Difference between revisions of "GPUs Basel 2018"

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* Create the tutorial project:
 
* Create the tutorial project:
 
  <tt>dtutorial myParticles -p myProject -M 128</tt>
 
  <tt>dtutorial myParticles -p myProject -M 128</tt>
This creates a tutorial dataset with 128 particles in the directory <code>myParticles</code> and a tutorial alignment project <code>myProject</code>.
+
We now have a tutorial dataset with 128 particles in the directory <code>myParticles</code> and a tutorial alignment project <code>myProject</code>.
  
 
* Open the alignment project window:
 
* Open the alignment project window:
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* Check and Unfold the project.
 
* Check and Unfold the project.
  
* Before moving the data to sciCORE we have to compress the project. In the dcp gui go to ''Tools'' and then "create a tarball"
+
* Before moving the data to sciCORE we have to compress the project. In the project window go to ''Tools'' and then "create a tarball".
  
  
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  <tt>nano  submit_job.sh</tt>
 
  <tt>nano  submit_job.sh</tt>
  
* Copy and adapt the following lines into the newly created script:
+
* Copy and adapt the following lines into the newly created script (note the difference between K80 and TitanX GPUs):
  
 
For using the K80 GPUs:
 
For using the K80 GPUs:
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* Note that depending on your project you might have to adapt the project name and the time requested (time=) in the script.
 
* Note that depending on your project you might have to adapt the project name and the time requested (time=) in the script.
  
 +
* You can now run your alignment project by submitting the previously created script to SLURM with:
 +
<tt>sbatch submit_job.sh</tt>
  
* You can now run your alignment project by submitting the previously created script to SLURM with:
+
* With the following commands you can check the overall status of the submitted jobs:
<tt>sbatch submit_job.sh</tt>
 
  
* To check your status in the queue type:
+
Check your status in the queue:
squeue -u USERNAME
+
<tt>squeue -u USERNAME</tt>
  
To see all users in queue for the K80 GPU:
+
See all users in queue for the K80 GPU:
squeue -q 30min
+
<tt>squeue -q 30min</tt>
  
To see all users in queue for the TitanX GPU:
+
See all users in queue for the TitanX GPU:
squeue -q empgu
+
<tt>squeue -q empgu</tt>
  
To cancel the job type ''scancel'' and the job ID that was shown by the squeue command:
+
To cancel the job type ''scancel'' followed by the job ID that was shown by the squeue command:
scancel my_job_id
+
<tt>scancel my_job_id</tt>
  
 
Some ways to check the last output:
 
Some ways to check the last output:
ls -rtl
+
<tt>ls -rtl</tt>
tail -f slurm-45994509.out
+
<tt>tail -f slurm-45994509.out</tt>
less slurm-45994509.out
+
<tt>less slurm-45994509.out</tt>
  
To check the last average:
+
To check the last average load the standalone Dynamo environment by typing <code>dynamo</code> into the terminal and use the usual commands, e.g.:
dynamo
+
<tt>ddb dTutorial:a -v</tt>
ddb dTutorial:a -v
 

Revision as of 16:11, 20 August 2018

Here we describe on how to use the GPUs provided for the Basel Workshop 2018. We go through each step by using a simple tutorial dataset and project as an example. You can use the same steps described on your dataset/project.

The GPUs we use are located on the high performance computing cluster of the University of Basel called sciCORE (https://scicore.unibas.ch) which uses the SLURM queuing system. A queuing system coordinates the access to the GPUs and is needed when there are many users using just a few GPUs.

The main idea is that we create an alignment project locally, move it to the cluster on sciCORE and then run it using a pre-installed Dynamo standalone version on sciCORE. To do that, we follow the following steps:


On your local Matlab session with Dynamo loaded:

  • Create the tutorial project:
dtutorial myParticles -p myProject -M 128

We now have a tutorial dataset with 128 particles in the directory myParticles and a tutorial alignment project myProject.

  • Open the alignment project window:
dcp myProject

and under computing environment select GPU (standalone) as an environment.

  • Check and Unfold the project.
  • Before moving the data to sciCORE we have to compress the project. In the project window go to Tools and then "create a tarball".


On your local Linux terminal:

  • Copy the project data (particles) to sciCORE:
rsync -avuP myParticles USERNAME@login.bc2.unibas.ch:/scicore/home/PATH/dynamo_projects
  • Copy the previously created tar file of the project to sciCORE:
rsync -avuP dTutorial.tar USERNAME@login.bc2.unibas.ch:/scicore/home/PATH/dynamo_projects
  • Login to your sciCORE account:
ssh -Y USERNAME@login.scicore.unibas.ch


While logged in to your sciCORE account:

  • Activate dynamo:
source PATH/dynamo_activate_linux_shipped_MCR.sh
  • Untar the Dynamo project:
dynamo dvuntar myProject.tar 
  • Create a blank SLURM submission script (text file) named submit_job.sh:
nano  submit_job.sh
  • Copy and adapt the following lines into the newly created script (note the difference between K80 and TitanX GPUs):

For using the K80 GPUs:

#!/bin/bash -l
#
#SBATCH --job-name=dTest
#SBATCH --qos=30min
#SBATCH --time=00:60:00
#SBATCH --mem=16G
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --cpus-per-task=1
#SBATCH --partition=k80
#SBATCH --gres=gpu:1
module load CUDA/7.5.18
source PATH/dynamo_activate_linux_shipped_MCR.sh
cd PATH/dynamo_projects
echo "dvput myProject -gpu_identifier_set $CUDA_VISIBLE_DEVICES" > dcommands.sh
echo "dvunfold myProject" >> dcommands.sh
dynamo dcommands.sh
chmod u=rxw ./myProject.m
./myProject.m

For using the TitanX GPUs:

#!/bin/bash -l
#
#SBATCH --job-name=dTest
#SBATCH --qos=emgpu
#SBATCH --time=00:60:00
#SBATCH --mem=16G
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --cpus-per-task=1
#SBATCH --partition=titanx
#SBATCH --gres=gpu:1
module load CUDA/7.5.18
source PATH/dynamo_activate_linux_shipped_MCR.sh
cd PATH/dynamo_projects
echo "dvput myProject -gpu_identifier_set $CUDA_VISIBLE_DEVICES" > dcommands.sh
echo "dvunfold myProject" >> dcommands.sh
dynamo dcommands.sh
chmod u=rxw ./myProject.m
./myProject.m
  • Note that depending on your project you might have to adapt the project name and the time requested (time=) in the script.
  • You can now run your alignment project by submitting the previously created script to SLURM with:
sbatch submit_job.sh
  • With the following commands you can check the overall status of the submitted jobs:

Check your status in the queue:

squeue -u USERNAME

See all users in queue for the K80 GPU:

squeue -q 30min

See all users in queue for the TitanX GPU:

squeue -q empgu

To cancel the job type scancel followed by the job ID that was shown by the squeue command:

scancel my_job_id

Some ways to check the last output:

ls -rtl
tail -f slurm-45994509.out
less slurm-45994509.out

To check the last average load the standalone Dynamo environment by typing dynamo into the terminal and use the usual commands, e.g.:

ddb dTutorial:a -v