DeepSeek AI Exploration Project

Model Overview: DeepSeek-R1

Key Characteristics

• Architecture: Mixture of Experts (MoE)
• Base Model: DeepSeek-V3-Base
• Activated Parameters: 37 billion
• Total Parameters: 671 billion
• Context Length: 128K tokens
• Version: V1.0

Model Capabilities

• State-of-the-art reasoning model
• Excels in math, code, and language tasks
• Trained using large-scale reinforcement learning (RL)
• Enhanced through supervised fine-tuning (SFT)

Recommended Configuration

• Temperature: 0.5-0.7 (recommended: 0.6)
• Avoid system prompts
• For math problems: Include step-by-step reasoning
• Conduct multiple tests and average results

Supported Environments

• Runtime Engines: vLLM and SGLang
• Hardware: NVIDIA Ampere, Blackwell, Jetson, Hopper, Lovelace, Pascal, Turing, Volta
• Preferred OS: Linux

Licensing

• Community-driven model by DeepSeek AI
• MIT License
• Ready for research and commercial use

Additional Resources

• DeepSeek Website
• GitHub Repository
• Research Paper

Project Overview

This project explores the capabilities of the DeepSeek-R1 model using NVIDIA AI Enterprise's integration with multiple language runtimes.

Prerequisites

• Python 3.8+
• Node.js 14+ (optional)
• Bash (with cURL)
• NVIDIA API Key

Setup Instructions

1. Clone the repository
2. Create a Python virtual environment:

   python3 -m venv venv
   source venv/bin/activate
   
3. Install Python dependencies:

   pip install -r requirements.txt
   
4. Set up API Key:

   export NVIDIA_API_KEY='your_api_key_here'
   

API Key Management

Secure Configuration

We use a .env file to manage sensitive configuration details securely:

1. Copy the .env.example to .env:

   cp .env.example .env

2. Edit the .env file and replace placeholders with your actual API keys:

   nano .env

Best Practices

• Never commit .env file to version control
• Use environment variables for sensitive information
• Rotate API keys periodically
• Limit API key permissions

Required Environment Variables

• NVIDIA_API_KEY: Your NVIDIA DeepSeek AI API key
• DEEPSEEK_MODEL_NAME: Model identifier
• DEEPSEEK_DEFAULT_TEMPERATURE: Model creativity setting
• DEEPSEEK_DEFAULT_TOP_P: Nucleus sampling parameter
• DEEPSEEK_MAX_TOKENS: Maximum response length

Python Dependency for .env Support

Install python-dotenv to load environment variables:

pip install python-dotenv

Example Usage in Python

from dotenv import load_dotenv
import os

# Load environment variables
load_dotenv()

# Access API key
api_key = os.getenv('NVIDIA_API_KEY')

Security Warning

Protect your API keys. Do not share them or expose them in public repositories.

Running Examples

Python Example

python deepseek_python_example.py

Node.js Example

# Install Node.js dependencies
npm init -y
npm install openai

# Run the script
node deepseek_node_example.js

Bash cURL Example

# Make the script executable
chmod +x deepseek_curl_example.sh

# Run the script
./deepseek_curl_example.sh

Running the Notebook

jupyter notebook DeepSeek_Exploration.ipynb

Jupyter Notebook JSON Structure

Understanding Notebook Metadata

Jupyter notebooks are stored as JSON files with a specific structure:

{
    "cells": [
        {
            "cell_type": "markdown|code",
            "metadata": {},
            "source": ["Cell content as an array of strings"]
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3",
            "language": "python",
            "name": "python3"
        },
        "language_info": {
            "name": "python",
            "version": "3.8.5"
        }
    },
    "nbformat": 4,
    "nbformat_minor": 4
}

Key Components

• cells: Array of notebook cells (markdown or code)
• metadata: Information about kernel and language
• nbformat: Notebook format version
• nbformat_minor: Minor version of the notebook format

Troubleshooting Tips

• Ensure valid JSON structure
• Check for proper cell formatting
• Verify metadata is correctly defined
• Use tools like nbconvert to validate notebooks

Automated Notebook Validation

GitHub Action Workflow

We've implemented an automated Jupyter notebook validation workflow that:

• Checks all .ipynb files on push and pull requests
• Automatically repairs notebook JSON structure
• Ensures compatibility with GitHub rendering
• Fixes common notebook formatting issues

Workflow Features

• Validates notebook JSON structure
• Adds missing outputs property to code cells
• Resets execution_count
• Commits and pushes repaired notebooks

How It Works

1. Scans all Jupyter notebooks in the repository
2. Checks for required notebook properties
3. Automatically repairs formatting issues
4. Prevents merging if critical issues are found

Note: This workflow helps maintain notebook quality and ensures consistent rendering across different platforms.

API Response Validation

Automated Testing Strategy

We've implemented comprehensive API response validation to ensure:

• Consistent and reliable AI model performance
• Robust error handling
• Quality of generated responses

Test Coverage

• Reasoning query validation
• Coding generation tests
• Multilingual response checks
• Token limit adherence

Validation Workflow

• Runs daily via GitHub Actions
• Triggered on pull requests
• Checks API responses against predefined criteria

Key Validation Checks

• Response structure integrity
• Content relevance
• Language understanding
• Token limit compliance

Monitoring and Alerts

• Automatic Slack notifications on test failures
• Detailed error reporting
• Continuous integration monitoring

Advanced API Performance Validation

Comprehensive Performance Benchmarking

Our validation strategy now includes advanced performance testing:

Performance Metrics

• Response Time Tracking

  • Maximum individual response time
  • Average response time across queries
  • Concurrent request performance

• Token Generation Analysis

  • Tokens generated per second
  • Consistency of token generation rate

Rate Limiting Validation

• Simulate high-frequency requests
• Test API's graceful handling under load
• Verify rate limit error handling

Performance Thresholds

• Maximum response time: 5 seconds
• Average response time: 2 seconds
• Concurrent request capacity: 10 simultaneous queries
• Maximum token generation rate: 100 tokens/second

Monitoring Capabilities

• Automated daily performance tests
• Detailed performance reports
• Slack notifications for critical issues
• Performance report artifacts for trend analysis

Key Testing Scenarios

1. Individual query performance
2. Concurrent request handling
3. Rate limiting behavior
4. Token generation consistency

Reporting

• JSON performance reports
• Timestamped test results
• Workflow status tracking

Note: Helps maintain API reliability and identify potential bottlenecks early

Multi-Model AI Performance Benchmarking

Comprehensive Model Comparison Framework

Benchmarking Objectives

• Compare performance across different AI models
• Evaluate real-world use case capabilities
• Provide data-driven model selection insights

Evaluated Models

• DeepSeek R1
• OpenAI GPT-3.5 Turbo
• (Expandable to more models)

Real-World Scenario Testing

Scenario Categories

1. Technical Documentation Generation

   • Assess structured content creation
   • Evaluate technical writing capabilities

2. Code Generation

   • Test programming task completion
   • Analyze code quality and complexity handling

3. Complex Reasoning

   • Evaluate multi-dimensional problem analysis
   • Test abstract thinking capabilities

4. Multilingual Translation

   • Check translation accuracy
   • Assess technical terminology preservation

Performance Metrics

• Average Response Time
• Median Response Time
• Token Generation Rate
• Task Success Rate
• Error Rate
• Total Execution Time

Benchmarking Workflow

• Automated weekly performance tests
• Comprehensive scenario-based evaluation
• Detailed JSON performance reports

Reporting

• Standardized performance metrics
• Comparative model analysis
• Trend tracking over time

Note: Provides transparent, data-driven insights into AI model capabilities

Advanced AI Model Performance Analytics 🚀

Comprehensive Multi-Model Benchmarking Framework

🔍 Benchmarking Evolution

• Expanded beyond initial two-model comparison
• Support for multiple AI model providers
• Advanced performance tracking and visualization

📊 Supported Models

• OpenAI GPT-3.5 Turbo
• DeepSeek R1
• Anthropic Claude
• Google PaLM 2
• (Easily extensible to more models)

Performance Tracking Innovations

1. Visualization Dashboard 📈

• Interactive Plotly graphs
• Real-time performance comparisons
• Stored in reports/ directory
  • response_time_comparison.html
  • token_generation_rate.html

2. Database-Driven Insights 💾

• SQLite performance logging
• Historical trend analysis
• Comprehensive metrics tracking
  • Timestamp
  • Model-specific performance
  • Scenario complexity

3. Expanded Scenario Testing 🧪

• Technical Documentation Generation
• Complex Code Generation
• Multi-dimensional Reasoning
• Multilingual Technical Translation

Performance Metrics Tracked

• Average Response Time
• Median Response Time
• Token Generation Rate
• Task Success Rate
• Error Rate
• Execution Time

Workflow Integration

• Weekly automated benchmarks
• GitHub Actions pipeline
• Artifact generation
• Slack notifications

Future Roadmap

• Machine learning trend prediction
• Automated model recommendation
• Continuous performance monitoring

Note: Provides data-driven insights into AI model capabilities across diverse scenarios

Interactive Performance Dashboard 🖥️

Streamlit Visualization Platform

📊 Dashboard Capabilities

• Real-time AI model performance tracking
• Interactive comparative visualizations
• Comprehensive performance metrics
• Historical trend analysis

🔍 Key Visualization Features

• Model Response Time Comparison
• Token Generation Rate Analysis
• Performance Trends Over Time
• Detailed Error Rate Insights

🚀 Quick Start

# Navigate to dashboard directory
cd dashboard

# Install dependencies
pip install -r requirements.txt

# Launch dashboard
streamlit run performance_dashboard.py

💡 Advanced Insights

• Machine learning trend prediction
• Predictive model performance scoring
• Customizable visualization framework

Note: Requires performance metrics database generated by benchmarking workflow

Performance Evaluation

DeepSeek Performance Notebook

The DeepSeek_Performance_Evaluation.ipynb provides a comprehensive assessment of the DeepSeek-R1 model's capabilities:

Evaluation Metrics

• Reasoning Performance

  • Tests across different difficulty levels
  • Measures response time and token count
  • Includes easy, medium, and hard reasoning tasks

• Coding Performance

  • Evaluates code generation in multiple languages
  • Assesses response time and code complexity
  • Languages tested: Python, JavaScript, Rust

Visualization

• Bar charts comparing response times
• Performance metrics across different task types
• Insights into model's strengths and limitations

How to Run

jupyter notebook DeepSeek_Performance_Evaluation.ipynb

Customization

• Easily extend test cases
• Add more programming languages
• Modify difficulty levels

Key Insights

• Quantitative analysis of AI model performance
• Benchmark DeepSeek-R1's reasoning and coding abilities
• Provides a structured approach to model evaluation

Full Performance Evaluation Notebook Content

Note

For the most up-to-date and interactive version, please refer to the DeepSeek_Performance_Evaluation.ipynb file in the repository.

# DeepSeek-R1 Performance Evaluation Notebook

# Project Overview
# This notebook provides a comprehensive performance evaluation 
# of the DeepSeek-R1 AI model across various domains and test scenarios.

# Import required libraries
from openai import OpenAI
from dotenv import load_dotenv
import os
import time
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# Load environment variables
load_dotenv()

# Initialize OpenAI client
client = OpenAI(
    base_url="https://integrate.api.nvidia.com/v1",
    api_key=os.getenv('NVIDIA_API_KEY')
)

# Test Case 1: Reasoning Performance
def test_reasoning_performance(prompts):
    results = []
    for difficulty, prompt in prompts.items():
        start_time = time.time()
        response = client.chat.completions.create(
            model="deepseek-ai/deepseek-r1",
            messages=[{"role": "user", "content": prompt}],
            max_tokens=500
        )
        end_time = time.time()
        
        results.append({
            'difficulty': difficulty,
            'prompt': prompt,
            'response': response.choices[0].message.content,
            'response_time': end_time - start_time,
            'token_count': len(response.choices[0].message.content.split())
        })
    
    return pd.DataFrame(results)

# Reasoning test prompts
reasoning_prompts = {
    'easy': "What is 15 * 7?",
    'medium': "If a train travels 120 miles in 2 hours, what is its speed?",
    'hard': "Solve this logic puzzle: A farmer has chickens and cows. The total number of heads is 50 and the total number of legs is 140. How many chickens and cows does the farmer have?"
}

# Run reasoning performance tests
reasoning_results = test_reasoning_performance(reasoning_prompts)
print(reasoning_results)

# Test Case 2: Coding Performance
def test_coding_performance(coding_tasks):
    results = []
    for language, task in coding_tasks.items():
        start_time = time.time()
        response = client.chat.completions.create(
            model="deepseek-ai/deepseek-r1",
            messages=[{"role": "user", "content": task}],
            max_tokens=500
        )
        end_time = time.time()
        
        results.append({
            'language': language,
            'task': task,
            'solution': response.choices[0].message.content,
            'response_time': end_time - start_time,
            'code_length': len(response.choices[0].message.content.split('\n'))
        })
    
    return pd.DataFrame(results)

# Coding tasks for different languages
coding_tasks = {
    'Python': "Write a function to calculate the Fibonacci sequence up to n terms",
    'JavaScript': "Create a function that checks if a string is a palindrome",
    'Rust': "Implement a basic bubble sort algorithm"
}

# Run coding performance tests
coding_results = test_coding_performance(coding_tasks)
print(coding_results)

# Visualization of Performance Metrics
plt.figure(figsize=(10, 5))
plt.subplot(1, 2, 1)
reasoning_results.plot(x='difficulty', y='response_time', kind='bar')
plt.title('Reasoning Task Response Time')
plt.xlabel('Difficulty Level')
plt.ylabel('Response Time (seconds)')

plt.subplot(1, 2, 2)
coding_results.plot(x='language', y='response_time', kind='bar')
plt.title('Coding Task Response Time')
plt.xlabel('Programming Language')
plt.ylabel('Response Time (seconds)')

plt.tight_layout()
plt.show()

# Performance Summary
# This notebook provides a comprehensive evaluation of the 
# DeepSeek-R1 model's performance across reasoning and coding tasks.

## Notebook Execution Notes
- Requires NVIDIA API Key
- Depends on environment variables
- Uses OpenAI client for model interaction
- Generates performance metrics and visualizations

## Key Components
- Python script for DeepSeek AI interaction
- Node.js script for DeepSeek AI interaction
- Bash cURL script for direct API interaction
- Jupyter Notebook for interactive exploration
- Flexible API key configuration

## Notes
- The scripts are designed to work with or without an API key
- Demonstrates model interaction in multiple languages and methods
- Supports streaming responses
- Provides multiple approaches to AI model interaction

## Acknowledgments
Thanks to the NVIDIA NIM AI Software Production Team and DeepSeek AI for providing this powerful reasoning model.

Improvement Plan

Performance Optimization

• Objective: Enhance the overall performance of the AI model.
• Rationale: Improved performance will lead to faster response times and better user experience.
• Actions:
  • Optimize the model's architecture for faster inference.
  • Implement caching mechanisms to reduce redundant computations.
  • Fine-tune hyperparameters for optimal performance.

Code Refactoring

• Objective: Improve the codebase for better maintainability and readability.
• Rationale: Clean and well-structured code is easier to maintain and extend.
• Actions:
  • Refactor the existing code to follow best practices and coding standards.
  • Modularize the codebase to separate concerns and improve reusability.
  • Add comprehensive comments and documentation to the code.

Additional Features

• Objective: Introduce new features to enhance the functionality of the project.
• Rationale: New features will provide more value to users and expand the project's capabilities.
• Actions:
  • Implement new visualization tools for better data analysis.
  • Add support for additional AI models and frameworks.
  • Develop user-friendly interfaces for easier interaction with the AI model.