Install itwinai

In this section, we will run you through the installation and give some instructions for the use of the itwinai framework for HPC and local systems.

for a quick overview of this platform for advanced AI/ML workflows in digital twin applications.

If you are a developer, please refer to the developers installation guide.

User installation

Requirements:

  • Linux or macOS environment. Windows was never tested.

Python virtual environment

Depending on your environment, there are different ways to select a specific python version.

Laptop or GPU node

If you are working on a laptop or on a simple on-prem setup, you could consider using pyenv. See the installation instructions. If you are using pyenv, make sure to read this.

HPC environment

In HPC systems it is more popular to load dependencies using Environment Modules or Lmod. If you don’t know what modules to load, contact the system administrator to learn how to select the proper modules.

PyTorch environment

Commands to execute every time before installing or activating the python virtual environment for PyTorch:

  • Juelich Supercomputer (JSC):

    ml --force purge
ml Stages/2024 GCC OpenMPI CUDA/12 cuDNN MPI-settings/CUDA
ml Python CMake HDF5 PnetCDF libaio mpi4py

  • Vega supercomputer:

    ml --force purge
ml Python CMake/3.24.3-GCCcore-11.3.0 mpi4py OpenMPI CUDA/11.7
ml GCCcore/11.3.0 NCCL/2.12.12-GCCcore-11.3.0-CUDA-11.7.0 cuDNN
TensorFlow environment

Commands to execute every time before installing or activating the python virtual environment for TensorFlow:

  • Juelich Supercomputer (JSC):

    ml --force purge
ml Stages/2024 GCC/12.3.0 OpenMPI CUDA/12 MPI-settings/CUDA
ml Python/3.11 HDF5 PnetCDF libaio mpi4py CMake cuDNN/8.9.5.29-CUDA-12

  • Vega supercomputer:

    ml --force purge
ml Python CMake/3.24.3-GCCcore-11.3.0 mpi4py OpenMPI CUDA/11.7
ml GCCcore/11.3.0 NCCL/2.12.12-GCCcore-11.3.0-CUDA-11.7.0 cuDNN

Install itwinai

Install itwinai and its dependencies using the following command, and follow the instructions:

# First, load the required environment modules, if on an HPC

# Second, create a python virtual environment and activate it
$ python -m venv ENV_NAME
$ source ENV_NAME/bin/activate

# Install itwinai inside the environment
(ENV_NAME) $ export ML_FRAMEWORK="pytorch" # or "tensorflow"
(ENV_NAME) $ curl -fsSL https://github.com/interTwin-eu/itwinai/raw/main/env-files/itwinai-installer.sh | bash

The ML_FRAMEWORK environment variable controls whether you are installing itwinai for PyTorch or TensorFlow.

[!WARNING] itwinai depends on Horovod, which requires CMake>=1.13 and other packages. Make sure to have them installed in your environment before proceeding.

Installation for developers

If you are contributing to this repository, please continue below for more advanced instructions.

[!WARNING] Branch protection rules are applied to all branches which names match this regex: [dm][ea][vi]* . When creating new branches, please avoid using names that match that regex, otherwise branch protection rules will block direct pushes to that branch.

Clone the itwinai repository

git clone [--recurse-submodules] git@github.com:interTwin-eu/itwinai.git

Install itwinai environment

You can create the Python virtual environments using our predefined Makefile targets.

PyTorch (+ Lightning) virtual environment

Makefile targets for environment installation:

  • Juelich Supercomputer (JSC): torch-gpu-jsc

  • Vega supercomputer: torch-env-vega

  • In any other cases, when CUDA is available: torch-env

  • In any other cases, when CUDA NOT is available (CPU-only installation): torch-env-cpu

For instance, on a laptop with a CUDA-compatible GPU you can use:

make torch-env

When not on an HPC system, you can activate the python environment directly with:

source .venv-pytorch/bin/activate

Otherwise, if you are on an HPC system, please refer to this section explaining how to load the required environment modules before the python environment.

To build a Docker image for the pytorch version (need to adapt TAG):

# Local
docker buildx build -t itwinai:TAG -f env-files/torch/Dockerfile .

# Ghcr.io
docker buildx build -t ghcr.io/intertwin-eu/itwinai:TAG -f env-files/torch/Dockerfile .
docker push ghcr.io/intertwin-eu/itwinai:TAG

TensorFlow virtual environment

Makefile targets for environment installation:

  • Juelich Supercomputer (JSC): tf-gpu-jsc

  • Vega supercomputer: tf-env-vega

  • In any other case, when CUDA is available: tensorflow-env

  • In any other case, when CUDA NOT is available (CPU-only installation): tensorflow-env-cpu

For instance, on a laptop with a CUDA-compatible GPU you can use:

make tensorflow-env

When not on an HPC system, you can activate the python environment directly with:

source .venv-tf/bin/activate

Otherwise, if you are on an HPC system, please refer to this section explaining how to load the required environment modules before the python environment.

To build a Docker image for the tensorflow version (need to adapt TAG):

# Local
docker buildx build -t itwinai:TAG -f env-files/tensorflow/Dockerfile .

# Ghcr.io
docker buildx build -t ghcr.io/intertwin-eu/itwinai:TAG -f env-files/tensorflow/Dockerfile .
docker push ghcr.io/intertwin-eu/itwinai:TAG

Activate itwinai environment on HPC

Usually, HPC systems organize their software in modules which need to be imported by the users every time they open a new shell, before activating a Python virtual environment.

Below you can find some examples on how to load the correct environment modules on the HPC systems we are currently working with.

Load modules before PyTorch virtual environment

Commands to be executed before activating the python virtual environment:

  • Juelich Supercomputer (JSC):

    ml --force purge
ml Stages/2024 GCC OpenMPI CUDA/12 cuDNN MPI-settings/CUDA
ml Python CMake HDF5 PnetCDF libaio mpi4py

  • Vega supercomputer:

    ml --force purge
ml Python CMake/3.24.3-GCCcore-11.3.0 mpi4py OpenMPI CUDA/11.7
ml GCCcore/11.3.0 NCCL/2.12.12-GCCcore-11.3.0-CUDA-11.7.0 cuDNN

  • When not on an HPC: do nothing.

For instance, on JSC you can activate the PyTorch virtual environment in this way:

# Load environment modules
ml --force purge
ml Stages/2024 GCC OpenMPI CUDA/12 cuDNN MPI-settings/CUDA
ml Python CMake HDF5 PnetCDF libaio mpi4py

# Activate virtual env
source envAI_hdfml/bin/activate

Load modules before TensorFlow virtual environment

Commands to be executed before activating the python virtual environment:

  • Juelich Supercomputer (JSC):

    ml --force purge
ml Stages/2024 GCC/12.3.0 OpenMPI CUDA/12 MPI-settings/CUDA
ml Python/3.11 HDF5 PnetCDF libaio mpi4py CMake cuDNN/8.9.5.29-CUDA-12

  • Vega supercomputer:

    ml --force purge
ml Python CMake/3.24.3-GCCcore-11.3.0 mpi4py OpenMPI CUDA/11.7
ml GCCcore/11.3.0 NCCL/2.12.12-GCCcore-11.3.0-CUDA-11.7.0 cuDNN

  • When not on an HPC: do nothing.

For instance, on JSC you can activate the TensorFlow virtual environment in this way:

# Load environment modules
ml --force purge
ml Stages/2024 GCC/12.3.0 OpenMPI CUDA/12 MPI-settings/CUDA
ml Python/3.11 HDF5 PnetCDF libaio mpi4py CMake cuDNN/8.9.5.29-CUDA-12

# Activate virtual env
source envAItf_hdfml/bin/activate

Test with pytest

Do this only if you are a developer wanting to test your code with pytest.

First, you need to create virtual environments both for torch and tensorflow, following the instructions above, depending on the system that you are using (e.g., JSC).

To select the name of the torch and tf environments in which the tests will be executed you can set the following environment variables. If these env variables are not set, the testing suite will assume that the PyTorch environment is under .venv-pytorch and the TensorFlow environment is under .venv-tf.

export TORCH_ENV="my_torch_env"
export TF_ENV="my_tf_env"

Functional tests (marked with pytest.mark.functional) will be executed under /tmp/pytest location to guarantee isolation among tests.

To run functional tests use:

pytest -v tests/ -m "functional"

[!NOTE] Depending on the system that you are using, we implemented a tailored Makefile target to run the test suite on it. Read these instructions until the end!

We provide some Makefile targets to run the whole test suite including unit, integration, and functional tests. Choose the right target depending on the system that you are using:

Makefile targets:

  • Juelich Supercomputer (JSC): test-jsc

  • In any other case: test

For instance, to run the test suite on your laptop user:

make test