itwinai

itwinai is a powerful Python toolkit designed to help scientists and researchers streamline AI and machine learning workflows, specifically for digital twin applications. It provides easy-to-use tools for distributed training, hyper-parameter optimization on HPC systems, and integrated ML logging, reducing engineering overhead and accelerating research. Developed primarily by CERN, itwinai supports modular and reusable ML workflows, with the flexibility to be extended through third-party plugins, empowering AI-driven scientific research in digital twins.

See the latest version of our docs here.

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/3.11.5-GCCcore-13.2.0 CMake/3.24.3-GCCcore-11.3.0 mpi4py OpenMPI CUDA/12.3
  ml GCCcore/11.3.0 NCCL cuDNN/8.9.7.29-CUDA-12.3.0 UCX-CUDA/1.15.0-GCCcore-13.2.0-CUDA-12.3.0

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/3.11.5-GCCcore-13.2.0 CMake/3.24.3-GCCcore-11.3.0 mpi4py OpenMPI CUDA/12.3
  ml GCCcore/11.3.0 NCCL cuDNN/8.9.7.29-CUDA-12.3.0 UCX-CUDA/1.15.0-GCCcore-13.2.0-CUDA-12.3.0

Install itwinai for users

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] [email protected]:interTwin-eu/itwinai.git

Install itwinai environment

In this project, we are using uv as a project-wide package manager. Therefore, if you are a developer, you should see the uv tutorial after reading the following pip tutorial.

Installation using pip

Creating a venv

You can install the itwinai environment for development using pip. First, however, you would want to make a Python venv if you haven't already. Make sure you have Python installed (on HPC you have to load it with module load Python), and then you can create a venv with the following command:

python -m venv <name-of-venv>

For example, if I wanted to create a venv in the directory .venv (which is useful if you use e.g. uv), then I would do:

python -m venv .venv

After this you can activate your venv using the following command:

source .venv/bin/activate

Now anything you pip install will be installed in your venv and if you run any python commands they will use the version from your venv.

Installation of packages

We provide some extras that can be activated depending on which platform you are using.

• macos, amd or nvidia depending on which platform you use. Changes the version of prov4ML.
• dev for development purposes. Includes libraries for testing and tensorboard etc.
• torch for installation with PyTorch.

If you want to install PyTorch using CUDA then you also have to add an --extra-index-url to the CUDA version that you want. Since you are developing the library, you also want to enable the editable flag, -e, so that you don't have to reinstall everything every time you make a change. If you are on HPC, then you will usually want to add the --no-cache-dir flag to avoid filling up your ~/.cache directory, as you can very easily reach your disk quota otherwise. An example of a complete command for installing as a developer on HPC with CUDA thus becomes:

pip install -e ".[torch,dev,nvidia,tf]" \
    --no-cache-dir \
    --extra-index-url https://download.pytorch.org/whl/cu121

If you wanted to install this locally on macOS (i.e. without CUDA) with PyTorch, you would do the following instead:

pip install -e ".[torch,dev,macos,tf]"

Horovod and DeepSpeed

The above does not install Horovod and DeepSpeed, however, as they require a specialized script. If you do not require CUDA, then you can install them using pip as follows:

pip install --no-cache-dir --no-build-isolation git+https://github.com/horovod/horovod.git
pip install --no-cache-dir --no-build-isolation deepspeed

PyTorch (+ Lightning) virtual environment with makefiles

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/3.11.5-GCCcore-13.2.0 CMake/3.24.3-GCCcore-11.3.0 mpi4py OpenMPI CUDA/12.3
  ml GCCcore/11.3.0 NCCL cuDNN/8.9.7.29-CUDA-12.3.0 UCX-CUDA/1.15.0-GCCcore-13.2.0-CUDA-12.3.0

• 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/3.11.5-GCCcore-13.2.0 CMake/3.24.3-GCCcore-11.3.0 mpi4py OpenMPI CUDA/12.3
  ml GCCcore/11.3.0 NCCL cuDNN/8.9.7.29-CUDA-12.3.0 UCX-CUDA/1.15.0-GCCcore-13.2.0-CUDA-12.3.0

• 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

Working with Docker containers

This section is intended for the developers of itwinai and outlines the practices used to manage container images through GitHub Container Registry (GHCR).

Terminology Recap

Our container images follow the convention:

ghcr.io/intertwin-eu/IMAGE_NAME:TAG

For example, in ghcr.io/intertwin-eu/itwinai:0.2.2-torch2.6-jammy:

• IMAGE_NAME is itwinai
• TAG is 0.2.2-torch2.6-jammy

The TAG follows the convention:

[jlab-]X.Y.Z-(torch|tf)x.y-distro

Where:

• X.Y.Z is the itwinai version
• (torch|tf) is an exclusive OR between "torch" and "tf". You can pick one or the other, but not both.
• x.y is the version of the ML framework (e.g., PyTorch or TensorFlow)
• distro is the OS distro in the container (e.g., Ubuntu Jammy)
• jlab- is prepended to the tag of images including JupyterLab

Image Names and Their Purpose

We use different image names to group similar images under the same namespace:

• itwinai: Production images. These should be well-maintained and orderly.
• itwinai-dev: Development images. Tags can vary, and may include random hashes.
• itwinai-cvmfs: Images that need to be made available through CVMFS via Unpacker.

Warning

It is very important to keep the number of tags for itwinai-cvmfs as low as possible. Tags should only be created under this namespace when strictly necessary. Otherwise, this could cause issues for the Unpacker.

Building a new container

Our docker manifests support labels to record provenance information, which can be lately accessed by docker inspect IMAGE_NAME:TAG.

A full example below:

export BASE_IMG_NAME="what goes after the last FROM"
export IMAGE_FULL_NAME="IMAGE_NAME:TAG"
docker build \
    -t "$IMAGE_FULL_NAME" \
    -f path/to/Dockerfile \
    --build-arg COMMIT_HASH="$(git rev-parse --verify HEAD)" \
    --build-arg BASE_IMG_NAME="$BASE_IMG_NAME" \
    --build-arg BASE_IMG_DIGEST="$(docker pull "$BASE_IMG_NAME" > /dev/null 2>&1 && docker inspect "$BASE_IMG_NAME" --format='{{index .RepoDigests 0}}')" \
    --build-arg ITWINAI_VERSION="$(grep -Po '(?<=^version = ")[^"]*' pyproject.toml)" \
    --build-arg CREATION_DATE="$(date +"%Y-%m-%dT%H:%M:%S%:z")" \
    --build-arg IMAGE_FULL_NAME=$IMAGE_FULL_NAME \ 
    .