• TL;DR
• Preparing your BOSE account
• Perform tracking
• Transfer files to be analyzed
• Prepare and run the Slurm script
• Monitoring jobs

TL;DR

Preparing your BOSE account

1. Clone the invision-tools GitHub repository into your home folder.
1. Login to BOSE using the terminal: ssh -p 50022 {UWEC username}@bose.hpc.uwec.edu
• Use your UWEC username and password.
• Login will require two-factor authentication via Okta.
• If off campus, first connect to the VPN (installation instructions here).
2. The repository should be cloned into your home directory (/data/users/{username}):
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$ cd # navigate to your home dir
$ mkdir GitHub # make a new GitHub dir (if you haven't already)
$ cd GitHub # navigate into the new dir
$ git clone https://github.com/wheelerlab-uwec/invision-tools.git # clone the repo

2. Create the conda environment required for invision-tools. This environment will contain all the software and Python libraries required for running the tracking scripts.
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$ cd # navigate to your home dir
$ module load python-libs # load the required modules from the BOSE shared software library
$ conda init bash # initialize conda
$ conda env create -f ~/GitHub/invision-tools/environment.yml # create the environment

1. This environment can be activated at anytime with the command: conda activate invision-env. It will be automatically activated during analysis by the Slurm scripts that manage each analysis job.

Perform tracking

Transfer files to be analyzed

1. Login to BOSE using the terminal: ssh {UWEC username}@bose.hpc.uwec.edu
• Use your UWEC username and password
• Login will require two-factor authentication via Okta
• If off campus, first connect to the VPN (installation instructions here)
• Alternatively, navigate to https://ondemand.hpc.uwec.edu/ and click BOSE Cluster Shell Access
2. Transfer videos from the Wheeler Lab’s server (https://data.wheelerlab.bio) to BOSE Note: When accessing the server via a browser, you may get a warning about the connection not being private. Click Advanced and Proceed.
1. On the server, all videos should be stored in the shared folder found at /volume1/WheelerLab_data/InVision/ . Here’s what it looks like using the File Station application (click the above link to access the sign-in page):

2. On BOSE, all videos should be stored in the Wheeler Lab’s group folder found at /data/groups/wheelenj. Mosquito videos are in the mosquitoes/ subfolder, miracidia videos are in the miracidia/ subfolder, and planaria videos are in the planaria/subfolder (here’s what it looks like when using the OnDemand file system):

3. Use rsync to transfer the files, for example (you will be prompted to enter the password for the server). The following shows the command run (on the first line) and the resulting output:
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$ rsync -Ppvrz wheelenj@data.wheelerlab.bio:/volume1/WheelerLab_data/InVision/20240301-a01-MRB_20240301_144112.24568709 /data/groups/wheelenj/mosquitoes

Could not chdir to home directory /var/services/homes/wheelenj: No such file or directory
receiving incremental file list
20240301-a01-MRB_20240301_144112.24568709/
20240301-a01-MRB_20240301_144112.24568709/000000.extra_data.json
        274,356 100%  261.65MB/s    0:00:00 (xfr#1, to-chk=4/6)
20240301-a01-MRB_20240301_144112.24568709/000000.hd5
  1,118,607,211 100%   85.02MB/s    0:00:12 (xfr#2, to-chk=3/6)
20240301-a01-MRB_20240301_144112.24568709/000000.mp4
  1,615,335,187 100%   24.12MB/s    0:01:03 (xfr#3, to-chk=2/6)
20240301-a01-MRB_20240301_144112.24568709/000000.npz
        274,972 100%  262.23MB/s    0:00:00 (xfr#4, to-chk=1/6)
20240301-a01-MRB_20240301_144112.24568709/metadata.yaml
            875 100%  213.62kB/s    0:00:00 (xfr#5, to-chk=0/6)

sent 123 bytes  received 1,647,720,372 bytes  16,728,126.85 bytes/sec
total size is 2,734,492,601  speedup is 1.66

1. General rsync syntax: rsync username@server:/path/to/source /path/to/destination
2. Explanation of options:
• -P - show progress
• -p - preserve permissions
• -v - be verbose
• -r - sync recursively (i.e., copy everything in the source directory)
• -z - compress while transferring

Prepare and run the Slurm script

1. Copy the Slurm scripts from the cloned invision-tools repository to the folder that was transferred:
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$ cp ~/GitHub/invision-tools/slurm/batch_track.sh /data/groups/wheelenj/mosquitoes/20240301-a01-MRB_20240301_144112.24568709

This script will track all MP4 videos in the experiment and link objects from all the videos together.

2. Navigate to the experiment directory: cd /data/groups/wheelenj/mosquitoes/20240301-a01-MRB_20240301_144112.24568709/ . This step is crucial; the job will not run properly if you are not in the experiment directory at the time of submission.
3. Run the tracking script to start the job(s): sbatch batch_track.sh
4. Verify the job(s) have been started by running the sacct command:
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$ sacct
JobID           JobName  Partition    Account  AllocCPUS      State ExitCode
------------ ---------- ---------- ---------- ---------- ---------- --------
71371_0        tracking highmemory wheelenj_+         64    RUNNING      0:0
71371_0.bat+      batch            wheelenj_+         64    RUNNING      0:0
71371_0.ext+     extern            wheelenj_+         64    RUNNING      0:0

You should see a JobID for each job that you are running.

Monitoring jobs

You will get an email when each job is finished. In addition to the sacct command shown above, there are several ways to monitor running jobs.

1. Navigate to the folder being analyzed and view the files being produced:
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$ cd /data/groups/wheelenj/mosquitoes/20240301-a01-MRB_20240301_144112.24568709
$ ls -lh
total 1.6G
drwxrwx--- 2 wheelenj SFU_Users 4.0K Mar  1 21:02 000000
-rw-r--r-- 1 wheelenj SFU_Users 268K Mar  1 17:07 000000.extra_data.json
-rw-r--r-- 1 wheelenj SFU_Users 1.6G Mar  1 17:07 000000.mp4
-rw-r--r-- 1 wheelenj SFU_Users 269K Mar  1 17:07 000000.npz
-rw-rw---- 1 wheelenj SFU_Users    0 Mar  1 21:58 70861_0_error.txt
-rw-rw---- 1 wheelenj SFU_Users  893 Mar  1 21:58 70861_0_output.txt
-rw-r----- 1 wheelenj SFU_Users 1.4K Mar  1 20:39 batch-track.sh
-rw-r--r-- 1 wheelenj SFU_Users  875 Mar  1 17:06 metadata.yaml

A new directory for each mp4 (i.e., 000000/) should have been created. This directory will contain the dynamically updated background (background.png), the background-subtracted frame for every 450 frames (i.e., 000000_16650.png), and the generated data (000000.hdf5)

Output and error files will be created that have the same JobID as each job (i.e., 70861_0_error.txt and 70861_0_output.txt ). Tail the output file to watch the logs written out in real time:

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$ tail -f 70861_0_output.txt
trackpy.feature.batch:  Frame 16933: 1 features
trackpy.feature.batch:  Frame 16934: 1 features
trackpy.feature.batch:  Frame 16935: 1 features
trackpy.feature.batch:  Frame 16936: 1 features
trackpy.feature.batch:  Frame 16937: 1 features
trackpy.feature.batch:  Frame 16938: 1 features
trackpy.feature.batch:  Frame 16939: 1 features
trackpy.feature.batch:  Frame 16940: 1 features
trackpy.feature.batch:  Frame 16941: 1 features
trackpy.feature.batch:  Frame 16942: 1 features
trackpy.feature.batch:  Frame 16943: 1 features

If the job quits due to an error, you can find the error messages int the error file. View the messages with the less or cat command: less 70861_0_error.txt. If there were no errors, the file will remain empty.