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Complete Phase 2 and 3 workflow documentation updates

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Updated remaining documentation to clarify the actual TinyTorch workflow and mark optional/future features appropriately.

**Phase 2 (Important files):**
- **learning-progress.md**: Added workflow context at top, clear modules vs checkpoints vs milestones explanation, module progression tables by tier, marked checkpoints as optional
- **checkpoint-system.md**: Added prominent "Optional Progress Tracking" banner at top, clarified this is not required for core workflow

**Phase 3 (Supporting files):**
- **classroom-use.md**: Added "Coming Soon" banner for NBGrader integration, clarified current status vs planned features, updated to reflect 18 modules (not 20)

Key clarifications across all files:
- Core workflow: Edit modules → `tito module complete N` → Run milestone scripts
- Checkpoints are optional capability tracking (helpful for self-assessment)
- Instructor features marked as "coming soon" / "under development"
- All pages reference canonical student-workflow.md

Completes the workflow documentation audit identified by website-manager.
This commit is contained in:
Vijay Janapa Reddi
2025-11-11 21:49:37 -05:00
parent 770d27c21b
commit c7bc68fa37
3 changed files with 131 additions and 62 deletions
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# 🎯 TinyTorch Checkpoint System
<div style="background: #fff3cd; border: 1px solid #ffc107; padding: 1.5rem; border-radius: 0.5rem; margin: 2rem 0;">
<h3 style="margin: 0 0 0.5rem 0; color: #856404;">📋 Optional Progress Tracking</h3>
<p style="margin: 0; color: #856404;">This checkpoint system is <strong>optional</strong> for tracking your learning progress. It's not required for the core TinyTorch workflow.</p>
<p style="margin: 0.5rem 0 0 0; color: #856404;"><strong>Core workflow</strong>: Edit modules → Export with <code>tito module complete N</code> → Validate with milestone scripts</p>
<p style="margin: 0.5rem 0 0 0;"><a href="student-workflow.html" style="color: #856404; font-weight: bold;">📖 See Student Workflow</a> for the essential development cycle.</p>
</div>
<div style="background: #f8f9fa; border: 1px solid #dee2e6; padding: 2rem; border-radius: 0.5rem; text-align: center; margin: 2rem 0;">
<h2 style="margin: 0 0 1rem 0; color: #495057;">Technical Implementation Guide</h2>
<p style="margin: 0; color: #6c757d;">Capability validation system architecture and implementation details</p>
@@ -7,7 +14,7 @@
**Purpose**: Technical documentation for the checkpoint validation system. Understand the architecture and implementation details of capability-based learning assessment.
The TinyTorch checkpoint system provides technical infrastructure for capability validation and progress tracking. This system transforms traditional module completion into measurable skill assessment through automated testing and validation.
The TinyTorch checkpoint system provides optional infrastructure for capability validation and progress tracking. This system transforms traditional module completion into measurable skill assessment through automated testing and validation.
<div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(250px, 1fr)); gap: 1rem; margin: 2rem 0;">

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<div style="background: #f8f9fa; padding: 2rem; border-radius: 0.5rem; margin: 2rem 0; text-align: center;">
<h2 style="margin: 0 0 1rem 0; color: #495057;">Monitor Your Learning Journey</h2>
<p style="margin: 0; font-size: 1.1rem; color: #6c757d;">Track your capability development through 16 essential ML systems skills</p>
<p style="margin: 0; font-size: 1.1rem; color: #6c757d;">Track your capability development through 18 modules and 6 historical milestones</p>
</div>
**Purpose**: Monitor your capability development through the 21-checkpoint system. Track progress from foundation skills to production ML systems mastery.
**Purpose**: Monitor your progress as you build a complete ML framework from scratch. Track module completion and milestone achievements.
Track your progression through 21 essential ML systems capabilities. Each checkpoint represents fundamental competencies you'll master through hands-on implementation—from tensor operations to production-ready systems.
## The Core Workflow
## How to Track Your Progress
TinyTorch follows a simple three-step cycle:
```
1. Edit modules → 2. Export to package → 3. Validate with milestones
```
**📖 See [Student Workflow](student-workflow.html)** for the complete development cycle.
## Understanding Modules vs Checkpoints vs Milestones
<div style="background: #e3f2fd; padding: 1.5rem; border-radius: 0.5rem; border-left: 4px solid #2196f3; margin: 1.5rem 0;">
<h4 style="margin: 0 0 1rem 0; color: #1976d2;">🎯 Capability-Based Learning</h4>
Use TinyTorch's 21-checkpoint system to monitor your capability development. Track progress from foundation skills to production ML systems mastery.
**Modules (18 total)**: What you're building - the actual code implementations
**📖 See [Essential Commands](tito-essentials.html)** for complete progress tracking commands and workflow.
- Located in `modules/source/`
- You implement each component from scratch
- Export with `tito module complete N`
**Milestones (6 total)**: How you validate - historical proof scripts
- Located in `milestones/`
- Run scripts that use YOUR implementations
- Recreate ML history (1957 Perceptron → 2018 MLPerf)
**Checkpoints (21 total)**: Optional progress tracking
- Use `tito checkpoint status` to view
- Tracks capability mastery
- Not required for the core workflow
**📖 See [Journey Through ML History](chapters/milestones.html)** for milestone details.
</div>
@@ -40,40 +63,66 @@ TinyTorch organizes learning through **three pedagogically-motivated tiers**, ea
**📖 See [Quick Start Guide](quickstart-guide.html)** for immediate hands-on experience with your first module.
## 21 Core Capabilities
## Module Progression
Track progress through essential ML systems competencies:
Your journey through 18 modules organized in three tiers:
```{admonition} Capability Tracking
:class: note
Each checkpoint validates mastery of fundamental ML systems skills.
### 🏗️ Foundation Tier (Modules 01-07)
Build the mathematical infrastructure:
| Module | Component | What You Build |
|--------|-----------|----------------|
| 01 | Tensor | N-dimensional arrays with operations |
| 02 | Activations | ReLU, Softmax, nonlinear functions |
| 03 | Layers | Linear layers, forward/backward |
| 04 | Losses | CrossEntropyLoss, MSELoss |
| 05 | Autograd | Automatic differentiation engine |
| 06 | Optimizers | SGD, Adam, parameter updates |
| 07 | Training | Complete training loops |
**Milestone unlocked**: M01 Perceptron (1957), M02 XOR (1969)
### 🏛️ Architecture Tier (Modules 08-13)
Implement modern architectures:
| Module | Component | What You Build |
|--------|-----------|----------------|
| 08 | DataLoader | Batching and data pipelines |
| 09 | Spatial | Conv2d, MaxPool2d for vision |
| 10 | Tokenization | Character-level tokenizers |
| 11 | Embeddings | Token and positional embeddings |
| 12 | Attention | Multi-head self-attention |
| 13 | Transformers | LayerNorm, TransformerBlock, GPT |
**Milestones unlocked**: M03 MLP (1986), M04 CNN (1998), M05 Transformers (2017)
### ⚡ Optimization Tier (Modules 14-18)
Optimize for production:
| Module | Component | What You Build |
|--------|-----------|----------------|
| 14 | Profiling | Performance measurement tools |
| 15 | Quantization | INT8/FP16 implementations |
| 16 | Compression | Pruning techniques |
| 17 | Memoization | KV-cache for generation |
| 18 | Acceleration | Batching strategies |
**Milestone unlocked**: M06 MLPerf (2018)
## Optional: Checkpoint System
Track capability mastery with the optional checkpoint system:
```bash
tito checkpoint status # View your progress
```
| Checkpoint | Capability Question | Modules Required | Status |
|------------|-------------------|------------------|--------|
| 00 | Can I set up my environment? | 01 | ⬜ Setup |
| 01 | Can I manipulate tensors? | 02 | ⬜ Foundation |
| 02 | Can I add nonlinearity? | 03 | ⬜ Intelligence |
| 03 | Can I build network layers? | 04 | ⬜ Components |
| 04 | Can I measure loss? | 05 | ⬜ Networks |
| 05 | Can I compute gradients? | 06 | ⬜ Learning |
| 06 | Can I optimize parameters? | 07 | ⬜ Optimization |
| 07 | Can I train models? | 08 | ⬜ Training |
| 08 | Can I process images? | 09 | ⬜ Vision |
| 09 | Can I load data efficiently? | 10 | ⬜ Data |
| 10 | Can I process text? | 11 | ⬜ Language |
| 11 | Can I create embeddings? | 12 | ⬜ Representation |
| 12 | Can I implement attention? | 13 | ⬜ Attention |
| 13 | Can I build transformers? | 14 | ⬜ Architecture |
| 14 | Can I profile performance? | 14 | ⬜ Deployment |
| 15 | Can I quantize models? | 15 | ⬜ Quantization |
| 16 | Can I compress networks? | 16 | ⬜ Compression |
| 17 | Can I cache computations? | 17 | ⬜ Memoization |
| 18 | Can I accelerate algorithms? | 18 | ⬜ Acceleration |
| 19 | Can I benchmark competitively? | 19 | ⬜ Competition |
| 20 | Can I build complete language models? | 20 | ⬜ TinyGPT Capstone |
This provides 21 capability checkpoints corresponding to modules and validates your understanding. Helpful for self-assessment but **not required** for the core workflow.
**📖 See [Essential Commands](tito-essentials.html)** for progress monitoring commands.
**📖 See [Essential Commands](tito-essentials.html)** for checkpoint commands.
---
@@ -121,10 +170,25 @@ Begin developing ML systems competencies immediately:
<a href="chapters/01-setup.html" style="display: inline-block; background: #28a745; color: white; padding: 0.75rem 1.5rem; border-radius: 0.25rem; text-decoration: none; font-weight: 500;">Begin Setup →</a>
</div>
## Track Your Progress
## How to Track Your Progress
To monitor your capability development and learning progression, use the TITO checkpoint commands.
The essential workflow:
**📖 See [Essential Commands](tito-essentials.html)** for complete command reference and usage examples.
```bash
# 1. Work on a module
cd modules/source/03_layers
jupyter lab 03_layers_dev.py
**Approach**: You're building ML systems engineering capabilities through hands-on implementation. Each capability checkpoint validates practical competency, not just theoretical understanding.
# 2. Export when ready
tito module complete 03
# 3. Validate with milestones
cd ../../milestones/01_1957_perceptron
python 01_rosenblatt_forward.py # Uses YOUR implementation!
```
**Optional**: Use `tito checkpoint status` to see capability tracking
**📖 See [Student Workflow](student-workflow.html)** for the complete development cycle.
**Approach**: You're building ML systems engineering capabilities through hands-on implementation. Each module adds new functionality to your framework, and milestones prove it works.

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# TinyTorch for Instructors: Complete ML Systems Course
<div style="background: #fff3cd; border: 1px solid #ffc107; padding: 1.5rem; border-radius: 0.5rem; margin: 2rem 0;">
<h3 style="margin: 0 0 0.5rem 0; color: #856404;">🚧 Classroom Integration: Coming Soon</h3>
<p style="margin: 0; color: #856404;">NBGrader integration and instructor tooling are under active development. Full documentation and automated grading workflows will be available in future releases.</p>
<p style="margin: 0.5rem 0 0 0; color: #856404;"><strong>Currently available</strong>: Students can use TinyTorch with the standard workflow (edit modules → export → validate with milestones)</p>
<p style="margin: 0.5rem 0 0 0;"><a href="../student-workflow.html" style="color: #856404; font-weight: bold;">📖 See Student Workflow</a> for the current development cycle.</p>
</div>
<div style="background: #e3f2fd; border: 1px solid #2196f3; padding: 1rem; border-radius: 0.5rem; margin: 1rem 0;">
<strong>📖 Course Overview & Benefits:</strong> This page explains WHAT TinyTorch offers for ML education and WHY it's effective.<br>
<strong>📖 For Setup & Daily Workflow:</strong> See <a href="../instructor-guide.html">Technical Instructor Guide</a> for step-by-step NBGrader setup and semester management.
<strong>📖 Course Vision:</strong> This page describes the planned TinyTorch classroom experience.<br>
<strong>📖 For Current Usage:</strong> Students should follow the <a href="../student-workflow.html">Student Workflow</a> guide.
</div>
<div style="background: #f8f9fa; border: 1px solid #dee2e6; padding: 2rem; border-radius: 0.5rem; text-align: center; margin: 2rem 0;">
<h2 style="margin: 0 0 1rem 0; color: #495057;">🏫 Turn-Key ML Systems Education</h2>
<h2 style="margin: 0 0 1rem 0; color: #495057;">🏫 Planned: Turn-Key ML Systems Education</h2>
<p style="font-size: 1.1rem; margin: 0; color: #6c757d;">Transform students from framework users to systems engineers</p>
</div>
**Transform Your ML Teaching:** Replace black-box API courses with deep systems understanding. Your students will build neural networks from scratch, understand every operation, and graduate job-ready for ML engineering roles.
**Vision:** Replace black-box API courses with deep systems understanding. Students will build neural networks from scratch, understand every operation, and graduate job-ready for ML engineering roles.
---
## 🎯 Complete Course Infrastructure
## 🎯 Planned Course Infrastructure
<div style="background: #f8f9fa; border-left: 4px solid #007bff; padding: 1.5rem; margin: 1.5rem 0;">
<h4 style="margin: 0 0 1rem 0; color: #0056b3;">What You Get: Production-Ready Course Materials</h4>
<h4 style="margin: 0 0 1rem 0; color: #0056b3;">Planned Features: Production-Ready Course Materials</h4>
<div style="display: grid; grid-template-columns: 1fr 1fr; gap: 1rem;">
<div>
<ul style="margin: 0; padding-left: 1rem;">
<li><strong>Three-tier progression</strong> (20 modules) with NBGrader integration</li>
<li><strong>200+ automated tests</strong> for immediate feedback</li>
<li><strong>Three-tier progression</strong> (18 modules) with NBGrader integration</li>
<li><strong>Automated grading</strong> for immediate feedback</li>
<li><strong>Professional CLI tools</strong> for development workflow</li>
<li><strong>Real datasets</strong> (CIFAR-10, text generation)</li>
</ul>
</div>
<div>
<ul style="margin: 0; padding-left: 1rem;">
<li><strong>Complete instructor guide</strong> with setup & grading</li>
<li><strong>Flexible pacing</strong> (8-20 weeks depending on depth)</li>
<li><strong>Complete instructor guide</strong> with setup & grading (coming soon)</li>
<li><strong>Flexible pacing</strong> (14-18 weeks depending on depth)</li>
<li><strong>Industry practices</strong> (Git, testing, documentation)</li>
<li><strong>Academic foundation</strong> from university research</li>
</ul>
@@ -38,19 +45,10 @@
</div>
</div>
**Course Duration:** 14-16 weeks (flexible pacing)
**Planned Course Duration:** 14-16 weeks (flexible pacing)
**Student Outcome:** Complete ML framework supporting vision AND language models
```{admonition} Complete Instructor Documentation
:class: tip
**See our comprehensive [Instructor Guide](../instructor-guide.md)** for:
- Complete setup walkthrough (30 minutes)
- Weekly assignment workflow with NBGrader
- Grading automation and feedback generation
- Student support and troubleshooting
- End-to-end course management
- Quick reference commands
```
**Current Status:** Students can work through modules individually using the standard workflow. Full classroom integration (NBGrader automation, instructor dashboards) coming soon.
---