ClementiGroup/mlcg
Nov 2024 – Feb 2026
15 Months active
Languages Used
PythonMarkdownRSTYAMLreStructuredTextCUDAGit IgnoreJSON
Technical Skills
Deep LearningMachine LearningModel TrainingCheckpointingCode FormattingData Management
Aldo S. Pasos-Trejo
PROFILE
Aldo S. Pasos-trejo
Over 15 months, Sayeg contributed to the ClementiGroup/mlcg repository by engineering robust simulation and machine learning workflows in Python, with deep integration of PyTorch and YAML-based configuration management. Sayeg delivered modular energy term architectures, improved checkpointing for simulation continuity, and enhanced CI/CD pipelines for reproducible builds and multi-architecture testing. Their work included refactoring project structure, optimizing Langevin simulation performance with torch.compile, and stabilizing neural network model search through recursive type checks. By introducing CPU-compatible checkpoints, Docker-based environments, and comprehensive documentation, Sayeg improved onboarding, reliability, and scalability, demonstrating strong skills in scientific computing, DevOps, and collaborative software development.
Overall Statistics
Feature vs Bugs
71%Features
Repository Contributions
97Total
Bugs
11
Commits
97
Features
27
Lines of code
18,611
Activity Months15
Your Network
8 people
Work History
February 2026
11 Commits • 3 Features
Feb 1, 2026February 2026 monthly work summary for ClementiGroup/mlcg focusing on delivering user-centric features, improving reliability, and clarifying release readiness.
11 Commits • 3 Features
Feb 1, 2026February 2026 monthly work summary for ClementiGroup/mlcg focusing on delivering user-centric features, improving reliability, and clarifying release readiness.
February 2026
January 2026
5 Commits • 1 Features
Jan 1, 2026January 2026 monthly summary for ClementiGroup/mlcg focused on delivering a robust CLN simulation demo and strengthening the documentation build pipeline. This period emphasized reproducibility, performance, and developer experience to drive faster onboarding and safer CI/CD.
January 2026
5 Commits • 1 Features
Jan 1, 2026January 2026 monthly summary for ClementiGroup/mlcg focused on delivering a robust CLN simulation demo and strengthening the documentation build pipeline. This period emphasized reproducibility, performance, and developer experience to drive faster onboarding and safer CI/CD.
December 2025
1 Commits • 1 Features
Dec 1, 2025Implemented major project-structure improvements and an upgraded testing workflow for ClementiGroup/mlcg. Deprecation of setup.py, refactored package layout, enhanced CI configurations, and reorganized tests. Introduced a new pytest configuration enabling light testing modes and improved test management. Updated documentation to reflect new usage commands and enforced Black formatting across the codebase.
1 Commits • 1 Features
Dec 1, 2025Implemented major project-structure improvements and an upgraded testing workflow for ClementiGroup/mlcg. Deprecation of setup.py, refactored package layout, enhanced CI configurations, and reorganized tests. Introduced a new pytest configuration enabling light testing modes and improved test management. Updated documentation to reflect new usage commands and enforced Black formatting across the codebase.
December 2025
November 2025
1 Commits
Nov 1, 2025Month: 2025-11 — ClementiGroup/mlcg monthly summary focused on stabilizing neural network model search through a targeted bug fix. The primary deliverable was precision stabilization in the recursive search by enforcing stricter type checks and ensuring all dependencies are included, leading to more reliable model search results. No new user-facing features were released; however, the improvement enhances model discovery reliability, reduces edge-case failures, and improves downstream pipeline confidence. This work strengthens the integrity of the model search workflow and reduces debugging time in future sprints.
November 2025
1 Commits
Nov 1, 2025Month: 2025-11 — ClementiGroup/mlcg monthly summary focused on stabilizing neural network model search through a targeted bug fix. The primary deliverable was precision stabilization in the recursive search by enforcing stricter type checks and ensuring all dependencies are included, leading to more reliable model search results. No new user-facing features were released; however, the improvement enhances model discovery reliability, reduces edge-case failures, and improves downstream pipeline confidence. This work strengthens the integrity of the model search workflow and reduces debugging time in future sprints.
October 2025
12 Commits • 7 Features
Oct 1, 2025Month 2025-10 — ClementiGroup/mlcg Key features delivered and improvements: - Langevin simulation performance optimization: Integrated PyTorch torch.compile to accelerate the Langevin model, with Dynamo logging configured to reduce noise and provide cleaner, reproducible outputs. - CI and testing improvements: Reduced CircleCI timeouts, added parallel execution for example tests, and updated CI to align with newer Python versions and dependencies for faster, more reliable builds. - Documentation and onboarding: Clarified installation steps, emphasized prerequisites ordering, and improved README formatting for readability. - PyTorch compatibility and dependencies: Updated to PyTorch 2.6+ to maintain compatibility with evolving tooling and dependencies. - Codebase organization: Reorganized assets by moving cgmatrix.pt into a dedicated assets directory and updated internal references. - Loading and checkpoint handling: Suppressed non-critical warnings by explicitly setting weights_only=False in torch.load paths across modules. - SchNet detection bug fix: Corrected model detection by enhancing recursive traversal to properly handle SumOut and GradientsOut modules. - Release version bumps: Bumped mlcg package version to 0.1.1 and synchronized version references across configuration and initialization files. Overall impact and business value: - Significant runtime improvements for Langevin simulations enable more extensive experimentation and faster iteration cycles. - More reliable, faster CI feedback reduces integration risk and accelerates shipping of features to users. - Improved developer onboarding and maintainability through clearer docs and a cleaner codebase. - Up-to-date dependencies and robust model loading increase stability in production-like environments. Technologies/skills demonstrated: - PyTorch torch.compile and Dynamo-based logging control - CI/CD optimization (CircleCI), parallel test execution - Dependency management and PyTorch 2.6+ compatibility - Codebase refactoring and asset management - Robust recursive model traversal and bug fixing - Versioning and release engineering
12 Commits • 7 Features
Oct 1, 2025Month 2025-10 — ClementiGroup/mlcg Key features delivered and improvements: - Langevin simulation performance optimization: Integrated PyTorch torch.compile to accelerate the Langevin model, with Dynamo logging configured to reduce noise and provide cleaner, reproducible outputs. - CI and testing improvements: Reduced CircleCI timeouts, added parallel execution for example tests, and updated CI to align with newer Python versions and dependencies for faster, more reliable builds. - Documentation and onboarding: Clarified installation steps, emphasized prerequisites ordering, and improved README formatting for readability. - PyTorch compatibility and dependencies: Updated to PyTorch 2.6+ to maintain compatibility with evolving tooling and dependencies. - Codebase organization: Reorganized assets by moving cgmatrix.pt into a dedicated assets directory and updated internal references. - Loading and checkpoint handling: Suppressed non-critical warnings by explicitly setting weights_only=False in torch.load paths across modules. - SchNet detection bug fix: Corrected model detection by enhancing recursive traversal to properly handle SumOut and GradientsOut modules. - Release version bumps: Bumped mlcg package version to 0.1.1 and synchronized version references across configuration and initialization files. Overall impact and business value: - Significant runtime improvements for Langevin simulations enable more extensive experimentation and faster iteration cycles. - More reliable, faster CI feedback reduces integration risk and accelerates shipping of features to users. - Improved developer onboarding and maintainability through clearer docs and a cleaner codebase. - Up-to-date dependencies and robust model loading increase stability in production-like environments. Technologies/skills demonstrated: - PyTorch torch.compile and Dynamo-based logging control - CI/CD optimization (CircleCI), parallel test execution - Dependency management and PyTorch 2.6+ compatibility - Codebase refactoring and asset management - Robust recursive model traversal and bug fixing - Versioning and release engineering
October 2025
September 2025
10 Commits • 3 Features
Sep 1, 2025In September 2025, the ClementiGroup/mlcg team delivered tooling and process improvements that streamline migration, enhance reliability, and boost developer productivity. Key outcomes include (1) a YAML training configuration migration tool with tests to enable seamless upgrading from legacy configs to PyTorch Lightning, (2) a bug fix correcting an extraneous character in the YAML attention config to ensure proper parsing, (3) CI/CD and environment reproducibility enhancements—CircleCI updates for dependency installation and conda environments, Docker image upgrade to Python 3.12, refined test execution and a CPU-only environment spec with hashes, and a version bump, and (4) documentation string formatting standardization for consistency across modules.
September 2025
10 Commits • 3 Features
Sep 1, 2025In September 2025, the ClementiGroup/mlcg team delivered tooling and process improvements that streamline migration, enhance reliability, and boost developer productivity. Key outcomes include (1) a YAML training configuration migration tool with tests to enable seamless upgrading from legacy configs to PyTorch Lightning, (2) a bug fix correcting an extraneous character in the YAML attention config to ensure proper parsing, (3) CI/CD and environment reproducibility enhancements—CircleCI updates for dependency installation and conda environments, Docker image upgrade to Python 3.12, refined test execution and a CPU-only environment spec with hashes, and a version bump, and (4) documentation string formatting standardization for consistency across modules.
August 2025
2 Commits • 1 Features
Aug 1, 2025August 2025 focused on strengthening the energy terms subsystem within ClementiGroup/mlcg and safeguarding the training pipeline against version changes. Delivered a modular energy terms architecture with new base classes and dedicated implementations (harmonic, repulsion, Fourier series, and polynomial interactions), improving modularity, maintainability, and future extensibility. Updated training configurations to be Lightning 1.9.4 compatible, preventing runtime issues by aligning max_epochs, accelerator, and callbacks with the new version. These changes reduce maintenance risk, accelerate feature iteration, and improve overall pipeline reliability.
2 Commits • 1 Features
Aug 1, 2025August 2025 focused on strengthening the energy terms subsystem within ClementiGroup/mlcg and safeguarding the training pipeline against version changes. Delivered a modular energy terms architecture with new base classes and dedicated implementations (harmonic, repulsion, Fourier series, and polynomial interactions), improving modularity, maintainability, and future extensibility. Updated training configurations to be Lightning 1.9.4 compatible, preventing runtime issues by aligning max_epochs, accelerator, and callbacks with the new version. These changes reduce maintenance risk, accelerate feature iteration, and improve overall pipeline reliability.
August 2025
July 2025
3 Commits
Jul 1, 2025July 2025 focused on reliability and correctness of the simulation workflow in ClementiGroup/mlcg. Delivered fixes to ensure robust restart behavior and parallel-tempering consistency, and improved test stability to reduce flaky results. These changes enhance overall simulation reliability, reproducibility, and trust in production experiments.
July 2025
3 Commits
Jul 1, 2025July 2025 focused on reliability and correctness of the simulation workflow in ClementiGroup/mlcg. Delivered fixes to ensure robust restart behavior and parallel-tempering consistency, and improved test stability to reduce flaky results. These changes enhance overall simulation reliability, reproducibility, and trust in production experiments.
May 2025
1 Commits • 1 Features
May 1, 2025May 2025 monthly summary for ClementiGroup/mlcg: Delivered portable SchNet training configuration to improve cross-environment portability and reproducibility, with YAML-based configuration, generic dataset path placeholders, and updated documentation. Fixed misconfigured paths to reduce setup friction and ensure robust training workflows. The work lays a foundation for scalable experiments and easier onboarding.
1 Commits • 1 Features
May 1, 2025May 2025 monthly summary for ClementiGroup/mlcg: Delivered portable SchNet training configuration to improve cross-environment portability and reproducibility, with YAML-based configuration, generic dataset path placeholders, and updated documentation. Fixed misconfigured paths to reduce setup friction and ensure robust training workflows. The work lays a foundation for scalable experiments and easier onboarding.
May 2025
April 2025
5 Commits • 2 Features
Apr 1, 2025April 2025 monthly highlights for ClementiGroup/mlcg: strengthened reliability, maintainability, and CI discipline through documentation and test readability improvements, a naming bug fix with robust test updates, and an automated GitHub Actions workflow. These deliverables reduce release risk, improve developer onboarding, and clarify internal expectations for simulation parameters and coordinate calculations.
April 2025
5 Commits • 2 Features
Apr 1, 2025April 2025 monthly highlights for ClementiGroup/mlcg: strengthened reliability, maintainability, and CI discipline through documentation and test readability improvements, a naming bug fix with robust test updates, and an automated GitHub Actions workflow. These deliverables reduce release risk, improve developer onboarding, and clarify internal expectations for simulation parameters and coordinate calculations.
March 2025
15 Commits • 2 Features
Mar 1, 2025March 2025 – ClementiGroup/mlcg: Focused on documentation improvements, test coverage, and notebook reliability. Delivered documentation improvements for MLCG examples and tutorials (clarified purpose, fixed typos, standardized wording across README files and notebooks). Added and refined unit tests for sparsification/desparsification of HarmonicAngles and Dihedral priors to verify dense-sparse round-trips. Fixed the coarse-grained model notebook to correct CG matrix calculations, integrate aggforce, and refine simulation parameters to ensure the notebook runs reliably. These changes improve onboarding, strengthen simulation demonstrations, and increase maintainability and confidence in the math and models. Demonstrated Python-based development and quality practices, including pytest-style tests, Black formatting, and codeowners updates.
15 Commits • 2 Features
Mar 1, 2025March 2025 – ClementiGroup/mlcg: Focused on documentation improvements, test coverage, and notebook reliability. Delivered documentation improvements for MLCG examples and tutorials (clarified purpose, fixed typos, standardized wording across README files and notebooks). Added and refined unit tests for sparsification/desparsification of HarmonicAngles and Dihedral priors to verify dense-sparse round-trips. Fixed the coarse-grained model notebook to correct CG matrix calculations, integrate aggforce, and refine simulation parameters to ensure the notebook runs reliably. These changes improve onboarding, strengthen simulation demonstrations, and increase maintainability and confidence in the math and models. Demonstrated Python-based development and quality practices, including pytest-style tests, Black formatting, and codeowners updates.
March 2025
February 2025
17 Commits • 3 Features
Feb 1, 2025February 2025: Focused on data compatibility, code quality, and test stability to enable scalable training and reliable execution in MLcg workflows. Key outcomes include H5/HDF5 dataset support with end-to-end docs and examples, repo-wide documentation and formatting improvements, stabilization of tests for SchNet and CUDA-dependent radius optimization, and the introduction of sparsify/desparsify utilities to enable in-place sparsification of priors for Dihedral/Repulsion/Harmonic models. These changes reduce onboarding time, improve training reliability, and enhance runtime efficiency on larger datasets.
February 2025
17 Commits • 3 Features
Feb 1, 2025February 2025: Focused on data compatibility, code quality, and test stability to enable scalable training and reliable execution in MLcg workflows. Key outcomes include H5/HDF5 dataset support with end-to-end docs and examples, repo-wide documentation and formatting improvements, stabilization of tests for SchNet and CUDA-dependent radius optimization, and the introduction of sparsify/desparsify utilities to enable in-place sparsification of priors for Dihedral/Repulsion/Harmonic models. These changes reduce onboarding time, improve training reliability, and enhance runtime efficiency on larger datasets.
January 2025
10 Commits • 2 Features
Jan 1, 2025January 2025 monthly summary for ClementiGroup/mlcg: Focused on code health, documentation, and governance to reduce technical debt and accelerate future feature work. No user-facing feature releases this month; instead, targeted maintenance, quality improvements, and process enhancements were delivered to strengthen reliability and collaboration.
10 Commits • 2 Features
Jan 1, 2025January 2025 monthly summary for ClementiGroup/mlcg: Focused on code health, documentation, and governance to reduce technical debt and accelerate future feature work. No user-facing feature releases this month; instead, targeted maintenance, quality improvements, and process enhancements were delivered to strengthen reliability and collaboration.
January 2025
December 2024
3 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for ClementiGroup/mlcg: Implemented a robust numpy file indexing initialization for simulation save and checkpoint resume, improving data continuity and reliability for long-running simulations. The work ensures correct initialization and calculation of _npy_file_index and _npy_starting_index for both new runs and resumes from checkpoints, with alignment to the current_timestep to prevent data gaps. Includes a readability refactor of the _npy_starting_index calculation and code quality improvements via Black formatting. Overall, this enhances data integrity, reproducibility, and developer productivity while preparing the repo for future scaling.
December 2024
3 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for ClementiGroup/mlcg: Implemented a robust numpy file indexing initialization for simulation save and checkpoint resume, improving data continuity and reliability for long-running simulations. The work ensures correct initialization and calculation of _npy_file_index and _npy_starting_index for both new runs and resumes from checkpoints, with alignment to the current_timestep to prevent data gaps. Includes a readability refactor of the _npy_starting_index calculation and code quality improvements via Black formatting. Overall, this enhances data integrity, reproducibility, and developer productivity while preparing the repo for future scaling.
November 2024
1 Commits
Nov 1, 2024Month: 2024-11 — ClementiGroup/mlcg: Stability-focused improvement addressing a PLModel hyperparameters saving crash. Implemented by invoking self.save_hyperparameters() with logger=False to disable logging during save, eliminating a crash path and reducing training downtime. This work enhances reliability for training runs and supports more consistent experiment results across environments.
1 Commits
Nov 1, 2024Month: 2024-11 — ClementiGroup/mlcg: Stability-focused improvement addressing a PLModel hyperparameters saving crash. Implemented by invoking self.save_hyperparameters() with logger=False to disable logging during save, eliminating a crash path and reducing training downtime. This work enhances reliability for training runs and supports more consistent experiment results across environments.
November 2024
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Quality Metrics
Correctness92.4%
Maintainability92.8%
Architecture90.2%
Performance88.2%
AI Usage20.8%
Skills & Technologies
Programming Languages
BashCUDADockerfileGit IgnoreJSONJupyter NotebookMarkdownPDBPythonRST
Technical Skills
CI/CDCUDACheckpointingCode FormattingCode OrganizationCode Ownership ManagementCode RefactoringCode Review ManagementConfigurationConfiguration ManagementContinuous IntegrationData AnalysisData HandlingData ManagementData Science
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