Progress & Data Management

Progress & Data Management#

Track Your Journey

Understanding progress tracking, data management, and reset commands

Purpose: Learn how TinyTorch tracks your progress, where your data lives, and how to manage it effectively.

Your Learning Journey: Two Tracking Systems#

TinyTorch uses a clean, simple approach to track your ML systems engineering journey:

        graph LR
 A[Build Modules] --> B[Complete 01-20]
 B --> C[Export to Package]
 C --> D[Unlock Milestones]
 D --> E[Achieve 1957-2018]
 E --> F[Track Progress]

 style A fill:#e3f2fd
 style B fill:#fffbeb
 style C fill:#f0fdf4
 style D fill:#fef3c7
 style E fill:#f3e5f5
 style F fill:#e8eaf6

Fig. 31 Progress Tracking Flow. Build modules, export to package, unlock historical milestones, and track achievements through two parallel systems.#

The Two Systems#

Module Progress

What you BUILD (01-20)

  • Tensor, Autograd, Optimizers
  • Layers, Training, DataLoader
  • Convolutions, Transformers
  • Your complete ML framework

Milestone Achievements

What you ACHIEVE (01-06)

  • Perceptron (1957)
  • MLP Revival (1986)
  • CNN Revolution (1998)
  • AlexNet Era (2012)
  • Transformer Era (2017)
  • MLPerf (2018)

Simple relationship:

  • Complete modules → Unlock milestones → Achieve historical ML recreations

  • Build capabilities → Validate with history → Track achievements

Where Your Data Lives#

All your progress is stored in the .tito/ folder:

TinyTorch/
├── .tito/ ← Your progress data
│ ├── config.json ← User preferences
│ ├── progress.json ← Module completion (01-20)
│ ├── milestones.json ← Milestone achievements (01-06)
│ └── backups/ ← Automatic safety backups
│ ├── 01_tensor_YYYYMMDD_HHMMSS.py
│ ├── 02_activations_YYYYMMDD_HHMMSS.py
│ └── ...
├── modules/ ← Where you edit
├── tinytorch/ ← Where code exports
└── ...

Understanding Each File#

config.json - User Preferences

{
 "logo_theme": "standard"
}

  • UI preferences

  • Display settings

  • Personal configuration

progress.json - Module Completion

{
 "version": "1.0",
 "completed_modules": [1, 2, 3, 4, 5, 6, 7],
 "completion_dates": {
 "1": "2025-11-16T10:00:00",
 "2": "2025-11-16T11:00:00",
 ...
 }
}

  • Tracks which modules (01-20) you’ve completed

  • Records when you completed each

  • Updated by tito module complete XX

milestones.json - Milestone Achievements

{
 "version": "1.0",
 "completed_milestones": ["03"],
 "completion_dates": {
 "03": "2025-11-16T15:00:00"
 }
}

  • Tracks which milestones (01-06) you’ve achieved

  • Records when you achieved each

  • Updated by tito milestone run XX

backups/ - Module Backups

  • Automatic backups before operations

  • Timestamped copies of your implementations

  • Safety net for module development

  • Format: XX_name_YYYYMMDD_HHMMSS.py

Progress Views#

Module Progress: tito module status#

tito module status

Shows your module completion progress:

╭─────────────── TinyTorch Progress ────────────────╮
│ │
│ Modules Completed: 7/20 (35%) │
│ Milestones Achieved: 1/6 (17%) │
│ Last Activity: Module 07 (2 hours ago) │
│ │
│ Next Steps: │
│ • Complete modules 08-09 to unlock Milestone 04 │
│ │
╰──────────────────────────────────────────────────────╯

Module Progress:
 01 Tensor
 02 Activations
 03 Layers
 04 Losses
 05 Autograd
 06 Optimizers
 07 Training
 08 DataLoader
 09 Convolutions
 10 Normalization
 ...

Milestone Achievements:
 03 - MLP Revival (1986)
 04 - CNN Revolution (1998) [Ready after modules 08-09]
 05 - Transformer Era (2017)
 06 - MLPerf (2018)

Use this to:

  • Check module completion

  • See next recommended steps

  • Track your learning journey

Milestone Achievements: tito milestone status#

tito milestone status

Shows your milestone achievements:

  • Which historical recreations you’ve completed

  • Which milestones are unlocked

  • What prerequisites remain

Data Management Commands#

Reset a Module#

Need to start a module over? The reset command lets you reset a specific module cleanly.

Reset a Specific Module#

tito module reset XX

What this does:

  • Resets the specified module to its clean state

  • Creates a backup of your current implementation

  • Keeps other modules untouched

  • Asks for confirmation before proceeding

Example:

tito module reset 03

Example output:

️ Warning: This will reset Module 03 (Layers)

This will:
 • Backup current implementation
 • Reset module to clean state
 • Clear module completion status

Your code will be backed up to .tito/backups/

Continue? [y/N]: y

 Creating backup at .tito/backups/03_layers_20251116_143000.py
 Resetting module to clean state

 Reset Complete!

You're ready to start fresh on Module 03.
Run: tito module start 03

Automatic Backups#

Before any reset, TinyTorch automatically:

  • Creates timestamped backup of your implementation

  • Stores it in .tito/backups/

  • Format: XX_name_YYYYMMDD_HHMMSS.py

  • Allows manual restore if needed

Data Safety & Recovery#

Automatic Backups#

TinyTorch automatically backs up your work:

When backups happen:

  1. Before module start: Backs up existing work

  2. Before reset: Creates full .tito/ backup

  3. Before module reset: Saves current implementation

Where backups go:

.tito/backups/
├── 01_tensor_20251116_100000.py
├── 01_tensor_20251116_143000.py
├── 03_layers_20251115_180000.py
└── ...

How to use backups:

# Backups are timestamped - find the one you need
ls -la .tito/backups/

# Manually restore if needed
cp .tito/backups/03_layers_20251115_180000.py modules/03_layers/layers_dev.py

What If .tito/ Is Deleted?#

No problem! TinyTorch recovers gracefully:

# If .tito/ is deleted, next command recreates it
tito system health

What happens:

  1. TinyTorch detects missing .tito/ folder

  2. Creates fresh folder structure

  3. Initializes empty progress tracking

  4. Your code in modules/ and tinytorch/ is safe

  5. You can continue from where you left off

Important: Your actual code (source in src/, notebooks in modules/, package in tinytorch/) is separate from progress tracking (in .tito/). Deleting .tito/ only resets progress tracking, not your implementations.

Data Health Checks#

Verify Data Integrity#

tito system health

Now includes data health checks:

╭────────── TinyTorch System Check ──────────╮
│ │
│ Environment setup │
│ Dependencies installed │
│ TinyTorch in development mode │
│ Data files intact │
│ .tito/progress.json valid │
│ .tito/milestones.json valid │
│ .tito/config.json valid │
│ Backups directory exists │
│ │
╰───────────────────────────────────────────────╯

All systems ready!

If data is corrupted:

 Data files corrupted
 .tito/progress.json is malformed

Fix by removing and recreating:
 rm .tito/progress.json
 tito system health # Recreates the file

Or restore from backup:
 cp .tito_backup_YYYYMMDD/.tito/progress.json .tito/

Best Practices#

Regular Progress Checks#

Good habits:

  1. Check status regularly:

tito module status
tito milestone status

See where you are, what’s next

  1. Verify environment before work:

tito system health

Catch issues early

  1. Let automatic backups work:

  • Don’t disable them

  • They’re your safety net

  • Cleanup happens automatically

  1. Reset modules when needed:

tito module reset XX # Reset a specific module

  1. Version control for code:

git commit -m "Completed Module 05: Autograd"

.tito/ is gitignored - use git for code versions

Understanding What Gets Tracked#

Modules (Build Progress)#

Tracked when: You run tito module complete XX

What’s recorded:

  • Module number (1-20)

  • Completion timestamp

  • Test results (passed/failed)

Visible in:

  • tito module status

  • .tito/progress.json

Milestones (Achievement Progress)#

Tracked when: You run tito milestone run XX

What’s recorded:

  • Milestone ID (01-06)

  • Achievement timestamp

  • Number of attempts (if multiple runs)

Visible in:

  • tito milestone status

  • .tito/milestones.json

What’s NOT Tracked#

TinyTorch does NOT track:

  • Your actual code implementations (source in src/, notebooks in modules/, package in tinytorch/)

  • How long you spent on each module

  • How many times you edited files

  • Your test scores or grades

  • Personal information

  • Usage analytics

Why: TinyTorch is a local, offline learning tool. Your privacy is protected. All data stays on your machine.

Common Data Scenarios#

Scenario 1: “I want to reset a specific module”#

# Reset module 03 to start fresh
tito module reset 03

# Start working on it again
tito module start 03

Result: Module 03 reset to clean state, backup created, other modules untouched

Scenario 2: “I want to re-run a milestone”#

# Just run the milestone again
tito milestone run 03

Result: Milestone re-runs using your current implementations

Scenario 3: “I accidentally deleted .tito/”#

# Just run any tito command
tito system health

# OR

# If you have a backup
cp -r .tito_backup_YYYYMMDD/ .tito/

Result: .tito/ folder recreated, either fresh or from backup

Scenario 4: “I want to share my progress with a friend”#

# Copy your progress folder
cp -r .tito/ ~/Desktop/my-tinytorch-progress/

Result: Friend can see your progress by copying to their .tito/ folder

FAQ#

Q: Will resetting delete my code?#

A: No! Reset commands only affect progress tracking in .tito/. Your source code in src/, notebooks in modules/, and exported code in tinytorch/ are never touched.

Q: Can I manually edit progress.json?#

A: Yes, but not recommended. Use tito commands instead. Manual edits might break validation.

Q: What if I want to re-export a module?#

A: Just run tito module complete XX again. It will re-run tests and re-export. Progress tracking remains unchanged.

Q: How do I see my completion dates?#

A: Run tito module status for a formatted view, or check .tito/progress.json and .tito/milestones.json directly.

Q: Can I delete backups?#

A: Yes, backups in .tito/backups/ can be deleted manually. They’re safety nets, not requirements.

Q: Is my data shared anywhere?#

A: No. TinyTorch is completely local. No data leaves your machine. No tracking, no analytics, no cloud sync.

Next Steps#

Keep Building!

Now that you understand data management, focus on what matters: building ML systems

Module Workflow → Milestone System →

Your progress is tracked, your data is safe, and your journey is yours. TinyTorch keeps track of what you’ve built and achieved - you focus on learning ML systems engineering.

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