How to make colabfold_batch use more CPU cores for large ensemble generation?

Hi,

I am running colabfold_batch inside a Docker container as part of a webserver pipeline.

We are trying to generate a large ensemble of models for one protein. In one test, we used n_models=1500, but the job ran for about 5 days and did not finish, so it had to be canceled.

During the run, we noticed that only about 6–9 CPUs were being used, even though 64 CPUs were available to the container.

My questions are:

1. Is there a recommended way to make colabfold_batch's use more CPU cores during the modeling/post-MSA steps?
2. Are there specific flags or environment variables for controlling CPU/thread usage?
3. For large ensemble generation, is it better to split the job into many smaller colabfold_batch runs instead of one run with a very large n_models value?

Any advice would be very helpful.

Thank you!

Start at the colabfold_batch entry point and review how Docker CPU allocation and the modeling/post-MSA steps affect thread usage. Compare a large n_models=1500 run with smaller runs; the issue is resolved when documented flags or environment variables, or a supported job-splitting recommendation, clearly explains how to use the available CPUs.