hayabusa-1/workflow.md Secret

## workflow.md

      
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              workflow.md
            
          
    Yes, absolutely. You've not only started to "get it," you've independently formulated an effective development workflow. This is an approach that many seasoned developers and data scientists also use, even if they don't explicitly articulate it this way.
Let's break down your proposed workflow. You've essentially created a three-stage development pipeline that balances an idea's conception, its implementation, and its execution at scale.
Your Proposed 3-Stage Workflow


Stage 1: The Blueprint & Scaffolding Stage


Environment: SolveIt
Goal: Build the basic workflow and code structure. Get the sequence of operations right (load data -> create DataLoaders -> create Learner -> find learning rate -> fine_tune).
Data: "Dummy" data (1-10 images).
Key Advantage: You leverage the AI assistant to get past initial "blank page" syndrome and syntax errors. By keeping compute and data minimal, your attention is 100% on the logic and structure of the code, not on waiting for cells to run. This is a massive productivity booster.

Why this is smart: You're separating the task of "designing the process" from "running the process." You're essentially creating a cheap, fast architectural blueprint.

Stage 2: The Refinement & Debugging Stage


Environment: JetBrains DataSpell on your local M4 Mac Mini
Goal: Make the code robust. Fix bugs, add documentation, use the profiler to find bottlenecks, and ensure it works with a slightly larger, more realistic subset of data.
Data: "Intermediate" data (100-200 images).
Key Advantage: You now have the power of a full-fledged IDE.

Intelligent Code Completion & Documentation: Much richer than in a standard notebook.
Interactive Debugger: You can set breakpoints and inspect variables step-by-step, which is invaluable for fixing tricky bugs.
Version Control (Git) Integration: Crucial for tracking changes as your project gets more complex.
Your M4's Power: The M4 Mac Mini is surprisingly capable. It can handle training on a few hundred or even a few thousand images just fine, allowing you to catch bugs that only appear when you move beyond 10 "dummy" images (e.g., issues with batching, data augmentation, etc.).


Why this is smart: This is the "quality assurance" step. You're hardening your blueprint into a reliable, well-documented, and efficient piece of code before you spend money on expensive cloud resources.

Stage 3: The Scaling & Performance Stage


Environment: Cloud GPU Provider (e.g., Paperspace, Jarvice, Colab Pro, Vast.ai, etc.)
Goal: Run the full-scale experiment. Train on the complete dataset, perform hyperparameter tuning, and focus on the results and analysis of the model.
Data: The "real," full dataset.
Key Advantage: You have access to powerful GPUs that can train your model in hours instead of days. Because you've already debugged your code in Stage 2, you can be confident that you're not wasting expensive GPU time on simple coding errors. Your focus shifts from "is my code working?" to "is my model learning?"

Why this is smart: You're using the most expensive resource (cloud GPU) only for the task that absolutely requires it: large-scale computation.
A Few Pro-Tips to Perfect This Workflow

You're already 99% of the way there, but here are a few things to keep in mind as you move between these stages:


Environment Consistency: The biggest potential headache is differences in library versions between SolveIt, your Mac, and the cloud.

Action: As soon as you move to Stage 2 (DataSpell), create a requirements.txt file. You can generate this with pip freeze > requirements.txt. When you set up your cloud machine in Stage 3, the first thing you'll do is run pip install -r requirements.txt. This ensures all your environments are identical.


Data Path Portability: A path like ~/Downloads/my_images on your Mac won't exist in the cloud.

Action: Use Python's pathlib library and avoid hardcoding absolute paths. Structure your project with a data folder and reference it with relative paths. This makes your code portable without any changes.


The Feedback Loop: Remember that this isn't always a one-way street. You might discover a subtle bug during your large-scale training in Stage 3. The correct response is not to try and debug it in the expensive cloud environment. It's to:

Go back to Stage 2 (DataSpell).
Replicate the bug with your intermediate dataset.
Fix it using the powerful debugger.
Push the fix and then return to Stage 3.


You have absolutely nailed the core concepts. This "start small, refine locally, scale in the cloud" workflow is the key to being productive and cost-effective in Machine Learning. You should feel very confident in this approach. Go for it