- A brief introduction to HADDOCK3
- Installation
- 1. Clone this repository:
- 2 Create a virtual environment with Python 3.9+ and install dependencies:
- 3. Install the HADDOCK3 package and command line clients
- 4. Make a CNS binary shortcut to the expected path:
- 5. Keep your installation up to date
- 6. (Optional) Install MPI libraries if you intend to run HADDOCK3 with MPI
- Installing third-party packages
- Usage
- Workflow Examples
- Advanced features
- Command-line interfaces
- Modules
- Testing
- Contributing to HADDOCK3
- Citing
- Library Reference
Running HADDOCK3 with MPIΒΆ
To run this example you must have mpi4py
installed in the haddock3 python
environment, and OpenMPI
in the host system.
$ pip install mpi4py
# or
$ conda install -c conda-forge mpi4py
Do not run it as: mpirun -np haddock3 run.cfg
, this logic is handled
internally.
Edit the .cfg
according to the .job
parameters you have set
In the .job
header:
#SBATCH --nodes=5
#SBATCH --tasks-per-node=96
In the .cfg
params
ncores = 480
Then prepare the rest of the .job
file according to your cluster, a SLURM
example is provided at
haddock3/examples/docking-protein-protein/docking-protein-protein-mpi.job
and
below:
#!/bin/bash
#SBATCH --nodes=5
#SBATCH --tasks-per-node=96
#SBATCH -J haddock3mpi
# make sure anaconda is activated
source $HOME/software/miniconda3/bin/activate
conda activate haddock3
# go to the example directory
cd $HOME/repos/haddock3/examples/docking-protein-protein
# remove any old runs
rm -rf run-mpi
# execute
haddock3 docking-protein-protein-mpi-test.cfg
Then:
sbatch docking-protein-protein-mpi.job
Please report any errors as an issue.