Run

1. If you haven’t yet, 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

3. Run a single run for testing

   assembline.py  --traj --models --prefix 0000000 -o out elongator.json params.py
   

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

4. Run 1000 runs to build 1000 models

   • 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 1000 elongator.json params.py
     

     • on a cluster, this will submit 1000 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 1000/N cluster jobs, each running N modeling jobs

     • on a multicore computer, it will run ntasks at a time, and keep running until all 1000 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 200 \
     --max_concurrent_jobs 1000 \
     -o out \
     --start_idx 0 \
     --njobs 1000 \
     elongator.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" 0 999)
         do
             srun assembline.py  --traj --models --prefix $i -o out elongator.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