My instructions are quite rudimentary since I don't have much time to write these blog posts anymore. Hopefully there's enough information to get you through.
Either way, sign up for AWS. If you already have an amazon ID I think you can use that. Go to https://aws.amazon.com/
Select Launch an Instance and pick the ubuntu AIM and do Launch and Review. I launched it as a t2.micro instance type, as it is free and it's sufficient for set up but not to run jobs.
Hit launch, and create a new key pair. I called mine myfirstkeypair and saved the pem file in my ~/Downloads folder
In my Downloads folder:
ssh -i "myfirstkeypair.pem" email@example.comI then set a password in the ubuntu AWS image:
sudo passwd ubuntu
I added my id_rsa.pub to ~/.ssh/authorized_keys on the ubuntu AWS image to make logging in via ssh easier -- that way I won't need the pem file.
Set up Gaussian
I then connected with SCP and uploaded my gaussian files -- I went straight for EM64T G09D. It went quite fast at +5 MB/s
scp E6L-103X.tgz firstname.lastname@example.org:/home/ubuntu/E6L-103X.tgz
Once that was done, on the ubuntu AWS instance I did:
sudo apt-get install csh sudo mkdir /opt/gaussian cd /opt sudo chown ubuntu gaussian -R cd /opt/gaussian cp ~/E6L-103X.tgz . tar xvf E6L-103X.tgz cd g09 csh bsd/install
echo 'export GAUSS_EXEDIR=/opt/gaussian/g09/bsd:/opt/gaussian/g09/local:/opt/gaussian/g09/extras:/opt/gaussian/g09' >> ~/.bashrc echo 'export GAUSS_SCRDIR=/home/ubuntu/scratch' >> ~/.bashrc echo 'export PATH=$PATH:/opt/gaussian/g09' >> ~/.bashrc source ~/.bashrc mkdir ~/scratch ~/jobs
NOTE that you can't run any gaussian jobs under a t2.micro instance. You will have to stop and relaunch as at least a t2.small instance or the jobs will be 'Killed' (that's what is echoed in the terminal when you try to run)
Note that if you terminate an image it will be deleted.
Stop the image and then create a snapshot or an image from it to keep everything you've installed.
Set up Slurm
You'll want a queue manager so that you can launch several jobs in serial. Also, you can set up your script so that it shuts down the image when your job is done to save money.
sudo apt-get update sudo apt-get install slurm-llnl
ControlMachine=localhost ControlAddr=127.0.0.1 MpiDefault=none ProctrackType=proctrack/pgid ReturnToService=2 SlurmctldPidFile=/var/run/slurm-llnl/slurmctld.pid SlurmdPidFile=/var/run/slurm-llnl/slurmd.pid SlurmdSpoolDir=/var/lib/slurm-llnl/slurmd SlurmUser=slurm StateSaveLocation=/var/lib/slurm-llnl/slurmctld SwitchType=switch/none TaskPlugin=task/none FastSchedule=1 SchedulerType=sched/backfill SelectType=select/linear AccountingStorageType=accounting_storage/none ClusterName=rupert JobAcctGatherType=jobacct_gather/none SlurmctldLogFile=/var/log/slurm-llnl/slurmctld.log SlurmdLogFile=/var/log/slurm-llnl/slurmd.log NodeName=localhost NodeAddr=127.0.0.1 PartitionName=All Nodes=localhost
sudo /usr/sbin/create-munge-keyEdit /etc/default/munge:
sudo service slurm-llnl restart
sudo service munge restartTest using slurm.batch
and submit with#!/bin/bash # #SBATCH -p All #SBATCH --job-name=test #SBATCH --output=res.txt # #SBATCH --ntasks=1 #SBATCH --time=10:00 srun hostname srun sleep 60
squeue JOBID PARTITION NAME USER ST TIME NODES NODELIST(REASON) 2 All test ubuntu R 0:08 1 localhost
Using the same opt/freq benchmark as in post 621.#!/bin/csh #SBATCH -p All #SBATCH --time=9999999 #SBATCH --output=slurm.out #SBATCH --job-name=benchmark setenv GAUSS_SCRDIR /home/ubuntu/scratch setenv GAUSS_EXEDIR /opt/gaussian/g09/bsd:/opt/gaussian/g09/local:/opt/gaussian/g09/extras:/opt/gaussian/g09 /opt/gaussian/g09/g09< benchmark.in > benchmark.out
c4.2xlarge 2h 11 min [1h 20 min] 8 vcpu/16 Gb
c4.4xlarge 1h 15 min [ 44 min] 16 vcpu/32 Gb
c4.8xlarge 41 min [ 25 min] 36 vcpu/60 Gb
It scales surprisingly well, although not perfectly linearly. It's clear that it's cheaper to use a smaller instance, so if time isn't critical or the larger memory isn't needed, c4.8xlarge is not the first choice.
You might want to use dropbox to transfer files back and forth, especially finished job files (useful if you shut down the machine using a slurm script as shown below)
cd ~ && wget -O - "https://www.dropbox.com/download?plat=lnx.x86_64" | tar xzf - ~/.dropbox-dist/dropboxdThis computer isn't linked to any Dropbox account... Please visit https://www.dropbox.com/cli_link_nonce?nonce=0011223344556677889900aabbccddeef to link this device. This computer isn't linked to any Dropbox account...
Open that link in a browser, then go back to the terminal.
wget -O - https://www.dropbox.com/download?dl=packages/dropbox.py > dropbox.py sudo mv dropbox.py /usr/local/bin sudo chmod +x d/usr/local/bin/dropbox.py dropbox.py autostart y
Now, since you don't want to use up space unnecessarily (you're paying for it after all), exclude as many directories as possible. To exclude all existing dropbox dirs, do
cd ~/Dropbox dropbox.py exclude add `ld -d */` dropbox.py exclude add `ld *.*` dropbox.py exclude list
Note that it can't handle directories with spaces in the name, so you'll need to polish the list by hand. Next create a directory where you want to run and store your jobs,e .g.
When you run a gaussian job, make sure to specify where the .chk files should end up, e.g.
so that you don't use up space/bandwidth for your chk files (unless of course you want to).%chk=/home/ubuntu/scratch/benchmark.chk
Stop after execution:
Use a batch script along these lines:
#!/bin/csh #SBATCH -p All #SBATCH --time=9999999 #SBATCH --output=slurm.out #SBATCH --job-name=benchmark setenv GAUSS_SCRDIR /home/ubuntu/scratch setenv GAUSS_EXEDIR /opt/gaussian/g09/bsd:/opt/gaussian/g09/local:/opt/gaussian/g09/extras:/opt/gaussian/g09 /opt/gaussian/g09/g09< benchmark.in > benchmark.out rm /home/ubuntu/scratch/*.* sudo shutdown -h now