When using multiple workers, I get a pickling error. Can I still use multiprocessing?

Yes, but you will have to make a few adjustments to your code.

Some background: When using num_workers > 1, you might experience an error that a certain object from your simulator could not be pickled (an example can be found here).

This can be fixed by forcing sbi to pickle with dill instead of the default cloudpickle. To do so, adjust your code as follows:

• Install dill:

pip install dill

• At the very beginning of your python script, set the pickler to dill:

from joblib.externals.loky import set_loky_pickler
set_loky_pickler("dill")

• Move all imports required by your simulator into the simulator:

# Imports specified outside of the simulator will break dill:
import torch
def my_simulator(parameters):
    return torch.ones(1,10)

# Therefore, move the imports into the simulator:
def my_simulator(parameters):
    import torch
    return torch.ones(1,10)

Alternative: parallelize yourself

You can also write your own code to parallelize simulations with whatever multiprocessing framework you prefer. You can then simulate your data outside of sbi and pass the simulated data using .append_simulations:

# Given pre-simulated theta and x
trainer = SNPE(prior)
trainer.append_simulations(theta, x).train()

Some more background

sbi uses joblib to parallelize simulations, which in turn uses pickle or cloudpickle to serialize the simulator. Almost all simulators will be picklable with cloudpickle, but we have experienced issues, e.g., with neuron simulators, see here.