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Agent Implement Command

The agent implement command generates step-by-step implementation guides for research papers, tailored to your framework and experience level.

Basic Usage

scoutml agent implement ARXIV_ID [OPTIONS]

Examples

Simple Implementation Guide

# Basic PyTorch implementation
scoutml agent implement 2010.11929

# TensorFlow implementation
scoutml agent implement 2010.11929 --framework tensorflow

Customized Guide

# Beginner-friendly PyTorch guide
scoutml agent implement 1810.04805 \
  --framework pytorch \
  --level beginner

Options

Option Type Default Description
--framework CHOICE pytorch Target framework: pytorch/tensorflow/jax/other
--level CHOICE intermediate Experience level: beginner/intermediate/advanced
--output CHOICE rich Output format: rich/json
--export PATH None Export guide to file

Framework Options

PyTorch (Default)

scoutml agent implement 2103.00020 --framework pytorch

Includes: - Module definitions - Forward pass implementation - Training loop structure - PyTorch-specific optimizations

TensorFlow

scoutml agent implement 2103.00020 --framework tensorflow

Includes: - Keras model architecture - Custom layers if needed - TF-specific features - TPU considerations

JAX

scoutml agent implement 2103.00020 --framework jax

Includes: - Functional programming approach - Flax/Haiku modules - JIT compilation tips - vmap usage

Other Frameworks

scoutml agent implement 2103.00020 --framework other

Provides: - Framework-agnostic pseudocode - General architecture description - Mathematical formulations - Implementation concepts

Experience Levels

Beginner

scoutml agent implement 1706.03762 --level beginner

Features: - Detailed explanations - Code comments - Common pitfalls - Prerequisite knowledge - Resource links

Intermediate (Default)

scoutml agent implement 1706.03762 --level intermediate

Features: - Balanced detail - Key implementation points - Optimization tips - Best practices

Advanced

scoutml agent implement 1706.03762 --level advanced

Features: - Concise implementation - Performance optimizations - Advanced techniques - Research extensions

Guide Components

1. Overview

  • Paper summary
  • Key contributions
  • Implementation complexity

2. Prerequisites

  • Required knowledge
  • Dependencies
  • Hardware requirements

3. Architecture

  • Model components
  • Data flow
  • Key algorithms

4. Implementation Steps

  • Step-by-step guide
  • Code snippets
  • Critical details

5. Training Process

  • Data preparation
  • Training loop
  • Hyperparameters
  • Evaluation metrics

6. Common Issues

  • Debugging tips
  • Performance tuning
  • Troubleshooting

7. Extensions

  • Improvements
  • Variations
  • Research directions

Use Cases

Quick Prototyping

# Get implementation for new paper
scoutml agent implement 2301.08727 \
  --framework pytorch \
  --level intermediate \
  --export vit_implementation.md

Learning New Concepts

# Understand transformer implementation
scoutml agent implement 1706.03762 \
  --framework pytorch \
  --level beginner \
  --export transformer_tutorial.md

Research Reproduction

# Reproduce paper results
scoutml agent implement 2010.11929 \
  --framework pytorch \
  --level advanced

Course Projects

# Student-friendly implementation
scoutml agent implement 1810.04805 \
  --framework tensorflow \
  --level beginner \
  --export bert_project.md

Advanced Usage

Batch Implementation Guides

# Generate guides for multiple papers
papers=("2010.11929" "1810.04805" "2103.00020")

for paper in "${papers[@]}"; do
    scoutml agent implement "$paper" \
        --framework pytorch \
        --output json \
        --export "impl_${paper}.json"
done

Framework Comparison

# Compare implementations across frameworks
PAPER="2103.00020"

for framework in pytorch tensorflow jax; do
    echo "=== $framework implementation ==="
    scoutml agent implement $PAPER \
        --framework $framework \
        --export "${PAPER}_${framework}.md"
done

Progressive Learning

# Start with beginner, progress to advanced
PAPER="1706.03762"

for level in beginner intermediate advanced; do
    scoutml agent implement $PAPER \
        --level $level \
        --export "transformer_${level}.md"
done

Output Examples

Rich Output (Default)

Displays formatted guide with: - Syntax-highlighted code - Structured sections - Visual hierarchy - Links to resources

JSON Output

scoutml agent implement 2010.11929 --output json

Returns: 

{
  "paper": {
    "arxiv_id": "2010.11929",
    "title": "An Image is Worth 16x16 Words..."
  },
  "implementation": {
    "overview": "...",
    "prerequisites": [...],
    "architecture": {...},
    "steps": [...],
    "code_snippets": {...}
  }
}

Best Practices

Choosing Papers

  1. Start with simpler papers if learning
  2. Check paper clarity - some explain better
  3. Look for papers with code as reference
  4. Consider computational requirements

Framework Selection

  1. Use familiar framework when possible
  2. Match paper's framework if specified
  3. Consider deployment target
  4. Check framework maturity for method

Level Selection

  1. Be honest about experience
  2. Start with lower level if unsure
  3. Use beginner for teaching
  4. Advanced for research/production

Common Workflows

Paper to Production

# 1. Get implementation guide
scoutml agent implement 2103.00020 --framework pytorch --level advanced

# 2. Get optimization tips
scoutml agent solve-limitations 2103.00020 --focus computational

# 3. Compare with alternatives
scoutml compare 2103.00020 2111.06377

Learning Path

# 1. Start with seminal paper
scoutml agent implement 1706.03762 --level beginner

# 2. Move to applications
scoutml agent implement 1810.04805 --level intermediate

# 3. Try latest research
scoutml agent implement 2010.11929 --level advanced

Tips and Tricks

Implementation Strategy

  1. Read paper first - understand concepts
  2. Start small - implement core first
  3. Validate often - check against paper
  4. Use checkpoints - save progress

Debugging Help

If implementation fails: 1. Check prerequisites section 2. Verify data format 3. Compare with paper's code 4. Reduce model size first

Performance Tips

  1. Profile code early
  2. Start with small data
  3. Use mixed precision when stable
  4. Monitor memory usage