Theta (ALCF)

Theta is a 11.69 petaflops system based on the second-generation Intel Xeon Phi processor at Argonne Leadership Computing Facility (ALCF). It serves as a stepping stone to the ALCF’s next leadership-class supercomputer, Aurora. Theta is a massively parallel, many-core system based on Intel processors and interconnect technology, a new memory search_space, and a Lustre-based parallel file system, all integrated by Cray’s HPC software stack.

User installation

Before installating DeepHyper, go to your project folder:

cd /lus/theta-fs0/projects/PROJECTNAME
mkdir theta && cd theta/

DeepHyper can be installed on Theta by following these commands:

git clone --depth 1

Then, restart your session.


You will note that a new file ~/.bashrc_theta was created and sourced in the ~/.bashrc. This is to avoid conflicting installations between the different systems available at the ALCF.


To test you installation run:


A manual installation can also be performed with the following set of commands:

module load postgresql
module load miniconda-3
conda create -p dh-theta python=3.8 -y
conda activate dh-theta/
conda install gxx_linux-64 gcc_linux-64 -y
# DeepHyper + Analytics Tools (Parsing logs, Plots, Notebooks)
pip install deephyper[analytics,balsam]
conda install tensorflow -c intel -y


Horovod can be installed to use data-parallelism during the evaluations of DeepHyper. To do so use pip install deephyper[analytics,hvd,balsam] while or after installing.


Follow the installation like Analytics to create a new IPython kernel. Then go to Theta Jupyter and use your regular authentication method. The Jupyter Hub tutorial from Argonne Leadership Computing Facility might help you in case of troubles.


Now when openning a generated notebook make sure to use the “Python (deephyper)” kernel before executing otherwise you will not have all required dependencies.

Developer installation

  1. Load the miniconda module:

    module load miniconda-3


The miniconda module is using the Intel channel which has optimized wheels using MKL/DNN (available on KNL nodes with Xeon Phi CPU) for some packages.

  1. Create a virtual environment for your deephyper installation as a developer:

    conda create -p dh-env --clone base
  2. Activate this freshly created virtual environment:

    conda activate dh-env
  3. Clone the deephyper repo:

    git clone deephyper_repo/
  4. Go to the root directory of the repo:

    cd deephyper_repo/
  5. Switch to the develop branch:

    git checkout develop
  6. Install the package (with analytics support):

    pip install -e .['analytics']
  7. Install an ipython kernel for analytics support:

    pip install ipykernel
    python -m ipykernel install --user --name deephyper-dev-env --display-name "Python deephyper-dev-env"