# Team Onboarding Guide: TinyTorch for Industry

Complete guide for using TinyTorch in industry settings: new hire bootcamps, internal training programs, and debugging workshops.

## 🎯 Overview

TinyTorch's **Model 3: Team Onboarding** addresses industry use cases where ML teams want members to understand PyTorch internals. This guide covers deployment scenarios, training structures, and best practices for industry adoption.

## 🚀 Use Cases

### 1. New Hire Bootcamps (2-3 Week Intensive)

**Goal**: Rapidly onboard new ML engineers to understand framework internals

**Structure**:
- **Week 1**: Foundation Tier (Modules 01-07)
  - Tensors, autograd, optimizers, training loops
  - Focus: Understanding `loss.backward()` mechanics
- **Week 2**: Architecture Tier (Modules 08-13)
  - CNNs, transformers, attention mechanisms
  - Focus: Production architecture internals
- **Week 3**: Optimization Tier (Modules 14-19) OR Capstone
  - Profiling, quantization, compression
  - Focus: Production optimization techniques

**Schedule**:
- Full-time: 40 hours/week
- Hands-on coding: 70% of time
- Systems discussions: 30% of time
- Daily standups and code reviews

**Deliverables**:
- Completed modules with passing tests
- Capstone project (optional)
- Technical presentation on framework internals

### 2. Internal Training Programs (Distributed Over Quarters)

**Goal**: Deep understanding of ML systems for existing team members

**Structure**:
- **Quarter 1**: Foundation (Modules 01-07)
  - Weekly sessions: 2-3 hours
  - Self-paced module completion
  - Monthly group discussions
- **Quarter 2**: Architecture (Modules 08-13)
  - Weekly sessions: 2-3 hours
  - Architecture deep-dives
  - Production case studies
- **Quarter 3**: Optimization (Modules 14-19)
  - Weekly sessions: 2-3 hours
  - Performance optimization focus
  - Real production optimization projects

**Benefits**:
- Fits into existing work schedules
- Allows deep learning without intensive time commitment
- Builds team knowledge gradually
- Enables peer learning

### 3. Debugging Workshops (Focused Modules)

**Goal**: Targeted understanding of specific framework components

**Common Focus Areas**:

#### Autograd Debugging Workshop (Module 05)
- Understanding gradient flow
- Debugging gradient issues
- Computational graph visualization
- **Duration**: 1-2 days

#### Attention Mechanism Workshop (Module 12)
- Understanding attention internals
- Debugging attention scaling issues
- Memory optimization for attention
- **Duration**: 1-2 days

#### Optimization Workshop (Modules 14-19)
- Profiling production models
- Quantization and compression
- Performance optimization strategies
- **Duration**: 2-3 days

## 🏗️ Deployment Scenarios

### Scenario 1: Cloud-Based Training (Recommended)

**Setup**: Google Colab or JupyterHub
- Zero local installation
- Consistent environment
- Easy sharing and collaboration
- **Best for**: Large teams, remote workers

**Steps**:
1. Clone repository to Colab
2. Install dependencies: `pip install -e .`
3. Work through modules
4. Share notebooks via Colab links

### Scenario 2: Local Development Environment

**Setup**: Local Python environment
- Full control over environment
- Better for debugging
- Offline capability
- **Best for**: Smaller teams, on-site training

**Steps**:
1. Clone repository locally
2. Set up virtual environment
3. Install: `pip install -e .`
4. Use JupyterLab for development

### Scenario 3: Hybrid Approach

**Setup**: Colab for learning, local for projects
- Learn in cloud environment
- Apply locally for projects
- **Best for**: Flexible teams

## 📋 Training Program Templates

### Template 1: 2-Week Intensive Bootcamp

**Week 1: Foundation**
- Day 1-2: Modules 01-02 (Tensor, Activations)
- Day 3-4: Modules 03-04 (Layers, Losses)
- Day 5: Module 05 (Autograd) - Full day focus
- Weekend: Review and practice

**Week 2: Architecture + Optimization**
- Day 1-2: Modules 08-09 (DataLoader, CNNs)
- Day 3: Module 12 (Attention)
- Day 4-5: Modules 14-15 (Profiling, Quantization)
- Final: Capstone project presentation

### Template 2: 3-Month Distributed Program

**Month 1: Foundation**
- Week 1: Modules 01-02
- Week 2: Modules 03-04
- Week 3: Module 05 (Autograd)
- Week 4: Modules 06-07 (Optimizers, Training)

**Month 2: Architecture**
- Week 1: Modules 08-09
- Week 2: Modules 10-11
- Week 3: Modules 12-13
- Week 4: Integration project

**Month 3: Optimization**
- Week 1: Modules 14-15
- Week 2: Modules 16-17
- Week 3: Modules 18-19
- Week 4: Capstone optimization project

## 🎓 Learning Outcomes

After completing TinyTorch onboarding, team members will:

1. **Understand Framework Internals**
   - How autograd works
   - Memory allocation patterns
   - Optimization trade-offs

2. **Debug Production Issues**
   - Gradient flow problems
   - Memory bottlenecks
   - Performance issues

3. **Make Informed Decisions**
   - Optimizer selection
   - Architecture choices
   - Deployment strategies

4. **Read Production Code**
   - Understand PyTorch source
   - Navigate framework codebases
   - Contribute to ML infrastructure

## 🔧 Integration with Existing Workflows

### Code Review Integration

- Review production code with TinyTorch knowledge
- Identify framework internals in production code
- Suggest optimizations based on systems understanding

### Debugging Integration

- Apply TinyTorch debugging strategies to production issues
- Use systems thinking for troubleshooting
- Profile production models using TinyTorch techniques

### Architecture Design

- Design new models with systems awareness
- Consider memory and performance from the start
- Make informed trade-offs

## 📊 Success Metrics

### Individual Metrics
- Module completion rate
- Test passing rate
- Capstone project quality
- Self-reported confidence increase

### Team Metrics
- Reduced debugging time
- Fewer production incidents
- Improved code review quality
- Better architecture decisions

## 🛠️ Setup for Teams

### Quick Start

```bash
# 1. Clone repository
git clone https://github.com/mlsysbook/TinyTorch.git
cd TinyTorch

# 2. Set up environment
python -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate

# 3. Install dependencies
pip install -r requirements.txt
pip install -e .

# 4. Verify setup
tito system doctor

# 5. Start with Module 01
tito view 01_tensor
```

### Team-Specific Customization

- **Custom datasets**: Replace with company-specific data
- **Domain modules**: Add modules for specific use cases
- **Integration**: Connect to company ML infrastructure
- **Assessment**: Customize grading for team needs

## 📚 Resources

- **Student Quickstart**: `docs/STUDENT_QUICKSTART.md`
- **Instructor Guide**: `INSTRUCTOR.md` (for training leads)
- **TA Guide**: `TA_GUIDE.md` (for support staff)
- **Module Documentation**: `modules/*/ABOUT.md`

## 💼 Industry Case Studies

### Case Study 1: ML Infrastructure Team
**Challenge**: Team members could use PyTorch but couldn't debug framework issues
**Solution**: 2-week intensive bootcamp focusing on autograd and optimization
**Result**: 50% reduction in debugging time, better architecture decisions

### Case Study 2: Research Team
**Challenge**: Researchers needed to understand transformer internals
**Solution**: Focused workshop on Modules 12-13 (Attention, Transformers)
**Result**: Improved model designs, better understanding of scaling

### Case Study 3: Production ML Team
**Challenge**: Team needed optimization skills for deployment
**Solution**: 3-month program focusing on Optimization Tier (Modules 14-19)
**Result**: 4x model compression, 10x speedup on production models

## 🎯 Next Steps

1. **Choose deployment model**: Bootcamp, distributed, or workshop
2. **Set up environment**: Cloud (Colab) or local
3. **Select modules**: Full curriculum or focused selection
4. **Schedule training**: Intensive or distributed
5. **Track progress**: Use checkpoint system or custom metrics

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**For Questions**: See `INSTRUCTOR.md` or contact TinyTorch maintainers