3. Execution on the ThetaGPU supercomputer (with MPI)#

In this tutorial we are going to learn how to use DeepHyper on the ThetaGPU supercomputer at the ALCF using MPI. ThetaGPU is a 3.9 petaflops system based on NVIDIA DGX A100.

3.1. Submission Script#

This section of the tutorial will show you how to submit script to the COBALT scheduler of ThetaGPU. To execute DeepHyper on ThetaGPU with a submission script it is required to:

  1. Define a Bash script to initialize the environment (e.g., load a module, activate a conda environment).

  2. Define an execution script, which will call the bash script defined in 1. and launch the python script using mpirun.

Start by creating a script named activate-dhenv.sh to initialize your environment. Replace the $CONDA_ENV_PATH by your personnal conda installation (e.g., it can be replaced by base if no virtual environment is used):

file: activate-dhenv.sh#

# Necessary for Bash shells
. /etc/profile

# Tensorflow optimized for A100 with CUDA 11
module load conda/2021-11-30

# Activate conda env
conda activate $CONDA_ENV_PATH


This activate-dhenv script can be very useful to tailor the execution’s environment to your needs. Here are a few tips that can be useful:

  • To activate XLA optimized compilation add export TF_XLA_FLAGS=--tf_xla_enable_xla_devices

  • To change the log level of Tensorflow add export TF_CPP_MIN_LOG_LEVEL=3

Then create a new file named job-deephyper.sh and make it executable. It will correspond to your submission script.

$ touch job-deephyper.sh && chmod +x job-deephyper.sh
file: job-deephyper.sh#
#COBALT -q full-node
#COBALT -n 2
#COBALT -t 20
#COBALT --attrs filesystems=home,grand,eagle,theta-fs0

# User Configuration

# Initialization of environment



About the COBALT directives :

#COBALT -q full-node

The queue your job will be submitted to. For ThetaGPU it can either be single-gpu, full-node, or bigmem ; you can find here the specificities of these queues.

#COBALT -n 2

The number of nodes your job will be submitted to.

#COBALT -t 20

The duration of the job submission, in minutes.


Your current project, e-g #COBALT -A datascience:

#COBALT --attrs filesystems=home,grand,eagle,theta-fs0

The filesystems your application should have access to, DeepHyper only requires home and theta-fs0, and it is unnecessary to let in this list a filesystem your application (and DeepHyper) doesn’t need.



COBALT_JOBSIZE and RANKS_PER_NODE correspond respectively to the number of nodes allocated and number of process per node. Unlike Theta on which COBALT_JOBSIZE is automatically instanciated to the correct value, on ThetaGPU it has to be done by hand : COBALT_JOBSIZE should always correspond to the number of nodes you submitted your application to (the number after #COBALT -n). e-g if you were to happen to have a #COBALT -n 4 you should have COBALT_JOBSIZE=4.

Adapt the executed Python application depending on your needs. Here is an application example of CBO using the mpi_comm evaluator:

file: myscript.py#
import pathlib
import os

os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"

import mpi4py

mpi4py.rc.initialize = False
mpi4py.rc.threads = True
mpi4py.rc.thread_level = "multiple"

from deephyper.evaluator import Evaluator
from deephyper.search.hps import CBO

from mpi4py import MPI

if not MPI.Is_initialized():

rank = comm.Get_rank()
size = comm.Get_size()

from deephyper.problem import HpProblem

hp_problem = HpProblem()
hp_problem.add_hyperparameter((-10.0, 10.0), "x")

def run(config):
    return - config["x"]**2

timeout = 10
search_log_dir = "search_log/"
pathlib.Path(search_log_dir).mkdir(parents=False, exist_ok=True)

if rank == 0:
    # Evaluator creation
    print("Creation of the Evaluator...")

with Evaluator.create(
) as evaluator:
    if evaluator is not None:
        print(f"Creation of the Evaluator done with {evaluator.num_workers} worker(s)")

        # Search creation
        print("Creation of the search instance...")
        search = CBO(
        print("Creation of the search done")

        # Search execution
        print("Starting the search...")
        results = search.search(timeout=timeout)
        print("Search is done")

        results.to_csv(os.path.join(search_log_dir, f"results.csv"))


If you are using tensorflow, you might want to add this snippet to your script in order to ensure that each worker is restricted to its own gpu (and doesn’t access other workers memory) :

from mpi4py import MPI
rank = comm.Get_rank()
size = comm.Get_size()
gpu_local_idx = rank % gpu_per_node
node = int(rank / gpu_per_node)

import tensorflow as tf
gpus = tf.config.list_physical_devices("GPU")
if gpus:
    # Restrict TensorFlow to only use the first GPU
        tf.config.set_visible_devices(gpus[gpu_local_idx], "GPU")
        tf.config.experimental.set_memory_growth(gpus[gpu_local_idx], True)
        logical_gpus = tf.config.list_logical_devices("GPU")
        logging.info(f"[r={rank}]: {len(gpus)} Physical GPUs, {len(logical_gpus)} Logical GPU")
    except RuntimeError as e:
        # Visible devices must be set before GPUs have been initialized

With gpu_per_node being equal to the RANKS_PER_NODE value specified in job-deephyper.sh.

Finally, submit the script using :

qsub-gpu job-deephyper.sh