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🧪 MLFlow Integration

Learn how to integrate SuperOptiX with MLFlow for comprehensive experiment tracking, model monitoring, and observability.

🎯 Overview

MLFlow is a popular open-source platform (third-party) for managing the complete machine learning lifecycle. This integration allows you to:

  • Track experiments with detailed metrics and parameters
  • Monitor model performance across different runs
  • Compare agent versions and configurations
  • Export trace data to MLFlow for analysis
  • Visualize agent behavior in MLFlow UI

🚀 Quick Setup

Prerequisites

Bash
# Install MLFlow
pip install mlflow

# Install SuperOptiX with MLFlow support
pip install superoptix[mlflow]

Basic Configuration

Create a configuration file mlflow_config.yaml:

YAML
# mlflow_config.yaml
mlflow:
  tracking_uri: "http://localhost:5000"  # MLFlow server URL
  experiment_name: "superoptix_agents"
  log_artifacts: true
  log_metrics: true
  log_params: true

# SuperOptiX observability configuration
observability:
  enabled: true
  backends:
    - mlflow
  trace_export:
    format: "mlflow"
    batch_size: 100

🔧 Integration Setup

Step 1: Start MLFlow Server

Bash
# Start MLFlow tracking server
mlflow server --host 0.0.0.0 --port 5000

# Or use MLFlow UI
mlflow ui --host 0.0.0.0 --port 5000

Step 2: Configure SuperOptiX

Update your agent playbook to include MLFlow integration:

YAML
# developer_playbook.yaml
name: "Developer Assistant"
description: "AI developer with MLFlow integration"
tier: "genies"

# ... existing configuration ...

observability:
  enabled: true
  backends:
    - mlflow
  mlflow:
    experiment_name: "developer_agent"
    tracking_uri: "http://localhost:5000"
    log_artifacts: true
    log_metrics: true
    log_params: true
    tags:
      agent_type: "developer"
      tier: "genies"
      version: "1.0.0"

Step 3: Run Agent with MLFlow Tracking

Bash
# Initialize project
super init mlflow_demo
cd mlflow_demo

# Pull and compile agent
super agent pull developer --tier genies
super agent compile developer

# Run with MLFlow tracking
super agent run developer --goal "Write a Python function to calculate fibonacci numbers"

📊 MLFlow Integration Features

Automatic Experiment Tracking

SuperOptiX automatically logs the following to MLFlow:

Parameters: - Agent configuration - Model settings - Tool configurations - Execution parameters

Metrics: - Execution time - Token usage - Tool usage frequency - Success/failure rates - Memory usage

Artifacts: - Trace files (JSONL format) - Generated code - Tool outputs - Error logs

Example MLFlow Run

Python
# MLFlow run structure
{
  "run_id": "abc123",
  "experiment_name": "developer_agent",
  "parameters": {
    "agent_name": "developer",
    "tier": "genies",
    "model": "llama3.1:8b",
    "temperature": 0.7,
    "max_tokens": 2048
  },
  "metrics": {
    "execution_time_ms": 10270.0,
    "total_tokens": 1250,
    "tools_used": 3,
    "success_rate": 1.0
  },
  "artifacts": {
    "traces.jsonl": "path/to/traces.jsonl",
    "generated_code.py": "path/to/output.py",
    "tool_outputs.json": "path/to/tools.json"
  }
}

🔍 Advanced Configuration

Custom Metrics Logging

Extend the MLFlow integration with custom metrics:

YAML
# Custom metrics configuration
observability:
  mlflow:
    custom_metrics:
      - name: "code_quality_score"
        type: "float"
        description: "Code quality assessment score"
      - name: "tool_efficiency"
        type: "float"
        description: "Tool usage efficiency ratio"
      - name: "response_relevance"
        type: "float"
        description: "Response relevance score"

Batch Processing

Configure batch processing for high-volume scenarios:

YAML
observability:
  mlflow:
    batch_processing:
      enabled: true
      batch_size: 100
      flush_interval: 30  # seconds
      max_queue_size: 1000

Custom Tags and Metadata

Add custom tags and metadata to MLFlow runs:

YAML
observability:
  mlflow:
    tags:
      environment: "production"
      team: "ai_engineering"
      project: "code_assistant"
    metadata:
      version: "1.2.0"
      deployment: "kubernetes"
      region: "us-west-2"

📈 Monitoring and Analysis

MLFlow UI Dashboard

Access the MLFlow UI to view experiments:

Bash
# Open MLFlow UI
open http://localhost:5000

Available Views: - Experiments: Compare different agent runs - Runs: Detailed run information - Metrics: Performance trends over time - Artifacts: Generated outputs and traces - Parameters: Configuration comparisons

Custom Dashboards

Create custom dashboards using MLFlow's API:

Python
import mlflow
import pandas as pd

# Query experiments
experiments = mlflow.search_experiments()
runs = mlflow.search_runs(experiment_names=["developer_agent"])

# Analyze performance trends
performance_df = runs[["execution_time_ms", "total_tokens", "success_rate"]]
print(performance_df.describe())

Alerting and Notifications

Set up alerts for performance degradation:

Python
# Example alerting script
def check_performance_alerts():
    runs = mlflow.search_runs(experiment_names=["developer_agent"])
    latest_run = runs.iloc[0]

    if latest_run["execution_time_ms"] > 15000:  # 15 seconds
        send_alert("Agent execution time exceeded threshold")

    if latest_run["success_rate"] < 0.9:
        send_alert("Agent success rate below threshold")

🔧 Troubleshooting

Common Issues

1. MLFlow Server Connection

Bash
# Check MLFlow server status
curl http://localhost:5000/health

# Restart MLFlow server
mlflow server --host 0.0.0.0 --port 5000

2. Authentication Issues

Bash
# Set MLFlow tracking URI
export MLFLOW_TRACKING_URI=http://localhost:5000

# Or configure in code
mlflow.set_tracking_uri("http://localhost:5000")

3. Artifact Storage

Bash
# Configure artifact storage
mlflow server --host 0.0.0.0 --port 5000 --backend-store-uri sqlite:///mlflow.db --default-artifact-root ./mlflow_artifacts

Debug Mode

Enable debug logging for MLFlow integration:

YAML
observability:
  mlflow:
    debug: true
    log_level: "DEBUG"

🚀 Production Deployment

MLFlow Server Setup

For production deployments, use a robust MLFlow setup:

Bash
# Using PostgreSQL backend
mlflow server \
  --backend-store-uri postgresql://user:pass@host:port/db \
  --default-artifact-root s3://bucket/mlflow \
  --host 0.0.0.0 \
  --port 5000

Kubernetes Deployment

Deploy MLFlow with Kubernetes:

YAML
# mlflow-deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: mlflow-server
spec:
  replicas: 1
  selector:
    matchLabels:
      app: mlflow
  template:
    metadata:
      labels:
        app: mlflow
    spec:
      containers:
      - name: mlflow
        image: python:3.9
        command: ["mlflow", "server"]
        args: ["--host", "0.0.0.0", "--port", "5000"]
        ports:
        - containerPort: 5000

🎯 Next Steps

  1. Set up MLFlow server for your environment
  2. Configure agent playbooks with MLFlow integration
  3. Monitor agent performance through MLFlow UI
  4. Set up alerts for performance degradation
  5. Scale to production with robust MLFlow deployment

Ready to track your SuperOptiX agents with MLFlow? Start with the basic setup and scale up as needed! 🚀