CIDS overview

CIDS and KadiAI readme

code style: black flake8 status pylint status test docs pipeline coverage

CIDS is a framework for Artificial Intelligence (AI) and Machine Learning (ML) for applications from engineering, materials, and natural sciences. It combines models, functions, and pipelines from libraries such as tensorflow/keras, sklearn, scipy, and pandas to build modular, flexible, and reproducible AI models.

The interface KadiAI integrates AI tools, such as CIDS, seamlessly into Kadi workflows and interacts with Kadi’s repositories and data management features.

The full documentation is available at:

https://intelligent-analysis.gitlab.io/cids/

The CIDS and KadiAI source codes are available at:

https://gitlab.com/intelligent-analysis/cids

Demo scripts of CIDS projects are available at:

The invite-only community repository demos contains scripts for applications ranging from motion analysis to hybrid finite elements in solid mechanics.

https://gitlab.com/intelligent-analysis/demos

Install

The reference configuration is a Linux (Ubuntu 20.04) OS. For any other configuration, the steps below are given as is, but not guaranteed to work.

  1. Download and install your favorite Python IDE (e.g. PyCharm Professional, VS Code)

  2. Download and install Git (Ubuntu: sudo apt-get install git-all)

    • Install your favorite Git Client (e.g. GitKraken)

There are two ways to install the required environments. Manual installation with pip requires all operating system components and packages (in particular Nvidia CUDA, CUDNN, and drivers) to be installed manually with compatible versions.

Manual install with conda and pip

  1. Install a Python (>3.10) distribution (e.g. Anaconda): https://www.tensorflow.org/install/pip

  2. Set up GPU support and CUDA: https://www.tensorflow.org/install/gpu

    • Requires CUDA, CUDNN and Nvidia drivers with compatible versions (may clash with requirements by other programs on the host machine)

    • Compatible combinations: https://www.tensorflow.org/install/source#gpu

    • Anaconda offers a convenient way that sets up CUDA and CUDNN, if the right driver is available. Select the CUDA toolkit and CUDNN version compatible with your GPU:

    conda install -c conda-forge cudatoolkit=11.2.2 cudnn=8.1.0

  3. Permanently add the CUDA library path to your environment, e.g., via conda activate:

    conda activate MY_ENVIRONMENT_NAME
mkdir -p $CONDA_PREFIX/etc/conda/activate.d
echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/' > $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh

  4. Go to the CIDS repository directory on your system.

  5. Linux (Ubuntu)/Mac shell or Windows command prompt:

    pip install -e .[dev]

  6. Install git pre-commit hooks for development

    pre-commit install

First steps

After installing and building the docker images, scripts can be executed from the project root directory with the following bash scripts (linux only). The scripts under demos/00_examples can serve as templates.

Clone the repository https://gitlab.com/intelligent-analysis/demos besides your cids repository:

$ git clone git@gitlab.com:intelligent-analysis/demos.git
$ ls
cids/ demos/

Run

python -u [ path to file in demo folder ]

Examples

python -u demos/00_examples/A1_convert_mnist.py

Tutorial and examples

Convert data to tfrecords
  1# Copyright 2022 Arnd Koeppe and the CIDS team
  2#
  3# Licensed under the Apache License, Version 2.0 (the "License");
  4# you may not use this file except in compliance with the License.
  5# You may obtain a copy of the License at
  6#
  7#     http://www.apache.org/licenses/LICENSE-2.0
  8#
  9# Unless required by applicable law or agreed to in writing, software
 10# distributed under the License is distributed on an "AS IS" BASIS,
 11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 12# See the License for the specific language governing permissions and
 13# limitations under the License.
 14#
 15import os
 16from pathlib import Path
 17
 18import tensorflow as tf
 19import tqdm
 20
 21from cids.data import DataDefinition
 22from cids.data import DataWriter
 23from cids.data import Feature
 24from kadi_ai import KadiAIProject
 25
 26
 27################################################################################
 28# Data paths
 29
 30project_name = "ex-mnist"
 31project_dir = Path.cwd().parent / "DATA" / project_name
 32project = KadiAIProject(project_name, root=project_dir)
 33
 34# Project creates an `input_dir` in the `project_dir`, which stores converted
 35#   input data as tfrecords in a subdirectory `tfrecord`
 36tfrecord_dir = Path(project.input_dir) / "tfrecord"
 37
 38
 39################################################################################
 40# Data definition
 41
 42data_definition = DataDefinition(
 43    Feature(
 44        "image",
 45        [None, 28, 28, 1],
 46        data_format="NXYF",
 47        dtype=tf.string,
 48        decode_str_to=tf.float32,
 49    ),
 50    Feature(
 51        "label", [None, 1], data_format="NF", dtype=tf.string, decode_str_to=tf.float32
 52    ),
 53    dtype=tf.float32,
 54)
 55
 56project.data_definition = data_definition
 57
 58
 59################################################################################
 60# Read data
 61
 62(train_images, train_labels), (
 63    test_images,
 64    test_labels,
 65) = tf.keras.datasets.mnist.load_data()
 66
 67src_data = list(zip(train_images, train_labels))
 68
 69
 70################################################################################
 71# Data processing
 72
 73
 74def read_and_process(src_sample):
 75    """Read and process source data."""
 76    # Do some preprocessing
 77    image = src_sample[0]
 78    image = (image - 127.5) / 127.5
 79    # Pack into dictionary
 80    sample = {}
 81    sample["image"] = image
 82    sample["label"] = src_sample[1]
 83    return sample
 84
 85
 86################################################################################
 87# Start processing
 88
 89# Create a data converter object
 90data_writer = DataWriter(data_definition)
 91
 92# Loop over all pairs of source files with a pretty progress bar
 93n = 0
 94for src_sample in tqdm.tqdm(
 95    src_data,
 96    total=len(src_data),
 97    file=project.stream_to_logger(),
 98    leave=True,
 99    desc="Conversion",
100    unit="sources",
101    dynamic_ncols=True,
102):
103    # Process sample
104    sample = read_and_process(src_sample)
105    out_file = tfrecord_dir / f"sample{n:05d}.tfrecord"
106    # Write sample to file
107    try:
108        data_writer.write_example(out_file, sample)
109    except KeyError as e:
110        project.warn(f"Missing key {e.args[0]} in: {os.fspath(out_file)}")
111        continue
112    n += 1
113    project.log(f"Done processing: {os.fspath(out_file)}")
114
115# Write the data definition and the features to a human-readable json file
116#   The json file can also be loaded directly later-on for training.
117project.data_definition = data_definition
118project.to_json(write_data_definition=True)
119
120project.log("Done.")

Policies

Citation

If you used CIDS, KadiAI or parts thereof in your research, please consider citing the following publications.

  • Koeppe, A., Bamer, F., Selzer, M., Nestler, B., Markert, B., 2022. Explainable Artificial Intelligence for Mechanics: Physics-Explaining Neural Networks for Constitutive Models. Front. Mater. 8, 824958. https://doi.org/10.3389/fmats.2021.824958

  • Koeppe, A., 2021. Deep Learning in the Finite Element Method. RWTH Aachen University, Aachen. http://doi.org/10.18154/RWTH-2021-04990

  • Mundt, M., Koeppe, A., David, S., Witter, T., Bamer, F., Potthast, W., Markert, B., 2020. Estimation of Gait Mechanics Based on Simulated and Measured IMU Data Using an Artificial Neural Network. Front. Bioeng. Biotechnol. 8. https://doi.org/10.3389/fbioe.2020.00041

  • Koeppe, A., Bamer, F., Markert, B., 2019. An efficient Monte Carlo strategy for elasto-plastic structures based on recurrent neural networks. Acta Mech 230, 3279–3293. https://doi.org/10.1007/s00707-019-02436-5

  • Koeppe, A., Hernandez Padilla, C.A., Voshage, M., Schleifenbaum, J.H., Markert, B., 2018. Efficient numerical modeling of 3D-printed lattice-cell structures using neural networks. MFGLET. https://doi.org/10.1016/j.mfglet.2018.01.002

Major contributors (alphabetical order)

  • Arnd Koeppe

  • Deepalaxmi Rajagopal

  • Julian Grolig

  • Marion Mundt

  • Tom Witter

  • Yinghan Zhao

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Appendix

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