Running Multiple Experiments

In this tutorial we show how you can execute a series of experiments, in order to benefit from Korali’s oversubscription capabilities.

Create a series of experiments:

for i in range(8):
  e = korali.Experiment()
  e["Problem"]["Type"] = "Evaluation/Bayesian/Inference/Reference"
  e["Problem"]["Likelihood Model"] = "Additive Normal"
  e["Problem"]["Reference Data"] = getReferenceData()
  e["Problem"]["Computational Model"] = lambda sampleData: model(sampleData, getReferencePoints())

  # Configuring CMA-ES parameters
  e["Solver"]["Type"] = "Optimizer/CMAES"

Set Experiment Vector

We can store experiments in a list eList:

# Adding Experiment to vector

Run Experiment Vector

We can runn all experiments in one Korali application

# Running first 100 generations


We are now ready to run our example: python3 ./run-cmaes

Resuming Previous Run

Runs with multiple Korali experiments can also be resumed from a previous execution. For more information, see Checkpoint / Resume.