ML Experiments Life-Cycle with Weights & Biases¶

Manage the life-cycle of your AI experiments effectively using Weights & Biases by following this structured guide. Ensure reproducibility, scalability, and collaboration through each phase of your experiment.

---
title: ML Experiment Life-Cycle with W&B
---
flowchart TB
    A(1. Create Issue/Story) --> B(2. Branching for the Experiment)
    B --> C(3. Coding the Experiment)
    C --> D(4. Configuring W&B)
    D --> |"Commit Changes ⚠️"| E(5. Running the Experiment)
    E --> F(6. Analyzing Results)
    F --> G(7. Refining the Experiment)
    G --> |Iterate| C
    G --> H(8. Document)
    H --> |Create a New Experiment| A

How to Create a GitHub Issue or a Jira Story for Your Experiment¶

Documenting your experiment's intent by opening a GitHub issue or Jira story is the first crucial step in the experiment life-cycle.

Preparation¶

1. Understand the Experiment: Before you start documenting, ensure you have a clear understanding of the experiment's goals and the methods you'll use.

Documentation Steps¶

1. Create a New Issue/Story: Navigate to your project's GitHub repository or Jira board and initiate a new issue or story.

2. Use a Template: If available use a pre-defined template that fits experiment documentation.

3. Title Your Issue/Story: Start with a succinct and descriptive title, prefixed with 'experiment/' followed by a brief identifier and issue number or Jira key.

4. Describe Your Experiment: Provide a detailed description of the experiment's background, objectives, and hypothesis.

5. List Your Goals: Clearly outline the specific goals or questions your experiment aims to address.

6. Link to Resources: Include links to any relevant documents, code repositories, or previous experiments.

7. Ensure Visibility: Make sure all potential stakeholders have visibility and encourage team members to comment.

8. Submit the Documentation: Once all details are filled in, submit your issue or story.

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How to Create a New Experiment Branch¶

This guide will provide a structured approach to creating a consistent branch naming convention for your experiment.

Create a Naming Convention¶

Structure your branch names to include the category, description, and issue or Jira key.

experiment/<description>-<issue_number_or_jira_key>

For example:

experiment/rag-fusion-GH15

How to Develop Python Code for ML Experiments¶

Developing Python code for machine learning (ML) experiments is a critical phase where your experimental concepts are transformed into executable models and analyses. This how-to guide outlines a structured approach to code development, focusing on integrating advanced ML components like LangChain RAG Fusion into your projects.

Preparing for Development¶

Before diving into coding, it's essential to align your development work with the experiment's goals and requirements.

Review Experiment Documentation¶

• Objective: Ensure your development efforts are guided by the experiment's defined goals and success criteria.
• Action: Revisit the experiment's description, goals, and success criteria detailed in the GitHub issue or Jira story.

Conduct Technical Research¶

• Objective: Gain a deep understanding of the technologies and components you plan to integrate, such as LangChain RAG Fusion.
• Action: Review technical documentation, research papers, and source code related to the technologies in use.

Implementing Your Experiment¶

Follow these steps to implement your experiment within your Python codebase.

Data Preparation¶

• Objective: Prepare your data to meet the input requirements of your ML components.
• Action:
• Write scripts for data preprocessing.
• Ensure data formats are compatible with LangChain RAG Fusion.

Model Integration¶

• Objective: Seamlessly integrate the LangChain RAG Fusion component into your ML pipeline.
• Action:
• Adapt existing code to accommodate the new component.
• Define new interfaces or integrate third-party libraries as necessary.

Configuration and Parameters¶

At this stage, identify your experiment's hyperparameters and hardcode them into your Python code. This decision is crucial for the experiment's setup.

Logging and Monitoring¶

Understanding what metrics and outputs to log is essential. The detailed setup for logging and monitoring will be covered next, using W&B.

Ensuring Code Quality and Reliability¶

Maintain high standards of code quality and reliability throughout the development process.

Write Unit Tests¶

• Objective: Verify that individual components of your code operate correctly.
• Action:
• Create unit tests for critical functions and modules.
• Use a testing framework like pytest to automate testing.

Conduct Integration Testing¶

• Objective: Ensure that different components of your system work together as intended.
• Action:
• Design and run integration tests after integrating new components.
• Pay special attention to the interaction between LangChain RAG Fusion and other parts of your system.

Perform Experiment Runs¶

• Objective: Validate the overall setup and functionality of your experiment.
• Action:
• Conduct initial small-scale runs to test the experimental setup.
• Adjust code and configurations based on initial outcomes.

By following this guide, you'll develop Python code for your ML experiments that is not only aligned with your scientific goals but also adheres to best practices in software engineering and ML research.

How to Configure and Track ML Experiments with Weights & Biases¶

Configuring your machine learning experiments with Weights & Biases (W&B) is crucial for effective tracking, logging, and optimization. Follow this guide to set up W&B for managing and automating your experiments.

Setting Up W&B for Your Experiments¶

Create a Sweep Configuration¶

1. Objective: Define the experiment's hyperparameters and their search space.
2. Action: Write a sweep_config.yaml file detailing parameters and search strategies (grid, random, Bayesian).
3. Version Control: Ensure this file and other configuration files are under version control for reproducibility.

Adapt Your Code for W&B Integration¶

Integrating W&B into your Python code allows dynamic parameter adjustment and comprehensive metric logging.

1. Dynamic Parameters:
2. Modify code to fetch and use parameters from W&B dynamically.

3. Utilize the W&B API to implement this functionality.

4. Logging Metrics:

5. Enhance your code to log training and evaluation metrics to W&B.

6. Ensure both outcome metrics and intermediate metrics are logged for in-depth analysis.

7. Automate Hyperparameter Search with Sweeps:

8. Set up W&B Sweeps in your code for automated hyperparameter optimization.
9. Handle sweep initialization, metric reporting, and parameter updates within your code.

Experiment Tracking Best Practices¶

Maximize the benefits of W&B for your ML experiments with these tracking best practices.

1. Comprehensive Logging:

2. Log every relevant piece of experiment data, including configurations, results, and binaries.

3. Iterative Improvement:

4. Use logged data to iteratively refine your experiments, aiming for continuous improvement.

5. Collaboration:

6. Share experiment results on W&B's platform for collaborative analysis and discussion.

Integrating W&B into your ML experiment workflow enhances tracking, optimization, and collaboration. By following this guide, you'll establish a solid foundation for managing your experiments, leading to more efficient and impactful research outcomes.

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Committing Code Changes Before Running ML Experiments¶

Ensuring that your code changes are committed before running machine learning experiments is critical for maintaining reproducibility and facilitating collaboration.

Importance of Pre-Experiment Commits¶

• Reproducibility: Committing changes before running experiments allows you to associate each W&B experiment run with the exact version of the code used. This is crucial for reproducibility, as it enables you and others to revisit and understand the conditions under which an experiment was conducted.
• Tracking and Collaboration: By committing before experiments, you ensure that the git SHA (a unique identifier for each commit) is captured by W&B. This allows team members to easily track which code changes correspond to which experiment results, facilitating collaboration and review.

Steps for Committing Code Changes¶

1. Review Your Changes: Before committing, review your changes to ensure that they are complete and aligned with the experiment's objectives. This includes new code, modifications, configuration files, and any documentation updates.
2. Stage Your Changes: Use git add to stage the files you intend to commit. This step allows you to select exactly which changes you want to include in your commit.
3. Commit with a Meaningful Message: Commit your staged changes using git commit, providing a descriptive message that clearly summarizes the changes made and their purpose. This message is invaluable for historical context and understanding the evolution of your project.
4. Push to Remote Repository: Push your commit to the remote repository to ensure it is shared with your team and integrated into the project's shared history. This step is critical for remote collaboration and backup.

Committing code changes before running experiments is a critical practice that underpins the reliability and integrity of your machine learning projects. It ensures that every experiment conducted with W&B is traceable to a specific code state, thereby enhancing reproducibility, facilitating collaboration, and supporting rigorous scientific inquiry. By embedding this practice into your workflow, you establish a robust foundation for conducting and sharing machine learning experiments.

How to Run Experiments and Manage Outcomes with Weights & Biases¶

Running experiments with Weights & Biases (W&B) and managing the outcomes efficiently is crucial for the success of machine learning projects. This guide provides a structured approach to executing experiments and handling the results, whether integrating successful outcomes into production or documenting learnings from unsuccessful attempts.

Initiating Experiments with W&B¶

Start Your W&B Runs¶

1. Objective: Log parameters, metrics, and outcomes of your experiment.
2. Action: Ensure your code initializes a W&B run at the start of your experiment script.

Automate with W&B Sweeps¶

1. Objective: Optimize hyperparameters and manage multiple configurations efficiently.
2. Action: Set up W&B Sweeps in your sweep_config.yaml and initiate them for automated parameter exploration.

Monitor and Analyze Results¶

1. Objective: Keep track of experiment progress and analyze data for insights.
2. Action: Use the W&B dashboard for real-time monitoring and result analysis.

Experiment Branch Strategy¶

For Successful Experiments¶

%%{init: { 'logLevel': 'debug', 'theme': 'base', 'gitGraph': {'rotateCommitLabel': true} } }%%
gitGraph TB:
    commit tag: "Initial commit"
    branch experiment
    checkout experiment
    commit tag: "Experiment changes"
    commit tag: "Interate Experiment"
    commit tag: "Successful approach found"
    checkout main
    commit tag: "Changes on master"
    merge experiment tag: "Merge successful experiment into master"
    commit tag: "Changes on master"

1. Opening Pull Requests: Prepare and open a PR for successful experiment branches, clearly documenting the experiment and its findings.
2. Peer Review: Undergo a review process, providing links to W&B pages for detailed metrics.
3. Documentation: Record the experiment's methodology and findings in the project's main documentation.

For Unsuccessful Experiments¶

%%{init: { 'logLevel': 'debug', 'theme': 'base' } }%%
gitGraph TB:
  commit tag: "Initial commit"
  branch experiment
  checkout experiment
  commit tag: "First Commit on experiment"
  commit tag: "Iterate Experiment"
  commit tag: "Failed Experiment"

1. Keep Branches Open: Retain the experimental branch open for documentation and to avoid repeating unsuccessful experiments.
2. Analysis and Documentation: Document the approach, results, and learnings from the experiment in the repository.

Best Practices¶

• Batch Running and Automation: Use scripts or CI/CD pipelines for automating experiments, especially when leveraging W&B Sweeps.
• Iterative Improvement: Apply insights from all experiments to refine future experimental designs.
• Collaboration and Sharing: Promote a collaborative environment by sharing results and insights within the team and using W&B for discussions.