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| experiments [2025/05/30 13:09] – [Example 1: Logging a Simple Experiment] eagleeyenebula | experiments [2025/06/06 12:53] (current) – [Experiment Manager] eagleeyenebula |
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| **[[https://autobotsolutions.com/god/templates/index.1.html|More Developers Docs]]**: | **[[https://autobotsolutions.com/god/templates/index.1.html|More Developers Docs]]**: |
| The AI Experiment Manager system is responsible for managing and logging configurations, results, and metadata for experiments, serving as the central hub for tracking the lifecycle of experimental workflows. By capturing every variable, parameter, and outcome, it ensures that each experiment is fully traceable and reproducible critical qualities for scientific rigor, iterative development, and compliance in regulated environments. Whether running isolated tests or large-scale batch experiments, the system enables researchers and developers to track progress, compare outcomes, and make informed decisions based on structured, historical data. | The AI Experiment Manager system is responsible for managing and logging configurations, results, and metadata for experiments, serving as the central hub for tracking the lifecycle of experimental workflows. By capturing every variable, parameter, and outcome, it ensures that each experiment is fully traceable and reproducible critical qualities for scientific rigor, iterative development, and compliance in regulated environments. Whether running isolated tests or large-scale batch experiments, the system enables researchers and developers to track progress, compare outcomes, and make informed decisions based on structured, historical data. |
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| Built with flexibility and performance in mind, the Experiment Manager supports versioning of configurations, tagging of experimental runs, and integration with external tools such as model registries, monitoring platforms, and data visualization dashboards. It can accommodate a variety of experiment types from hyperparameter tuning in machine learning models to performance benchmarking in software systems. Through its modular architecture, users can define custom logging behavior, attach contextual metadata, and link results with code snapshots or datasets. This not only promotes reproducibility but also accelerates collaboration and knowledge sharing across teams. With the Experiment Manager, experimentation becomes a disciplined, transparent, and scalable process aligned with best practices in modern research and development workflows. | Built with flexibility and performance in mind, the Experiment Manager supports versioning of configurations, tagging of experimental runs, and integration with external tools such as model registries, monitoring platforms, and data visualization dashboards. It can accommodate a variety of experiment types from hyperparameter tuning in machine learning models to performance benchmarking in software systems. Through its modular architecture, users can define custom logging behavior, attach contextual metadata, and link results with code snapshots or datasets. This not only promotes reproducibility but also accelerates collaboration and knowledge sharing across teams. With the Experiment Manager, experimentation becomes a disciplined, transparent, and scalable process aligned with best practices in modern research and development workflows. |
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| <code> | <code> |
| ```python | python |
| config = { | config = { |
| "model": "SVM", | "model": "SVM", |
| file_path = "custom_logs/svm_experiment.json" | file_path = "custom_logs/svm_experiment.json" |
| ExperimentManager.log_experiment(config, results, file_path=file_path) | ExperimentManager.log_experiment(config, results, file_path=file_path) |
| ``` | |
| </code> | </code> |
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| <code> | <code> |
| ```python | python |
| import datetime | import datetime |
| import uuid | import uuid |
| |
| ExperimentManager.log_experiment(config, results) | ExperimentManager.log_experiment(config, results) |
| ``` | |
| </code> | </code> |
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| <code> | <code> |
| ```json | json |
| { | { |
| "config": { | "config": { |
| } | } |
| } | } |
| ``` | |
| </code> | </code> |
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| <code> | <code> |
| ```python | python |
| batch = [ | batch = [ |
| { | { |
| for experiment in batch: | for experiment in batch: |
| ExperimentManager.log_experiment(experiment["config"], experiment["results"]) | ExperimentManager.log_experiment(experiment["config"], experiment["results"]) |
| ``` | |
| </code> | </code> |
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| <code> | <code> |
| ```python | python |
| try: | try: |
| ExperimentManager.log_experiment({"model": "XGBoost"}, {"accuracy": 0.94}, file_path="/invalid/path.json") | ExperimentManager.log_experiment({"model": "XGBoost"}, {"accuracy": 0.94}, file_path="/invalid/path.json") |
| except Exception as e: | except Exception as e: |
| print(f"Logging failed: {e}") | print(f"Logging failed: {e}") |
| ``` | |
| </code> | </code> |
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| 1. **Cloud Storage**: | 1. **Cloud Storage**: |
| Modify `log_experiment` to send logs to Amazon S3, Google Cloud Storage, or Azure Blob. | * Modify **log_experiment** to send logs to **Amazon S3**, **Google Cloud Storage**, or **Azure Blob**. |
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| 2. **Database Integration**: | 2. **Database Integration**: |
| Replace file storage with SQL/NoSQL databases for scalable operations. | * Replace file storage with **SQL/NoSQL** databases for scalable operations. |
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| 3. **Real-Time Monitoring**: | 3. **Real-Time Monitoring**: |
| Stream results into a dashboard for live experiment tracking. | * Stream results into a dashboard for live experiment tracking. |
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| 4. **Summarized Logging**: | 4. **Summarized Logging**: |
| Automatically summarize metrics (e.g., show only the top 5 accuracies). | * Automatically summarize metrics (e.g., show only the top 5 accuracies). |
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| ===== Best Practices ===== | ===== Best Practices ===== |
