Continuing simulations

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Continuing simulations#

This notebook will show you:

  • how to continue an existing ANN assisted TPS simulation from storage

Note: This notebook depends on files created in the first notebook 1_Toy_pytorch_simple_setup.ipynb. Please do it first if you have not already.

%matplotlib inline

import os
import aimmd
import torch
import numpy as np
import matplotlib.pyplot as plt
import openpathsampling as paths

Tensorflow/Keras not available

# change to the working directory of choice
# (should be the same as in the first notebook)
wdir = '/home/think/scratch/SimData_pytorch_toy_22dim'
#wdir = None
if wdir is not None:
    os.chdir(wdir)

The easy way#

By continuing an openpathsampling TPS simulation that has been set up with aimmd you will autmatically load the RCModel saved automagically after the simulation. This will furhtermore repopulate the attached TrainSet with the shooting results from the openpathsampling.Storage. To do this, simply continue a TPS simulation the OPS way.

# open old ops storage for appending
storage = paths.Storage('pytorch_toy_22dim.nc', 'a')

# get the PathSampling from storage and set its state to the last MCStep in storage
sampler = storage.pathsimulators[0]
sampler.restart_at_step(storage.steps[-1])

Restoring RCModelSelector without model.Please take care of resetting the model yourself.

# lets get the model and the trainset from the aimmd storage
aimmd_store = aimmd.Storage('aimmd_storage.h5', mode='a')
model = aimmd_store.rcmodels["most_recent"]  # the last model will alwyas be saved as "most_recent"
# this will restore any ops collective variables used as descriptor transform for the model
model = model.complete_from_ops_storage(storage)
# for the traisnet passing an ops storage automatically resets all ops objects to the values they had at save time
trainset = aimmd_store.load_trainset()

# create our hooks
trainhook = aimmd.ops.TrainingHook(model, trainset)
densityhook = aimmd.ops.DensityCollectionHook(model)
storehook = aimmd.ops.AimmdStorageHook(aimmd_store, model, trainset)

# and attach them
sampler.attach_hook(trainhook)
sampler.attach_hook(densityhook)
sampler.attach_hook(storehook)

# the only thing left is to take care of the waring above:
# we need to put the model into the RCModel selector 
# (because it can not be saved to ops storages together with the selector)
# if you want to (re)set all RCModels in all RCModelSelectors of a ops simulation to the same model,
# you can use one of the aimmd utility functions
# Note: I think this is what you most likely want, since most people will either use only one RCModelSelector or the same Model in all Selectors (?)
aimmd.ops.utils.set_rcmodel_in_all_selectors(model=model, simulation=sampler)

# now we can simply run the simulation again and it will start where we left of
sampler.run(1000)

Working on Monte Carlo cycle number 2000
Running for 18 minutes 38 seconds -  1.12 seconds per step
Estimated time remaining: 1 second
DONE! Completed 2000 Monte Carlo cycles.

# close the storages
#storage.sync_all()
storage.close()
aimmd_store.close()
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