NNPDF/nnpdf
Jul 2024 – Feb 2026
19 Months active
Languages Used
PythonYAMLBashHCLpythonyamlRSTShell
Technical Skills
Deep LearningKerasMachine LearningPyTorchTensorFlowPython programming
juacrumar
PROFILE
Juacrumar
Juan Cruz-Martinez contributed to the NNPDF/nnpdf repository by engineering robust data analysis and machine learning workflows for high-energy physics applications. He developed modular backend systems and enhanced PDF evolution pipelines, integrating technologies such as Python, JAX, and TensorFlow to support reproducible experiments and scalable model training. His work included modernizing configuration management with YAML, optimizing CI/CD pipelines, and improving metadata governance for dataset reliability. By refactoring core components and introducing compatibility layers, Juan ensured maintainability and cross-platform stability. His disciplined approach to documentation and testing resulted in a codebase that supports rapid onboarding, reliable model evaluation, and streamlined scientific computing.
Overall Statistics
Feature vs Bugs
82%Features
Repository Contributions
215Total
Bugs
21
Commits
215
Features
97
Lines of code
281,606
Activity Months19
Your Network
25 peopleShared Repositories
25
Andrew PietraszkiewiczMember
achiefaMember
Giovanni De CrescenzoMember
Eva GroenendijkMember
Ella ColeMember
Eva Doortje Zee GroenendijkMember
Eva Doortje Zee GroenendijkMember
Eva Doortje Zee GroenendijkMember
Eleanor ColeMember
Work History
February 2026
2 Commits • 1 Features
Feb 1, 2026February 2026: NNPDF/nnpdf focused on documentation and usability improvements to strengthen model evaluation and onboarding. Key actions include updating metadata.yaml to note covariance regularization and facilitating better chi-squared results; clarifying the FK table format; and introducing CFACTOR usage documentation (README) with pointers to CFACTOR docs. This work demonstrates strong documentation discipline, metadata governance, and cross-team knowledge transfer, enabling more reliable model evaluation, faster onboarding, and fewer misconfigurations in production workflows.
2 Commits • 1 Features
Feb 1, 2026February 2026: NNPDF/nnpdf focused on documentation and usability improvements to strengthen model evaluation and onboarding. Key actions include updating metadata.yaml to note covariance regularization and facilitating better chi-squared results; clarifying the FK table format; and introducing CFACTOR usage documentation (README) with pointers to CFACTOR docs. This work demonstrates strong documentation discipline, metadata governance, and cross-team knowledge transfer, enabling more reliable model evaluation, faster onboarding, and fewer misconfigurations in production workflows.
February 2026
January 2026
14 Commits • 4 Features
Jan 1, 2026January 2026 monthly summary for NNPDF/nnpdf: Delivered a set of stability- and performance- oriented updates across core data workflows and ML experiments. Key improvements included Pandas 3.x compatibility and workflow updates, MongoDB-backed hyperparameter optimization with enhanced restart/reproducibility and optional DB upload, and strategic CI/CD and code organization optimizations. Fixed NaN accumulation in model loss to improve training robustness. Upgraded dependencies for compatibility (ruamel.yaml). These changes collectively decrease risk, accelerate iteration, and improve production readiness of experimentation pipelines.
January 2026
14 Commits • 4 Features
Jan 1, 2026January 2026 monthly summary for NNPDF/nnpdf: Delivered a set of stability- and performance- oriented updates across core data workflows and ML experiments. Key improvements included Pandas 3.x compatibility and workflow updates, MongoDB-backed hyperparameter optimization with enhanced restart/reproducibility and optional DB upload, and strategic CI/CD and code organization optimizations. Fixed NaN accumulation in model loss to improve training robustness. Upgraded dependencies for compatibility (ruamel.yaml). These changes collectively decrease risk, accelerate iteration, and improve production readiness of experimentation pipelines.
December 2025
11 Commits • 4 Features
Dec 1, 2025December 2025 monthly summary for NNPDF/nnpdf focusing on delivering stable, business-value features and reliability improvements across testing, CI/CD, data reporting, and infrastructure. The work aligns defaults with updated model requirements, hardens artifact handling, and strengthens dataset validation and compatibility. Resulting in more reliable pipelines, cleaner artifacts, and higher confidence in model comparisons.
11 Commits • 4 Features
Dec 1, 2025December 2025 monthly summary for NNPDF/nnpdf focusing on delivering stable, business-value features and reliability improvements across testing, CI/CD, data reporting, and infrastructure. The work aligns defaults with updated model requirements, hardens artifact handling, and strengthens dataset validation and compatibility. Resulting in more reliable pipelines, cleaner artifacts, and higher confidence in model comparisons.
December 2025
November 2025
13 Commits • 7 Features
Nov 1, 2025Monthly summary for 2025-11 (NNPDF/nnpdf). Focused on delivering business value through modeling enhancements, reliability improvements, and streamlined workflows with clear cross-hardware compatibility. Key outcomes: - Delivered feature-rich enhancements enabling advanced NNLO modeling and user-facing configuration via NNPDF4.1 runcard updates and NNLO EXA scale variations. - Strengthened validation and reliability for feature scaling in N3fit with backward-compatible migrations, reducing risk of silent misconfigurations. - Improved hardware compatibility and performance predictability by default TensorFloat32 handling in TensorFlow backends, plus plotting refinements for clearer data visualization. - Enhanced CI/CD and environment support to improve training reproducibility and workflow stability, including cache management and fitbot workflow adjustments. Overall impact: - Expanded modeling capabilities and configurability for end users, enabling more accurate predictions and more robust experimentation. - Reduced operational risk through stricter validation, regression test alignment, and improved testing stability across environments. - Faster, more reliable training and evaluation cycles thanks to CI/CD and environment improvements. Technologies/skills demonstrated: - Python-based ML tooling and model configuration (NNPDF4.1, N3fit) - Validation logic, regression testing, and test stability practices - TensorFlow backend management and hardware compatibility considerations - Data visualization improvements and plotting refactors - CI/CD processes, fitbot integration, and reproducibility practices
November 2025
13 Commits • 7 Features
Nov 1, 2025Monthly summary for 2025-11 (NNPDF/nnpdf). Focused on delivering business value through modeling enhancements, reliability improvements, and streamlined workflows with clear cross-hardware compatibility. Key outcomes: - Delivered feature-rich enhancements enabling advanced NNLO modeling and user-facing configuration via NNPDF4.1 runcard updates and NNLO EXA scale variations. - Strengthened validation and reliability for feature scaling in N3fit with backward-compatible migrations, reducing risk of silent misconfigurations. - Improved hardware compatibility and performance predictability by default TensorFloat32 handling in TensorFlow backends, plus plotting refinements for clearer data visualization. - Enhanced CI/CD and environment support to improve training reproducibility and workflow stability, including cache management and fitbot workflow adjustments. Overall impact: - Expanded modeling capabilities and configurability for end users, enabling more accurate predictions and more robust experimentation. - Reduced operational risk through stricter validation, regression test alignment, and improved testing stability across environments. - Faster, more reliable training and evaluation cycles thanks to CI/CD and environment improvements. Technologies/skills demonstrated: - Python-based ML tooling and model configuration (NNPDF4.1, N3fit) - Validation logic, regression testing, and test stability practices - TensorFlow backend management and hardware compatibility considerations - Data visualization improvements and plotting refactors - CI/CD processes, fitbot integration, and reproducibility practices
October 2025
2 Commits • 2 Features
Oct 1, 2025Monthly summary for 2025-10 (NNPDF/nnpdf). Delivered two high-impact updates that improve documentation accuracy and Monte Carlo (MC) uncertainty handling, with positive implications for data provenance, model evaluation, and reproducibility across analyses. No major bugs fixed this month. Key work focused on aligning collaborator affiliations and refining ATLAS Z0J 8 TeV MC uncertainty handling.
2 Commits • 2 Features
Oct 1, 2025Monthly summary for 2025-10 (NNPDF/nnpdf). Delivered two high-impact updates that improve documentation accuracy and Monte Carlo (MC) uncertainty handling, with positive implications for data provenance, model evaluation, and reproducibility across analyses. No major bugs fixed this month. Key work focused on aligning collaborator affiliations and refining ATLAS Z0J 8 TeV MC uncertainty handling.
October 2025
September 2025
8 Commits • 4 Features
Sep 1, 2025In September 2025, the NNPDF/nnpdf effort delivered a cohesive set of dataset configuration enhancements, a baseline runcard for the NNPDF4.1 series, and CI/CD improvements that collectively improve data analysis reliability, reproducibility, and maintainability. The work emphasized compatibility with existing workflows while enabling new physics analyses, and it was complemented by clear documentation improvements to boost discoverability and collaboration.
September 2025
8 Commits • 4 Features
Sep 1, 2025In September 2025, the NNPDF/nnpdf effort delivered a cohesive set of dataset configuration enhancements, a baseline runcard for the NNPDF4.1 series, and CI/CD improvements that collectively improve data analysis reliability, reproducibility, and maintainability. The work emphasized compatibility with existing workflows while enabling new physics analyses, and it was complemented by clear documentation improvements to boost discoverability and collaboration.
August 2025
4 Commits • 3 Features
Aug 1, 2025Concise monthly summary for 2025-08 focusing on key business value and technical achievements for NNPDF/nnpdf. 1) Key features delivered - JAX backend integration and training optimization: Added CI job to run tests with the JAX backend, configured environment for JAX, installed dependencies, and adapted MetaModel to correctly handle JAX backend when determining training replicas. Commit: 072673d0dd7a71c0b178a0663293201bd78a9ab7 ("add jax backend to the CI tests"). - JAX backend training step optimization: Introduced STEPS_PER_EPOCH = 100 with a JAX-specific override to use 1 step per epoch; simplified training step logic to leverage the constant or 1 when epochs are fewer than the constant to reduce overhead. Commit: d2aa5569b72f7b8bfbef634753f215c83c2aead9 ("fix STEPS_PER_EPOCH"). - NNPDF configuration: ekos_path option: Updated nnprofile_example.yaml to add ekos_path configuration and clarified that downloaded theories, ekos, and related data are stored under subdirectories in the NNPDF share path. Commit: a768963799014ee9fef1f911ab716070de513914 ("Update nnprofile_example.yaml"). 2) Major bugs fixed - N3LHAPDFSet t0 central value handling bug fix: Ensure N3LHAPDFSet correctly returns only the central PDF value when is_t0 is true, avoiding processing all replicas. Commit: 6b6ddf441838ef1f57b99c4db7a410b88ad199b2 ("fix t0"). 3) Overall impact and accomplishments - Strengthened reliability and reproducibility by expanding automated testing to include JAX backend and by clarifying data storage paths for NNPDF components. - Reduced training overhead for JAX runs by introducing a fixed-step training regime per epoch, enabling faster iteration cycles and lower CI runtime cost. - Fixed correctness issue in central value selection for t0 scenarios, preventing incorrect replica processing and ensuring accurate downstream results. 4) Technologies, skills demonstrated - CI integration and environment management for ML backends (JAX) - ML training optimization and pipeline simplification - YAML configuration management for data and theory storage paths - Bug diagnosis and targeted fixes in PDF set handling Commit references: - 072673d0dd7a71c0b178a0663293201bd78a9ab7 - d2aa5569b72f7b8bfbef634753f215c83c2aead9 - 6b6ddf441838ef1f57b99c4db7a410b88ad199b2 - a768963799014ee9fef1f911ab716070de513914
4 Commits • 3 Features
Aug 1, 2025Concise monthly summary for 2025-08 focusing on key business value and technical achievements for NNPDF/nnpdf. 1) Key features delivered - JAX backend integration and training optimization: Added CI job to run tests with the JAX backend, configured environment for JAX, installed dependencies, and adapted MetaModel to correctly handle JAX backend when determining training replicas. Commit: 072673d0dd7a71c0b178a0663293201bd78a9ab7 ("add jax backend to the CI tests"). - JAX backend training step optimization: Introduced STEPS_PER_EPOCH = 100 with a JAX-specific override to use 1 step per epoch; simplified training step logic to leverage the constant or 1 when epochs are fewer than the constant to reduce overhead. Commit: d2aa5569b72f7b8bfbef634753f215c83c2aead9 ("fix STEPS_PER_EPOCH"). - NNPDF configuration: ekos_path option: Updated nnprofile_example.yaml to add ekos_path configuration and clarified that downloaded theories, ekos, and related data are stored under subdirectories in the NNPDF share path. Commit: a768963799014ee9fef1f911ab716070de513914 ("Update nnprofile_example.yaml"). 2) Major bugs fixed - N3LHAPDFSet t0 central value handling bug fix: Ensure N3LHAPDFSet correctly returns only the central PDF value when is_t0 is true, avoiding processing all replicas. Commit: 6b6ddf441838ef1f57b99c4db7a410b88ad199b2 ("fix t0"). 3) Overall impact and accomplishments - Strengthened reliability and reproducibility by expanding automated testing to include JAX backend and by clarifying data storage paths for NNPDF components. - Reduced training overhead for JAX runs by introducing a fixed-step training regime per epoch, enabling faster iteration cycles and lower CI runtime cost. - Fixed correctness issue in central value selection for t0 scenarios, preventing incorrect replica processing and ensuring accurate downstream results. 4) Technologies, skills demonstrated - CI integration and environment management for ML backends (JAX) - ML training optimization and pipeline simplification - YAML configuration management for data and theory storage paths - Bug diagnosis and targeted fixes in PDF set handling Commit references: - 072673d0dd7a71c0b178a0663293201bd78a9ab7 - d2aa5569b72f7b8bfbef634753f215c83c2aead9 - 6b6ddf441838ef1f57b99c4db7a410b88ad199b2 - a768963799014ee9fef1f911ab716070de513914
August 2025
July 2025
8 Commits • 6 Features
Jul 1, 2025Concise monthly summary for 2025-07 focusing on stability, reproducibility, and maintainability for NNPDF/nnpdf. Key features delivered include: (1) JAX compatibility enhancements and compute_loss stability achieved by making input a class attribute to avoid tracking and saving weights as NumPy objects, and investigating spurious compute_loss calls each epoch; (2) JSON logging of chi2 values and dependency pinning to exact version to ensure stable builds; (3) LHAPDF compatibility layer moved to wrappers for maintainability; (4) Testing workflow enhancement with postfit integration to Nolha tests and lhapdf-like functions; (5) CI workflow alignment to use the latest stable reference set (fitbot 4.1.0 tag). Additional improvements include refactoring for rule uniqueness checks in FilterRule/CoreConfig. Major bugs fixed include test suite compatibility with dependencies (eko 0.15.2, numpy >=2.0) and corrections in-data metric calculation and data filtering in ModelTrainer. Overall impact: increased reliability and reproducibility of experiments, tighter validation, and streamlined dependency management, enabling faster iteration and onboarding. Technologies demonstrated: Python, JAX, LHAPDF integration, dependency pinning, CI/CD (fitbot), and test modernization.
July 2025
8 Commits • 6 Features
Jul 1, 2025Concise monthly summary for 2025-07 focusing on stability, reproducibility, and maintainability for NNPDF/nnpdf. Key features delivered include: (1) JAX compatibility enhancements and compute_loss stability achieved by making input a class attribute to avoid tracking and saving weights as NumPy objects, and investigating spurious compute_loss calls each epoch; (2) JSON logging of chi2 values and dependency pinning to exact version to ensure stable builds; (3) LHAPDF compatibility layer moved to wrappers for maintainability; (4) Testing workflow enhancement with postfit integration to Nolha tests and lhapdf-like functions; (5) CI workflow alignment to use the latest stable reference set (fitbot 4.1.0 tag). Additional improvements include refactoring for rule uniqueness checks in FilterRule/CoreConfig. Major bugs fixed include test suite compatibility with dependencies (eko 0.15.2, numpy >=2.0) and corrections in-data metric calculation and data filtering in ModelTrainer. Overall impact: increased reliability and reproducibility of experiments, tighter validation, and streamlined dependency management, enabling faster iteration and onboarding. Technologies demonstrated: Python, JAX, LHAPDF integration, dependency pinning, CI/CD (fitbot), and test modernization.
June 2025
13 Commits • 5 Features
Jun 1, 2025June 2025 (2025-06) monthly summary for NNPDF/nnpdf focusing on delivering business value through a modernized, robust PDF evolution workflow, expanded documentation, and stronger test coverage. The work emphasizes maintainability, reproducibility, and build stability, with clear traceability to commits.
13 Commits • 5 Features
Jun 1, 2025June 2025 (2025-06) monthly summary for NNPDF/nnpdf focusing on delivering business value through a modernized, robust PDF evolution workflow, expanded documentation, and stronger test coverage. The work emphasizes maintainability, reproducibility, and build stability, with clear traceability to commits.
June 2025
May 2025
11 Commits • 5 Features
May 1, 2025May 2025 delivered targeted feature enhancements and performance optimizations for NNPDF/nnpdf, with a focus on reproducibility, efficiency, and maintainability. Key deliverables include: 1) Extended Legacy Data/Theory support for ATLAS_Z0J_8TEV_PT-M, including a legacy_data_10 variant to ensure reproducible analyses across legacy and updated grids; 2) Covariance Matrix Generation API improvements enabling construction from a list of DataSetSpecs with optional data_input and added safety checks; 3) Memoization of predictions/central_predictions to reduce recomputation in plotting and analysis workflows; 4) Speedups in Pineappl theories convolutions via pre-ordered fktables and einsum; 5) Documentation and code comments improvements for EKO/Evolven3fit. Business value realized includes more reliable cross-grid reproducibility, faster analysis pipelines, and clearer developer/docs. No major bugs fixed this month; primary focus on feature delivery and performance gains.
May 2025
11 Commits • 5 Features
May 1, 2025May 2025 delivered targeted feature enhancements and performance optimizations for NNPDF/nnpdf, with a focus on reproducibility, efficiency, and maintainability. Key deliverables include: 1) Extended Legacy Data/Theory support for ATLAS_Z0J_8TEV_PT-M, including a legacy_data_10 variant to ensure reproducible analyses across legacy and updated grids; 2) Covariance Matrix Generation API improvements enabling construction from a list of DataSetSpecs with optional data_input and added safety checks; 3) Memoization of predictions/central_predictions to reduce recomputation in plotting and analysis workflows; 4) Speedups in Pineappl theories convolutions via pre-ordered fktables and einsum; 5) Documentation and code comments improvements for EKO/Evolven3fit. Business value realized includes more reliable cross-grid reproducibility, faster analysis pipelines, and clearer developer/docs. No major bugs fixed this month; primary focus on feature delivery and performance gains.
April 2025
15 Commits • 9 Features
Apr 1, 2025April 2025 performance summary for NNPDF/nnpdf: Delivered a cohesive set of features, reliability fixes, and performance improvements that streamline onboarding, standardize configuration, and enhance the accuracy and scalability of modeling workflows. Focused on improving installation ease, dataset configurability, hyperparameter optimization relevance, and CI/CD robustness. Demonstrated strong collaboration between configuration-driven design, reproducible experiments, and robust deployment practices to accelerate deliverables and maintainability.
15 Commits • 9 Features
Apr 1, 2025April 2025 performance summary for NNPDF/nnpdf: Delivered a cohesive set of features, reliability fixes, and performance improvements that streamline onboarding, standardize configuration, and enhance the accuracy and scalability of modeling workflows. Focused on improving installation ease, dataset configurability, hyperparameter optimization relevance, and CI/CD robustness. Demonstrated strong collaboration between configuration-driven design, reproducible experiments, and robust deployment practices to accelerate deliverables and maintainability.
April 2025
March 2025
41 Commits • 19 Features
Mar 1, 2025Monthly summary for 2025-03 (NNPDF/nnpdf): Delivered user-facing onboarding improvements, developer-oriented quality upgrades, and stability fixes that collectively reduce setup friction, accelerate releases, and improve maintainability. Highlights include onboarding-focused doc updates, API-aligned tutorials, and a new theory card enriching the content library. Implemented robust CI/CD and packaging enhancements to shorten release cycles and ensure cross-platform reliability, while updating dependencies to maintain security and compatibility.
March 2025
41 Commits • 19 Features
Mar 1, 2025Monthly summary for 2025-03 (NNPDF/nnpdf): Delivered user-facing onboarding improvements, developer-oriented quality upgrades, and stability fixes that collectively reduce setup friction, accelerate releases, and improve maintainability. Highlights include onboarding-focused doc updates, API-aligned tutorials, and a new theory card enriching the content library. Implemented robust CI/CD and packaging enhancements to shorten release cycles and ensure cross-platform reliability, while updating dependencies to maintain security and compatibility.
February 2025
14 Commits • 4 Features
Feb 1, 2025February 2025 (NNPDF/nnpdf): Delivered core API enhancements, data processing robustness, and CI/config improvements, resulting in more reliable fits, consistent data naming, and easier maintenance. Key features include: 1) ValidPhys: Flexible parse_pdf API now accepts PDF objects directly, returning the PDF instance when applicable; type hints added and unnecessary annotation removed to streamline usage. 2) Dataset naming convention modernization in n3fit: default enforcement of new data names, deprecation of legacy names, and removal of the old fallback/config option to improve data consistency and user guidance. 3) Data handling and fitting robustness across single-point and replicated data: unified treatment of 1-point datasets for sequential and parallel fits; per-replica pseudodata storage; improved masking, covariance handling, chi2 calculations, and reinforced reliability through testing. 4) CI/Dependency/Config hygiene and testing infrastructure: optional pymongo dependency, aligned fitbot environment, ignoring untracked files, and enforced import sorting for maintainability. These changes collectively improve data integrity, reproduceability, and operational reliability, enabling faster, more reliable model validation and easier future maintenance.
14 Commits • 4 Features
Feb 1, 2025February 2025 (NNPDF/nnpdf): Delivered core API enhancements, data processing robustness, and CI/config improvements, resulting in more reliable fits, consistent data naming, and easier maintenance. Key features include: 1) ValidPhys: Flexible parse_pdf API now accepts PDF objects directly, returning the PDF instance when applicable; type hints added and unnecessary annotation removed to streamline usage. 2) Dataset naming convention modernization in n3fit: default enforcement of new data names, deprecation of legacy names, and removal of the old fallback/config option to improve data consistency and user guidance. 3) Data handling and fitting robustness across single-point and replicated data: unified treatment of 1-point datasets for sequential and parallel fits; per-replica pseudodata storage; improved masking, covariance handling, chi2 calculations, and reinforced reliability through testing. 4) CI/Dependency/Config hygiene and testing infrastructure: optional pymongo dependency, aligned fitbot environment, ignoring untracked files, and enforced import sorting for maintainability. These changes collectively improve data integrity, reproduceability, and operational reliability, enabling faster, more reliable model validation and easier future maintenance.
February 2025
January 2025
22 Commits • 8 Features
Jan 1, 2025January 2025 performance summary for NNPDF/nnpdf focused on delivering a stable, scalable data foundation for downstream analyses and improved packaging for broader distribution. The month emphasized data organization, metadata consistency, dataset freshness, and targeted bug fixes to enhance reliability and scientific rigor, while improving developer efficiency through tooling and labeling improvements.
January 2025
22 Commits • 8 Features
Jan 1, 2025January 2025 performance summary for NNPDF/nnpdf focused on delivering a stable, scalable data foundation for downstream analyses and improved packaging for broader distribution. The month emphasized data organization, metadata consistency, dataset freshness, and targeted bug fixes to enhance reliability and scientific rigor, while improving developer efficiency through tooling and labeling improvements.
December 2024
4 Commits • 4 Features
Dec 1, 2024December 2024 Monthly Summary for repository NNPDF/nnpdf. This period focused on delivering robust backend improvements, standardizing plotting behavior, and improving documentation and version reporting to enhance reproducibility and developer experience.
4 Commits • 4 Features
Dec 1, 2024December 2024 Monthly Summary for repository NNPDF/nnpdf. This period focused on delivering robust backend improvements, standardizing plotting behavior, and improving documentation and version reporting to enhance reproducibility and developer experience.
December 2024
November 2024
28 Commits • 9 Features
Nov 1, 2024November 2024 (2024-11) monthly summary for NNPDF/nnpdf focused on delivering business value through robustness, reproducibility, and expanded experimentation. Key efforts spanned CI/CD reliability, code quality, testing, and data packaging, with targeted fixes to ensure correctness and clearer diagnostics. The team stabilized the core workflow, broadened testing and experimental scope, and introduced new capabilities for performance and variant analysis, while addressing foundational bugs that affected input handling, kinematics, error messages, and tolerances.
November 2024
28 Commits • 9 Features
Nov 1, 2024November 2024 (2024-11) monthly summary for NNPDF/nnpdf focused on delivering business value through robustness, reproducibility, and expanded experimentation. Key efforts spanned CI/CD reliability, code quality, testing, and data packaging, with targeted fixes to ensure correctness and clearer diagnostics. The team stabilized the core workflow, broadened testing and experimental scope, and introduced new capabilities for performance and variant analysis, while addressing foundational bugs that affected input handling, kinematics, error messages, and tolerances.
October 2024
2 Commits • 1 Features
Oct 1, 2024October 2024 monthly summary for NNPDF/nnpdf: Delivered two high-impact items: backward compatibility for legacy theory cards in EKO and FIATLUX_NOTFIXED dataset support. Implemented through deprecation-aware key mapping and new meta package, enhancing data ingestion reliability, maintaining compatibility with older EKO configurations, and enabling handling of FIATLUX_NOTFIXED datasets.
2 Commits • 1 Features
Oct 1, 2024October 2024 monthly summary for NNPDF/nnpdf: Delivered two high-impact items: backward compatibility for legacy theory cards in EKO and FIATLUX_NOTFIXED dataset support. Implemented through deprecation-aware key mapping and new meta package, enhancing data ingestion reliability, maintaining compatibility with older EKO configurations, and enabling handling of FIATLUX_NOTFIXED datasets.
October 2024
September 2024
2 Commits • 1 Features
Sep 1, 2024September 2024 – NNPDF/nnpdf Key features delivered: - Loss calculation enhancements for hyper-optimization: integrated validation loss into the hyper-optimization averaging scheme and exposed flexible keyword-arguments for averages and losses. This also involved clarifying the empirical proportions used for validation and k-fold losses. Major bugs fixed: - No explicitly tracked major bugs fixed for this repo in September 2024. Overall impact and accomplishments: - Strengthened the reliability and interpretability of hyper-parameter tuning by making loss computations more robust and configurable. - Enabled more transparent experimentation with averaging schemes, which should accelerate iteration cycles and improve generalization in downstream models. - Delivered clear, reproducible changes anchored by two commits, facilitating future maintenance and review. Technologies/skills demonstrated: - Python-based ML experimentation and loss function design - Hyper-parameter optimization workflows and configurable averaging - Clear commit discipline, documentation, and change traceability - Emphasis on business value through improved model robustness and faster experimentation cycles
September 2024
2 Commits • 1 Features
Sep 1, 2024September 2024 – NNPDF/nnpdf Key features delivered: - Loss calculation enhancements for hyper-optimization: integrated validation loss into the hyper-optimization averaging scheme and exposed flexible keyword-arguments for averages and losses. This also involved clarifying the empirical proportions used for validation and k-fold losses. Major bugs fixed: - No explicitly tracked major bugs fixed for this repo in September 2024. Overall impact and accomplishments: - Strengthened the reliability and interpretability of hyper-parameter tuning by making loss computations more robust and configurable. - Enabled more transparent experimentation with averaging schemes, which should accelerate iteration cycles and improve generalization in downstream models. - Delivered clear, reproducible changes anchored by two commits, facilitating future maintenance and review. Technologies/skills demonstrated: - Python-based ML experimentation and loss function design - Hyper-parameter optimization workflows and configurable averaging - Clear commit discipline, documentation, and change traceability - Emphasis on business value through improved model robustness and faster experimentation cycles
July 2024
1 Commits • 1 Features
Jul 1, 2024Concise monthly summary for 2024-07 focusing on key accomplishments, business impact, and technical achievements for NNPDF/nnpdf.
1 Commits • 1 Features
Jul 1, 2024Concise monthly summary for 2024-07 focusing on key accomplishments, business impact, and technical achievements for NNPDF/nnpdf.
July 2024
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Quality Metrics
Correctness87.6%
Maintainability87.2%
Architecture83.4%
Performance78.6%
AI Usage21.0%
Skills & Technologies
Programming Languages
BashBibTeXC++HCLJSONJupyter NotebookMakefileMarkdownPythonRST
Technical Skills
API DevelopmentAPI IntegrationBackend DevelopmentBug FixingBuild ConfigurationBuild System ConfigurationBuild SystemsCI/CDCI/CD ConfigurationCLI DevelopmentCachingCode CleanupCode CoverageCode DocumentationCode Formatting
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