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slurm [2022/10/03 13:38]
vodrazka [Cluster info]
slurm [2024/01/09 19:54] (current)
popel
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 LRC (Linguistic Research Cluster) is the name of ÚFAL's computational grid/cluster. The cluster is built on top of [[https://slurm.schedmd.com/|SLURM]] and is using [[https://www.lustre.org/|Lustre]] for [[internal:linux-network#directory-structure|data storage]]. LRC (Linguistic Research Cluster) is the name of ÚFAL's computational grid/cluster. The cluster is built on top of [[https://slurm.schedmd.com/|SLURM]] and is using [[https://www.lustre.org/|Lustre]] for [[internal:linux-network#directory-structure|data storage]].
 +
 +See Milan Straka's intro to Slurm (and Spark and possibly also the [[https://ufal.mff.cuni.cz/courses/npfl118#assignments|NPFL118 assingments]] if you want). Use the username=ufal and small linguistic password:
 +
 +  * https://lectures.ms.mff.cuni.cz/video/rec/npfl118/2324/npfl118-2324-winter-slurm.mp4
 +  * https://lectures.ms.mff.cuni.cz/video/rec/npfl118/2324/npfl118-2324-winter-spark.mp4
 +  * https://lectures.ms.mff.cuni.cz/video/rec/npfl118/2324/npfl118-2324-winter-assignments.mp4
  
 Currently there are following partitions (queues) available for computing: Currently there are following partitions (queues) available for computing:
  
-**Partition name** **Nodes**  **Note** | +===== Node list by partitions ===== 
-| cpu-troja      7x CPU default partition + 
-| gpu-troja      6x GPU | features: gpuram48G,gpuram40G | +The naming convention is straightforward for CPU nodes - nodes in each group are numbered. For GPU nodes the format is: [t]dll-**X**gpu**N** where **X** gives total number of GPUs equipped and **N** is just enumerating the order of the node with the given configuration. 
-| gpu-ms         7x GPU | features: gpuram48G,gpuram24G |+The prefix **t** is for nodes at Troja and **dll** stands for Deep Learning Laboratory.  
 +==== cpu-troja ==== 
 + 
 +| Node name | Thread count | Socket:Core:Thread | RAM (MB) 
 +achilles[1-8] | 32 | 2:8:2 | 128810 | 
 +| hector[1-8] | 32 | 2:8:2 | 128810 | 
 +| helena[1-8] | 32 | 2:8:2 | 128811 | 
 +| paris[1-8] | 32 | 2:8:2 | 128810 | 
 +| hyperion[2-8] | 64 | 2:16:2 | 257667 | 
 +==== cpu-ms ==== 
 + 
 +Node name Thread count | Socket:Core:Thread | RAM (MB) 
 +iridium | 16 | 2:4:2 | 515977 | 
 +| orion[1-8] | 40 | 2:10:2 | 128799 | 
 +==== gpu-troja ==== 
 + 
 +| Node name | Thread count | Socket:Core:Thread | RAM (MB) | Features | GPU type | 
 +| tdll-3gpu[1-4] | 64 | 2:16:2 | 128642 | gpuram48G gpu_cc8.6 | NVIDIA A40 | 
 +| tdll-8gpu[1,2] | 64 | 2:16:2 | 257666 | gpuram40G gpu_cc8.0 | NVIDIA A100 
 +tdll-8gpu[3-7] | 32 | 2:8:2 | 253725 | gpuram16G gpu_cc7.5 | NVIDIA Quadro P5000 | 
 +==== gpu-ms ==== 
 + 
 +| Node name | Thread count | Socket:Core:Thread | RAM (MB) | Features | GPU type | 
 +| dll-3gpu[1-5] | 64 | 2:16:2 | 128642 | gpuram48G gpu_cc8.6 | NVIDIA A40 | 
 +| dll-4gpu[1,2] | 40 | 2:10:2 | 187978 | gpuram24G gpu_cc8.6 NVIDIA RTX 3090 | 
 +| dll-4gpu3 | 62 | 1:32:2 | 515652 | gpuram48G gpu_cc8.9 | NVIDIA L40 | 
 +| dll-4gpu4 | 30 | 1:16:2 | 257616 | gpuram48G gpu_cc8.6 | NVIDIA A40 | 
 +| dll-8gpu[1,2] | 64 | 2:16:2 | 515838 | gpuram24G gpu_cc8.0 | NVIDIA A30 | 
 +| dll-8gpu[3,4] | 32 | 2:8:2 | 257830 | gpuram16G gpu_cc8.6 | NVIDIA RTX A4000 | 
 +| dll-8gpu[5,6] | 40 | 2:10:2 | 385595 | gpuram16G gpu_cc7.5 | NVIDIA Quadro RTX 5000 | 
 +| dll-10gpu1 | 32 | 2:8:2 | 257830 | gpuram16G gpu_cc8.6 | NVIDIA RTX A4000 | 
 +| dll-10gpu[2,3] | 32 | 2:8:2 | 257830 | gpuram11G gpu_cc6.1 | NVIDIA GeForce GTX 1080 Ti | 
 + 
 + 
 +==== Submit nodes ==== 
  
 In order to submit a job you need to login to one of the head nodes: In order to submit a job you need to login to one of the head nodes:
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    lrc1.ufal.hide.ms.mff.cuni.cz    lrc1.ufal.hide.ms.mff.cuni.cz
    lrc2.ufal.hide.ms.mff.cuni.cz    lrc2.ufal.hide.ms.mff.cuni.cz
 +   sol1.ufal.hide.ms.mff.cuni.cz
 +   sol2.ufal.hide.ms.mff.cuni.cz
 +   sol3.ufal.hide.ms.mff.cuni.cz
 +   sol4.ufal.hide.ms.mff.cuni.cz
 ===== Basic usage ===== ===== Basic usage =====
  
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 #!/bin/bash #!/bin/bash
 #SBATCH -J helloWorld   # name of job #SBATCH -J helloWorld   # name of job
-#SBATCH -p cpu-troja   # name of partition or queue (if not specified default partition is used)+#SBATCH -p cpu-troja   # name of partition or queue (default=cpu-troja)
 #SBATCH -o helloWorld.out   # name of output file for this submission script #SBATCH -o helloWorld.out   # name of output file for this submission script
 #SBATCH -e helloWorld.err   # name of error file for this submission script #SBATCH -e helloWorld.err   # name of error file for this submission script
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 #SBATCH -D /some/path/                        # change directory before executing the job    #SBATCH -D /some/path/                        # change directory before executing the job   
 #SBATCH -N 2                                  # number of nodes (default 1) #SBATCH -N 2                                  # number of nodes (default 1)
-#SBATCH --nodelist=node1,node2...             # required node, or comma separated list of required nodes+#SBATCH --nodelist=node1,node2...             # execute on *all* the specified nodes (and possibly more)
 #SBATCH --cpus-per-task=4                     # number of cores/threads per task (default 1) #SBATCH --cpus-per-task=4                     # number of cores/threads per task (default 1)
 #SBATCH --gres=gpu:                         # number of GPUs to request (default 0) #SBATCH --gres=gpu:                         # number of GPUs to request (default 0)
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 </code> </code>
  
-==== Running jobs ====+=== Rudolf's template === 
 + 
 +The main point is for log files to have the job name and job id in them automatically. 
 + 
 +<code> 
 +#SBATCH -J RuRjob 
 +#SBATCH -o %x.%j.out 
 +#SBATCH -e %x.%j.err 
 +#SBATCH -p gpu-troja 
 +#SBATCH --gres=gpu:
 +#SBATCH --mem=16G 
 +#SBATCH --constraint="gpuram16G|gpuram24G" 
 + 
 +# Print each command to STDERR before executing (expanded), prefixed by "+ " 
 +set -o xtrace 
 +</code> 
 + 
 +==== Inspecting jobs ====
  
 In order to inspect all running jobs on the cluster use: In order to inspect all running jobs on the cluster use:
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 In the example above you can see comments at all lines relevant to CPU allocation. In the example above you can see comments at all lines relevant to CPU allocation.
  
 +=== Priority ====
  
 +When running srun or sbatch, you can pass ''-q high/normal/low/preempt-low''. These represent priorities 300/200/100/100, with ''normal'' (200) being the default. Furthermore, the ''preempt-low'' QOS is actually preemptible -- if there is a job with normal or high QOS, they can interrupt your ''preempt-low'' job.
  
 +The preemption has probably not been used by anyone yet; some documentation about it is on https://slurm.schedmd.com/preempt.html, we use the REQUEUE regime (so your job is killed, very likely with some signal, so you could monitor it and for example save a checkpoint; but currently I do not know any details), and then started again when there are resources.
  
 ==== Interactive mode ==== ==== Interactive mode ====
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 There are many more parameters available to use. For example: There are many more parameters available to use. For example:
  
-<code>srun -p cpu-troja --mem=64G --pty bash</code>+**To get an interactive CPU job with 64GB of reserved memory:** 
 +<code>srun -p cpu-troja,cpu-ms --mem=64G --pty bash</code>
  
   * ''-p cpu-troja'' explicitly requires partition ''cpu-troja''. If not specified slurm will use default partition.   * ''-p cpu-troja'' explicitly requires partition ''cpu-troja''. If not specified slurm will use default partition.
-  * ''--mem=64G'' requires 64G of memory for the job+  * ''-''''-mem=64G'' requires 64G of memory for the job 
 + 
 +**To get interactive job with a single GPU of any kind:** 
 +<code>srun -p gpu-troja,gpu-ms --gres=gpu:1 --pty bash</code> 
 +  * ''-p gpu-troja,gpu-ms'' require only nodes from these two partitions 
 +  * ''-''''-gres=gpu:1'' requires 1 GPUs
  
 <code>srun -p gpu-troja,gpu-ms --nodelist=tdll-3gpu1 --mem=64G --gres=gpu:2 --pty bash</code> <code>srun -p gpu-troja,gpu-ms --nodelist=tdll-3gpu1 --mem=64G --gres=gpu:2 --pty bash</code>
   * ''-p gpu-troja,gpu-ms'' require only nodes from these two partitions   * ''-p gpu-troja,gpu-ms'' require only nodes from these two partitions
-  * ''--nodelist=tdll-3gpu1'' explicitly requires one specific node +  * ''-''''-nodelist=tdll-3gpu1'' explicitly requires one specific node 
-  * ''--gres=gpu:2'' requires 2 GPUs+  * Note that e.g. ''-''''-nodelist=tdll-3gpu[1-4]'' would execute 4 jobs on **all** the four machines ''tdll-3gpu[1-4]''. The documentation says "The job will contain all of these hosts and possibly additional hosts as needed to satisfy resource requirements." I am not aware of any [[https://stackoverflow.com/a/37555321/3310232|simple way]] how to specify that **any** of the listed nodes can be used, i.e. an equivalent of SGE ''-q '*@hector[14]'''
 +  * ''-''''-gres=gpu:2'' requires 2 GPUs 
 + 
 +<code>srun -p gpu-troja --constraint="gpuram48G|gpuram40G" --mem=64G --gres=gpu:2 --pty bash</code> 
 +  * ''-''''-constraint="gpuram48G|gpuram40G"'' only consider nodes that have either ''gpuram48G'' or ''gpuram40G'' feature defined 
 + 
 + 
 +\\ 
 +**Unexpected Behavior of ''srun -c1''** 
 +When you execute a command using ''srun'' and pass ''-c1'' like 
 +<code>srun -c1 date</code> 
 +then the command is actually executed **twice in parallel**. To avoid it, you have to either **remove the ''-c1''** or also **add explicit ''-n1''.** 
 +==== Delete Job ==== 
 +<code>scancel <job_id> </code> 
 + 
 +<code>scancel -n <job_name> </code>
  
-<code>srun -p gpu-troja --constraint="gpuram44G|gpuram39G" --mem=64G --gres=gpu:2 --pty bash</code> 
-  * ''--constraint="gpuram44G|gpuram39G"'' only consider nodes that have either ''gpuram44G'' or ''gpuram39G'' feature defined 
  
 To see all the available options type: To see all the available options type:
  
-<code>man srun</code>+<code>man scancel</code> 
 + 
 +==== Basic commands on cluster machines ==== 
 + 
 +  lspci 
 +    # is any such hardware there? 
 +  nvidia-smi 
 +    # more details, incl. running processes on the GPU 
 +    # nvidia-* are typically located in /usr/bin 
 +  watch nvidia-smi 
 +    # For monitoring GPU activity in a separate terminal (thanks to Jindrich Libovicky for this!) 
 +    # You can also use nvidia-smi -l TIME 
 +  nvcc --version 
 +    # this should tell CUDA version 
 +    # nvcc is typically installed in /usr/local/cuda/bin/ 
 +  theano-test 
 +    # dela to vubec neco uzitecneho? :-) 
 +    # theano-* are typically located in /usr/local/bin/ 
 +  /usr/local/cuda/samples/1_Utilities/deviceQuery/deviceQuery 
 +    # shows CUDA capability etc. 
 +  ssh dll1; ~popel/bin/gpu_allocations 
 +    # who occupies which card on a given machine 
 +     
 + 
 + 
 +===== See also ===== 
 + 
 +https://www.msi.umn.edu/slurm/pbs-conversion 
  

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