RunΒΆ

  1. In order to run global optimization with Assembline, activate the environment before using the software by

    source activate Assembline

    or depending on your computer setup:

    conda activate Assembline

  2. Enter the main project directory (e.g. scnpc_tutorial/CR_Y_complex)

  3. Run a single run for testing

    assembline.py --traj --models --prefix 0000000 -o out config.json params.py &>log&

    Output is saved to out directory specified by -o option.

  4. Run N amount of runs to build N number of models (following example from CR Y-complex)

    • Method 1: Submit all runs to the computer cluster or run on a workstation in chunks of N according to the number of processors:

      assembline.py --traj --models -o out --multi --start_idx 0 --njobs 20000 config.json params.py

      • on a cluster, this will submit 20000 modeling jobs in the queue, each job leading to one model (if ntasks in params.py is set to 1)

      • if ntasks params.py is N, it will run submit 20000/N cluster jobs, each running N modeling jobs

      • on a multicore computer, it will run ntasks at a time, and keep running until all 20000 jobs are done.

      Note

      The number of processors or cluster submission commands and templates are specified in params.py

    • Method 2: Dynamically adjust the number of concurrent runs (e.g. to not to overload a cluster or annoy other users):

      Warning

      The following works out of the box only on the EMBL cluster. To adjust to your cluster, modify the assembline.py for your cluster environment following the guidelines in the script.

      assembline.py --traj --models --multi --daemon --min_concurrent_jobs 500 --max_concurrent_jobs 5000 -o out --start_idx 0 --njobs 20000 config.json params.py &>log&

    • Method 3: If none of the above solutions work for you, you could probably submit multiple jobs manually using a mad shell loop e.g. on a computer cluster with the Slurm queuing system:

      for i in $(seq -f "%07g" 0 19999)
    do
        srun assembline.py  --traj --models --prefix $i -o out config.json params.py &>log&
    done

      Just remember to make the prefix unique for every run.

    Read more about how to run many runs on different platforms in the manual