Stage Data
Large datasets are often stored off-cluster on departmental servers, Storage@Yale, in cloud storage, etc. Since the permanent home of the data remains on off-cluster storage, you need to transfer a working copy to the cluster temporarily. When your computation finishes, you would then remove the copy and transmit the results to a more permanent location.
A sample workflow using rsync would be:
# copy data to temporary cluster storage
[netID@cluster ~]$ rsync -avP netID@department_server:/path/to/data $HOME/scratch60/
# process data on cluster
[netID@cluster ~]$ sbatch data_processing.sh
# return results to permanent storage for safe-keeping
[netID@cluster ~]$ rsync -avP $HOME/scratch60/output_data netID@department_server:/path/to/outputs/
The working copy of the data can then be removed manually or left to be deleted (which will happen automatically after 60-days). See the Archive Data and Transfer Data pages for more ways to move data efficiently to and from the clusters.
Transfer Partition
Both Grace and Farnam have dedicated data transfer partitions (named transfer) designed for staging data onto the cluster.
All users are able to submit jobs to these partitions. Note each users is limited to running two transfer jobs at one time.
If your workflow requires more simultaneuous transfers, contact us for assistance.
Interactive Transfers
We recommend using tmux to launch interactive transfers so that you are able to log out and leave the job running in the background.
# enter tmux shell
[netID@grace1 ~]$ tmux
# launch transfer job
[netID@grace1 ~]$ srun --pty --partition transfer --cpus-per-task=1 --time=6:00:00 bash
# start transfer
[netID@c01n03 ~]$ rsync -avP netID@department_server:/path/to/data $HOME/scratch60/
tmux session.
This will leave your transfer running in the background inside the tmux session.
For more details about how to use tmux, look at our guide here.
Transfers as Batch Jobs
A sample sbatch script for an rsync transfer is show here:
#!/bin/bash
#SBATCH --partition=transfer
#SBATCH --time=6:00:00
#SBATCH --cpus-per-task=1
#SBATCH --job-name=my_transfer
#SBATCH --output=transfer.txt
rsync -av netID@department_server:/path/to/data $HOME/scratch60/
remote.host.yale.edu to your scratch60 directory.
Note, this will only work if you have set up password-less logins on the remote host.
Transfer Job Dependencies
There are sbatch options that allow you to hold a job from running until a previous job finishes.
These are called Job Dependencies, and they allow you to include a data-staging step as part of your data processing pipe-line.
Consider a workflow where we would like to process data located on a remote server. We can break this into two separate Slurm jobs: a transfer job followed by a processing job.
transfer.sbatch
#!/bin/bash
#SBATCH --partition=transfer
#SBATCH --time=6:00:00
#SBATCH --cpus-per-task=1
#SBATCH --job-name=my_transfer
rsync -av netID@department_server:/path/to/data $HOME/scratch60/
process.sbatch
#!/bin/bash
#SBATCH --partition=day
#SBATCH --time=6:00:00
#SBATCH --cpus-per-task=1
#SBATCH --job-name=my_process
module purge
module load miniconda
conda activate my_env
python $HOME/process_script.py $HOME/scratch60/data
First we would submit the transfer job to Slurm:
$ sbatch transfer.sbatch
Submitted batch job 12345678
Then we can pass this jobID as a dependency for the processing job:
$ sbatch --dependency=afterok:12345678 process.sbatch
Submitted batch job 12345679
$ squeue
JOBID PARTITION NAME USER ST TIME NODES NODELIST(REASON)
12345679 day process netID PD 0:00 1 (Dependency)
12345678 transfer transfer netID R 0:15 1 c01n04
Storage@Yale Transfers
Storage@Yale shares are mounted on the transfer partition, enabling you to stage data from these remove servers.
The process is somewhat simpler than the above example because we do not need to rsync the data, and can instead use cp directly.
Here, we have modified the transfer.sbatch file from above:
transfer.sbatch
#!/bin/bash
#SBATCH --partition=transfer
#SBATCH --time=6:00:00
#SBATCH --cpus-per-task=1
#SBATCH --job-name=my_transfer
cp /SAY/standard/my_say_share/data $HOME/scratch60/
This will transfer data from the Storage@Yale share to scratch60 where it can be processed on any of the compute nodes.