QUICK_START.md

🚀 QUICK START - 5 EASY STEPS

✅ SERVER IS ALREADY RUNNING!

Your FastAPI server is currently active at:

http://localhost:8000

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📋 STEP-BY-STEP RUN GUIDE

STEP 1: Open Terminal

# Open PowerShell or Command Prompt
# Navigate to project directory
cd "d:\Projects\Pytorch x hugging face\he_demo"

STEP 2: Start Virtual Environment (Optional)

# Activate Python virtual environment
.venv\Scripts\Activate.ps1

STEP 3: Run the Server

# Start FastAPI server with uv
uv run server

Expected Output:

INFO:     Started server process [pid]
INFO:     Application startup complete.
INFO:     Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)

STEP 4: Verify Server is Running (New Terminal)

# In a new terminal, test the API
curl http://localhost:8000/graders

# Or with PowerShell
Invoke-WebRequest -Uri "http://localhost:8000/graders" -UseBasicParsing

Expected Response:

{
  "graders": {...},
  "total_graders": 5,
  "grader_names": [...]
}

STEP 5: Run Validation Tests

# Test environment and graders
python validate.py

# Or comprehensive tests
python validate_comprehensive.py

Expected Output:

✅ Grader count requirement met (>= 3)
✅ All validation tests passed
✅ 5 graders WORKING

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🧪 TEST COMMANDS (While Server Running)

Test 1: Check All Graders

curl http://localhost:8000/graders

Test 2: Get Specific Grader

curl "http://localhost:8000/graders/balanced_optimization"

Test 3: Get Grader Info

curl http://localhost:8000/graders/info

Test 4: Reset Environment

curl -X POST http://localhost:8000/reset `
  -H "Content-Type: application/json" `
  -d '{}'

Test 5: Execute Action

curl -X POST http://localhost:8000/step `
  -H "Content-Type: application/json" `
  -d '{"action_type": "reduce_ram", "intensity": 0.8}'

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🎓 TRAINING & INFERENCE

Run Training Script

python train_agent.py

• Trains RL agent on environment
• Evaluates with graders
• Saves model as energy_optimization_ppo.zip

Run Inference Script

# Set environment variables first
$env:ENERGY_TASK = "balanced_optimization"
$env:HF_TOKEN = "your_token"
$env:MODEL_NAME = "Qwen/Qwen2.5-72B-Instruct"

# Then run
python -m he_demo.inference

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🐳 RUN WITH DOCKER (Alternative)

Build Docker Image

docker build -t energy-optimization-env .

Run Docker Container

# Port 8000 on your machine maps to 8000 in container
docker run -p 8000:8000 he_demo:latest

# Or with interactive terminal
docker run -it -p 8000:8000 he_demo:latest

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⏹️ STOP THE SERVER

# In the terminal running the server:
Press CTRL+C

# Or if running Docker:
docker stop <container_id>

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📊 VERIFY EVERYTHING WORKS

Run this quick verification:

# 1. Check server status
curl http://localhost:8000/graders

# 2. Run validation
python validate.py

# 3. Check all graders
python validate_comprehensive.py

All three should ✅ PASS

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🎯 WHAT'S RUNNING

Component	Status	Port	Command
FastAPI Server	✅ RUNNING	8000	uv run server
5 Graders	✅ ACTIVE	8000/graders	Built-in
WebSocket	✅ READY	8000/ws	Real-time updates
Validation	✅ READY	N/A	python validate.py

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🔗 IMPORTANT LINKS

• Local Server: http://localhost:8000
• GitHub Repo: https://github.com/Sushruth-21/Energy-and-Memory-Ram-Optimization
• HF Space: https://sushruth21-energy-optimization-space.hf.space
• Complete Guide: See RUN_INSTRUCTIONS.md

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✅ TROUBLESHOOTING

Port 8000 already in use?

# Find process using port
netstat -ano | findstr :8000
# Kill process
taskkill /PID <pid> /F

Module not found error?

# Reinstall dependencies
uv sync

Docker image not building?

# Use pre-built image
docker run -p 8000:8000 he_demo:latest

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🎉 YOU'RE READY!

1. ✅ Server is running
2. ✅ 5 Graders are working
3. ✅ API is responding
4. ✅ Ready to submit to hackathon

Next: Run validation tests and submit!

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Current Server Status: 🟢 RUNNING ON http://localhost:8000