C3 - the CLAIMED Component Compiler

TL;DR

• takes arbitrary assets (Jupyter notebooks, python scripts, R scripts) as input
• automatically creates container images and pushes to container registries
• automatically installs all required dependencies into the container image
• creates KubeFlow Pipeline components (target workflow execution engines are pluggable)
• creates Kubernetes job configs for execution on Kubernetes/Openshift clusters
• can be triggered from CICD pipelines

C3 (CLAIMED Component Compiler) is the central project of the CLAIMED framework. It automates the transformation of arbitrary code assets — such as Jupyter notebooks, Python scripts, or R scripts — into fully portable, executable AI components.

While the component library is now maintained primarily as an example repository, C3 is where active development and innovation take place. The most utilized and powerful feature of C3 is grid compute parallelization, enabling distributed execution of AI workloads across heterogeneous compute environments.

MLX Integration

The Machine Learning eXchange (MLX) is now fully integrated as the backend for C3’s grid computing system, responsible for tracking all assets, including:

• data

• models

• jobs

• and other related resources

This integration allows C3 to seamlessly manage asset lifecycle, provenance, and discovery within a unified infrastructure.

To learn more on how this library works in practice, please have a look at the following video

Getting started

Install

pip install claimed

Usage

Just run the following command with your python script or notebook:

c3_create_operator "<your-operator-script>.py" --repository "<registry>/<namespace>"

Your code needs to follow certain requirements which are explained in Getting Started.

Getting Help

c3_create_operator --help

We welcome your questions, ideas, and feedback. Please create an issue or a discussion thread. Please see VULNERABILITIES.md for reporting vulnerabilities.

Contributing to CLAIMED

Interested in helping make CLAIMED better? We encourage you to take a look at our Contributing page.

Credits

CLAIMED is supported by the EU’s Horizon Europe program under Grant Agreement number 101131841 and also received funding from the Swiss State Secretariat for Education, Research and Innovation (SERI) and the UK Research and Innovation (UKRI).

Co-Funded by the European Union

License

This software is released under Apache License v2.0.

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terratorch-iterate (bundled)

TerraTorch-iterate

A tool for benchmarking and hyper-parameter optimization using TerraTorch.

Leverages MLFlow for experiment logging, optuna for hyperparameter optimization and ray for parallelization.

Installation

We recommend using python 3.10, 3.11 or 3.12 and also using a virtual environment for all commands in this guide.

Package installation

pip install terratorch-iterate

New instructions for iterate v0.3

Iterate v0.3 can optimize over arbitrary code running on arbitrary workload managers. Slurm and LSF are supported, Kubernetes/OpenShift and PBS coming soon.

From version 0.3 on the current iterate can be used using iterate-classig. Here are some usage examples

Prerequisites

mkdir deleteme.iterate
cd deleteme.iterate
python -m venv .venv
source ./venv/bin/activate
wget https://raw.githubusercontent.com/terrastackai/iterate/refs/heads/main/examples/bumpy_function.py
wget https://raw.githubusercontent.com/terrastackai/iterate/refs/heads/main/examples/bumpy_hpo.yaml
pip install terratorch-iterate==0.3

Run locally

iterate \
        --script bumpy_function.py \
        --root-dir . \
        --optuna-study-name terratorch_hpo_nas_2 \
        --optuna-db-path "sqlite:///iterate_study.db" \
        --hpo-yaml bumpy_hpo.yaml \
        --wlm none \
        --metric yval

Run on LSF

iterate \
        --script bumpy_function.py \
        --root-dir . \
        --optuna-study-name terratorch_hpo_nas_2 \
        --optuna-db-path "sqlite:///iterate_study.db" \
        --hpo-yaml bumpy_hpo.yaml \
        --wlm lsf \
        --metric yval \
        --gpu-count 0

Useful commands

pip install optuna-dashboard
optuna-dashboard --host 0.0.0.0 sqlite:///iterate_study.db

Suggested setup for development

git clone https://github.com/IBM/terratorch-iterate.git
cd terratorch-iterate
pip install --upgrade pip setuptools wheel
pip install -e .
pip install -e ".[dev]"
pip install -e ".[test]"
pip install -e ".[utilities]"

Usage for benchmarking

This tool allows you to design a benchmark test for a backbone that exists in TerraTorch over:

• Several tasks

• Several hyperparameter configurations

To do this it relies on a configuration file where the benchmark is defined. This consists of:

• experiment_name: MLFLow experiment to run the benchmark on. This is the highest level grouping of runs in MLFLow.

• run_name: Name of the parent (top-level) run under the experiment. NOTE: This item should not be included in the config if you wish to use the parameters extraction function in mlfow_utils to compile results.

• defaults: Defaults that are set for all tasks. Can be overriden under each task.

• tasks: List of tasks to perform. Tasks specify parameters for the decoder, datamodule to be used and training parameters.

• n_trials: Number of trials to be carried out per task, in the case of hyperparameter tuning.

• save_models: Whether to save models. Defaults to False. (Setting this to true can take up a lot of space). Models will be logged as artifacts for each run in MLFlow.

• optimization_space: Hyperparameter space to search over. Bayesian optimization tends to work well with a small number of hyperparameters.

• run_repetitions: number of repetitions to be run using best setting from optimization.

• storage_uri: Location to use for mlflow storage for the hyperparameter optimization (hpo) stage. During optimization, additional folders will be created in parent directory of storage_uri. For example, if storage_uri is /opt/benchmark_experiments/hpo, additional folders will include:

/opt/benchmark_experiments/
        └──hpo_results
        └──hpo_repeated_exp
        └──repeated_exp_output_csv
        └──job_logs
        └──optuna_db

Please see configs/benchmark_v2_template.yaml in the git repo for an example.

Besides the --config argument, terratorch-iterate also has two other arguments:

• if users include --hpo argument, then terratorch-iterate will optimize hyperparameters. Otherwise, it will rerun best experiment
• if users include --repeat argument, then terratorch-iterate will repeat the best experiment. Otherwise, terratorch-iterate will not rerun any experiment

If users want to optimize hyperparameters:

terratorch iterate --hpo --config <config-file>

Another way to run terratorch-iterate is to omit terratorch by running:

iterate --hpo --config <config-file>

For instance:

iterate --hpo --config configs/dofa_large_patch16_224_upernetdecoder_true_modified.yaml

If users want to rerun best experiment, please use the same config file. Additionally, the parent_run_id, which is the mlflow run id from optimization, should be added as shown below:

iterate --repeat --config <config-file> --parent_run_id <mlflow run_id from hpo>

For instance:

iterate --repeat --config configs/dofa_large_patch16_224_upernetdecoder_true_modified.yaml --parent_run_id 61bdee4a35a94f988ad30c46c87d4fbd

If users want to optimize hyperparameters then the rerun best experiment in a single command, please use both settings as shown below:

iterate --hpo --repeat --config <config-file>

For instance:

iterate --hpo --repeat --config configs/dofa_large_patch16_224_upernetdecoder_true_modified.yaml

To check the experiment results, use mlflow ui --host $(hostname -f) --port <port> --backend-store-uri <storage_uri>

Summarizing results

Summarizing results and hyperparameters of multiple experiments relies on a configuration file where the experiments and tasks are defined. This consists of:

• list_of_experiment_names: List of MLFLOW experiment names which had been completed.

• task_names: List of tasks found in each experiment in list_of_experiment_names.

• run_repetitions: Number of repetitions to be expected for each experiment. This should be the same as the value used in in each experiment config file during optimization.

• storage_uri: Location to use for mlflow storage for the hyperparameter optimization (hpo) stage. This should be the same value used as storage_uri in each experiment config file during optimization (see above).

• benchmark_name: string to be used to name resulting csv file

See configs/summarize_results_template.yaml in the git repo for an example.

To summarize results and hyperparameters, please run the following:

iterate --summarize --config <summarize-config-file>

For instance:

iterate --summarize --config configs/summarize_results.yaml

The results and hyperparameters are extracted into a csv file. For example, if storage_uri is /opt/benchmark_experiments/hpo, then sumarized results will be saved in last file as shown below:

/opt/benchmark_experiments/
        └──hpo_results
        └──hpo_repeated_exp
        └──repeated_exp_output_csv
        └──job_logs
        └──optuna_db
        └──summarized_results/
            └──<benchmark_name>/
                └──results_and_parameters.csv

Ray

You can also parallelize your runs over a ray cluster.

Check out instructions in the docs

terratorch integration

terratorch-iterate provides an utility to convert terratorch's config file into a single terratorch-iterate's config file. You have to follow these steps:

1. Go to <$TERRATORCH-ITERATE-HOME>/benchmark/config_util
2. Copy all terratorch config files that you want to be converted into a new directory. Note that all yaml files within this directory will be parsed to generate the terratorch-iterate file, so make sure that only yaml files that you want to be parsed are located in this directory.
3. Run build_geobench_configs.py script by specifying 3 input parameters:
   1. input_dir - Full path to the directory that contains all terratorch config yaml files
   2. output_dir - Full path to the directory that will stored the generated files
   3. template - Full or relative path to the template file
   4. prefix - prefix to the generated file names, e.g., if set prefix to my_prefix then a generated filename could be my_prefix_Clay.yaml

For instance, this is an example of such command:

python3 build_geobench_configs.py --input_dir /Users/john/terratorch/examples/confs/geobenchv2_detection --output_dir /Users/john/terratorch-iterate/benchmark/config_util --template geobenchv2_template.yaml --prefix my_prefix_