCH --cpus-per-task=20
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#SBATCH --mem-per-cpu=1GB
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#SBATCH --nodes=5
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#SBATCH --tasks-per-node 1
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worker_num=4 # Must be one less that the total number of nodes
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module load Langs/Python/3.6.4 # This will vary depending on your environment
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source venv/bin/activate
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nodes=$(scontrol show hostnames $SLURM_JOB_NODELIST) # Getting the node names
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nodes_array=( $nodes )
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node1=${nodes_array[0]}
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ip_prefix=$(srun --nodes=1 --ntasks=1 -w $node1 hostname --ip-address) # Making address
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suffix=':6379'
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ip_head=$ip_prefix$suffix
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export ip_head # Exporting for latter access by trainer.py
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srun --nodes=1 --ntasks=1 -w $node1 ray start --block --head --redis-port=6379 & # Starting the head
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sleep 5
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for (( i=1; i<=$worker_num; i++ ))
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do
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node2=${nodes_array[$i]}
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srun --nodes=1 --ntasks=1 -w $node2 ray start --block --address=$ip_head & # Starting the workers
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sleep 5
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done
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python trainer.py 100 # Pass the total number of allocated CPUs
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.. code-block:: python
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# trainer.py
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import os
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import sys
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import time
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import ray
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ray.init(address=os.environ["ip_head"])
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@ray.remote
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def f():
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time.sleep(1)
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# The following takes one second (assuming that ray was able to access all of the allocated nodes).
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start = time.time()
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num_cpus = int(sys.argv[1])
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ray.get([f.remote() for _ in range(num_cpus)])
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end = time.time()
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print(end - start)
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