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- You can create directories/folders inside your "home" directory, and upload your files using the "Upload" option in the menu bar.
- For this specific example, you will need the files (in the working directory) shown in the following screenshot
- The files that needed to be modified are Full_ML_fit_evaluation.py, Job1.slurm with the correct paths. See the following screen-shots as an example: Notice the paths in "red".
- In order to run SLURM or interactive jobs (ijobs), it is best practice to create a local copy of the container image in your own /home or /scratch directory. Initially (most probably) you will not see the file "tensorflow-2.1.0-py37.sif" file in your home directory. If so, "click" on the ">_Open in Terminal" on the menu bar; a new tab on your web browser will show up with a terminal environment and issue this commands:
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module load singularity/3.5.2
module load tensorflow/2.1.0-py37
cp $CONTAINERDIR/tensorflow-2.1.0-py37.sif /home/$USER
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#!/usr/bin/env bash
#SBATCH -p standard
#SBATCH --output=result_%a.out
#SBATCH -c 1
#SBATCH -t 16:30:00
#SBATCH -A spinquest
module purge
module load anaconda/2019.10-py3.7
module load singularity/3.5.2
module load tensorflow/2.1.0-py37
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- You will need to make sure the locations/paths mentioned in your program files contain your change computing ID. Also, within the python file, you may modify parameters such as the learning rate and numSamples with specific values .in the Full_ML_fit_evaluation.py .
- If all the above steps are completed, then issue Issue the command: sbatch --array=0-14 Job1.slurm (please note that this "Job1" is an example, you can have your own filename). If there are no error messages, then you should be able to see your job(s) running, using the Jobs tab > Active Jobs (see the following screen-shot)
3. Secure Shell Access:
- You will need a UVA's VPN in order to SSH to Rivanna. Follow this link and follow the instructions on the page to download and configure the VPN. There are three types of network accesss available: "UVA More Secure Network", "UVA Anywhere", and "High Security VPN". "UVA More Secure Network" would be the prefered one, but "UVA Anywhere" would also work if "UVA More Secure Network" is not available.
- Open a UNIX terminal and connect to Rivanna with the command "ssh -Y mst3k@rivanna.hpc.virginia.edu" (replacing mst3k with your computing id). The password is the same as your UVA netbadge password. If you are not now at the Rivanna command line check that the step(1) + above steps are successfully completed.
- To move the code and associated resources into your Rivanna directory, you can use secure copy from another terminal. The resources you need for running the code are the same as with the Colab notebook, except that it is necessary to run a pure python file as opposed to a notebook on Rivanna. I have uploaded a python version of the same code to the Github. One additional resource that was not previously necessary is the bash script to run the Rivanna job, called as "Job1.slurm" for this example. You will need to make sure the locations/paths mentioned in your program files contain your change computing ID. Additionally, within the python file you will want to change the learning rate and numSamples parameters to the specified values.
- Run the job with the command "sbatch --array=0-14 Job1.slurm". For more information about this command see this link. This will run all the kinematic sets simultaneously for however many replicas you specified with the numSamples parameter. For 1000 replicas, the process may take up to six or seven hours. If you desire to run only a handful of kinematic sets for 1000 replicas that can be done much more quickly but requires some slight changes to the code (The replicas would be parallelized instead of the kinematic sets. In fact, both could be parallelized, but Rivanna prevents you from running more than a few thousand jobs simultaneously). Let me know if that is the case and I can edit the code for you.
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