The Experiment acts as both a factory class for constructing the stages of an experiment (Model, Ensemble, Orchestrator, etc.) as well as an interface to interact with the entities created by the experiment.

Users can initialize an Experiment at the beginning of a Jupyter notebook, interactive python session, or Python file and use the Experiment to iteratively create, configure and launch computational kernels on the system through the specified launcher.

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The interface was designed to be simple, with as little complexity as possible, and agnostic to the backend launching mechanism (local, Slurm, PBSPro, etc.).


Model(s) are subclasses of SmartSimEntity(s) and are created through the Experiment API. Models represent any computational kernel. Models are flexible enough to support many different applications, however, to be used with our clients (SmartRedis) the application will have to be written in Python, C, C++, or Fortran.

Models are given RunSettings objects that specify how a kernel should be executed with regard to the workload manager (e.g. Slurm) and the available compute resources on the system.

Each launcher supports specific types of RunSettings.

These settings can be manually specified by the user, or auto-detected by the SmartSim Experiment through the Experiment.create_run_settings method.

A simple example of using the Experiment API to create a model and run it locally:

from smartsim import Experiment

exp = Experiment("simple", launcher="local")

settings = exp.create_run_settings("echo", exe_args="Hello World")
model = exp.create_model("hello_world", settings)

exp.start(model, block=True)

If the launcher has been specified, or auto-detected through setting launcher=auto in the Experiment initialization, the create_run_settings method will automatically create the appropriate RunSettings object and return it.

For example with Slurm

from smartsim import Experiment

exp = Experiment("hello_world_exp", launcher="slurm")
srun = exp.create_run_settings(exe="echo", exe_args="Hello World!")

# helper methods for configuring run settings are available in
# each of the implementations of RunSettings

model = exp.create_model("hello_world", srun)
exp.start(model, block=True, summary=True)


The above will run srun -n 32 -N 1 echo Hello World!, monitor it’s execution, and inform the user when it is completed. This driver script can be executed in an interactive allocation, or placed into a batch script as follows:

#SBATCH --exclusive
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=32
#SBATCH --time=00:10:00

python /path/to/script.py


In addition to a single model, SmartSim has the ability to launch an Ensemble of Model applications simultaneously.

An Ensemble can be constructed in three ways:
  1. Parameter expansion (by specifying params and perm_strat argument)

  2. Replica creation (by specifying replicas argument)

  3. Manually (by adding created Model objects) if launching as a batch job

Ensembles can be given parameters and permutation strategies that define how the Ensemble will create the underlying model objects.

Three strategies are built in:
  1. all_perm: for generating all permutations of model parameters

  2. step: for creating one set of parameters for each element in n arrays

  3. random: for random selection from predefined parameter spaces

Here is an example that uses the random strategy to intialize four models with random parameters within a set range. We use the params_as_args field to specify that the randomly selected learning rate parameter should be passed to the created models as a executable argument.

import numpy as np
from smartsim import Experiment

exp = Experiment("Training-Run", launcher="auto")

# setup ensemble parameter space
learning_rate = list(np.linspace(.01, .5))
train_params = {"LR": learning_rate}

# define how each member should run
run = exp.create_run_settings(exe="python",

ensemble = exp.create_ensemble("Training-Ensemble",
exp.start(ensemble, summary=True)

A callable function can also be supplied for custom permutation strategies. The function should take two arguments: a list of parameter names, and a list of lists of potential parameter values. The function should return a list of dictionaries that will be supplied as model parameters. The length of the list returned will determine how many Model instances are created.

For example, the following is the built-in strategy all_perm:

from itertools import product

def create_all_permutations(param_names, param_values):
    perms = list(product(*param_values))
    all_permutations = []
    for p in perms:
        temp_model = dict(zip(param_names, p))
    return all_permutations

After Ensemble initialization, Ensemble instances can be passed as arguments to Experiment.generate() to write assigned parameter values into attached and tagged configuration files.

Launching Ensembles

Ensembles can be launched in previously obtained interactive allocations and as a batch. Similar to RunSettings, BatchSettings specify how an application(s) in a batch job should be executed with regards to the system workload manager and available compute resources.

If it only passed RunSettings, Ensemble, objects will require either a replicas argument or a params argument to expand parameters into Model instances. At launch, the Ensemble will look for interactive allocations to launch models in.

If it passed BatchSettings without other arguments, an empty Ensemble will be created that Model objects can be added to manually. All Model objects added to the Ensemble will be launched in a single batch.

If it passed BatchSettings and RunSettings, the BatchSettings will determine the allocation settings for the entire batch, and the RunSettings will determine how each individual Model instance is executed within that batch.

This is the same example as above, but tailored towards a running as a batch job on a slurm system:

import numpy as np
from smartsim import Experiment

exp = Experiment("Training-Run", launcher="slurm")

# setup ensemble parameter space
learning_rate = list(np.linspace(.01, .5))
train_params = {"LR": learning_rate}

# define resources for all ensemble members
sbatch = exp.create_batch_settings(nodes=4,

# define how each member should run
srun = exp.create_run_settings(exe="python",

ensemble = exp.create_ensemble("Training-Ensemble",
exp.start(ensemble, summary=True)

This will generate and execute a batch script that looks something like the following:



#SBATCH --output=/lus/smartsim/Training-Ensemble.out
#SBATCH --error=/lus/smartsim/Training-Ensemble.err
#SBATCH --job-name=Training-Ensemble-CHTN0UI2DORX
#SBATCH --nodes=4
#SBATCH --time=01:00:00
#SBATCH --partition=gpu
#SBATCH --account=12345-Cray

cd /lus/smartsim ; /usr/bin/srun --output /lus/smartsim/Training-Ensemble_0.out --error /lus/smartsim/Training-Ensemble_0.err --job-name Training-Ensemble_0-CHTN0UI2E5DX --nodes=1 --ntasks=24 /lus/miniconda/envs/smartsim-0.4.1-pre/bin/python ./train-model.py --LR=0.17 &

cd /lus/smartsim ; /usr/bin/srun --output /lus/smartsim/Training-Ensemble_1.out --error /lus/smartsim/Training-Ensemble_1.err --job-name Training-Ensemble_1-CHTN0UI2JQR5 --nodes=1 --ntasks=24 /lus/miniconda/envs/smartsim-0.4.1-pre/bin/python ./train-model.py --LR=0.32 &

cd /lus/smartsim ; /usr/bin/srun --output /lus/smartsim/Training-Ensemble_2.out --error /lus/smartsim/Training-Ensemble_2.err --job-name Training-Ensemble_2-CHTN0UI2P2AR --nodes=1 --ntasks=24 /lus/miniconda/envs/smartsim-0.4.1-pre/bin/python ./train-model.py --LR=0.060000000000000005 &

cd /lus/smartsim ; /usr/bin/srun --output /lus/smartsim/Training-Ensemble_3.out --error /lus/smartsim/Training-Ensemble_3.err --job-name Training-Ensemble_3-CHTN0UI2TRE7 --nodes=1 --ntasks=24 /lus/miniconda/envs/smartsim-0.4.1-pre/bin/python ./train-model.py --LR=0.35000000000000003 &