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Simulation Data Versioning and Reproducible AI Studies

30 Chapters
View Course Samples Online
01
Course overview
scope, core concepts, and technical context of simulation data versioning and reproducible AI studies.
technicalsimulation
02
Simulation data characteristics
large-scale time-series, parameter sweeps, and metadata requirements for versioning.
Simulationcharacteristics
03
Version control fundamentals for data
Git, DVC, Git LFS, and their applicability to simulation outputs.
DVCLFS
04
Data versioning strategies
snapshots, deltas, content-addressable storage, and immutable artifacts.
versioningstrategies
05
Metadata management
capturing simulation parameters, environment, and provenance for reproducibility.
Metadatamanagement
06
Experiment tracking systems
MLflow, Weights & Biases, and TensorBoard for simulation-based AI workflows.
Experimenttracking
07
Reproducibility challenges in AI studies
nondeterminism, hardware dependencies, and software drift.
Reproducibilitychallenges
08
Containerization for reproducible simulations
Docker, Singularity, and environment locking.
Containerizationreproducible
09
Workflow orchestration
Nextflow, Snakemake, and Airflow for simulation data pipelines.
Workfloworchestration
10
Data lineage and provenance
tracking transformations from raw simulation to AI training datasets.
provenancetracking
11
Storage backends for versioned simulation data
object stores, filesystems, and cloud services.
backendsversioned
12
Efficient diffing and merging of simulation datasets
challenges and tooling.
Efficientsimulation
13
Handling large binary simulation outputs
chunking, compression, and remote access.
Handlingsimulation
14
Versioning of AI models trained on simulation data
model registries and artifact management.
Versioningsimulation
15
Reproducible training pipelines
seeding, deterministic operations, and checkpointing.
Reproducibletraining
16
Case study
versioning CFD simulation data for machine learning surrogate models.
versioningCFD
17
Case study
reproducible AI studies in molecular dynamics simulations.
reproduciblemolecular
18
Case study
climate model data versioning for downstream AI analysis.
versioningdownstream
19
Integration of version control with HPC job schedulers and batch systems.
Integration of version control with HPC job schedulers and batch systems.
IntegrationHPC
20
Automated testing for reproducibility
unit tests, integration tests, and regression checks.
Automatedreproducibility
21
Continuous integration and continuous delivery (CI/CD) for simulation and AI workflows.
Continuous integration and continuous delivery (CI/CD) for simulation and AI workflows.
Continuousintegration
22
Documentation and reporting
generating reproducible reports with Jupyter, Quarto, and R Markdown.
Documentationreporting
23
Collaboration and sharing
remote repositories, access control, and data licensing.
Collaborationrepositories
24
Auditing and compliance
ensuring traceability and reproducibility for regulatory requirements.
Auditingcompliance
25
Performance considerations
caching, lazy loading, and parallel access to versioned data.
Performanceconsiderations
26
Scaling versioning to petabytes
distributed version control and federated repositories.
versioningpetabytes
27
Emerging standards and tools
FAIR principles, RO-Crate, and ML reproducibility checklists.
Emergingstandards
28
Practical engineering task
setting up a DVC pipeline for a simulation parameter sweep.
DVCpipeline
29
Practical engineering task
building a reproducible AI training workflow with MLflow and Docker.
buildingreproducible
30
Capstone project
end-to-end versioning and reproducibility for a simulation-based AI study.
Capstoneend-to-end