Setting up MSP

The msp package is based on low-level API of tensorflow. To install it and all its dependencies, you need to download a github code repository and run a make build command. Make sure you have make utility install on your system.

make build

There are two ways to use this package:

1. Command Line

2. Programmatically

1. Command Line

To run the program on your system. Pass the following command with appropriate configuration file and action. Configuration files are available inside the bin folder. Possible values of actions are run_solver, train_model, test_model, or train_and_test_model. Refer flag.py file inside bin folder for more details about flags.

python bin/main.py --config_path=bin/configs/exact_solver_msp_5_2_copy.yml --action='train_model'

To run it on HPC (High Performance Computing) cluster managed by slurm.

export config_path=bin/configs/rl_model_msp_5_2.yml
export action=train_model
sbatch slurm.sh export=config_path,action

2. Programmatically

Sample code to run the ExactSolver on synthetic data.

# Run the exact solver
from msp.solvers import ExactSolver
from msp.datasets import make_sparse_data

n_instances = 640
dataset = make_sparse_data(n_instances, seed=2021)
solver = ExactSolver()

for msp_batch_instance in dataset.batch(32):
    best_schedule, makespan = solver(msp_batch_instance)
    # store all best_schedule in an appropriate way

Sample code to create the instanace of MSP model.

# create a model instanace
from msp.models import MSPModel
from msp.models.encoders import GGCNEncoder
from msp.models.decoders import AttentionDecoder

encoder_params = {'units': 32}
decoder_params = {'units': 32}
model = MSPModel(GGCNEncoder, encoder_params, AttentionDecoder, decoder_params)